diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 4e72e7bcedf790e6a087099f3defb5bc9ae36bd5..0000000000000000000000000000000000000000 Binary files a/.DS_Store and /dev/null differ diff --git a/README.md b/README.md index 81d904f36dc9c06eccc46c7150c52d103d208ffa..8ab1e1daca1f7dcdeea0e8c076222d44e033987a 100644 --- a/README.md +++ b/README.md @@ -9,60 +9,67 @@ license: mit app_port: 7860 --- -# Brain AI - Advanced Multi-Agent AI System +# ๐Ÿง  Brain AI - Advanced Multi-Agent AI System -Brain AI is a sophisticated multi-agent artificial intelligence system designed for complex reasoning, code generation, and autonomous problem-solving. +An sophisticated multi-agent AI system designed for complex reasoning, research, and problem-solving tasks. -## ๐ŸŒŸ Features +## Features -- **Multi-Agent Architecture**: Specialized agents for different domains (coding, analysis, debugging, documentation) -- **Advanced Reasoning**: Cognitive processing with meta-memory and learning capabilities -- **Code Generation**: Support for multiple programming languages with real execution -- **Benchmarking**: Comprehensive testing against academic and industry benchmarks -- **Real-time Learning**: Continuous improvement through experience and feedback +- **Multi-Agent Architecture**: Specialized agents for different domains and tasks +- **Advanced Reasoning**: Complex problem-solving and analytical capabilities +- **Research Integration**: Academic research and web search capabilities +- **Benchmark Testing**: Performance evaluation and optimization tools +- **Cognitive Framework**: Memory, learning, and adaptation systems -## ๐Ÿš€ Getting Started +## System Architecture -The Brain AI system is deployed as a containerized application. Simply interact with the interface to access the AI capabilities. +The Brain AI system consists of several key components: -## ๐Ÿ“Š Architecture +### Core Agents +- **Academic Agent**: Research paper analysis and academic query handling +- **Web Agent**: Real-time web search and information gathering +- **Reasoning Agent**: Complex logical reasoning and problem solving +- **Memory Agent**: Information storage, retrieval, and knowledge management -- **brain-core**: Core system components and orchestration -- **brain-api**: REST API and web services -- **brain-cognitive**: Advanced reasoning and learning systems -- **brain-benchmark**: Testing and evaluation framework -- **brain-cli**: Command-line interface +### Cognitive Framework +- **Memory Systems**: Short-term and long-term memory management +- **Learning Mechanisms**: Adaptive learning from interactions +- **Insight Generation**: Pattern recognition and knowledge synthesis +- **Performance Monitoring**: System optimization and benchmarking -## ๐ŸŽฏ Performance +### Benchmark Suite +- **SWE-Bench**: Software engineering problem solving evaluation +- **Academic Benchmarks**: Research task performance assessment +- **Reasoning Tests**: Logic and analytical thinking evaluation +- **Memory Tests**: Information retention and retrieval assessment -Brain AI has achieved remarkable performance across multiple benchmarks: -- HumanEval code generation benchmark -- MBPP programming problems -- Academic reasoning tasks -- Real-world problem solving +## Technical Implementation -## ๐Ÿ“ˆ Status +Built with: +- **Rust**: High-performance core system implementation +- **Python**: Web interface and API integration +- **Docker**: Containerized deployment +- **Flask**: Web interface for interaction -**Current Version**: v0.8.0 -**Build Status**: โœ… Compiles successfully with 0 errors -**Deployment Date**: August 07, 2025 +## Getting Started -## ๐Ÿ”ง Technology Stack +1. The system will automatically start when deployed +2. Access the web interface to interact with the Brain AI system +3. Use the control panel to start/stop the AI agents +4. Monitor system logs and performance metrics -- **Core**: Rust with async/await architecture -- **AI Integration**: OpenAI GPT models, Anthropic Claude -- **Web Framework**: Axum with REST API -- **Database**: SQLite with vector search capabilities -- **Containerization**: Docker with optimized multi-stage builds +## Performance -## ๐Ÿค Contributing +The Brain AI system is designed for: +- High concurrency and parallel processing +- Memory-efficient operation +- Scalable agent architecture +- Real-time response capabilities -Brain AI is an open-source project. Feel free to explore the codebase and contribute improvements. +## Development -## ๐Ÿ“ License - -MIT License - see LICENSE file for details. +This is an active research and development project exploring advanced AI agent architectures and multi-agent collaboration patterns. --- -**Deployed on Hugging Face Spaces** | **August 07, 2025** \ No newline at end of file +*Deployed on Hugging Face Spaces* ๐Ÿค— diff --git a/__pycache__/demonstrate_brain_ai.cpython-313.pyc b/__pycache__/demonstrate_brain_ai.cpython-313.pyc deleted file mode 100644 index d840113d1b9b5373dbd68166278417fe2771c546..0000000000000000000000000000000000000000 Binary files a/__pycache__/demonstrate_brain_ai.cpython-313.pyc and /dev/null differ diff --git a/__pycache__/python_api_demo.cpython-313.pyc b/__pycache__/python_api_demo.cpython-313.pyc deleted file mode 100644 index cf2f2f2190c056d11866d856406615faf2130e1b..0000000000000000000000000000000000000000 Binary files a/__pycache__/python_api_demo.cpython-313.pyc and /dev/null differ diff --git a/academic_integration_verification_demo.rs b/academic_integration_verification_demo.rs deleted file mode 100644 index b0cd0e1403e9b686e6c4353ec5f725ff1c99d35c..0000000000000000000000000000000000000000 --- a/academic_integration_verification_demo.rs +++ /dev/null @@ -1,506 +0,0 @@ -use std::collections::HashMap; -use tokio; -use anyhow::Result; - -use brain_cognitive::agents::{ - AgentRegistry, AgentInput, CognitiveContext, -}; - -/// Academic Integration Verification Demo -/// -/// This demo verifies that all academic domain experts are properly integrated -/// with the Brain AI agent orchestration system and can be discovered and -/// executed through the standard agent registry and orchestration mechanisms. -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŽ“ Brain AI Academic Integration Verification"); - println!("=============================================="); - println!("Target: Verify all 5 domain experts are properly integrated with orchestration system"); - println!("Goal: Confirm agents can be discovered by capabilities and respond to academic queries"); - println!(); - - // Phase 1: Initialize Agent Registry with all agents - println!("๐Ÿ”ง Phase 1: Initializing Agent Registry..."); - let start_time = std::time::Instant::now(); - - let registry = AgentRegistry::new_with_defaults(); - - // Register async agents (including domain experts) - registry.register_async_agents().await - .map_err(|e| anyhow::anyhow!("Failed to register async agents: {}", e))?; - - let init_time = start_time.elapsed(); - println!("โœ… Registry initialized in {}ms", init_time.as_millis()); - println!(" โ€ข Agent registry operational"); - println!(" โ€ข All async agents registered"); - println!(); - - // Phase 2: Verify Domain Expert Registration - println!("๐Ÿ“Š Phase 2: Verifying Domain Expert Registration..."); - let verification_results = verify_domain_expert_registration(®istry).await?; - print_registration_verification(&verification_results); - println!(); - - // Phase 3: Test Capability-Based Agent Discovery - println!("๐Ÿ” Phase 3: Testing Capability-Based Agent Discovery..."); - let discovery_results = test_capability_discovery(®istry).await?; - print_discovery_results(&discovery_results); - println!(); - - // Phase 4: Test Academic Query Execution - println!("๐Ÿงช Phase 4: Testing Academic Query Execution..."); - let execution_results = test_academic_query_execution(®istry).await?; - print_execution_results(&execution_results); - println!(); - - // Phase 5: Integration Health Check - println!("๐Ÿฅ Phase 5: Integration Health Check..."); - let health_results = perform_integration_health_check(®istry).await?; - print_health_check_results(&health_results); - println!(); - - println!("๐Ÿ† Academic Integration Verification Complete!"); - println!("๐ŸŽฏ All domain experts successfully integrated with orchestration system"); - - Ok(()) -} - -#[derive(Debug)] -struct RegistrationVerification { - total_agents: usize, - academic_agents_found: usize, - universal_agent_found: bool, - domain_experts_found: HashMap, - registry_statistics: RegistryStats, -} - -#[derive(Debug)] -struct RegistryStats { - total_capabilities: usize, - academic_capabilities: Vec, - agents_by_category: HashMap, -} - -async fn verify_domain_expert_registration(registry: &AgentRegistry) -> Result { - // Get registry statistics - let stats = registry.get_statistics() - .map_err(|e| anyhow::anyhow!("Failed to get registry statistics: {}", e))?; - - let total_agents = stats.total_agents; - let agents_by_category = stats.agents_by_category; - - // Check for Universal Academic Agent - let universal_agent = registry.get_agent("universal_academic_agent") - .map_err(|e| anyhow::anyhow!("Failed to get universal agent: {}", e))?; - let universal_agent_found = universal_agent.is_some(); - - // Check for domain experts by capability - let academic_capabilities = vec![ - "TheoreticalPhysics", - "AdvancedMathematics", - "AdvancedChemistry", - "MolecularBiology", - "ComputerScienceTheory", - "AcademicReasoning", - "DomainExpertise", - ]; - - let mut domain_experts_found = HashMap::new(); - let mut academic_agents_found = 0; - - for capability in &academic_capabilities { - let agents = registry.get_agents_by_capability(capability) - .map_err(|e| anyhow::anyhow!("Failed to get agents for capability {}: {}", capability, e))?; - - domain_experts_found.insert(capability.to_string(), !agents.is_empty()); - if !agents.is_empty() { - academic_agents_found += agents.len(); - } - } - - Ok(RegistrationVerification { - total_agents, - academic_agents_found, - universal_agent_found, - domain_experts_found, - registry_statistics: RegistryStats { - total_capabilities: stats.total_capabilities, - academic_capabilities: academic_capabilities.iter().map(|s| s.to_string()).collect(), - agents_by_category, - }, - }) -} - -fn print_registration_verification(verification: &RegistrationVerification) { - println!(" Registration Verification Results:"); - println!(" ================================="); - println!(" Total Agents Registered: {}", verification.total_agents); - println!(" Academic Agents Found: {}", verification.academic_agents_found); - println!(" Universal Academic Agent: {}", if verification.universal_agent_found { "โœ… Found" } else { "โŒ Missing" }); - - println!(" Domain Expert Capabilities:"); - for (capability, found) in &verification.domain_experts_found { - let status = if *found { "โœ…" } else { "โŒ" }; - println!(" {}: {} Available", capability, status); - } - - println!(" Registry Statistics:"); - println!(" Total Capabilities: {}", verification.registry_statistics.total_capabilities); - - if let Some(academic_count) = verification.registry_statistics.agents_by_category.get("academic") { - println!(" Academic Tag Count: {}", academic_count); - } - - // Assessment - let missing_capabilities = verification.domain_experts_found.values().filter(|&&found| !found).count(); - if missing_capabilities == 0 && verification.universal_agent_found { - println!(" โœ… EXCELLENT: All academic agents properly registered"); - } else { - println!(" โš ๏ธ ISSUES: {} missing capabilities detected", missing_capabilities); - } -} - -#[derive(Debug)] -struct CapabilityDiscoveryResults { - capabilities_tested: Vec, - discovery_results: HashMap, - total_agents_discovered: usize, -} - -#[derive(Debug)] -struct DiscoveryResult { - agents_found: usize, - agent_names: Vec, - discovery_time_ms: u128, -} - -async fn test_capability_discovery(registry: &AgentRegistry) -> Result { - let capabilities_to_test = vec![ - "TheoreticalPhysics".to_string(), - "AdvancedMathematics".to_string(), - "AdvancedChemistry".to_string(), - "MolecularBiology".to_string(), - "ComputerScienceTheory".to_string(), - "AcademicReasoning".to_string(), - "MultipleChoiceProcessing".to_string(), - ]; - - let mut discovery_results = HashMap::new(); - let mut total_agents_discovered = 0; - - for capability in &capabilities_to_test { - let start_time = std::time::Instant::now(); - - let agents = registry.get_agents_by_capability(capability) - .map_err(|e| anyhow::anyhow!("Discovery failed for {}: {}", capability, e))?; - - let discovery_time = start_time.elapsed(); - - let agent_names: Vec = agents.iter() - .map(|agent| agent.metadata().name.clone()) - .collect(); - - total_agents_discovered += agents.len(); - - discovery_results.insert(capability.clone(), DiscoveryResult { - agents_found: agents.len(), - agent_names, - discovery_time_ms: discovery_time.as_millis(), - }); - } - - Ok(CapabilityDiscoveryResults { - capabilities_tested: capabilities_to_test, - discovery_results, - total_agents_discovered, - }) -} - -fn print_discovery_results(results: &CapabilityDiscoveryResults) { - println!(" Capability Discovery Results:"); - println!(" ============================"); - println!(" Total Capabilities Tested: {}", results.capabilities_tested.len()); - println!(" Total Agents Discovered: {}", results.total_agents_discovered); - - for capability in &results.capabilities_tested { - if let Some(result) = results.discovery_results.get(capability) { - println!(" {}:", capability); - println!(" Agents Found: {}", result.agents_found); - println!(" Discovery Time: {}ms", result.discovery_time_ms); - - if !result.agent_names.is_empty() { - println!(" Agent Names: {}", result.agent_names.join(", ")); - } - } - } - - // Assessment - let successful_discoveries = results.discovery_results.values() - .filter(|result| result.agents_found > 0) - .count(); - - if successful_discoveries == results.capabilities_tested.len() { - println!(" โœ… SUCCESS: All capabilities have discoverable agents"); - } else { - println!(" โš ๏ธ PARTIAL: {}/{} capabilities have agents", - successful_discoveries, results.capabilities_tested.len()); - } -} - -#[derive(Debug)] -struct QueryExecutionResults { - queries_tested: usize, - successful_executions: usize, - execution_details: Vec, - average_response_time_ms: f64, -} - -#[derive(Debug)] -struct ExecutionDetail { - capability: String, - agent_name: String, - query: String, - success: bool, - response_time_ms: u128, - error_message: Option, -} - -async fn test_academic_query_execution(registry: &AgentRegistry) -> Result { - let test_queries = vec![ - ("TheoreticalPhysics", "What is the significance of gauge invariance in quantum field theory?"), - ("AdvancedMathematics", "Explain the fundamental group of a topological space"), - ("AdvancedChemistry", "Describe molecular orbital theory for diatomic molecules"), - ("MolecularBiology", "What are topologically associating domains in chromatin?"), - ("ComputerScienceTheory", "Explain the P vs NP problem significance"), - ]; - - let mut execution_details = Vec::new(); - let mut successful_executions = 0; - let mut total_response_time = 0u128; - - for (capability, query) in &test_queries { - let start_time = std::time::Instant::now(); - - let agents = registry.get_agents_by_capability(capability) - .map_err(|e| anyhow::anyhow!("Failed to find agents for {}: {}", capability, e))?; - - if let Some(agent) = agents.first() { - let agent_name = agent.metadata().name.clone(); - - // Create test input - let input = AgentInput::new( - "academic_question".to_string(), - query.to_string(), - "integration_test_session".to_string() - ); - let context = CognitiveContext::default(); - - // Execute query - match agent.execute(input, &context).await { - Ok(_output) => { - let response_time = start_time.elapsed(); - successful_executions += 1; - total_response_time += response_time.as_millis(); - - execution_details.push(ExecutionDetail { - capability: capability.to_string(), - agent_name, - query: query.to_string(), - success: true, - response_time_ms: response_time.as_millis(), - error_message: None, - }); - } - Err(e) => { - let response_time = start_time.elapsed(); - - execution_details.push(ExecutionDetail { - capability: capability.to_string(), - agent_name, - query: query.to_string(), - success: false, - response_time_ms: response_time.as_millis(), - error_message: Some(e.to_string()), - }); - } - } - } else { - execution_details.push(ExecutionDetail { - capability: capability.to_string(), - agent_name: "None".to_string(), - query: query.to_string(), - success: false, - response_time_ms: 0, - error_message: Some("No agent found for capability".to_string()), - }); - } - } - - let average_response_time_ms = if successful_executions > 0 { - total_response_time as f64 / successful_executions as f64 - } else { - 0.0 - }; - - Ok(QueryExecutionResults { - queries_tested: test_queries.len(), - successful_executions, - execution_details, - average_response_time_ms, - }) -} - -fn print_execution_results(results: &QueryExecutionResults) { - println!(" Query Execution Results:"); - println!(" ======================="); - println!(" Queries Tested: {}", results.queries_tested); - println!(" Successful Executions: {}", results.successful_executions); - println!(" Success Rate: {:.1}%", - (results.successful_executions as f64 / results.queries_tested as f64) * 100.0); - println!(" Average Response Time: {:.1}ms", results.average_response_time_ms); - - println!(" Execution Details:"); - for detail in &results.execution_details { - let status = if detail.success { "โœ…" } else { "โŒ" }; - println!(" {} {} ({}ms): {}", - status, detail.capability, detail.response_time_ms, detail.agent_name); - - if let Some(error) = &detail.error_message { - println!(" Error: {}", error); - } - } - - // Assessment - let success_rate = (results.successful_executions as f64 / results.queries_tested as f64) * 100.0; - if success_rate >= 80.0 { - println!(" โœ… EXCELLENT: High success rate for academic query execution"); - } else if success_rate >= 60.0 { - println!(" โš ๏ธ GOOD: Acceptable success rate, some optimization needed"); - } else { - println!(" โŒ NEEDS IMPROVEMENT: Low success rate, integration issues detected"); - } -} - -#[derive(Debug)] -struct IntegrationHealthCheck { - registry_health: RegistryHealth, - orchestration_health: OrchestrationHealth, - academic_system_health: AcademicSystemHealth, - overall_health_score: f64, -} - -#[derive(Debug)] -struct RegistryHealth { - total_agents: usize, - academic_agents: usize, - capability_coverage: f64, - health_score: f64, -} - -#[derive(Debug)] -struct OrchestrationHealth { - discovery_latency_ms: f64, - execution_success_rate: f64, - health_score: f64, -} - -#[derive(Debug)] -struct AcademicSystemHealth { - domain_coverage: f64, - integration_completeness: f64, - health_score: f64, -} - -async fn perform_integration_health_check(registry: &AgentRegistry) -> Result { - // Registry Health - let stats = registry.get_statistics() - .map_err(|e| anyhow::anyhow!("Failed to get stats: {}", e))?; - - let academic_agents = stats.agents_by_category.get("academic").unwrap_or(&0); - let capability_coverage = if stats.total_capabilities > 0 { - (*academic_agents as f64 / stats.total_agents as f64) * 100.0 - } else { - 0.0 - }; - - let registry_health = RegistryHealth { - total_agents: stats.total_agents, - academic_agents: *academic_agents, - capability_coverage, - health_score: if capability_coverage > 10.0 { 90.0 } else { 60.0 }, - }; - - // Orchestration Health - let discovery_start = std::time::Instant::now(); - let _academic_agents = registry.get_agents_by_capability("AcademicReasoning") - .map_err(|e| anyhow::anyhow!("Discovery test failed: {}", e))?; - let discovery_latency = discovery_start.elapsed().as_millis() as f64; - - let orchestration_health = OrchestrationHealth { - discovery_latency_ms: discovery_latency, - execution_success_rate: 85.0, // Estimated based on previous tests - health_score: if discovery_latency < 50.0 { 95.0 } else { 80.0 }, - }; - - // Academic System Health - let expected_domains = 5; // Physics, Math, Chemistry, Biology, CS - let found_domains = ["TheoreticalPhysics", "AdvancedMathematics", "AdvancedChemistry", - "MolecularBiology", "ComputerScienceTheory"] - .iter() - .map(|domain| registry.get_agents_by_capability(domain)) - .filter_map(|result| result.ok()) - .filter(|agents| !agents.is_empty()) - .count(); - - let domain_coverage = (found_domains as f64 / expected_domains as f64) * 100.0; - let integration_completeness = 95.0; // High due to proper registration - - let academic_system_health = AcademicSystemHealth { - domain_coverage, - integration_completeness, - health_score: (domain_coverage + integration_completeness) / 2.0, - }; - - // Overall Health Score - let overall_health_score = (registry_health.health_score + - orchestration_health.health_score + - academic_system_health.health_score) / 3.0; - - Ok(IntegrationHealthCheck { - registry_health, - orchestration_health, - academic_system_health, - overall_health_score, - }) -} - -fn print_health_check_results(health: &IntegrationHealthCheck) { - println!(" Integration Health Check Results:"); - println!(" ================================"); - - println!(" Registry Health: {:.1}%", health.registry_health.health_score); - println!(" Total Agents: {}", health.registry_health.total_agents); - println!(" Academic Agents: {}", health.registry_health.academic_agents); - println!(" Capability Coverage: {:.1}%", health.registry_health.capability_coverage); - - println!(" Orchestration Health: {:.1}%", health.orchestration_health.health_score); - println!(" Discovery Latency: {:.1}ms", health.orchestration_health.discovery_latency_ms); - println!(" Execution Success Rate: {:.1}%", health.orchestration_health.execution_success_rate); - - println!(" Academic System Health: {:.1}%", health.academic_system_health.health_score); - println!(" Domain Coverage: {:.1}%", health.academic_system_health.domain_coverage); - println!(" Integration Completeness: {:.1}%", health.academic_system_health.integration_completeness); - - println!(" Overall Health Score: {:.1}%", health.overall_health_score); - - // Assessment - if health.overall_health_score >= 90.0 { - println!(" โœ… EXCELLENT: Academic integration is fully operational"); - } else if health.overall_health_score >= 75.0 { - println!(" โœ… GOOD: Academic integration is working well with minor optimizations needed"); - } else if health.overall_health_score >= 60.0 { - println!(" โš ๏ธ FAIR: Academic integration has some issues that need attention"); - } else { - println!(" โŒ POOR: Academic integration has significant issues requiring immediate action"); - } -} \ No newline at end of file diff --git a/academic_intelligence_demonstration.rs b/academic_intelligence_demonstration.rs deleted file mode 100644 index ef33adeddfd2effed4acdb7ac3b47e8414d13d1d..0000000000000000000000000000000000000000 --- a/academic_intelligence_demonstration.rs +++ /dev/null @@ -1,413 +0,0 @@ -use anyhow::Result; -use std::time::{Duration, Instant}; -use brain_cognitive::agents::{ - registry::AgentRegistry, - intelligence::{ - academic_reasoning::UniversalAcademicAgent, - multiple_choice_processor::MultipleChoiceProcessor, - }, - traits::{AgentInput, BrainAgent}, - AcademicDomain, - }; -use brain_types::error::BrainError; -use serde::{Deserialize, Serialize}; -use std::collections::HashMap; - -#[derive(Debug, Clone, Serialize, Deserialize)] -struct MockHLEQuestion { - id: String, - question: String, - options: Vec, - correct_answer: usize, - domain: AcademicDomain, - difficulty: String, -} - -#[derive(Debug, Clone, Serialize, Deserialize)] -struct TestResult { - question_id: String, - question: String, - selected_answer: usize, - correct_answer: usize, - is_correct: bool, - confidence: f32, - processing_time_ms: u64, - domain: AcademicDomain, - reasoning: String, -} - -#[derive(Debug)] -struct AcademicIntelligenceDemo { - academic_agent: UniversalAcademicAgent, - multiple_choice_processor: std::cell::RefCell, - agent_registry: AgentRegistry, -} - -impl AcademicIntelligenceDemo { - pub async fn new() -> Result { - println!("๐Ÿš€ Initializing Brain AI Academic Intelligence Demonstration..."); - - let academic_agent = UniversalAcademicAgent::new().await?; - let multiple_choice_processor = std::cell::RefCell::new(MultipleChoiceProcessor::new()); - let agent_registry = AgentRegistry::new_with_defaults(); - - println!("โœ… Academic Intelligence System initialized"); - println!(" โ€ข Universal Academic Agent: READY"); - println!(" โ€ข Multiple Choice Processor: READY"); - println!(" โ€ข Agent Registry: READY"); - - Ok(Self { - academic_agent, - multiple_choice_processor, - agent_registry, - }) - } - - pub async fn run_demonstration(&self) -> Result<(), BrainError> { - println!("\n๐Ÿงช Brain AI Academic Intelligence Phase 1 Demonstration"); - println!("======================================================="); - println!("๐ŸŽฏ Goal: Validate 40%+ accuracy with advanced bias mitigation"); - println!("๐Ÿง  Testing: Universal Academic Reasoning + Multiple Choice Processing"); - - let test_questions = self.create_realistic_hle_questions(); - println!("๐Ÿ“Š Generated {} realistic HLE-style questions", test_questions.len()); - - let mut results = Vec::new(); - let total_start_time = Instant::now(); - - for (index, question) in test_questions.iter().enumerate() { - println!("\n๐Ÿ“ Question {}/{}: {}", - index + 1, - test_questions.len(), - self.truncate_text(&question.question, 60) - ); - - let result = self.process_question(question).await?; - results.push(result); - - // Small delay between questions for realistic processing - tokio::time::sleep(Duration::from_millis(100)).await; - } - - let total_time = total_start_time.elapsed(); - - // Analyze and display comprehensive results - self.analyze_results(&results, total_time).await; - - Ok(()) - } - - async fn process_question(&self, question: &MockHLEQuestion) -> Result { - let start_time = Instant::now(); - - // Create academic input - let academic_input = AgentInput::new( - "academic_question".to_string(), - serde_json::json!({ - "question": question.question, - "options": question.options, - "domain": format!("{:?}", question.domain), - "type": "multiple_choice_academic" - }).to_string(), - format!("demo_session_{}", question.id), - ); - - // Process through Universal Academic Agent - println!(" ๐Ÿง  Processing through Universal Academic Agent..."); - let academic_result = self.academic_agent - .execute(academic_input, &Default::default()) - .await?; - - // Process through Multiple Choice Processor for bias mitigation - println!(" ๐ŸŽฏ Applying bias mitigation through Multiple Choice Processor..."); - let mc_result = self.multiple_choice_processor - .borrow_mut() - .process_options(&question.question, &question.options, &question.domain) - .await?; - - // Extract results - let selected_answer = self.parse_option_letter(&mc_result.recommended_answer); - let confidence = mc_result.recommendation_confidence; - let is_correct = selected_answer == question.correct_answer; - let processing_time = start_time.elapsed(); - - let reasoning = format!( - "Academic Analysis: {} | MC Processing: {} (confidence: {:.1}%)", - academic_result.content.chars().take(100).collect::(), - mc_result.recommended_answer, - confidence * 100.0 - ); - - println!(" โœ… Selected: {} | Correct: {} | Accuracy: {} | Time: {}ms", - self.get_option_letter(selected_answer), - self.get_option_letter(question.correct_answer), - if is_correct { "โœ…" } else { "โŒ" }, - processing_time.as_millis() - ); - - Ok(TestResult { - question_id: question.id.clone(), - question: question.question.clone(), - selected_answer, - correct_answer: question.correct_answer, - is_correct, - confidence, - processing_time_ms: processing_time.as_millis() as u64, - domain: question.domain.clone(), - reasoning, - }) - } - - async fn analyze_results(&self, results: &[TestResult], total_time: Duration) { - println!("\n๐Ÿ† Brain AI Academic Intelligence Phase 1 Results"); - println!("================================================="); - - // Overall Performance - let correct_count = results.iter().filter(|r| r.is_correct).count(); - let total_count = results.len(); - let accuracy = (correct_count as f32 / total_count as f32) * 100.0; - - println!("๐Ÿ“Š Overall Performance:"); - println!(" โ€ข Accuracy: {:.1}% ({}/{})", accuracy, correct_count, total_count); - - // Compare to target - let target_accuracy = 40.0; - if accuracy >= target_accuracy { - println!(" ๐ŸŽฏ TARGET ACHIEVED: Exceeded {:.1}% target accuracy!", target_accuracy); - } else { - println!(" โš ๏ธ TARGET MISSED: {:.1}% below {:.1}% target", - target_accuracy - accuracy, target_accuracy); - } - - // Performance by Domain - let mut domain_stats: HashMap = HashMap::new(); - for result in results { - let entry = domain_stats.entry(result.domain.clone()).or_insert((0, 0)); - entry.1 += 1; // total - if result.is_correct { - entry.0 += 1; // correct - } - } - - println!("\n๐Ÿ”ฌ Performance by Academic Domain:"); - for (domain, (correct, total)) in &domain_stats { - let domain_accuracy = (*correct as f32 / *total as f32) * 100.0; - println!(" โ€ข {:?}: {:.1}% ({}/{})", domain, domain_accuracy, correct, total); - } - - // Confidence Analysis - let avg_confidence = results.iter().map(|r| r.confidence).sum::() / results.len() as f32; - let correct_confidence = results.iter() - .filter(|r| r.is_correct) - .map(|r| r.confidence) - .sum::() / correct_count as f32; - let incorrect_confidence = results.iter() - .filter(|r| !r.is_correct) - .map(|r| r.confidence) - .sum::() / (total_count - correct_count).max(1) as f32; - - println!("\n๐ŸŽฏ Confidence Analysis:"); - println!(" โ€ข Average Confidence: {:.1}%", avg_confidence * 100.0); - println!(" โ€ข Correct Answer Confidence: {:.1}%", correct_confidence * 100.0); - println!(" โ€ข Incorrect Answer Confidence: {:.1}%", incorrect_confidence * 100.0); - - // Bias Analysis (Option Distribution) - let mut option_counts = [0; 4]; - for result in results { - if result.selected_answer < 4 { - option_counts[result.selected_answer] += 1; - } - } - - println!("\n๐Ÿ“ˆ Bias Analysis (Option Distribution):"); - for (i, count) in option_counts.iter().enumerate() { - let percentage = (*count as f32 / total_count as f32) * 100.0; - let bias_indicator = if percentage > 35.0 { "โš ๏ธ" } else { "โœ…" }; - println!(" {} Option {}: {:.1}% ({})", - bias_indicator, - self.get_option_letter(i), - percentage, - count - ); - } - - // Performance Metrics - let avg_processing_time = results.iter().map(|r| r.processing_time_ms).sum::() / results.len() as u64; - - println!("\nโšก Performance Metrics:"); - println!(" โ€ข Total Processing Time: {}ms", total_time.as_millis()); - println!(" โ€ข Average Time per Question: {}ms", avg_processing_time); - println!(" โ€ข Questions per Second: {:.2}", results.len() as f32 / total_time.as_secs_f32()); - - // System Status Summary - println!("\n๐Ÿ”ง System Validation Summary:"); - println!(" โ€ข Universal Academic Agent: โœ… OPERATIONAL"); - println!(" โ€ข Multiple Choice Processor: โœ… OPERATIONAL"); - println!(" โ€ข Bias Mitigation: โœ… ACTIVE"); - println!(" โ€ข Domain Expertise: โœ… 5 SPECIALISTS ACTIVE"); - - // Final Assessment - if accuracy >= 45.0 { - println!("\n๐Ÿ† BREAKTHROUGH: Global #1 HLE Leadership Potential Demonstrated!"); - } else if accuracy >= 40.0 { - println!("\n๐ŸŽฏ SUCCESS: Phase 1 Target Achieved - Ready for Live HLE Testing!"); - } else if accuracy >= 30.0 { - println!("\n๐Ÿ“ˆ PROGRESS: Significant improvement detected - Continue optimization!"); - } else { - println!("\n๐Ÿ”ง OPTIMIZATION NEEDED: Focus on domain specialists and knowledge base expansion"); - } - - println!("\nโœ… Brain AI Academic Intelligence Phase 1 Demonstration Complete!"); - println!("๐Ÿš€ System validated and ready for real-time HLE integration!"); - } - - fn create_realistic_hle_questions(&self) -> Vec { - vec![ - MockHLEQuestion { - id: "hle_demo_01".to_string(), - question: "In quantum mechanics, what is the fundamental principle that prevents us from simultaneously knowing both the exact position and momentum of a particle?".to_string(), - options: vec![ - "Pauli exclusion principle".to_string(), - "Heisenberg uncertainty principle".to_string(), - "Schrรถdinger wave equation".to_string(), - "Einstein-Podolsky-Rosen paradox".to_string(), - ], - correct_answer: 1, - domain: AcademicDomain::TheoreticalPhysics, - difficulty: "intermediate".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_02".to_string(), - question: "Which of the following mathematical structures forms a group under matrix multiplication?".to_string(), - options: vec![ - "All 2ร—2 matrices with real entries".to_string(), - "All invertible 2ร—2 matrices with real entries".to_string(), - "All symmetric 2ร—2 matrices with real entries".to_string(), - "All 2ร—2 matrices with determinant equal to 1".to_string(), - ], - correct_answer: 1, - domain: AcademicDomain::AdvancedMathematics, - difficulty: "advanced".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_03".to_string(), - question: "In protein folding, what type of interaction primarily stabilizes the tertiary structure of globular proteins?".to_string(), - options: vec![ - "Hydrogen bonds between backbone atoms".to_string(), - "Hydrophobic interactions between nonpolar side chains".to_string(), - "Ionic bonds between charged residues".to_string(), - "Van der Waals forces between all atoms".to_string(), - ], - correct_answer: 1, - domain: AcademicDomain::MolecularBiology, - difficulty: "intermediate".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_04".to_string(), - question: "Which of the following best describes the mechanism of SN2 nucleophilic substitution?".to_string(), - options: vec![ - "Two-step mechanism with carbocation intermediate".to_string(), - "One-step mechanism with simultaneous bond breaking and forming".to_string(), - "Radical mechanism involving homolytic bond cleavage".to_string(), - "Elimination mechanism forming alkene products".to_string(), - ], - correct_answer: 1, - domain: AcademicDomain::AdvancedChemistry, - difficulty: "intermediate".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_05".to_string(), - question: "In computational complexity theory, which class contains problems that are efficiently verifiable but not necessarily efficiently solvable?".to_string(), - options: vec![ - "P (Polynomial time)".to_string(), - "NP (Nondeterministic polynomial time)".to_string(), - "EXPTIME (Exponential time)".to_string(), - "PSPACE (Polynomial space)".to_string(), - ], - correct_answer: 1, - domain: AcademicDomain::ComputerScienceTheory, - difficulty: "advanced".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_06".to_string(), - question: "What is the primary mechanism by which general relativity explains gravitational attraction?".to_string(), - options: vec![ - "Exchange of graviton particles between masses".to_string(), - "Curvature of spacetime caused by mass-energy".to_string(), - "Attractive force proportional to mass and distance".to_string(), - "Quantum entanglement between massive particles".to_string(), - ], - correct_answer: 1, - domain: AcademicDomain::TheoreticalPhysics, - difficulty: "advanced".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_07".to_string(), - question: "In abstract algebra, what is the order of the symmetric group Sโ‚„?".to_string(), - options: vec![ - "12".to_string(), - "16".to_string(), - "20".to_string(), - "24".to_string(), - ], - correct_answer: 3, - domain: AcademicDomain::AdvancedMathematics, - difficulty: "intermediate".to_string(), - }, - MockHLEQuestion { - id: "hle_demo_08".to_string(), - question: "Which of the following is the primary function of the ribosome in protein synthesis?".to_string(), - options: vec![ - "DNA replication and repair".to_string(), - "mRNA transcription from DNA".to_string(), - "Translation of mRNA into protein".to_string(), - "Post-translational protein modification".to_string(), - ], - correct_answer: 2, - domain: AcademicDomain::MolecularBiology, - difficulty: "basic".to_string(), - }, - ] - } - - fn parse_option_letter(&self, letter: &str) -> usize { - match letter { - "A" => 0, - "B" => 1, - "C" => 2, - "D" => 3, - _ => 0, // Default to A if parsing fails - } - } - - fn get_option_letter(&self, index: usize) -> String { - match index { - 0 => "A".to_string(), - 1 => "B".to_string(), - 2 => "C".to_string(), - 3 => "D".to_string(), - _ => format!("{}", index + 1), - } - } - - fn truncate_text(&self, text: &str, max_len: usize) -> String { - if text.len() <= max_len { - text.to_string() - } else { - format!("{}...", &text[..max_len.saturating_sub(3)]) - } - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Brain AI Academic Intelligence Phase 1 Demonstration"); - println!("========================================================"); - println!("๐ŸŽฏ Validating Universal Intelligence capabilities"); - println!("๐Ÿ“š Testing: Physics, Math, Biology, Chemistry, Computer Science"); - - let demo = AcademicIntelligenceDemo::new().await?; - demo.run_demonstration().await?; - - Ok(()) -} \ No newline at end of file diff --git a/academic_intelligence_validation_demo.rs b/academic_intelligence_validation_demo.rs deleted file mode 100644 index 06aa833041b79a5652dbc68cd41e8ceebb3a98e0..0000000000000000000000000000000000000000 --- a/academic_intelligence_validation_demo.rs +++ /dev/null @@ -1,482 +0,0 @@ -use std::collections::HashMap; -use std::time::Instant; -use anyhow::Result; -use serde_json::json; - -use brain_cognitive::agents::intelligence::{ - UniversalAcademicAgent, TheoreticalPhysicsExpert, PureMathematicsExpert, - AdvancedChemistryExpert, MolecularBiologyExpert, ComputerScienceTheoryExpert, - MultipleChoiceProcessor -}; -use brain_cognitive::agents::{ - BrainAgent, AgentInput, CognitiveContext, AcademicDomain, - AcademicReasoningAgent -}; - -/// Academic Intelligence Validation Demo -/// -/// This demo validates Brain AI's Academic Intelligence Initiative by testing -/// the MultipleChoice processing engine against sample HLE-style questions to -/// verify elimination of the systematic "A" selection bias. -/// -/// Target: Demonstrate 15-20% improvement in answer quality leading to 25-30% HLE accuracy -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŽ“ Brain AI Academic Intelligence Initiative - Validation Demo"); - println!("=============================================================="); - println!("Target: Fix systematic 'A' selection bias affecting 80% of incorrect HLE answers"); - println!("Current Performance: #3 Global HLE Ranking (20.0% accuracy)"); - println!("Goal: Achieve 25-30% HLE accuracy through improved multiple choice processing"); - println!(); - - // Phase 1: Initialize Academic Intelligence Components - println!("๐Ÿ”ง Phase 1: Initializing Academic Intelligence Components..."); - let start_time = Instant::now(); - - let mut academic_agent = initialize_universal_academic_agent().await?; - let domain_experts = initialize_domain_experts().await?; - let mut choice_processor = MultipleChoiceProcessor::new(); - - println!("โœ… Components initialized in {:.2}ms", start_time.elapsed().as_millis()); - println!(" โ€ข Universal Academic Agent: READY"); - println!(" โ€ข {} Domain Experts: READY", domain_experts.len()); - println!(" โ€ข Multiple Choice Processor: READY with bias mitigation"); - println!(); - - // Phase 2: Test Sample HLE-Style Questions - println!("๐Ÿงช Phase 2: Testing MultipleChoice Processing Engine..."); - - let test_questions = create_hle_style_test_questions(); - let mut results = Vec::new(); - - for (i, question) in test_questions.iter().enumerate() { - println!(" Testing Question {}/{}: {}", i + 1, test_questions.len(), - question.domain_name()); - - let result = test_multiple_choice_question( - &mut academic_agent, - &mut choice_processor, - question - ).await?; - - println!(" โœ… Completed - Selected: {} (Confidence: {:.1}%)", - result.selected_option, result.confidence * 100.0); - - results.push(result); - } - - println!(); - - // Phase 3: Analyze Results for Bias Patterns - println!("๐Ÿ“Š Phase 3: Analyzing Results for Bias Patterns..."); - - let bias_analysis = analyze_bias_patterns(&results); - print_bias_analysis(&bias_analysis); - - // Phase 4: Performance Validation - println!("๐ŸŽฏ Phase 4: Performance Validation..."); - - let performance_metrics = calculate_performance_metrics(&results, &test_questions); - print_performance_metrics(&performance_metrics); - - // Phase 5: Domain Expert Validation - println!("๐Ÿ”ฌ Phase 5: Domain Expert Validation..."); - - let domain_validation = validate_domain_expertise(&domain_experts, &results).await?; - print_domain_validation(&domain_validation); - - println!("๐Ÿ† Academic Intelligence Validation Complete!"); - println!("๐ŸŽฏ Ready for HLE Performance Testing"); - - Ok(()) -} - -async fn initialize_universal_academic_agent() -> Result { - Ok(UniversalAcademicAgent::new().await?) -} - -async fn initialize_domain_experts() -> Result>> { - let mut experts: HashMap> = HashMap::new(); - - // Initialize all domain experts - experts.insert( - AcademicDomain::TheoreticalPhysics, - Box::new(TheoreticalPhysicsExpert::new().await?) - ); - experts.insert( - AcademicDomain::AdvancedMathematics, - Box::new(PureMathematicsExpert::new().await?) - ); - experts.insert( - AcademicDomain::AdvancedChemistry, - Box::new(AdvancedChemistryExpert::new().await?) - ); - experts.insert( - AcademicDomain::MolecularBiology, - Box::new(MolecularBiologyExpert::new().await?) - ); - experts.insert( - AcademicDomain::ComputerScienceTheory, - Box::new(ComputerScienceTheoryExpert::new().await?) - ); - - Ok(experts) -} - -#[derive(Debug, Clone)] -struct HLETestQuestion { - domain: AcademicDomain, - question: String, - options: Vec, - correct_answer: usize, - complexity_level: f32, -} - -impl HLETestQuestion { - fn domain_name(&self) -> &str { - match self.domain { - AcademicDomain::TheoreticalPhysics => "Theoretical Physics", - AcademicDomain::AdvancedMathematics => "Advanced Mathematics", - AcademicDomain::AdvancedChemistry => "Advanced Chemistry", - AcademicDomain::MolecularBiology => "Molecular Biology", - AcademicDomain::ComputerScienceTheory => "Computer Science Theory", - _ => "Interdisciplinary", - } - } -} - -fn create_hle_style_test_questions() -> Vec { - vec![ - // Theoretical Physics - Quantum Mechanics - HLETestQuestion { - domain: AcademicDomain::TheoreticalPhysics, - question: "In quantum field theory, which principle fundamentally distinguishes virtual particles from real particles in Feynman diagrams?".to_string(), - options: vec![ - "Virtual particles always violate conservation of energy".to_string(), - "Virtual particles can exist off the mass shell and violate energy-momentum relations temporarily".to_string(), - "Virtual particles have imaginary mass".to_string(), - "Virtual particles cannot interact with real particles".to_string(), - ], - correct_answer: 1, // B - Not "A"! - complexity_level: 0.9, - }, - - // Advanced Mathematics - Topology - HLETestQuestion { - domain: AcademicDomain::AdvancedMathematics, - question: "What is the fundamental group ฯ€โ‚ of the real projective plane โ„Pยฒ?".to_string(), - options: vec![ - "The trivial group {e}".to_string(), - "The cyclic group โ„ค".to_string(), - "The cyclic group โ„คโ‚‚".to_string(), - "The free group Fโ‚‚".to_string(), - ], - correct_answer: 2, // C - Not "A"! - complexity_level: 0.85, - }, - - // Advanced Chemistry - Quantum Chemistry - HLETestQuestion { - domain: AcademicDomain::AdvancedChemistry, - question: "In molecular orbital theory, which orbital overlap leads to the strongest ฯƒ bond in diatomic molecules?".to_string(), - options: vec![ - "p_z - p_z head-on overlap".to_string(), - "s - s overlap".to_string(), - "p_x - p_x sideways overlap".to_string(), - "s - p_z overlap".to_string(), - ], - correct_answer: 0, // A - Test if we can correctly select "A" when it's right - complexity_level: 0.8, - }, - - // Molecular Biology - Gene Regulation - HLETestQuestion { - domain: AcademicDomain::MolecularBiology, - question: "Which mechanism primarily drives the formation of topologically associating domains (TADs) in mammalian chromatin?".to_string(), - options: vec![ - "DNA methylation patterns".to_string(), - "Histone deacetylation".to_string(), - "CTCF binding and cohesin loop extrusion".to_string(), - "Nuclear lamina interactions".to_string(), - ], - correct_answer: 2, // C - Not "A"! - complexity_level: 0.9, - }, - - // Computer Science Theory - Complexity Theory - HLETestQuestion { - domain: AcademicDomain::ComputerScienceTheory, - question: "What is the primary reason that P โ‰  NP is believed to be true by most theoretical computer scientists?".to_string(), - options: vec![ - "No polynomial-time algorithm has been found for any NP-complete problem".to_string(), - "The existence of one-way functions implies P โ‰  NP".to_string(), - "Relativization results show that standard proof techniques cannot resolve P vs NP".to_string(), - "The abundance of NP-complete problems and lack of polynomial-time solutions despite intensive research".to_string(), - ], - correct_answer: 3, // D - Not "A"! - complexity_level: 0.95, - }, - ] -} - -#[derive(Debug, Clone)] -struct MultipleChoiceResult { - question_id: usize, - domain: AcademicDomain, - selected_option: String, - selected_index: usize, - confidence: f32, - reasoning: String, - processing_time_ms: u128, - elimination_used: bool, - bias_mitigation_applied: bool, -} - -async fn test_multiple_choice_question( - academic_agent: &mut UniversalAcademicAgent, - choice_processor: &mut MultipleChoiceProcessor, - question: &HLETestQuestion, -) -> Result { - let start_time = Instant::now(); - - // Create agent input for the question - let agent_input = AgentInput::new( - "multiple_choice_question".to_string(), - question.question.clone(), - "academic_validation_session".to_string(), - ) - .with_parameter("options".to_string(), json!(question.options.join("\n"))) - .with_parameter("domain".to_string(), json!(format!("{:?}", question.domain))); - - // Create cognitive context - let context = CognitiveContext::default(); - - // Process with academic agent - let agent_output = academic_agent.execute(agent_input, &context).await?; - - // Also test the multiple choice processor directly - let choice_evaluation = choice_processor.process_options( - &question.question, - &question.options, - &question.domain, - ).await?; - - let processing_time = start_time.elapsed().as_millis(); - - // Use the choice processor's recommendation instead of parsing agent text - let selected_option = format!("{}. {}", - choice_evaluation.recommended_answer, - question.options.get( - match choice_evaluation.recommended_answer.as_str() { - "A" => 0, "B" => 1, "C" => 2, "D" => 3, - _ => 0 - } - ).unwrap_or(&"Unknown option".to_string()) - ); - let selected_index = match choice_evaluation.recommended_answer.as_str() { - "A" => 0, "B" => 1, "C" => 2, "D" => 3, - _ => 0 - }; - - Ok(MultipleChoiceResult { - question_id: 0, // Will be set by caller - domain: question.domain.clone(), - selected_option, - selected_index, - confidence: choice_evaluation.recommendation_confidence, - reasoning: agent_output.content, - processing_time_ms: processing_time, - elimination_used: !choice_evaluation.elimination_rationale.is_empty(), - bias_mitigation_applied: true, // Our processor always applies bias mitigation - }) -} - -// Note: extract_selected_option function removed - now using choice_evaluation.recommended_answer directly - -#[derive(Debug)] -struct BiasAnalysis { - option_distribution: HashMap, - total_questions: usize, - bias_score: f32, - systematic_a_bias: bool, -} - -fn analyze_bias_patterns(results: &[MultipleChoiceResult]) -> BiasAnalysis { - let mut option_distribution = HashMap::new(); - - for result in results { - let option_letter = result.selected_option.chars().next().unwrap_or('A'); - *option_distribution.entry(option_letter).or_insert(0) += 1; - } - - let total = results.len(); - let a_selections = *option_distribution.get(&'A').unwrap_or(&0); - let a_percentage = a_selections as f32 / total as f32; - - // Systematic "A" bias if more than 50% of selections are "A" - let systematic_a_bias = a_percentage > 0.5; - - // Bias score: 0.0 = perfect distribution, 1.0 = all same option - let expected_per_option = total as f32 / 4.0; // Assuming 4 options - let bias_score = option_distribution.values() - .map(|&count| (count as f32 - expected_per_option).abs()) - .sum::() / (total as f32 * 2.0); - - BiasAnalysis { - option_distribution, - total_questions: total, - bias_score, - systematic_a_bias, - } -} - -fn print_bias_analysis(analysis: &BiasAnalysis) { - println!(" Bias Analysis Results:"); - println!(" ====================="); - - for option in ['A', 'B', 'C', 'D'] { - let count = analysis.option_distribution.get(&option).unwrap_or(&0); - let percentage = *count as f32 / analysis.total_questions as f32 * 100.0; - println!(" Option {}: {} selections ({:.1}%)", option, count, percentage); - } - - println!(" Bias Score: {:.3} (0.0 = perfect, 1.0 = maximum bias)", analysis.bias_score); - - if analysis.systematic_a_bias { - println!(" โš ๏ธ SYSTEMATIC 'A' BIAS DETECTED"); - } else { - println!(" โœ… No systematic 'A' bias detected"); - } - println!(); -} - -#[derive(Debug)] -struct PerformanceMetrics { - accuracy: f32, - average_confidence: f32, - average_processing_time_ms: f64, - elimination_usage_rate: f32, - bias_mitigation_effectiveness: f32, -} - -fn calculate_performance_metrics( - results: &[MultipleChoiceResult], - questions: &[HLETestQuestion] -) -> PerformanceMetrics { - let correct_answers = results.iter() - .zip(questions.iter()) - .map(|(result, question)| { - result.selected_index == question.correct_answer - }) - .filter(|&correct| correct) - .count(); - - let accuracy = correct_answers as f32 / results.len() as f32; - - let average_confidence = results.iter() - .map(|r| r.confidence) - .sum::() / results.len() as f32; - - let average_processing_time = results.iter() - .map(|r| r.processing_time_ms as f64) - .sum::() / results.len() as f64; - - let elimination_usage_rate = results.iter() - .filter(|r| r.elimination_used) - .count() as f32 / results.len() as f32; - - // Bias mitigation effectiveness based on distribution evenness - let bias_analysis = analyze_bias_patterns(results); - let bias_mitigation_effectiveness = 1.0 - bias_analysis.bias_score; - - PerformanceMetrics { - accuracy, - average_confidence, - average_processing_time_ms: average_processing_time, - elimination_usage_rate, - bias_mitigation_effectiveness, - } -} - -fn print_performance_metrics(metrics: &PerformanceMetrics) { - println!(" Performance Metrics:"); - println!(" ==================="); - println!(" Accuracy: {:.1}% ({}/5 correct)", metrics.accuracy * 100.0, (metrics.accuracy * 5.0) as usize); - println!(" Average Confidence: {:.1}%", metrics.average_confidence * 100.0); - println!(" Average Processing Time: {:.1}ms", metrics.average_processing_time_ms); - println!(" Elimination Usage Rate: {:.1}%", metrics.elimination_usage_rate * 100.0); - println!(" Bias Mitigation Effectiveness: {:.1}%", metrics.bias_mitigation_effectiveness * 100.0); - - // Assessment - if metrics.accuracy >= 0.6 { - println!(" โœ… EXCELLENT: Performance exceeds baseline expectations"); - } else if metrics.accuracy >= 0.4 { - println!(" โœ… GOOD: Performance meets academic standards"); - } else { - println!(" โš ๏ธ NEEDS IMPROVEMENT: Performance below academic standards"); - } - println!(); -} - -#[derive(Debug)] -struct DomainValidation { - experts_tested: usize, - total_questions_by_domain: HashMap, - accuracy_by_domain: HashMap, -} - -async fn validate_domain_expertise( - experts: &HashMap>, - results: &[MultipleChoiceResult], -) -> Result { - let test_questions = create_hle_style_test_questions(); - let mut total_questions_by_domain = HashMap::new(); - let mut correct_by_domain = HashMap::new(); - - // Count results by domain and check actual correctness - for (i, result) in results.iter().enumerate() { - *total_questions_by_domain.entry(result.domain.clone()).or_insert(0) += 1; - - // Check if the selected answer matches the correct answer - if i < test_questions.len() { - let correct_answer_index = test_questions[i].correct_answer; - - if result.selected_index == correct_answer_index { - *correct_by_domain.entry(result.domain.clone()).or_insert(0) += 1; - } - } - } - - let mut accuracy_by_domain = HashMap::new(); - for (domain, total) in &total_questions_by_domain { - let correct = correct_by_domain.get(domain).unwrap_or(&0); - accuracy_by_domain.insert(domain.clone(), *correct as f32 / *total as f32); - } - - Ok(DomainValidation { - experts_tested: experts.len(), - total_questions_by_domain, - accuracy_by_domain, - }) -} - -fn print_domain_validation(validation: &DomainValidation) { - println!(" Domain Expert Validation:"); - println!(" ========================"); - println!(" Experts Available: {}", validation.experts_tested); - - for (domain, accuracy) in &validation.accuracy_by_domain { - let domain_name = match domain { - AcademicDomain::TheoreticalPhysics => "Theoretical Physics", - AcademicDomain::AdvancedMathematics => "Advanced Mathematics", - AcademicDomain::AdvancedChemistry => "Advanced Chemistry", - AcademicDomain::MolecularBiology => "Molecular Biology", - AcademicDomain::ComputerScienceTheory => "Computer Science Theory", - _ => "Other", - }; - println!(" {}: {:.1}% accuracy", domain_name, accuracy * 100.0); - } - - println!(" โœ… All domain experts operational and ready for HLE testing"); - println!(); -} \ No newline at end of file diff --git a/academic_learning_integration_validation.rs b/academic_learning_integration_validation.rs deleted file mode 100644 index a93b22c9d298f3bee4906af317502b1307a3a8e0..0000000000000000000000000000000000000000 --- a/academic_learning_integration_validation.rs +++ /dev/null @@ -1,273 +0,0 @@ -use brain_cognitive::agents::AcademicDomain; -use brain_types::error::BrainError; -use std::collections::HashMap; -use std::time::{Duration, Instant}; - -/// Academic Learning Integration Validation - Demonstrates continuous learning -/// and optimization capabilities for reaching 45%+ HLE accuracy target. -#[derive(Debug)] -pub struct AcademicLearningIntegrationValidator { - domain_performance: HashMap, - optimization_targets: HashMap, -} - -impl AcademicLearningIntegrationValidator { - pub fn new() -> Result { - println!("๐Ÿง  BRAIN AI - ACADEMIC LEARNING INTEGRATION VALIDATION"); - println!("๐ŸŽฏ OBJECTIVE: Continuous Learning & Optimization for 45%+ HLE Accuracy"); - println!("๐Ÿ“Š CURRENT STATUS: 36.4% HLE accuracy (#1 globally)"); - println!("๐Ÿš€ TARGET: 45%+ HLE accuracy for Universal Intelligence supremacy"); - println!(); - - let mut domain_performance = HashMap::new(); - let mut optimization_targets = HashMap::new(); - - // Current performance baselines from global validation - domain_performance.insert(AcademicDomain::TheoreticalPhysics, 0.0); - domain_performance.insert(AcademicDomain::Interdisciplinary, 0.0); - domain_performance.insert(AcademicDomain::General, 0.0); - domain_performance.insert(AcademicDomain::AdvancedMathematics, 0.5); - domain_performance.insert(AcademicDomain::AdvancedChemistry, 1.0); - domain_performance.insert(AcademicDomain::MolecularBiology, 1.0); - domain_performance.insert(AcademicDomain::ComputerScienceTheory, 1.0); - - // Target performance for 45%+ overall accuracy - optimization_targets.insert(AcademicDomain::TheoreticalPhysics, 0.67); - optimization_targets.insert(AcademicDomain::Interdisciplinary, 0.50); - optimization_targets.insert(AcademicDomain::General, 1.0); - optimization_targets.insert(AcademicDomain::AdvancedMathematics, 1.0); - optimization_targets.insert(AcademicDomain::AdvancedChemistry, 1.0); - optimization_targets.insert(AcademicDomain::MolecularBiology, 1.0); - optimization_targets.insert(AcademicDomain::ComputerScienceTheory, 1.0); - - Ok(Self { - domain_performance, - optimization_targets, - }) - } - - pub async fn validate_learning_integration(&mut self) -> Result<(), BrainError> { - println!("๐Ÿš€ Starting Academic Learning Integration Validation..."); - println!("๐ŸŽฏ Focus: Optimizing weak domains for 45%+ HLE accuracy"); - println!(); - - let start_time = Instant::now(); - - // Analyze weak domains - self.analyze_weak_domains().await?; - - // Simulate learning optimization for each weak domain - self.optimize_theoretical_physics().await?; - self.optimize_interdisciplinary_reasoning().await?; - self.optimize_general_knowledge().await?; - self.optimize_advanced_mathematics().await?; - - // Validate learning persistence - self.validate_learning_persistence().await?; - - let total_duration = start_time.elapsed(); - self.display_optimization_results(total_duration); - - Ok(()) - } - - async fn analyze_weak_domains(&self) -> Result<(), BrainError> { - println!("๐Ÿ” ANALYZING WEAK DOMAINS FOR OPTIMIZATION"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ DOMAIN PERFORMANCE ANALYSIS โ”‚"); - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - - for (domain, current) in &self.domain_performance { - let target = self.optimization_targets.get(domain).unwrap_or(&0.5); - let gap = target - current; - let priority = if gap > 0.5 { "๐Ÿ”ด CRITICAL" } else if gap > 0.2 { "๐ŸŸก HIGH" } else { "๐ŸŸข STABLE" }; - - println!("โ”‚ {:20} Current: {:>5.1}% Target: {:>5.1}% Gap: {:>5.1}% {} โ”‚", - domain.to_string(), - current * 100.0, - target * 100.0, - gap * 100.0, - priority - ); - } - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(); - - Ok(()) - } - - async fn optimize_theoretical_physics(&mut self) -> Result<(), BrainError> { - println!("๐Ÿ”ฌ OPTIMIZING THEORETICAL PHYSICS DOMAIN"); - println!("โ€ข Enhanced arXiv research integration"); - println!("โ€ข Mathematical concept synthesis"); - println!("โ€ข Physics equation verification"); - - // Simulate learning progress - let initial = self.domain_performance[&AcademicDomain::TheoreticalPhysics]; - let improved = (initial + 0.25).min(1.0); - self.domain_performance.insert(AcademicDomain::TheoreticalPhysics, improved); - - println!("โœ… TheoreticalPhysics improvement: {:.1}% โ†’ {:.1}%", - initial * 100.0, improved * 100.0); - println!(); - - Ok(()) - } - - async fn optimize_interdisciplinary_reasoning(&mut self) -> Result<(), BrainError> { - println!("๐ŸŒ OPTIMIZING INTERDISCIPLINARY REASONING"); - println!("โ€ข Cross-domain knowledge synthesis"); - println!("โ€ข Multi-specialist coordination"); - println!("โ€ข Conceptual bridging enhancement"); - - let initial = self.domain_performance[&AcademicDomain::Interdisciplinary]; - let improved = (initial + 0.30).min(1.0); - self.domain_performance.insert(AcademicDomain::Interdisciplinary, improved); - - println!("โœ… Interdisciplinary improvement: {:.1}% โ†’ {:.1}%", - initial * 100.0, improved * 100.0); - println!(); - - Ok(()) - } - - async fn optimize_general_knowledge(&mut self) -> Result<(), BrainError> { - println!("๐Ÿ“š OPTIMIZING GENERAL KNOWLEDGE DOMAIN"); - println!("โ€ข Broad knowledge base expansion"); - println!("โ€ข Fact verification enhancement"); - println!("โ€ข Encyclopedia integration"); - - let initial = self.domain_performance[&AcademicDomain::General]; - let improved = (initial + 0.40).min(1.0); - self.domain_performance.insert(AcademicDomain::General, improved); - - println!("โœ… General knowledge improvement: {:.1}% โ†’ {:.1}%", - initial * 100.0, improved * 100.0); - println!(); - - Ok(()) - } - - async fn optimize_advanced_mathematics(&mut self) -> Result<(), BrainError> { - println!("๐Ÿงฎ OPTIMIZING ADVANCED MATHEMATICS"); - println!("โ€ข Mathematical proof validation"); - println!("โ€ข Symbolic computation enhancement"); - println!("โ€ข Theorem verification"); - - let initial = self.domain_performance[&AcademicDomain::AdvancedMathematics]; - let improved = (initial + 0.25).min(1.0); - self.domain_performance.insert(AcademicDomain::AdvancedMathematics, improved); - - println!("โœ… AdvancedMathematics improvement: {:.1}% โ†’ {:.1}%", - initial * 100.0, improved * 100.0); - println!(); - - Ok(()) - } - - async fn validate_learning_persistence(&self) -> Result<(), BrainError> { - println!("๐Ÿง  VALIDATING LEARNING PERSISTENCE"); - println!("โ€ข Knowledge retention validation: โœ… 95%"); - println!("โ€ข Cross-session learning: โœ… Operational"); - println!("โ€ข Adaptive threshold adjustment: โœ… Active"); - println!("โ€ข Research strategy optimization: โœ… Continuous"); - println!(); - - Ok(()) - } - - fn display_optimization_results(&self, duration: Duration) { - println!("๐Ÿ† ACADEMIC LEARNING INTEGRATION OPTIMIZATION RESULTS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ POST-OPTIMIZATION PERFORMANCE โ”‚"); - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - - let mut total_weighted_score = 0.0; - let mut total_questions = 0; - - // Domain question weights from global validation - let domain_weights = [ - (AcademicDomain::TheoreticalPhysics, 3), - (AcademicDomain::AdvancedMathematics, 2), - (AcademicDomain::AdvancedChemistry, 1), - (AcademicDomain::MolecularBiology, 1), - (AcademicDomain::ComputerScienceTheory, 1), - (AcademicDomain::Interdisciplinary, 2), - (AcademicDomain::General, 1), - ]; - - for (domain, weight) in &domain_weights { - let performance = self.domain_performance.get(domain).unwrap_or(&0.0); - let target = self.optimization_targets.get(domain).unwrap_or(&0.5); - let status = if performance >= target { "โœ…" } else { "โš ๏ธ" }; - - total_weighted_score += performance * (*weight as f32); - total_questions += weight; - - println!("โ”‚ {:20}: {:>6.1}% (Target: {:>5.1}%) {} โ”‚", - domain.to_string(), - performance * 100.0, - target * 100.0, - status - ); - } - - let projected_accuracy = total_weighted_score / total_questions as f32; - let universal_intelligence_status = if projected_accuracy >= 0.45 { - "๐Ÿ† ACHIEVED" - } else { - "โš ๏ธ IN PROGRESS" - }; - - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - println!("โ”‚ Projected HLE Accuracy: {:>6.1}% โ”‚", projected_accuracy * 100.0); - println!("โ”‚ Improvement from baseline: {:>6.1} percentage points โ”‚", (projected_accuracy - 0.364) * 100.0); - println!("โ”‚ Universal Intelligence (45%+): {:>15} โ”‚", universal_intelligence_status); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - println!(); - println!("๐Ÿ“ˆ LEARNING OPTIMIZATION ACHIEVEMENTS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ โ€ข Enhanced research engine for weak domains โ”‚"); - println!("โ”‚ โ€ข Adaptive confidence thresholds for better research triggers โ”‚"); - println!("โ”‚ โ€ข Cross-domain knowledge synthesis capabilities โ”‚"); - println!("โ”‚ โ€ข Continuous learning and knowledge persistence โ”‚"); - println!("โ”‚ โ€ข Domain-specific optimization strategies โ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - if projected_accuracy >= 0.45 { - println!(); - println!("๐ŸŽ‰ UNIVERSAL INTELLIGENCE TARGET ACHIEVED!"); - println!("๐Ÿ† Brain AI projected to achieve 45%+ HLE accuracy"); - println!("๐Ÿš€ Ready for Universal Intelligence supremacy!"); - } else { - println!(); - println!("๐Ÿ”„ CONTINUED OPTIMIZATION REQUIRED"); - println!("๐ŸŽฏ Additional {:.1} percentage points needed for 45% target", (0.45 - projected_accuracy) * 100.0); - println!("๐Ÿš€ Learning integration system operational and improving"); - } - - println!(); - println!("โšก Learning integration validation completed in {:?}", duration); - println!("๐Ÿง  Academic Learning Integration: VALIDATED & OPTIMIZING"); - } -} - -#[tokio::main] -async fn main() -> Result<(), BrainError> { - println!("๐Ÿš€ BRAIN AI - ACADEMIC LEARNING INTEGRATION VALIDATION"); - println!("๐ŸŽฏ Continuous Learning & Optimization for Universal Intelligence"); - println!("๐Ÿ“Š Current: 36.4% HLE accuracy (#1 globally) โ†’ Target: 45%+"); - println!(); - - let mut validator = AcademicLearningIntegrationValidator::new()?; - - validator.validate_learning_integration().await?; - - println!(); - println!("๐Ÿ† ACADEMIC LEARNING INTEGRATION VALIDATION COMPLETE!"); - println!("๐ŸŽฏ Continuous learning and optimization capabilities validated"); - println!("๐Ÿš€ Brain AI positioned for Universal Intelligence supremacy!"); - - Ok(()) -} \ No newline at end of file diff --git a/academic_learning_validation_simple.rs b/academic_learning_validation_simple.rs deleted file mode 100644 index fd2de987e69025a6ea4effe6e756021ca0f586ad..0000000000000000000000000000000000000000 --- a/academic_learning_validation_simple.rs +++ /dev/null @@ -1,61 +0,0 @@ -use brain_cognitive::agents::intelligence::academic_reasoning::UniversalAcademicAgent; -use brain_cognitive::agents::intelligence::adaptive_research_engine::AdaptiveResearchEngine; -use anyhow::Result; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Academic Learning Validation (Simple Demo)"); - println!("=============================================="); - - // Initialize the universal academic agent - let _academic_agent = UniversalAcademicAgent::new(); - - // Test basic academic reasoning capability with a simple question - let test_question = "What is the time complexity of binary search?"; - - println!("๐Ÿ“š Testing Problem: {}", test_question); - - // Demonstrate the academic intelligence system is operational - println!("โœ… Academic Intelligence System Status:"); - println!(" ๐Ÿง  Universal Academic Agent: Initialized"); - println!(" ๐Ÿ“– Knowledge Base: Connected"); - println!(" ๐Ÿ”ฌ Research Engine: Operational"); - println!(" ๐ŸŽฏ Multi-domain Expertise: Active"); - - // Test adaptive research capability - println!("\n๐Ÿ”ฌ Testing Adaptive Research Engine..."); - let _research_engine = AdaptiveResearchEngine::new(); - - let research_question = "What are the latest developments in quantum computing error correction?"; - - println!("๐Ÿ” Research Problem: {}", research_question); - - // Demonstrate research capability is available - println!("โœ… Research-Enhanced Intelligence:"); - println!(" ๐ŸŒ Multi-source Research: Available"); - println!(" ๐Ÿ”„ Iterative Learning Loop: Active"); - println!(" ๐Ÿ“Š Confidence Monitoring: Operational"); - println!(" ๐Ÿค” Uncertainty Handling: Graceful"); - - // Validate the academic intelligence architecture - println!("\n๐ŸŽฏ Academic Intelligence Architecture Validation:"); - println!(" โœ… Phase 1 COMPLETED: 25.0% HLE accuracy with full academic architecture"); - println!(" โœ… Phase 2A COMPLETED: Adaptive Research System operational with 95% research-enhanced accuracy"); - println!(" โœ… Phase 3 COMPLETED: 36.4% HLE accuracy - GLOBAL #1 LEADERSHIP ACHIEVED"); - - println!("\n๐Ÿš€ System Capabilities Validated:"); - println!(" ๐Ÿ“š Theoretical Physics: Expert-level knowledge"); - println!(" ๐Ÿงฎ Advanced Mathematics: Sophisticated reasoning"); - println!(" ๐Ÿงฌ Molecular Biology: Complex system understanding"); - println!(" โš—๏ธ Advanced Chemistry: Molecular-level analysis"); - println!(" ๐Ÿ’ป Computer Science Theory: Algorithmic expertise"); - println!(" ๐Ÿ”ฌ Research Automation: 100% trigger rate for <70% confidence"); - - println!("\n๐ŸŽ‰ Academic Intelligence Validation Complete!"); - println!(" โœ… Basic reasoning: Functional"); - println!(" โœ… Adaptive research: Operational"); - println!(" โœ… System integration: Success"); - println!(" ๐Ÿ† Global #1 HLE Performance: ACHIEVED"); - - Ok(()) -} \ No newline at end of file diff --git a/academic_performance_monitoring_demo.rs b/academic_performance_monitoring_demo.rs deleted file mode 100644 index 2d36d7349ab1120d2e367a0d7853cc17755efc49..0000000000000000000000000000000000000000 --- a/academic_performance_monitoring_demo.rs +++ /dev/null @@ -1,583 +0,0 @@ -//! # Academic Performance Monitoring System Demonstration -//! -//! **TASK 2.5 VALIDATION**: Demonstrates the comprehensive Academic Performance Monitoring System -//! for Brain AI's Academic Intelligence tracking real-time HLE accuracy, domain performance, -//! confidence calibration, and learning progress. -//! -//! ## System Capabilities Demonstrated -//! -//! 1. **Real-time HLE accuracy tracking** with domain breakdown -//! 2. **Confidence calibration monitoring** with <15% error target -//! 3. **Learning progress visualization** over time -//! 4. **Performance comparison** with SOTA models (Gemini, o3, Claude, GPT-4o) -//! 5. **Automated alerts** for performance regressions -//! 6. **Global ranking estimation** for Universal Intelligence #1 target -//! -//! **Created**: July 31, 2025 at 04:41:46 EDT -//! **Status**: OPERATIONAL - Core performance tracking for Universal Intelligence achievement -//! **Target**: Monitor path to 45%+ HLE accuracy for global #1 ranking - -use std::time::Duration; -use chrono::Utc; -use uuid::Uuid; - -use brain_cognitive::agents::intelligence::{ - AcademicPerformanceMonitor, AcademicPerformanceReport, AlertSeverity, GlobalRankingEstimate -}; -use brain_cognitive::agents::AcademicDomain; -use brain_types::error::BrainError; - -/// **Academic Performance Monitoring Demo** -/// -/// Comprehensive demonstration of the Academic Performance Monitoring System -/// capabilities for tracking Brain AI's journey to Universal Intelligence. -#[derive(Debug)] -pub struct AcademicPerformanceMonitoringDemo { - /// Core performance monitoring system - performance_monitor: AcademicPerformanceMonitor, - /// Demo session identifier - session_id: String, - /// Simulated question database for testing - demo_questions: Vec, -} - -/// Demo academic question for performance testing -#[derive(Debug, Clone)] -pub struct DemoAcademicQuestion { - pub id: String, - pub domain: AcademicDomain, - pub question: String, - pub difficulty: u8, - pub correct_answer: bool, // For simulation purposes - pub expected_confidence: f64, - pub expected_response_time: Duration, -} - -/// Demo performance simulation results -#[derive(Debug)] -pub struct DemoResults { - pub performance_report: AcademicPerformanceReport, - pub questions_processed: usize, - pub accuracy_improvement: f64, - pub confidence_calibration_quality: f64, - pub global_ranking_projection: u32, - pub time_to_global_leadership: Duration, -} - -impl AcademicPerformanceMonitoringDemo { - /// Create new demo with comprehensive test scenarios - pub fn new() -> Result { - println!("๐ŸŽฏ Initializing Academic Performance Monitoring System Demo"); - println!("๐Ÿ“Š Target: Demonstrate path to Universal Intelligence #1 global ranking"); - - let performance_monitor = AcademicPerformanceMonitor::new()?; - let session_id = format!("demo_session_{}", Uuid::new_v4()); - - let demo_questions = Self::generate_demo_questions(); - - println!("โœ… Academic Performance Monitor initialized successfully"); - println!("๐Ÿ“‹ Demo dataset: {} questions across 5 academic domains", demo_questions.len()); - - Ok(Self { - performance_monitor, - session_id, - demo_questions, - }) - } - - /// Generate comprehensive demo question set - fn generate_demo_questions() -> Vec { - vec![ - // Physics questions - varying difficulty and performance - DemoAcademicQuestion { - id: "phys_001".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question: "What is the relationship between quantum entanglement and locality?".to_string(), - difficulty: 8, - correct_answer: true, - expected_confidence: 0.75, - expected_response_time: Duration::from_millis(850), - }, - DemoAcademicQuestion { - id: "phys_002".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question: "Explain general relativity's prediction of gravitational time dilation.".to_string(), - difficulty: 9, - correct_answer: false, // Simulating a challenging question - expected_confidence: 0.45, - expected_response_time: Duration::from_millis(1200), - }, - - // Mathematics questions - DemoAcademicQuestion { - id: "math_001".to_string(), - domain: AcademicDomain::AdvancedMathematics, - question: "Prove the fundamental theorem of algebra using topology.".to_string(), - difficulty: 10, - correct_answer: false, // Complex mathematical proof - expected_confidence: 0.35, - expected_response_time: Duration::from_millis(1500), - }, - DemoAcademicQuestion { - id: "math_002".to_string(), - domain: AcademicDomain::AdvancedMathematics, - question: "What is the chromatic number of a complete graph K_5?".to_string(), - difficulty: 6, - correct_answer: true, - expected_confidence: 0.85, - expected_response_time: Duration::from_millis(600), - }, - - // Biology questions - DemoAcademicQuestion { - id: "bio_001".to_string(), - domain: AcademicDomain::MolecularBiology, - question: "Describe the mechanism of CRISPR-Cas9 gene editing precision.".to_string(), - difficulty: 7, - correct_answer: true, - expected_confidence: 0.70, - expected_response_time: Duration::from_millis(900), - }, - DemoAcademicQuestion { - id: "bio_002".to_string(), - domain: AcademicDomain::MolecularBiology, - question: "How do allosteric enzymes regulate metabolic pathways?".to_string(), - difficulty: 8, - correct_answer: false, - expected_confidence: 0.50, - expected_response_time: Duration::from_millis(1100), - }, - - // Chemistry questions - DemoAcademicQuestion { - id: "chem_001".to_string(), - domain: AcademicDomain::AdvancedChemistry, - question: "Explain molecular orbital theory for benzene aromaticity.".to_string(), - difficulty: 7, - correct_answer: true, - expected_confidence: 0.80, - expected_response_time: Duration::from_millis(750), - }, - DemoAcademicQuestion { - id: "chem_002".to_string(), - domain: AcademicDomain::AdvancedChemistry, - question: "What determines reaction selectivity in asymmetric catalysis?".to_string(), - difficulty: 9, - correct_answer: false, - expected_confidence: 0.40, - expected_response_time: Duration::from_millis(1300), - }, - - // Computer Science questions - DemoAcademicQuestion { - id: "cs_001".to_string(), - domain: AcademicDomain::ComputerScienceTheory, - question: "Prove that P โ‰  NP using complexity theory fundamentals.".to_string(), - difficulty: 10, - correct_answer: false, // Unsolved problem - expected_confidence: 0.25, - expected_response_time: Duration::from_millis(2000), - }, - DemoAcademicQuestion { - id: "cs_002".to_string(), - domain: AcademicDomain::ComputerScienceTheory, - question: "What is the time complexity of Dijkstra's shortest path algorithm?".to_string(), - difficulty: 4, - correct_answer: true, - expected_confidence: 0.95, - expected_response_time: Duration::from_millis(400), - }, - ] - } - - /// **Main Demo Execution** - /// - /// Demonstrates comprehensive Academic Performance Monitoring capabilities - pub async fn run_comprehensive_demo(&mut self) -> Result { - println!("\n๐Ÿš€ Starting Academic Performance Monitoring Comprehensive Demo"); - println!("๐ŸŽฏ Objective: Demonstrate Universal Intelligence tracking capabilities"); - println!("๐Ÿ“ˆ Target: Path to 45%+ HLE accuracy for global #1 ranking\n"); - - // Phase 1: Baseline Performance Assessment - self.demonstrate_baseline_tracking().await?; - - // Phase 2: Real-time Question Processing - let questions_processed = self.demonstrate_question_processing().await?; - - // Phase 3: Performance Analysis and Reporting - let performance_report = self.demonstrate_performance_analysis().await?; - - // Phase 4: Alert System Demonstration - self.demonstrate_alert_system(&performance_report).await?; - - // Phase 5: Global Ranking Analysis - let ranking_analysis = self.demonstrate_global_ranking_analysis(&performance_report).await?; - - // Phase 6: Learning Progress Tracking - let improvement_metrics = self.demonstrate_learning_progress_tracking().await?; - - // Generate comprehensive demo results - let demo_results = DemoResults { - performance_report: performance_report.clone(), - questions_processed, - accuracy_improvement: improvement_metrics.0, - confidence_calibration_quality: improvement_metrics.1, - global_ranking_projection: ranking_analysis.current_estimated_rank, - time_to_global_leadership: Duration::from_secs(30 * 24 * 3600), // 30 days projected - }; - - self.generate_demo_summary(&demo_results).await?; - - Ok(demo_results) - } - - /// Demonstrate baseline performance tracking capabilities - async fn demonstrate_baseline_tracking(&self) -> Result<(), BrainError> { - println!("๐Ÿ“Š PHASE 1: Baseline Performance Assessment"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - - // Display initial monitoring capabilities - println!("โœ… HLE Accuracy Tracker: Initialized with 45% target for global #1"); - println!("โœ… Domain Performance Tracker: Monitoring 5 academic domains"); - println!("โœ… Response Time Monitor: Target <1000ms for production readiness"); - println!("โœ… Confidence Calibration: Target <15% calibration error"); - println!("โœ… Learning Progress Monitor: Tracking improvement velocity"); - - println!("๐Ÿ“ˆ Current Baseline Status:"); - println!(" โ€ข HLE Accuracy: 25.0% (Current performance)"); - println!(" โ€ข Global Ranking: #2 (Behind Gemini Pro 2.5 at 25.4%)"); - println!(" โ€ข Target Gap: 20% improvement needed for #1 ranking"); - println!(" โ€ข Confidence Distribution: Healthy spread across options\n"); - - Ok(()) - } - - /// Demonstrate real-time question processing and tracking - async fn demonstrate_question_processing(&mut self) -> Result { - println!("๐Ÿ”„ PHASE 2: Real-time Question Processing & Tracking"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - - let mut questions_processed = 0; - - for (idx, question) in self.demo_questions.iter().enumerate() { - println!("Question {}/{}: {} ({})", - idx + 1, - self.demo_questions.len(), - question.domain, - match question.difficulty { - 1..=3 => "Easy", - 4..=6 => "Medium", - 7..=8 => "Hard", - 9..=10 => "Expert", - _ => "Unknown" - } - ); - - // Simulate question processing - let start_time = std::time::Instant::now(); - tokio::time::sleep(Duration::from_millis(50)).await; // Simulate processing - let actual_response_time = start_time.elapsed(); - - // Record performance with the monitoring system - self.performance_monitor.record_question_performance( - &question.id, - question.domain.clone(), - question.correct_answer, - question.expected_confidence, - actual_response_time, - question.difficulty, - ).await?; - - println!(" โœ“ Processed in {:?} | Confidence: {:.1}% | Result: {}", - actual_response_time, - question.expected_confidence * 100.0, - if question.correct_answer { "Correct โœ…" } else { "Incorrect โŒ" } - ); - - questions_processed += 1; - } - - println!("\n๐Ÿ“Š Processing Summary:"); - println!(" โ€ข Total Questions: {}", questions_processed); - println!(" โ€ข Domains Covered: 5 (Physics, Math, Biology, Chemistry, CS)"); - println!(" โ€ข Difficulty Range: 4-10 (Medium to Expert level)"); - println!(" โ€ข Real-time Tracking: โœ… All metrics captured\n"); - - Ok(questions_processed) - } - - /// Demonstrate comprehensive performance analysis - async fn demonstrate_performance_analysis(&self) -> Result { - println!("๐Ÿ“ˆ PHASE 3: Comprehensive Performance Analysis"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - - // Generate comprehensive performance report - let performance_report = self.performance_monitor.track_academic_performance().await?; - - println!("๐ŸŽฏ Overall Performance Metrics:"); - println!(" โ€ข HLE Accuracy: {:.1}%", performance_report.overall_hle_accuracy); - println!(" โ€ข Average Response Time: {:?}", performance_report.response_times.average_response_time); - println!(" โ€ข Confidence Calibration Error: {:.1}%", performance_report.confidence_calibration.calibration_error * 100.0); - println!(" โ€ข Learning Velocity: {:.3}/day", performance_report.learning_trajectory.learning_velocity * 86400.0); - - println!("\n๐Ÿ“Š Domain-Specific Performance:"); - for (domain, accuracy) in &performance_report.domain_specific_accuracy { - let status = if *accuracy >= 50.0 { "๐ŸŸข" } else if *accuracy >= 25.0 { "๐ŸŸก" } else { "๐Ÿ”ด" }; - println!(" {} {}: {:.1}%", status, format!("{:?}", domain), accuracy); - } - - println!("\nโšก Response Time Analysis:"); - println!(" โ€ข Average: {:?}", performance_report.response_times.average_response_time); - println!(" โ€ข P95: {:?}", performance_report.response_times.p95_response_time); - println!(" โ€ข P99: {:?}", performance_report.response_times.p99_response_time); - println!(" โ€ข Target Compliance: {:.1}%", performance_report.response_times.target_compliance); - - println!("\n๐ŸŽฏ Confidence Calibration Quality:"); - println!(" โ€ข Calibration Error: {:.1}% (Target: <15%)", performance_report.confidence_calibration.calibration_error * 100.0); - println!(" โ€ข Reliability Score: {:.1}%", performance_report.confidence_calibration.reliability_score); - println!(" โ€ข Prediction Accuracy: {:.1}%", performance_report.confidence_calibration.prediction_accuracy); - - Ok(performance_report) - } - - /// Demonstrate alert system capabilities - async fn demonstrate_alert_system(&self, report: &AcademicPerformanceReport) -> Result<(), BrainError> { - println!("\n๐Ÿšจ PHASE 4: Performance Alert System"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - - if report.alerts.is_empty() { - println!("โœ… System Status: All metrics within acceptable ranges"); - println!(" โ€ข No performance regressions detected"); - println!(" โ€ข Response times meeting production targets"); - println!(" โ€ข Confidence calibration within threshold"); - } else { - println!("โš ๏ธ Active Performance Alerts:"); - for (idx, alert) in report.alerts.iter().enumerate() { - let severity_icon = match alert.severity { - AlertSeverity::Info => "โ„น๏ธ", - AlertSeverity::Warning => "โš ๏ธ", - AlertSeverity::Critical => "๐Ÿšจ", - AlertSeverity::Emergency => "๐Ÿ”ฅ", - }; - - println!(" {}. {} {} - {}", - idx + 1, - severity_icon, - format!("{:?}", alert.alert_type), - alert.message - ); - println!(" Current: {:.2} | Threshold: {:.2}", - alert.current_value, - alert.threshold_value - ); - - if !alert.recommendations.is_empty() { - println!(" Recommendations:"); - for rec in &alert.recommendations { - println!(" โ€ข {}", rec); - } - } - } - } - - println!("\n๐Ÿ”ง Alert System Features:"); - println!(" โœ… Real-time monitoring across all performance metrics"); - println!(" โœ… Automated threshold-based alerting"); - println!(" โœ… Severity classification (Info โ†’ Emergency)"); - println!(" โœ… Actionable recommendations for each alert"); - println!(" โœ… Historical alert tracking and trend analysis\n"); - - Ok(()) - } - - /// Demonstrate global ranking analysis and competitive positioning - async fn demonstrate_global_ranking_analysis(&self, report: &AcademicPerformanceReport) -> Result { - println!("๐Ÿ† PHASE 5: Global Ranking & Competitive Analysis"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - - let ranking = &report.global_ranking; - - println!("๐ŸŒ Current Global Position:"); - println!(" โ€ข Estimated Rank: #{}", ranking.current_estimated_rank); - println!(" โ€ข Confidence Interval: #{}-#{}", ranking.confidence_interval.0, ranking.confidence_interval.1); - println!(" โ€ข Performance Gap to #1: {:.1}%", report.comparison_to_sota.performance_gap); - - println!("\n๐ŸฅŠ Competitive Analysis:"); - for competitor in &ranking.competitive_analysis { - let gap = competitor.estimated_accuracy - report.overall_hle_accuracy; - let status = if gap <= 0.0 { "๐ŸŸข AHEAD" } else { "๐Ÿ”ด BEHIND" }; - println!(" โ€ข {}: {:.1}% ({} by {:.1}%)", - competitor.model_name, - competitor.estimated_accuracy, - status, - gap.abs() - ); - } - - println!("\n๐Ÿš€ Path to Global #1 Leadership:"); - for (idx, step) in ranking.path_to_number_one.iter().enumerate() { - println!(" {}. {} (+{:.1}% accuracy gain)", - idx + 1, - step.step_description, - step.estimated_accuracy_gain - ); - println!(" Priority: {} | Timeline: {} days | Effort: {}", - step.priority, - step.timeline.as_secs() / (24 * 3600), - step.implementation_effort - ); - } - - println!("\n๐ŸŽฏ Competitive Advantages:"); - for advantage in &report.comparison_to_sota.competitive_advantages { - println!(" โœ… {}", advantage); - } - - println!("\n๐ŸŽฏ Improvement Targets:"); - for target in &report.comparison_to_sota.improvement_targets { - println!(" ๐ŸŽฏ {}", target); - } - - let total_gain: f64 = ranking.path_to_number_one.iter() - .map(|step| step.estimated_accuracy_gain) - .sum(); - let projected_accuracy = report.overall_hle_accuracy + total_gain; - - println!("\n๐Ÿ“Š Universal Intelligence Projection:"); - println!(" โ€ข Current: {:.1}% HLE accuracy", report.overall_hle_accuracy); - println!(" โ€ข Projected: {:.1}% HLE accuracy (after improvements)", projected_accuracy); - println!(" โ€ข Global Ranking: #{} โ†’ #1 (Universal Intelligence Leader)", ranking.current_estimated_rank); - println!(" โ€ข Coding Excellence: 100% SWE-Bench + HumanEval (maintained)"); - - Ok(ranking.clone()) - } - - /// Demonstrate learning progress tracking and improvement analytics - async fn demonstrate_learning_progress_tracking(&self) -> Result<(f64, f64), BrainError> { - println!("\n๐Ÿ“š PHASE 6: Learning Progress & Improvement Analytics"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - - // Simulate learning progress data - let baseline_accuracy = 20.0; - let current_accuracy = 30.0; // Simulated improvement - let accuracy_improvement = current_accuracy - baseline_accuracy; - - let baseline_calibration = 0.25; - let current_calibration = 0.12; // Improved calibration - let calibration_improvement = baseline_calibration - current_calibration; - - println!("๐Ÿ“ˆ Learning Progress Metrics:"); - println!(" โ€ข Accuracy Improvement: +{:.1}% (from {:.1}% to {:.1}%)", - accuracy_improvement, baseline_accuracy, current_accuracy); - println!(" โ€ข Calibration Improvement: -{:.1}% error (from {:.1}% to {:.1}%)", - calibration_improvement * 100.0, baseline_calibration * 100.0, current_calibration * 100.0); - println!(" โ€ข Learning Velocity: {:.2}%/week", accuracy_improvement / 4.0); // 4 weeks - println!(" โ€ข Knowledge Acquisition Rate: 15 concepts/day"); - - println!("\n๐Ÿ† Learning Milestones Achieved:"); - println!(" โœ… 25% HLE Accuracy Threshold (Week 2)"); - println!(" โœ… Systematic Bias Elimination (Week 3)"); - println!(" โœ… Multi-Domain Processing (Week 3)"); - println!(" โœ… Real-time Research Integration (Week 4)"); - println!(" ๐ŸŽฏ 30% HLE Accuracy (In Progress)"); - - println!("\n๐Ÿ“Š Learning Trajectory Analysis:"); - println!(" โ€ข Improvement Trend: Consistent upward trajectory"); - println!(" โ€ข Learning Efficiency: High (multiple domains simultaneously)"); - println!(" โ€ข Knowledge Retention: Excellent (no performance regression)"); - println!(" โ€ข Cross-Domain Transfer: Active (physics โ†” chemistry connections)"); - - println!("\n๐Ÿ”ฎ Performance Projections:"); - println!(" โ€ข 30-Day Target: 35-40% HLE accuracy"); - println!(" โ€ข 60-Day Target: 40-45% HLE accuracy"); - println!(" โ€ข 90-Day Target: 45-50% HLE accuracy (Global #1)"); - println!(" โ€ข Learning Acceleration: Expected with adaptive research system"); - - println!("\n๐Ÿง  Continuous Learning Features:"); - println!(" โœ… Real-time performance tracking"); - println!(" โœ… Automated knowledge gap identification"); - println!(" โœ… Adaptive research triggering (confidence < 70%)"); - println!(" โœ… Cross-domain knowledge synthesis"); - println!(" โœ… Learning velocity optimization"); - - Ok((accuracy_improvement, calibration_improvement)) - } - - /// Generate comprehensive demo summary - async fn generate_demo_summary(&self, results: &DemoResults) -> Result<(), BrainError> { - println!("\n"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - println!("๐ŸŽ‰ ACADEMIC PERFORMANCE MONITORING DEMO COMPLETE"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - println!("\n๐Ÿ“Š Demo Results Summary:"); - println!(" โ€ข Questions Processed: {}", results.questions_processed); - println!(" โ€ข Current HLE Accuracy: {:.1}%", results.performance_report.overall_hle_accuracy); - println!(" โ€ข Accuracy Improvement: +{:.1}%", results.accuracy_improvement); - println!(" โ€ข Confidence Calibration: {:.1}% error", results.confidence_calibration_quality * 100.0); - println!(" โ€ข Global Ranking: #{}", results.global_ranking_projection); - println!(" โ€ข Time to Global #1: {} days", results.time_to_global_leadership.as_secs() / (24 * 3600)); - - println!("\n๐Ÿ† System Capabilities Validated:"); - println!(" โœ… Real-time HLE accuracy tracking with domain breakdown"); - println!(" โœ… Confidence calibration monitoring (<15% error target)"); - println!(" โœ… Learning progress visualization over time"); - println!(" โœ… Performance comparison with SOTA models"); - println!(" โœ… Automated alerts for performance regressions"); - println!(" โœ… Global ranking estimation for Universal Intelligence"); - - println!("\n๐Ÿš€ Path to Universal Intelligence #1:"); - println!(" 1. ๐Ÿ”ฌ Adaptive Research System (AUTO-RESEARCH at confidence < 70%)"); - println!(" 2. ๐Ÿ“š Knowledge Base Expansion (curated academic datasets)"); - println!(" 3. ๐Ÿ”— RAG Integration (live academic database connections)"); - println!(" 4. ๐Ÿง  Domain Fine-tuning (specialist enhancement)"); - println!(" 5. ๐Ÿ“ˆ Continuous Learning (performance pattern recognition)"); - - println!("\n๐ŸŽฏ Key Achievements:"); - println!(" โ€ข TASK 2.5 โœ… COMPLETED: Academic Performance Monitoring System operational"); - println!(" โ€ข Real-time tracking across 6 critical performance dimensions"); - println!(" โ€ข Comprehensive alerting with actionable recommendations"); - println!(" โ€ข Global competitive analysis with path to #1 ranking"); - println!(" โ€ข Learning analytics for continuous improvement"); - - println!("\n๐ŸŒŸ Next Steps for Global Leadership:"); - println!(" โ€ข Deploy to production HLE testing environment"); - println!(" โ€ข Integrate with adaptive research system for auto-learning"); - println!(" โ€ข Scale monitoring to 100+ academic domains"); - println!(" โ€ข Implement real-time dashboard for performance visualization"); - println!(" โ€ข Enable automated academic intelligence optimization"); - - println!("\n๐Ÿ’ซ Expected Impact:"); - println!(" โ€ข Universal Intelligence Achievement: 100% Coding + 45%+ Academic"); - println!(" โ€ข Global AI Leadership: First comprehensive universal system"); - println!(" โ€ข Academic Excellence: Real-time research and learning capabilities"); - println!(" โ€ข Continuous Evolution: Self-improving academic intelligence"); - - println!("\n๐Ÿ Demo Status: SUCCESS โœ…"); - println!("๐Ÿ“… Session: {}", self.session_id); - println!("โฑ๏ธ Completed: {}", Utc::now().format("%Y-%m-%d %H:%M:%S UTC")); - - Ok(()) - } -} - -/// **Main Demo Entry Point** -/// -/// Executes the comprehensive Academic Performance Monitoring System demonstration -#[tokio::main] -async fn main() -> Result<(), BrainError> { - println!("๐Ÿง  Brain AI Academic Performance Monitoring System"); - println!("๐ŸŽฏ TASK 2.5 IMPLEMENTATION VALIDATION"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - // Initialize and run comprehensive demo - let mut demo = AcademicPerformanceMonitoringDemo::new()?; - let _results = demo.run_comprehensive_demo().await?; - - // Success validation - println!("\nโœ… VALIDATION SUCCESSFUL"); - println!("๐Ÿ“ˆ Academic Performance Monitoring System is fully operational"); - println!("๐Ÿ† Ready for Universal Intelligence #1 global ranking pursuit"); - - Ok(()) -} \ No newline at end of file diff --git a/adaptive_research_demo.rs b/adaptive_research_demo.rs deleted file mode 100644 index b5810ee88e23dea9c4e751e56d4b4241fd5556fc..0000000000000000000000000000000000000000 --- a/adaptive_research_demo.rs +++ /dev/null @@ -1,226 +0,0 @@ -//! # Adaptive Research System Demo -//! -//! **Live Demonstration**: Shows the Adaptive Research Engine in action with real low-confidence questions -//! that trigger research automation to boost confidence from 37% โ†’ 70%+. -//! -//! ## Demo Flow -//! -//! 1. **Setup**: Initialize AdaptiveResearchEngine with all components -//! 2. **Low-Confidence Questions**: Test questions with < 70% confidence -//! 3. **Research Triggering**: Automatic research activation for uncertain responses -//! 4. **Multi-Source Research**: Academic databases, fact-checking, cross-domain synthesis -//! 5. **Confidence Boost**: Demonstrate improvement from research findings -//! 6. **Results**: Show before/after confidence and accuracy improvements -//! -//! **Created**: July 31, 2023 -//! **Purpose**: Demonstration of research automation system - -use std::time::{Duration, Instant}; -use anyhow::Result; - -use brain_cognitive::agents::{AcademicDomain, UniversalAcademicAgent}; -use brain_cognitive::agents::traits::{AcademicQuestion, QuestionType}; -use std::collections::HashMap; - -/// **Demo Academic Question** -/// -/// Represents a test question designed to trigger research workflow -#[derive(Debug, Clone)] -pub struct DemoQuestionSetup { - pub question: AcademicQuestion, - pub expected_confidence_before: f64, - pub expected_confidence_after: f64, -} - -/// **Demo Academic Analysis** -/// -/// Simulates initial analysis with intentionally low confidence to trigger research -#[derive(Debug, Clone)] -pub struct DemoAcademicAnalysis { - pub domain: AcademicDomain, - pub confidence: f64, - pub evidence: Vec, - pub reasoning_chain: Vec, -} - -// AcademicQuestion is now a struct, not a trait, so no impl needed - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  ADAPTIVE RESEARCH SYSTEM - LIVE DEMONSTRATION"); - println!("================================================"); - println!("๐ŸŽฏ Mission: Demonstrate research automation for uncertain AI responses"); - println!("๐Ÿ”ฌ Innovation: First AI that researches rather than guesses when uncertain"); - println!("๐Ÿ“Š Target: Transform 37% confidence โ†’ 70%+ through intelligent research"); - println!(); - - // Initialize the Universal Academic Agent with research capabilities - println!("โšก Initializing Universal Academic Agent with research capabilities..."); - let _academic_agent = UniversalAcademicAgent::new().await?; - println!("โœ… Academic Agent operational with confidence monitoring"); - println!(); - - // Demo test questions designed to trigger research workflow - let demo_questions = create_demo_questions(); - - println!("๐Ÿ”ฌ TESTING {} LOW-CONFIDENCE QUESTIONS", demo_questions.len()); - println!("๐Ÿ“ˆ Each question designed to trigger research automation"); - println!(); - - let mut total_confidence_improvement = 0.0; - let mut research_triggered_count = 0; - - for (i, question_setup) in demo_questions.iter().enumerate() { - println!("๐Ÿ“ QUESTION {}/{}: Testing research workflow", i + 1, demo_questions.len()); - println!(" Domain: {:?}", question_setup.question.domain); - println!(" Question: {}", question_setup.question.question); - - // Demonstrate academic analysis - let initial_confidence = question_setup.expected_confidence_before; - - println!(" ๐Ÿ” Initial Confidence: {:.1}% (Simulated low confidence)", initial_confidence * 100.0); - - // Simulate research triggering logic - if initial_confidence < 0.70 { - println!(" ๐Ÿšจ RESEARCH TRIGGERED: Confidence below 70% threshold"); - research_triggered_count += 1; - - // Simulate research workflow execution - let _start_time = Instant::now(); - let simulated_research_time = Duration::from_millis(750); - tokio::time::sleep(simulated_research_time).await; - - let final_confidence = question_setup.expected_confidence_after; - let confidence_improvement = final_confidence - initial_confidence; - total_confidence_improvement += confidence_improvement; - - println!(" โœ… RESEARCH SIMULATION COMPLETE:"); - println!(" ๐Ÿ“Š Final Confidence: {:.1}% ({:+.1} percentage points)", - final_confidence * 100.0, confidence_improvement * 100.0); - println!(" โฑ๏ธ Research Time: {}ms", simulated_research_time.as_millis()); - println!(" ๐ŸŽฏ Status: {}", get_confidence_status(final_confidence)); - println!(" ๐Ÿ”ฌ Research Components: AcademicDatabaseAccess, FactCheckingServices, CrossDomainSynthesis"); - } else { - println!(" ๐Ÿ’Ž High Confidence: No research needed"); - } - - println!(); - } - - // Display comprehensive results - println!("๐Ÿ† ADAPTIVE RESEARCH SYSTEM DEMONSTRATION RESULTS"); - println!("================================================"); - println!("๐Ÿ“Š Questions Processed: {}", demo_questions.len()); - println!("๐Ÿ”ฌ Research Triggered: {} questions ({:.1}%)", - research_triggered_count, - (research_triggered_count as f64 / demo_questions.len() as f64) * 100.0); - - if research_triggered_count > 0 { - let avg_improvement = total_confidence_improvement / research_triggered_count as f64; - println!("๐Ÿ“ˆ Average Confidence Improvement: {:.1} percentage points", avg_improvement * 100.0); - - let success_rate = research_triggered_count as f64 / demo_questions.len() as f64 * 100.0; - println!("๐ŸŽฏ Research Success Rate: {:.1}%", success_rate); - } - - println!(); - println!("๐Ÿš€ REVOLUTIONARY FEATURES DEMONSTRATED:"); - println!(" โœ… Automatic research triggering for uncertain responses"); - println!(" โœ… Multi-source research integration (Academic databases, fact-checking, synthesis)"); - println!(" โœ… Confidence-driven research workflow"); - println!(" โœ… Real-time research execution with performance tracking"); - println!(" โœ… Graceful uncertainty handling when research incomplete"); - println!(); - println!("๐Ÿ† Brain AI is now the ONLY AI that researches instead of guessing when uncertain!"); - - Ok(()) -} - -/// Create demo questions designed to trigger research workflow -fn create_demo_questions() -> Vec { - vec![ - DemoQuestionSetup { - question: AcademicQuestion { - id: "demo_1".to_string(), - question: "What is the relationship between quantum entanglement and thermodynamic entropy in black hole information paradox?".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question_type: QuestionType::OpenEnded, - options: None, - metadata: HashMap::new(), - }, - expected_confidence_before: 0.35, - expected_confidence_after: 0.75, - }, - DemoQuestionSetup { - question: AcademicQuestion { - id: "demo_2".to_string(), - question: "How does the mechanism of autocatalytic RNA synthesis contribute to origin of life theories?".to_string(), - domain: AcademicDomain::AdvancedChemistry, - question_type: QuestionType::OpenEnded, - options: None, - metadata: HashMap::new(), - }, - expected_confidence_before: 0.42, - expected_confidence_after: 0.78, - }, - DemoQuestionSetup { - question: AcademicQuestion { - id: "demo_3".to_string(), - question: "What are the implications of the Riemann hypothesis for modern cryptographic algorithms?".to_string(), - domain: AcademicDomain::AdvancedMathematics, - question_type: QuestionType::OpenEnded, - options: None, - metadata: HashMap::new(), - }, - expected_confidence_before: 0.38, - expected_confidence_after: 0.72, - }, - DemoQuestionSetup { - question: AcademicQuestion { - id: "demo_4".to_string(), - question: "How do topological insulators enable fault-tolerant quantum computation architectures?".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question_type: QuestionType::OpenEnded, - options: None, - metadata: HashMap::new(), - }, - expected_confidence_before: 0.45, - expected_confidence_after: 0.80, - }, - DemoQuestionSetup { - question: AcademicQuestion { - id: "demo_5".to_string(), - question: "What is the role of molecular chaperones in protein folding under cellular stress conditions?".to_string(), - domain: AcademicDomain::AdvancedChemistry, - question_type: QuestionType::OpenEnded, - options: None, - metadata: HashMap::new(), - }, - expected_confidence_before: 0.40, - expected_confidence_after: 0.76, - } - ] -} - -/// Create initial analysis with low confidence to trigger research -fn create_low_confidence_analysis(question_setup: &DemoQuestionSetup) -> DemoAcademicAnalysis { - DemoAcademicAnalysis { - domain: question_setup.question.domain.clone(), - confidence: question_setup.expected_confidence_before, - evidence: vec!["Limited initial knowledge available".to_string()], - reasoning_chain: vec!["Preliminary analysis incomplete".to_string()], - } -} - -/// Get status description based on confidence level -fn get_confidence_status(confidence: f64) -> &'static str { - if confidence >= 0.80 { - "High Confidence - Reliable Answer" - } else if confidence >= 0.70 { - "Research Threshold Met - Acceptable Answer" - } else if confidence >= 0.50 { - "Moderate Confidence - Further Research Beneficial" - } else { - "Low Confidence - Uncertainty Acknowledged" - } -} \ No newline at end of file diff --git a/adaptive_research_demonstration_simplified.rs b/adaptive_research_demonstration_simplified.rs deleted file mode 100644 index 864af9db9f22f01987369de15dbec1ee13a90c7f..0000000000000000000000000000000000000000 --- a/adaptive_research_demonstration_simplified.rs +++ /dev/null @@ -1,173 +0,0 @@ -//! # Adaptive Research Engine Demonstration -//! -//! **TASK 1.2 VALIDATION**: Demonstrates the REAL Adaptive Research Engine -//! for Brain AI's Academic Intelligence, showing actual research automation. -//! -//! ## Revolutionary Capabilities Demonstrated -//! -//! 1. **Confidence-Triggered Research**: Automatically research when confidence < 70% -//! 2. **Multi-Source Research**: Database lookup, fact verification, conceptual synthesis -//! 3. **Academic Intelligence**: Real research for theoretical physics, mathematics, biology -//! 4. **Uncertainty Handling**: Gracefully acknowledge limits when research insufficient -//! -//! **Created**: July 31, 2025 at 06:22:39 EDT -//! **Purpose**: Demonstrate REAL research automation for Universal Intelligence #1 global ranking - -use brain_cognitive::agents::intelligence::adaptive_research_engine::{ - AdaptiveResearchEngine, - ResearchStrategy, -}; -use brain_cognitive::agents::traits::AcademicDomain; -use std::time::{Duration, Instant}; - - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("{}", "=".repeat(70)); - println!("๐Ÿง  ADAPTIVE RESEARCH ENGINE DEMONSTRATION"); - println!("{}", "=".repeat(70)); - - // Initialize the Adaptive Research Engine - println!("๐Ÿš€ Initializing Adaptive Research Engine..."); - let _research_engine = AdaptiveResearchEngine::new(); - - println!("โœ… Research Engine initialized successfully!"); - - // Define research confidence threshold (70%) - const RESEARCH_CONFIDENCE_THRESHOLD: f64 = 0.70; - println!("๐ŸŽฏ Confidence Threshold: {:.1}%", RESEARCH_CONFIDENCE_THRESHOLD * 100.0); - - // Demonstrate available research strategies - let available_strategies = vec![ - ResearchStrategy::DatabaseLookup, - ResearchStrategy::FactVerification, - ResearchStrategy::ConceptualSynthesis, - ResearchStrategy::IterativeRefinement, - ]; - - println!("\n๐Ÿ“‹ Available Research Strategies:"); - for (i, strategy) in available_strategies.iter().enumerate() { - println!(" {}. {:?}", i + 1, strategy); - } - - // Demonstrate academic domains - let academic_domains = vec![ - AcademicDomain::TheoreticalPhysics, - AcademicDomain::AdvancedMathematics, - AcademicDomain::MolecularBiology, - AcademicDomain::AdvancedChemistry, - AcademicDomain::ComputerScienceTheory, - ]; - - println!("\n๐Ÿ”ฌ Supported Academic Domains:"); - for (i, domain) in academic_domains.iter().enumerate() { - println!(" {}. {:?}", i + 1, domain); - } - - // Simulate research scenarios - println!("\n{}", "=".repeat(50)); - println!("๐Ÿงช RESEARCH AUTOMATION SCENARIOS"); - println!("{}", "=".repeat(50)); - - let test_scenarios = vec![ - ("Low Confidence Physics (35%)", AcademicDomain::TheoreticalPhysics, 0.35), - ("Low Confidence Math (25%)", AcademicDomain::AdvancedMathematics, 0.25), - ("Medium Confidence Biology (45%)", AcademicDomain::MolecularBiology, 0.45), - ("High Confidence Chemistry (75%)", AcademicDomain::AdvancedChemistry, 0.75), - ]; - - let mut research_triggered_count = 0; - let total_scenarios = test_scenarios.len(); - - for (i, (scenario_name, domain, initial_confidence)) in test_scenarios.iter().enumerate() { - println!("\n๐Ÿ“Š SCENARIO {}: {}", i + 1, scenario_name); - println!(" ๐ŸŽฏ Domain: {:?}", domain); - println!(" ๐Ÿ“ˆ Initial Confidence: {:.1}%", initial_confidence * 100.0); - - // Check if research would be triggered - if *initial_confidence < RESEARCH_CONFIDENCE_THRESHOLD { - research_triggered_count += 1; - println!(" ๐Ÿ”ฌ RESEARCH TRIGGERED: Confidence below {:.1}% threshold", RESEARCH_CONFIDENCE_THRESHOLD * 100.0); - - // Simulate research process - let research_start = Instant::now(); - - // Simulate research with appropriate strategies for domain - let strategies_used = match domain { - AcademicDomain::TheoreticalPhysics => vec![ - ResearchStrategy::DatabaseLookup, - ResearchStrategy::IterativeRefinement, - ], - AcademicDomain::AdvancedMathematics => vec![ - ResearchStrategy::IterativeRefinement, - ResearchStrategy::ConceptualSynthesis, - ], - AcademicDomain::MolecularBiology => vec![ - ResearchStrategy::DatabaseLookup, - ResearchStrategy::FactVerification, - ], - _ => vec![ResearchStrategy::DatabaseLookup], - }; - - let mut current_confidence = *initial_confidence; - - for (step, strategy) in strategies_used.iter().enumerate() { - tokio::time::sleep(Duration::from_millis(100)).await; // Simulate research time - - let confidence_gain = match strategy { - ResearchStrategy::DatabaseLookup => 0.15, - ResearchStrategy::FactVerification => 0.12, - ResearchStrategy::ConceptualSynthesis => 0.10, - ResearchStrategy::IterativeRefinement => 0.08, - _ => 0.05, - }; - - current_confidence += confidence_gain; - current_confidence = current_confidence.min(0.95_f64); // Cap at 95% - - println!(" Step {}: {:?} โ†’ {:.1}% (+{:.1}%)", - step + 1, strategy, current_confidence * 100.0, confidence_gain * 100.0); - - // Stop if threshold reached - if current_confidence >= RESEARCH_CONFIDENCE_THRESHOLD { - break; - } - } - - let research_duration = research_start.elapsed(); - - if current_confidence >= RESEARCH_CONFIDENCE_THRESHOLD { - println!(" โœ… RESEARCH SUCCESSFUL: {:.1}% confidence achieved", current_confidence * 100.0); - println!(" โฑ๏ธ Research Time: {:?}", research_duration); - } else { - println!(" โ“ UNCERTAINTY ACKNOWLEDGED: {:.1}% confidence (below threshold)", current_confidence * 100.0); - println!(" ๐Ÿ’ญ Status: Research attempted but insufficient"); - } - } else { - println!(" โšก HIGH CONFIDENCE: No research needed"); - println!(" โœจ Direct response with {:.1}% confidence", initial_confidence * 100.0); - } - } - - println!("\n{}", "=".repeat(70)); - println!("๐Ÿ“Š RESEARCH AUTOMATION PERFORMANCE SUMMARY"); - println!("{}", "=".repeat(70)); - - println!("๐Ÿ”ฌ Total Test Scenarios: {}", total_scenarios); - println!("๐Ÿš€ Research Triggered: {}", research_triggered_count); - println!("๐ŸŽฏ Research Efficiency: {:.1}%", - (research_triggered_count as f64 / total_scenarios as f64) * 100.0); - println!("๐Ÿ“ˆ Confidence Threshold: {:.1}%", RESEARCH_CONFIDENCE_THRESHOLD * 100.0); - - println!("\nโœ… ADAPTIVE RESEARCH ENGINE: FULLY OPERATIONAL"); - println!("๐ŸŽฏ Core Innovation: Never guess when uncertain - research until confident"); - println!("๐ŸŒŸ Game Changer: First AI that researches rather than guesses"); - println!("๐Ÿ† Universal Intelligence: READY for 45%+ academic excellence"); - - println!("\n{}", "=".repeat(70)); - println!("๐ŸŒŸ DEMONSTRATION COMPLETE"); - println!("๐ŸŒŸ REAL RESEARCH AUTOMATION: Pushing Brain AI to #1 global ranking"); - println!("{}", "=".repeat(70)); - - Ok(()) -} \ No newline at end of file diff --git a/adaptive_research_engine_hle_demo.rs b/adaptive_research_engine_hle_demo.rs deleted file mode 100644 index 97f4ec9c7c9bc5f275d37e32c247f1c6135e685c..0000000000000000000000000000000000000000 --- a/adaptive_research_engine_hle_demo.rs +++ /dev/null @@ -1,261 +0,0 @@ -/// Adaptive Research Engine HLE Integration Demo -/// -/// This demonstrates the Adaptive Research & Learning System (TASK 2.4) working with -/// HLE-style academic questions, showcasing the revolutionary uncertainty handling -/// that researches instead of guessing when confidence falls below threshold. - -use std::time::Instant; -use brain_cognitive::agents::intelligence::adaptive_research_engine::AdaptiveResearchEngine; -use brain_cognitive::agents::{ - AcademicDomain, OptionEvaluation, QuestionType -}; -use brain_cognitive::agents::traits::AcademicQuestion; -use uuid::Uuid; -use std::collections::HashMap; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  ADAPTIVE RESEARCH ENGINE HLE INTEGRATION DEMO"); - println!("================================================"); - println!("Demonstrating TASK 2.4: Revolutionary uncertainty handling that"); - println!("researches instead of guessing when confidence < 70%"); - println!(); - - // Create the Adaptive Research Engine - let mut research_engine = AdaptiveResearchEngine::new(); - println!("โœ… AdaptiveResearchEngine initialized"); - println!(" ๐Ÿ“Š Confidence threshold: 70%"); - println!(" ๐Ÿ”ฌ Research sources: Academic databases, fact-checking, synthesis"); - println!(); - - // Test with HLE-style questions across different domains - let test_scenarios = create_hle_test_scenarios(); - - let mut scenario_results = Vec::new(); - - for (i, (domain, question, low_confidence_evaluation)) in test_scenarios.iter().enumerate() { - println!("๐ŸŽฏ Scenario {}: {} Domain", i + 1, format!("{:?}", domain)); - println!(" Question: {}", question.question); - println!(" Initial confidence: {:.1}%", low_confidence_evaluation.recommendation_confidence * 100.0); - - let start_time = Instant::now(); - - // Process with research engine - match research_engine.process_with_research( - question, - low_confidence_evaluation, - domain - ).await { - Ok(research_result) => { - let duration = start_time.elapsed(); - - println!(" ๐Ÿ“Š Research result:"); - println!(" Final confidence: {:.1}%", research_result.final_confidence * 100.0); - println!(" Threshold reached: {}", if research_result.threshold_reached { "โœ… YES" } else { "โŒ NO" }); - println!(" Strategies used: {:?}", research_result.strategies_used); - println!(" Sources consulted: {} sources", research_result.sources_consulted.len()); - println!(" Knowledge gathered: {} snippets", research_result.knowledge_gathered.len()); - println!(" Research iterations: {}", research_result.iterations_performed); - println!(" Research duration: {:?}", research_result.research_duration); - - let confidence_improvement = (research_result.final_confidence - low_confidence_evaluation.recommendation_confidence) * 100.0; - println!(" Confidence improvement: +{:.1}%", confidence_improvement); - - scenario_results.push(( - format!("{:?}", domain), - research_result.threshold_reached, - confidence_improvement, - research_result.iterations_performed, - duration, - )); - - if research_result.threshold_reached { - println!(" ๐ŸŽ‰ SUCCESS: Research achieved confidence threshold!"); - } else { - println!(" โš ๏ธ UNCERTAINTY: Gracefully handled insufficient confidence"); - } - } - Err(e) => { - println!(" โŒ Error: {}", e); - scenario_results.push(( - format!("{:?}", domain), - false, - 0.0, - 0, - start_time.elapsed(), - )); - } - } - - println!(); - } - - // Display overall results - println!("๐Ÿ“‹ ADAPTIVE RESEARCH ENGINE PERFORMANCE SUMMARY"); - println!("=============================================="); - - let stats = research_engine.get_statistics(); - println!("๐Ÿ”ง Engine Statistics:"); - println!(" Research triggers: {}", stats.total_triggers); - println!(" Confidence threshold: {:.1}%", stats.average_threshold * 100.0); - println!(" Confidence history: {} readings", stats.confidence_history_size); - - println!(); - println!("๐Ÿ“Š Scenario Results:"); - - let mut successful_research = 0; - let mut total_confidence_improvement = 0.0; - let mut total_iterations = 0; - - for (i, (domain, success, improvement, iterations, duration)) in scenario_results.iter().enumerate() { - println!(" Scenario {}: {} - {} ({}% improvement, {} iterations, {:?})", - i + 1, - domain, - if *success { "โœ… SUCCESS" } else { "โš ๏ธ UNCERTAINTY" }, - improvement, - iterations, - duration); - - if *success { - successful_research += 1; - } - total_confidence_improvement += improvement; - total_iterations += iterations; - } - - println!(); - println!("๐ŸŽฏ PERFORMANCE METRICS:"); - println!(" Research success rate: {}/{} ({:.1}%)", - successful_research, - scenario_results.len(), - (successful_research as f32 / scenario_results.len() as f32) * 100.0); - println!(" Average confidence improvement: {:.1}%", - total_confidence_improvement / scenario_results.len() as f32); - println!(" Average research iterations: {:.1}", - total_iterations as f32 / scenario_results.len() as f32); - - println!(); - println!("โœ… TASK 2.4 VALIDATION COMPLETE"); - println!("==============================="); - println!("โœ… System triggers research automatically when confidence < 70%"); - println!("โœ… Research continues until 70%+ confidence achieved or timeout reached"); - println!("โœ… Multiple research strategies attempted (databases, synthesis, reasoning)"); - println!("โœ… Graceful uncertainty acknowledgment when threshold not reached"); - println!("โœ… Learning integration - new knowledge persisted for future questions"); - println!("โœ… Performance improvement - measurable accuracy increase through research"); - println!(); - println!("๐Ÿš€ Adaptive Research Engine successfully transforms low-confidence"); - println!(" guesses into high-confidence researched answers!"); - - Ok(()) -} - -/// Helper function to create a proper OptionEvaluation with low confidence -fn create_low_confidence_evaluation(recommended_answer: &str, confidence: f32) -> OptionEvaluation { - let mut option_scores = HashMap::new(); - let mut option_reasoning = HashMap::new(); - - // Set up scores for A, B, C, D options - for option in ["A", "B", "C", "D"] { - if option == recommended_answer { - option_scores.insert(option.to_string(), confidence); - option_reasoning.insert(option.to_string(), "Tentative best guess based on limited analysis".to_string()); - } else { - option_scores.insert(option.to_string(), (1.0 - confidence) / 3.0); - option_reasoning.insert(option.to_string(), "Less likely option requiring further research".to_string()); - } - } - - OptionEvaluation { - option_scores, - option_reasoning, - recommended_answer: recommended_answer.to_string(), - recommendation_confidence: confidence, - elimination_rationale: vec!["Initial analysis incomplete - research needed".to_string()], - } -} - -/// Create HLE-style test scenarios with low initial confidence -fn create_hle_test_scenarios() -> Vec<(AcademicDomain, AcademicQuestion, OptionEvaluation)> { - vec![ - // Theoretical Physics scenario - ( - AcademicDomain::TheoreticalPhysics, - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "In quantum field theory, what is the significance of the renormalization group flow for asymptotic freedom in non-Abelian gauge theories?".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question_type: QuestionType::ConceptualExplanation, - options: Some(vec!["A) Flow toward strong coupling".to_string(), "B) Flow toward weak coupling".to_string(), "C) Flow remains constant".to_string(), "D) Flow is undefined".to_string()]), - metadata: { - let mut meta = HashMap::new(); - meta.insert("difficulty_level".to_string(), "9".to_string()); - meta.insert("expected_time_minutes".to_string(), "15".to_string()); - meta.insert("context".to_string(), "Advanced theoretical physics question requiring deep understanding of quantum field theory".to_string()); - meta - }, - }, - create_low_confidence_evaluation("B", 0.45) // Below 70% threshold - triggers research - ), - - // Advanced Mathematics scenario - ( - AcademicDomain::AdvancedMathematics, - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "For a compact Riemann surface of genus g โ‰ฅ 2, what is the dimension of the space of holomorphic differentials?".to_string(), - domain: AcademicDomain::AdvancedMathematics, - question_type: QuestionType::CalculationBased, - options: Some(vec!["A) g".to_string(), "B) g-1".to_string(), "C) 2g".to_string(), "D) 2g-2".to_string()]), - metadata: { - let mut meta = HashMap::new(); - meta.insert("difficulty_level".to_string(), "8".to_string()); - meta.insert("expected_time_minutes".to_string(), "12".to_string()); - meta.insert("context".to_string(), "Complex geometry question involving Riemann surfaces and holomorphic forms".to_string()); - meta - }, - }, - create_low_confidence_evaluation("A", 0.52) // Below 70% threshold - triggers research - ), - - // Computer Science Theory scenario - ( - AcademicDomain::ComputerScienceTheory, - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "In computational complexity theory, what is the relationship between PSPACE and the polynomial hierarchy (PH)?".to_string(), - domain: AcademicDomain::ComputerScienceTheory, - question_type: QuestionType::ConceptualExplanation, - options: Some(vec!["A) PH โŠ† PSPACE".to_string(), "B) PSPACE โŠ† PH".to_string(), "C) PH = PSPACE".to_string(), "D) PH and PSPACE are incomparable".to_string()]), - metadata: { - let mut meta = HashMap::new(); - meta.insert("difficulty_level".to_string(), "7".to_string()); - meta.insert("expected_time_minutes".to_string(), "10".to_string()); - meta.insert("context".to_string(), "Computational complexity theory question about complexity class relationships".to_string()); - meta - }, - }, - create_low_confidence_evaluation("A", 0.62) // Below 70% threshold - triggers research - ), - - // Molecular Biology scenario - ( - AcademicDomain::MolecularBiology, - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "In CRISPR-Cas9 gene editing, what determines the specificity of the guide RNA targeting?".to_string(), - domain: AcademicDomain::MolecularBiology, - question_type: QuestionType::ConceptualExplanation, - options: Some(vec!["A) PAM sequence only".to_string(), "B) Guide RNA sequence only".to_string(), "C) Both PAM and guide RNA sequence".to_string(), "D) Cas9 protein conformation".to_string()]), - metadata: { - let mut meta = HashMap::new(); - meta.insert("difficulty_level".to_string(), "6".to_string()); - meta.insert("expected_time_minutes".to_string(), "8".to_string()); - meta.insert("context".to_string(), "Molecular biology question about CRISPR mechanism specificity".to_string()); - meta - }, - }, - create_low_confidence_evaluation("C", 0.68) // Just below 70% threshold - triggers research - ), - ] -} \ No newline at end of file diff --git a/adaptive_research_hle_validation.rs b/adaptive_research_hle_validation.rs deleted file mode 100644 index d0f803a1f1d73e3b6cfb1b7beb8ab1680a44bbe8..0000000000000000000000000000000000000000 --- a/adaptive_research_hle_validation.rs +++ /dev/null @@ -1,698 +0,0 @@ -//! # Adaptive Research HLE Validation Framework -//! -//! **Critical Validation**: Proves the Adaptive Research & Learning System can boost Brain AI -//! from 25% to 45%+ HLE accuracy by researching instead of guessing when uncertain. -//! -//! ## Revolutionary Validation Strategy -//! -//! 1. **Baseline Testing**: Measure current 25% HLE accuracy without research -//! 2. **Research Triggering**: Identify questions with < 70% confidence (100% of current questions) -//! 3. **Research Execution**: Apply multi-source research to boost confidence -//! 4. **Performance Measurement**: Validate 37% โ†’ 70%+ confidence improvement -//! 5. **Accuracy Projection**: Demonstrate path to 45%+ HLE accuracy -//! -//! **Created**: July 30, 2023 -//! **Purpose**: Validate research automation for academic intelligence -//! **Status**: PRIORITY - Core validation for research-driven academic performance - -use std::collections::HashMap; -use std::time::{Duration, Instant}; -use serde::{Deserialize, Serialize}; -use uuid::Uuid; -use chrono::Utc; -use rand; - -use brain_cognitive::agents::{UniversalAcademicAgent, AcademicDomain}; -use brain_cognitive::agents::intelligence::adaptive_research_engine::{ - AdaptiveResearchEngine, ResearchStrategy -}; -use brain_cognitive::agents::traits::{AgentInput, BrainAgent}; -use brain_cognitive::agents::CognitiveContext; -use brain_types::error::BrainError; - -/// **Revolutionary HLE Validation Framework** -/// -/// Proves that Brain AI's Adaptive Research System transforms low-confidence guesses -/// into high-confidence researched answers, achieving 45%+ HLE accuracy breakthrough. -#[derive(Debug)] -pub struct AdaptiveResearchHLEValidator { - /// Universal academic agent with research capabilities - academic_agent: UniversalAcademicAgent, - /// Adaptive research engine for uncertainty handling - research_engine: AdaptiveResearchEngine, - /// Test question database for HLE simulation - test_questions: Vec, - /// Validation metrics and results - validation_metrics: ValidationMetrics, - /// Research performance tracking - research_performance: ResearchPerformanceTracker, -} - -/// Real HLE test question with research validation data -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct HLETestQuestion { - /// Unique question identifier - pub id: String, - /// Question text from HLE dataset - pub question: String, - /// Multiple choice options (A, B, C, D) - pub options: Vec, - /// Correct answer for validation - pub correct_answer: String, - /// Academic domain classification - pub domain: AcademicDomain, - /// Question difficulty level (1-10) - pub difficulty: u8, - /// Expected research sources for this question type - pub expected_sources: Vec, -} - -/// Comprehensive validation metrics for research system evaluation -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct ValidationMetrics { - /// Total questions processed - pub total_questions: usize, - /// Baseline accuracy without research - pub baseline_accuracy: f64, - /// Research-enhanced accuracy - pub research_accuracy: f64, - /// Average baseline confidence - pub avg_baseline_confidence: f64, - /// Average research-enhanced confidence - pub avg_research_confidence: f64, - /// Questions requiring research (< 70% confidence) - pub questions_requiring_research: usize, - /// Research success rate (reaching 70%+ confidence) - pub research_success_rate: f64, - /// Total research time spent - pub total_research_time: Duration, - /// Average research time per question - pub avg_research_time: Duration, - /// Confidence improvement distribution - pub confidence_improvements: Vec, -} - -/// Research performance tracking for continuous improvement -#[derive(Debug, Clone)] -pub struct ResearchPerformanceTracker { - /// Research execution history - research_history: Vec, - /// Source effectiveness mapping - source_effectiveness: HashMap, - /// Strategy performance by domain - strategy_performance: HashMap, - /// Learning progression over time - learning_progression: Vec, -} - -/// Individual research execution record -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct ResearchExecution { - /// Question ID - pub question_id: String, - /// Initial confidence before research - pub initial_confidence: f64, - /// Final confidence after research - pub final_confidence: f64, - /// Research strategies used - pub strategies_used: Vec, - /// Sources consulted during research - pub sources_consulted: Vec, - /// Research duration - pub research_duration: Duration, - /// Whether threshold was reached - pub threshold_reached: bool, - /// Knowledge gained during research - pub knowledge_gained: Vec, - /// Research success (correct answer found) - pub research_success: bool, -} - -/// Source effectiveness analysis -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct SourceEffectiveness { - /// Source name (PubMed, arXiv, Wikipedia, etc.) - pub source_name: String, - /// Times consulted - pub consultations: usize, - /// Successful confidence boosts - pub successful_boosts: usize, - /// Average confidence improvement - pub avg_confidence_boost: f64, - /// Response time statistics - pub avg_response_time: Duration, - /// Domain specialization effectiveness - pub domain_effectiveness: HashMap, -} - -/// Strategy performance by academic domain -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct StrategyPerformance { - /// Strategy name - pub strategy_name: String, - /// Success rate in this domain - pub success_rate: f64, - /// Average confidence improvement - pub avg_confidence_improvement: f64, - /// Average execution time - pub avg_execution_time: Duration, - /// Question types best suited for this strategy - pub optimal_question_types: Vec, -} - -/// Learning milestone tracking -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct LearningMilestone { - /// Milestone timestamp - pub timestamp: chrono::DateTime, - /// Questions processed at this point - pub questions_processed: usize, - /// Cumulative accuracy improvement - pub accuracy_improvement: f64, - /// Research efficiency gain - pub efficiency_gain: f64, - /// New knowledge domains discovered - pub new_domains_discovered: Vec, -} - -impl AdaptiveResearchHLEValidator { - /// Create new HLE validation framework with research capabilities - pub async fn new() -> Result { - let academic_agent = UniversalAcademicAgent::new().await?; - let research_engine = AdaptiveResearchEngine::new(); - - Ok(Self { - academic_agent, - research_engine, - test_questions: Vec::new(), - validation_metrics: ValidationMetrics::default(), - research_performance: ResearchPerformanceTracker::new(), - }) - } - - /// Load real HLE test questions for validation - pub async fn load_test_questions(&mut self, question_count: usize) -> Result<(), BrainError> { - println!("๐Ÿ”ฌ Loading {} HLE test questions for adaptive research validation...", question_count); - - // Generate realistic HLE questions across all domains - let domains = vec![ - AcademicDomain::TheoreticalPhysics, - AcademicDomain::AdvancedMathematics, - AcademicDomain::MolecularBiology, - AcademicDomain::ComputerScienceTheory, - AcademicDomain::AdvancedChemistry, - AcademicDomain::QuantumInformation, - AcademicDomain::AlgebraicGeometry, - ]; - - for i in 0..question_count { - let domain = domains[i % domains.len()].clone(); - let question = self.generate_hle_question(&domain, i + 1).await?; - self.test_questions.push(question); - } - - println!("โœ… Loaded {} test questions across {} academic domains", - question_count, domains.len()); - Ok(()) - } - - /// **CRITICAL VALIDATION**: Execute baseline vs research-enhanced HLE testing - pub async fn validate_research_system(&mut self) -> Result { - println!("๐ŸŽฏ STARTING CRITICAL VALIDATION: Adaptive Research System HLE Performance"); - println!("๐Ÿ“Š Expected Outcome: Transform 25% โ†’ 45%+ HLE accuracy through intelligent research"); - - let start_time = Instant::now(); - let mut baseline_correct = 0; - let mut research_correct = 0; - let mut total_baseline_confidence = 0.0; - let mut total_research_confidence = 0.0; - let mut questions_needing_research = 0; - let mut research_successes = 0; - let mut research_executions = Vec::new(); - - println!("\n๐Ÿ” Phase 1: Baseline Performance Measurement (No Research)"); - - for (index, question) in self.test_questions.iter().enumerate() { - let progress = (index + 1) as f64 / self.test_questions.len() as f64 * 100.0; - println!(" ๐Ÿ“ Question {}/{} ({:.1}%): Testing baseline performance...", - index + 1, self.test_questions.len(), progress); - - // Step 1: Baseline evaluation without research - let baseline_result = self.evaluate_question_baseline(question).await?; - total_baseline_confidence += baseline_result.confidence; - - if baseline_result.selected_answer == question.correct_answer { - baseline_correct += 1; - println!(" โœ… Baseline: CORRECT (confidence: {:.1}%)", baseline_result.confidence * 100.0); - } else { - println!(" โŒ Baseline: INCORRECT (confidence: {:.1}%) - Expected: {}, Got: {}", - baseline_result.confidence * 100.0, question.correct_answer, baseline_result.selected_answer); - } - - // Step 2: Check if research would be triggered (< 70% confidence) - if baseline_result.confidence < 0.70 { - questions_needing_research += 1; - println!(" ๐Ÿ”ฌ Research TRIGGERED: Low confidence ({:.1}%) - Executing adaptive research...", - baseline_result.confidence * 100.0); - - // Step 3: Execute research-enhanced evaluation - let research_result = self.evaluate_question_with_research(question, &baseline_result).await?; - total_research_confidence += research_result.final_confidence; - - if research_result.threshold_reached { - research_successes += 1; - println!(" โœ… Research SUCCESS: Confidence boosted to {:.1}%", - research_result.final_confidence * 100.0); - } else { - println!(" โš ๏ธ Research PARTIAL: Confidence improved to {:.1}% (below 70% threshold)", - research_result.final_confidence * 100.0); - } - - // Check research-enhanced accuracy - if research_result.research_answer == question.correct_answer { - research_correct += 1; - println!(" ๐ŸŽฏ Research ANSWER: CORRECT - {} (confidence: {:.1}%)", - research_result.research_answer, research_result.final_confidence * 100.0); - } else { - println!(" โŒ Research ANSWER: INCORRECT - Expected: {}, Got: {} (confidence: {:.1}%)", - question.correct_answer, research_result.research_answer, research_result.final_confidence * 100.0); - } - - // Track research execution - research_executions.push(ResearchExecution { - question_id: question.id.clone(), - initial_confidence: baseline_result.confidence, - final_confidence: research_result.final_confidence, - strategies_used: research_result.strategies_used, - sources_consulted: research_result.sources_consulted, - research_duration: research_result.research_duration, - threshold_reached: research_result.threshold_reached, - knowledge_gained: research_result.knowledge_gathered, - research_success: research_result.research_answer == question.correct_answer, - }); - } else { - // High confidence baseline - no research needed - total_research_confidence += baseline_result.confidence; - if baseline_result.selected_answer == question.correct_answer { - research_correct += 1; - } - println!(" ๐Ÿ’Ž High CONFIDENCE: No research needed ({:.1}%)", baseline_result.confidence * 100.0); - } - - println!(); - } - - let total_questions = self.test_questions.len(); - let validation_duration = start_time.elapsed(); - - // Calculate final metrics - let baseline_accuracy = baseline_correct as f64 / total_questions as f64; - let research_accuracy = research_correct as f64 / total_questions as f64; - let avg_baseline_confidence = total_baseline_confidence / total_questions as f64; - let avg_research_confidence = total_research_confidence / total_questions as f64; - let research_success_rate = if questions_needing_research > 0 { - research_successes as f64 / questions_needing_research as f64 - } else { - 1.0 - }; - - // Create validation report - let report = ValidationReport { - validation_timestamp: Utc::now(), - total_questions, - baseline_accuracy, - research_accuracy, - accuracy_improvement: research_accuracy - baseline_accuracy, - avg_baseline_confidence, - avg_research_confidence, - confidence_improvement: avg_research_confidence - avg_baseline_confidence, - questions_requiring_research: questions_needing_research, - research_success_rate, - total_validation_time: validation_duration, - research_executions, - projected_hle_accuracy: self.calculate_hle_projection(research_accuracy, research_success_rate), - competitive_position: self.analyze_competitive_position(research_accuracy), - }; - - self.print_validation_results(&report); - Ok(report) - } - - /// Generate realistic HLE question for testing - async fn generate_hle_question(&self, domain: &AcademicDomain, sequence: usize) -> Result { - let questions_by_domain = match domain { - AcademicDomain::TheoreticalPhysics => vec![ - ("What is the fundamental principle behind quantum entanglement?", - vec!["A) Wave-particle duality".to_string(), "B) Superposition collapse".to_string(), - "C) Non-local correlation".to_string(), "D) Uncertainty principle".to_string()], - "C"), - ("In general relativity, what causes gravitational time dilation?", - vec!["A) Mass-energy equivalence".to_string(), "B) Spacetime curvature".to_string(), - "C) Gravitational waves".to_string(), "D) Black hole formation".to_string()], - "B"), - ], - AcademicDomain::AdvancedMathematics => vec![ - ("What defines a topological space as compact?", - vec!["A) Every open cover has finite subcover".to_string(), "B) It is closed and bounded".to_string(), - "C) It has no isolated points".to_string(), "D) It is path-connected".to_string()], - "A"), - ("Which property characterizes a Banach space?", - vec!["A) Inner product completeness".to_string(), "B) Norm completeness".to_string(), - "C) Metric completeness".to_string(), "D) Algebraic completeness".to_string()], - "B"), - ], - _ => vec![ - ("What is the primary mechanism of enzyme catalysis?", - vec!["A) Lowering activation energy".to_string(), "B) Increasing substrate affinity".to_string(), - "C) Changing reaction enthalpy".to_string(), "D) Altering product stability".to_string()], - "A"), - ], - }; - - let (question_text, options, correct_answer) = &questions_by_domain[sequence % questions_by_domain.len()]; - - Ok(HLETestQuestion { - id: format!("hle_test_{:?}_{}_{}", domain, sequence, Uuid::new_v4()), - question: question_text.to_string(), - options: options.clone(), - correct_answer: correct_answer.to_string(), - domain: domain.clone(), - difficulty: 7 + (sequence % 3) as u8, // 7-9 difficulty for realistic HLE - expected_sources: vec![ - "Academic Database".to_string(), - "Fact Checking".to_string(), - "Cross-Domain Synthesis".to_string(), - ], - }) - } - - /// Evaluate question with baseline agent (no research) - NOW USING REAL AGENT - async fn evaluate_question_baseline(&self, question: &HLETestQuestion) -> Result { - // Use the ACTUAL UniversalAcademicAgent instead of simulation - let options_str = question.options.join("\n"); - - let input = AgentInput { - input_type: "multiple_choice_question".to_string(), - content: question.question.clone(), - parameters: { - let mut params = HashMap::new(); - params.insert("options".to_string(), serde_json::Value::String(options_str)); - params - }, - previous_outputs: Vec::new(), - session_id: "hle_validation".to_string(), - timestamp: chrono::Utc::now(), - user_preferences: HashMap::new(), - }; - - // Create a minimal context for validation testing - let context = CognitiveContext::default(); - let output = self.academic_agent.execute(input, &context).await?; - - // Extract answer and confidence from actual agent response - let selected_answer = output.content - .lines() - .find(|line| line.starts_with("Answer:")) - .and_then(|line| line.split(':').nth(1)) - .map(|s| s.trim().to_string()) - .unwrap_or_else(|| "A".to_string()); // fallback - - let confidence = output.confidence as f64; - - Ok(BaselineEvaluation { - selected_answer, - confidence, - reasoning: format!("Real agent evaluation: {}", output.content.lines().take(2).collect::>().join(" ")), - }) - } - - /// Evaluate question with adaptive research system - async fn evaluate_question_with_research(&self, question: &HLETestQuestion, baseline: &BaselineEvaluation) -> Result { - let research_start = Instant::now(); - - // Simulate research process that significantly improves confidence - let research_confidence_boost = 0.25 + (rand::random::() * 0.25); // 25-50% boost - let final_confidence = (baseline.confidence + research_confidence_boost).min(0.95); - - // Research dramatically improves accuracy - let research_answer = if final_confidence > 0.70 && rand::random::() < 0.75 { - question.correct_answer.clone() // 75% accuracy with research - } else if final_confidence > 0.60 && rand::random::() < 0.60 { - question.correct_answer.clone() // 60% accuracy for medium confidence - } else { - baseline.selected_answer.clone() // Fall back to baseline - }; - - let research_duration = research_start.elapsed(); - - Ok(ResearchEvaluation { - research_answer, - final_confidence, - strategies_used: vec![ - ResearchStrategy::DatabaseLookup, - ResearchStrategy::FactVerification, - ResearchStrategy::ConceptualSynthesis, - ], - sources_consulted: vec![ - "PubMed".to_string(), - "arXiv".to_string(), - "Wikipedia".to_string(), - "Wolfram Alpha".to_string(), - ], - knowledge_gathered: vec![ - format!("Domain knowledge: {:?}", question.domain), - "Cross-referenced multiple authoritative sources".to_string(), - "Applied iterative reasoning refinement".to_string(), - ], - research_duration, - threshold_reached: final_confidence >= 0.70, - }) - } - - /// Calculate projected HLE accuracy based on research results - fn calculate_hle_projection(&self, research_accuracy: f64, research_success_rate: f64) -> f64 { - // Conservative projection based on research effectiveness - let base_projection = research_accuracy; - let research_multiplier = 1.0 + (research_success_rate * 0.5); // Up to 50% boost - let learning_factor = 1.1; // 10% improvement from continuous learning - - (base_projection * research_multiplier * learning_factor).min(0.60) // Cap at 60% for realistic projection - } - - /// Analyze competitive position based on research accuracy - fn analyze_competitive_position(&self, research_accuracy: f64) -> CompetitivePosition { - let global_leaderboard = vec![ - ("Gemini Pro 2.5 Experimental", 0.254), - ("o3", 0.203), - ("Brain AI (Current)", 0.250), - ("Claude 3.5 Sonnet", 0.041), - ("GPT-4o", 0.027), - ]; - - let mut new_ranking = 1; - for (_, accuracy) in &global_leaderboard { - if research_accuracy <= *accuracy { - new_ranking += 1; - } - } - - CompetitivePosition { - current_ranking: 3, - projected_ranking: new_ranking, - accuracy_gap_to_first: 0.254 - research_accuracy, - competitive_advantage: if research_accuracy > 0.30 { - "Significant research-driven advantage".to_string() - } else if research_accuracy > 0.254 { - "Leading position achieved".to_string() - } else { - "Strong improvement demonstrated".to_string() - }, - } - } - - /// Print comprehensive validation results - fn print_validation_results(&self, report: &ValidationReport) { - println!("\n๐Ÿ† ========== ADAPTIVE RESEARCH SYSTEM VALIDATION RESULTS =========="); - println!("๐Ÿ“… Validation Date: {}", report.validation_timestamp.format("%Y-%m-%d %H:%M:%S UTC")); - println!("โฑ๏ธ Total Validation Time: {:.2}s", report.total_validation_time.as_secs_f64()); - println!(); - - println!("๐Ÿ“Š ACCURACY ANALYSIS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ Baseline Accuracy (No Research): {:.1}% ({}/{}) โ”‚", - report.baseline_accuracy * 100.0, - (report.baseline_accuracy * report.total_questions as f64).round() as usize, - report.total_questions); - println!("โ”‚ Research-Enhanced Accuracy: {:.1}% ({}/{}) โ”‚", - report.research_accuracy * 100.0, - (report.research_accuracy * report.total_questions as f64).round() as usize, - report.total_questions); - println!("โ”‚ Accuracy Improvement: +{:.1} percentage points โ”‚", - report.accuracy_improvement * 100.0); - println!("โ”‚ Projected HLE Accuracy: {:.1}% โ”‚", - report.projected_hle_accuracy * 100.0); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(); - - println!("๐Ÿ”ฌ CONFIDENCE ANALYSIS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ Average Baseline Confidence: {:.1}% โ”‚", - report.avg_baseline_confidence * 100.0); - println!("โ”‚ Average Research-Enhanced Confidence: {:.1}% โ”‚", - report.avg_research_confidence * 100.0); - println!("โ”‚ Confidence Improvement: +{:.1} percentage points โ”‚", - report.confidence_improvement * 100.0); - println!("โ”‚ Questions Requiring Research: {} ({:.1}%) โ”‚", - report.questions_requiring_research, - (report.questions_requiring_research as f64 / report.total_questions as f64) * 100.0); - println!("โ”‚ Research Success Rate: {:.1}% โ”‚", - report.research_success_rate * 100.0); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(); - - println!("๐Ÿ COMPETITIVE POSITION"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ Current Global Ranking: #{} โ”‚", - report.competitive_position.current_ranking); - println!("โ”‚ Projected Global Ranking: #{} โ”‚", - report.competitive_position.projected_ranking); - println!("โ”‚ Gap to #1 Position: {:.1} percentage points โ”‚", - report.competitive_position.accuracy_gap_to_first * 100.0); - println!("โ”‚ Competitive Advantage: {} โ”‚", - report.competitive_position.competitive_advantage); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(); - - println!("โœ… VALIDATION CONCLUSION:"); - if report.research_accuracy > report.baseline_accuracy { - println!("๐ŸŽฏ RESEARCH SYSTEM VALIDATED: {:.1}% accuracy improvement demonstrated", - report.accuracy_improvement * 100.0); - println!("๐Ÿ”ฌ ADAPTIVE RESEARCH WORKS: Transforms low-confidence guesses into researched answers"); - println!("๐Ÿ† PATH TO #1 GLOBAL RANKING: Research-driven approach shows clear competitive advantage"); - } else { - println!("โš ๏ธ Research system needs optimization - no significant improvement shown"); - } - - println!("๐Ÿš€ NEXT STEPS: Deploy to full HLE dataset for comprehensive validation"); - println!("๐Ÿ† ULTIMATE GOAL: Achieve 45-50% HLE accuracy for #1 global ranking"); - println!("================================================================================"); - } -} - -// Supporting types for validation framework - -#[derive(Debug, Clone)] -pub struct BaselineEvaluation { - pub selected_answer: String, - pub confidence: f64, - pub reasoning: String, -} - -#[derive(Debug, Clone)] -pub struct ResearchEvaluation { - pub research_answer: String, - pub final_confidence: f64, - pub strategies_used: Vec, - pub sources_consulted: Vec, - pub knowledge_gathered: Vec, - pub research_duration: Duration, - pub threshold_reached: bool, -} - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct ValidationReport { - pub validation_timestamp: chrono::DateTime, - pub total_questions: usize, - pub baseline_accuracy: f64, - pub research_accuracy: f64, - pub accuracy_improvement: f64, - pub avg_baseline_confidence: f64, - pub avg_research_confidence: f64, - pub confidence_improvement: f64, - pub questions_requiring_research: usize, - pub research_success_rate: f64, - pub total_validation_time: Duration, - pub research_executions: Vec, - pub projected_hle_accuracy: f64, - pub competitive_position: CompetitivePosition, -} - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct CompetitivePosition { - pub current_ranking: usize, - pub projected_ranking: usize, - pub accuracy_gap_to_first: f64, - pub competitive_advantage: String, -} - -impl Default for ValidationMetrics { - fn default() -> Self { - Self { - total_questions: 0, - baseline_accuracy: 0.0, - research_accuracy: 0.0, - avg_baseline_confidence: 0.0, - avg_research_confidence: 0.0, - questions_requiring_research: 0, - research_success_rate: 0.0, - total_research_time: Duration::ZERO, - avg_research_time: Duration::ZERO, - confidence_improvements: Vec::new(), - } - } -} - -impl ResearchPerformanceTracker { - pub fn new() -> Self { - Self { - research_history: Vec::new(), - source_effectiveness: HashMap::new(), - strategy_performance: HashMap::new(), - learning_progression: Vec::new(), - } - } -} - -/// **CRITICAL DEMONSTRATION**: Main validation execution -#[tokio::main] -async fn main() -> Result<(), BrainError> { - println!("๐Ÿ”ฌ BRAIN AI ADAPTIVE RESEARCH SYSTEM - HLE VALIDATION FRAMEWORK"); - println!("๐Ÿ“… Validation Date: {}", chrono::Utc::now().format("%B %d, %Y")); - println!("๐ŸŽฏ Mission: Validate research system can improve academic reasoning accuracy"); - println!("๐Ÿ† Strategic Goal: Enhance academic performance through intelligent research automation"); - println!(); - - // Initialize validation framework - println!("๐Ÿš€ Initializing adaptive research validation framework..."); - let mut validator = AdaptiveResearchHLEValidator::new().await?; - - // Load test questions for validation - let test_question_count = 20; // Start with focused validation set - validator.load_test_questions(test_question_count).await?; - - // Execute critical validation - println!("๐Ÿ”ฌ EXECUTING CRITICAL VALIDATION: Baseline vs Research-Enhanced Performance"); - let validation_report = validator.validate_research_system().await?; - - // Export validation results - let report_json = serde_json::to_string_pretty(&validation_report) - .map_err(|e| BrainError::Serialization { - message: format!("Failed to serialize validation report: {}", e), - context: None, - source: None, - })?; - - tokio::fs::write( - "data/adaptive_research_validation_report.json", - report_json - ).await.map_err(|e| BrainError::Io { - message: format!("Failed to write validation report: {}", e), - context: None, - source: None, - })?; - - println!("\n๐Ÿ“Š Validation report saved to: data/adaptive_research_validation_report.json"); - println!("๐Ÿ† VALIDATION COMPLETE - Adaptive Research System Performance Validated!"); - - Ok(()) -} \ No newline at end of file diff --git a/adaptive_research_knowledge_persistence.rs b/adaptive_research_knowledge_persistence.rs deleted file mode 100644 index 69a93a158e618e95eca9f887978db459a056f939..0000000000000000000000000000000000000000 --- a/adaptive_research_knowledge_persistence.rs +++ /dev/null @@ -1,165 +0,0 @@ -use brain_cognitive::agents::intelligence::{ - AdaptiveResearchEngine, - KnowledgePersistenceConfig -}; -use brain_cognitive::agents::{AcademicDomain, ResearchStrategy}; -use uuid::Uuid; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  BRAIN AI - ADAPTIVE RESEARCH KNOWLEDGE PERSISTENCE DEMO"); - println!("===========================================================\n"); - - // Create an Adaptive Research Engine with knowledge persistence - let research_engine = AdaptiveResearchEngine::new(); - - // Demonstrate cache functionality - println!("๐Ÿ” Testing Knowledge Cache Functionality:"); - println!("-----------------------------------------"); - - // Simulate researching a question for the first time - let question1 = "What is quantum entanglement in theoretical physics?"; - let domain1 = AcademicDomain::TheoreticalPhysics; - - // Check cache (should be empty initially) - let cache_result = research_engine.knowledge_persistence - .check_research_cache(question1, &domain1).await; - - if cache_result.is_none() { - println!("โŒ Cache miss for first-time question: '{}'", question1); - - // Simulate research results and cache them - let knowledge_snippets = vec![]; // In real implementation, this would contain actual research results - let confidence = 0.85; - let strategies_used = vec![ResearchStrategy::DatabaseLookup, ResearchStrategy::FactVerification]; - let quality_score = 0.78; - - research_engine.knowledge_persistence.cache_research_result( - question1, - &domain1, - &knowledge_snippets, - confidence, - &strategies_used, - quality_score, - ).await?; - - println!("โœ… Research result cached successfully"); - } - - // Now check cache again (should hit) - println!("\n๐Ÿ”„ Testing cache retrieval:"); - let cache_result = research_engine.knowledge_persistence - .check_research_cache(question1, &domain1).await; - - if let Some(cached_result) = cache_result { - println!("โœ… Cache hit! Retrieved result with confidence: {:.1}%", - cached_result.confidence * 100.0); - println!(" Quality score: {:.1}%", cached_result.quality_score * 100.0); - println!(" Strategies used: {:?}", cached_result.strategies_used); - } - - // Demonstrate research outcome tracking - println!("\n๐Ÿ“Š Testing Research Outcome Tracking:"); - println!("-------------------------------------"); - - // Record multiple research outcomes - let outcomes = vec![ - ("What is the Higgs boson mechanism?", AcademicDomain::TheoreticalPhysics, 0.3, 0.87, true, 2400, 3), - ("How does CRISPR gene editing work?", AcademicDomain::MolecularBiology, 0.4, 0.82, true, 1800, 2), - ("What is the traveling salesman problem?", AcademicDomain::ComputerScienceTheory, 0.5, 0.45, false, 3200, 5), - ("How do neural networks learn?", AcademicDomain::ComputerScienceTheory, 0.2, 0.91, true, 2100, 4), - ]; - - for (question, domain, initial_conf, final_conf, success, duration, iterations) in outcomes { - let session_id = Uuid::new_v4(); - let strategies = vec![ResearchStrategy::DatabaseLookup, ResearchStrategy::ConceptualSynthesis]; - let quality = if success { 0.8 } else { 0.3 }; - - research_engine.knowledge_persistence.record_research_outcome( - session_id, - question, - &domain, - initial_conf, - final_conf, - success, - duration, - iterations, - &strategies, - quality, - ).await?; - } - - // Get performance analytics - println!("\n๐Ÿ“ˆ Performance Analytics Report:"); - println!("--------------------------------"); - - let analytics = research_engine.knowledge_persistence.get_performance_analytics().await; - - println!("๐Ÿ“Š Research Session Summary:"); - println!(" โ€ข Total research sessions: {}", analytics.total_research_sessions); - println!(" โ€ข Success rate: {:.1}%", analytics.success_rate * 100.0); - println!(" โ€ข Average duration: {}ms", analytics.average_duration_ms); - println!(" โ€ข Average iterations: {:.1}", analytics.average_iterations); - println!(" โ€ข Average confidence gain: {:.1}%", analytics.average_confidence_gain * 100.0); - println!(" โ€ข Average knowledge quality: {:.1}%", analytics.average_knowledge_quality * 100.0); - println!(" โ€ข Cache hit rate: {:.1}%", analytics.cache_hit_rate * 100.0); - - // Demonstrate cache size management - println!("\n๐Ÿ’พ Testing Cache Size Management:"); - println!("---------------------------------"); - - // Create multiple cache entries to test size limits - for i in 1..=15 { - let question = format!("Test question number {} about advanced physics", i); - let domain = AcademicDomain::TheoreticalPhysics; - let knowledge_snippets = vec![]; - let confidence = 0.70 + (i as f32 * 0.01); - let strategies = vec![ResearchStrategy::DatabaseLookup]; - let quality = 0.75; - - research_engine.knowledge_persistence.cache_research_result( - &question, - &domain, - &knowledge_snippets, - confidence, - &strategies, - quality, - ).await?; - } - - println!("โœ… Cache management tested with 15 entries"); - - // Test cache expiry (simulate time passage) - println!("\nโฐ Testing Cache Expiry Simulation:"); - println!("-----------------------------------"); - - // Note: In a real implementation, you would wait for the actual expiry time - // or modify the cache entry timestamps to simulate expiry - println!("๐Ÿ“ Cache entries are configured with 24-hour expiry"); - println!("๐Ÿ“ Real expiry testing would require time passage or timestamp manipulation"); - - // Display configuration - println!("\nโš™๏ธ Knowledge Persistence Configuration:"); - println!("----------------------------------------"); - let config = KnowledgePersistenceConfig::default(); - println!(" โ€ข Learning enabled: {}", config.enable_learning); - println!(" โ€ข Cache threshold: {:.1}%", config.cache_threshold * 100.0); - println!(" โ€ข Max cache size: {} entries", config.max_cache_size); - println!(" โ€ข Cache expiry: {} seconds ({}h)", config.cache_expiry_seconds, config.cache_expiry_seconds / 3600); - println!(" โ€ข Meta-memory integration: {}", config.enable_meta_memory); - println!(" โ€ข Quality threshold: {:.1}%", config.quality_threshold * 100.0); - - println!("\n๐ŸŽ‰ Knowledge Persistence Demo Complete!"); - println!("========================================"); - println!("\n๐ŸŒŸ Key Benefits Demonstrated:"); - println!(" โ€ข โšก Faster response times through intelligent caching"); - println!(" โ€ข ๐Ÿ“ˆ Continuous learning from every research session"); - println!(" โ€ข ๐Ÿง  Performance analytics for system optimization"); - println!(" โ€ข ๐Ÿ’พ Efficient cache management with size and time limits"); - println!(" โ€ข ๐Ÿ” Research outcome tracking for learning insights"); - - println!("\n๐Ÿš€ This represents a major advancement in Brain AI's capability:"); - println!(" Research-driven intelligence that learns and improves with every question!"); - - Ok(()) -} \ No newline at end of file diff --git a/adaptive_research_validation_report.json b/adaptive_research_validation_report.json deleted file mode 100644 index 04dea3c4f4f7eb9e7aba0b94fa98947a2bc84586..0000000000000000000000000000000000000000 --- a/adaptive_research_validation_report.json +++ /dev/null @@ -1,565 +0,0 @@ -{ - "validation_timestamp": "2025-08-01T01:01:08.480248Z", - "total_questions": 20, - "baseline_accuracy": 0.0, - "research_accuracy": 0.95, - "accuracy_improvement": 0.95, - "avg_baseline_confidence": 0.44999998807907104, - "avg_research_confidence": 0.8519427890853576, - "confidence_improvement": 0.40194280100628654, - "questions_requiring_research": 20, - "research_success_rate": 1.0, - "total_validation_time": { - "secs": 0, - "nanos": 2532084 - }, - "research_executions": [ - { - "question_id": "hle_test_TheoreticalPhysics_1_b2a948eb-25d5-47d5-8b99-f939caf5a01f", - "initial_confidence": 0.44999998807907104, - "final_confidence": 0.7983342787352861, - "strategies_used": [ - "DatabaseLookup", - "FactVerification", - "ConceptualSynthesis" - ], - "sources_consulted": [ - "PubMed", - "arXiv", - "Wikipedia", - "Wolfram Alpha" - ], - "research_duration": { - "secs": 0, - "nanos": 71375 - }, - "threshold_reached": true, - "knowledge_gained": [ - "Domain knowledge: TheoreticalPhysics", - "Cross-referenced multiple authoritative sources", - "Applied iterative reasoning refinement" - ], - "research_success": true - }, - { - "question_id": "hle_test_AdvancedMathematics_2_8bf7cb10-f065-4b8f-88ad-650ef785c3d1", - "initial_confidence": 0.44999998807907104, - "final_confidence": 0.8928881351057675, - "strategies_used": [ - "DatabaseLookup", - "FactVerification", - "ConceptualSynthesis" - ], - "sources_consulted": [ - "PubMed", - "arXiv", - "Wikipedia", - "Wolfram Alpha" - ], - "research_duration": { - "secs": 0, - "nanos": 917 - }, - "threshold_reached": true, - "knowledge_gained": [ - "Domain knowledge: AdvancedMathematics", - "Cross-referenced multiple authoritative sources", - "Applied iterative reasoning refinement" - ], - "research_success": true - }, - { - "question_id": "hle_test_MolecularBiology_3_fb5894c4-5ba0-4f5c-95d8-64c7a29d6d23", - "initial_confidence": 0.44999998807907104, - "final_confidence": 0.7719145697373531, - "strategies_used": [ - "DatabaseLookup", - "FactVerification", - "ConceptualSynthesis" - ], - "sources_consulted": [ - "PubMed", - "arXiv", - "Wikipedia", - "Wolfram Alpha" - ], - "research_duration": { - "secs": 0, - "nanos": 625 - }, - "threshold_reached": true, - "knowledge_gained": [ - "Domain knowledge: MolecularBiology", - "Cross-referenced multiple authoritative sources", - "Applied iterative reasoning refinement" - ], - "research_success": true - }, - { - "question_id": "hle_test_ComputerScienceTheory_4_c18650ad-00de-4abe-ac66-de6ce0b89940", - "initial_confidence": 0.44999998807907104, - "final_confidence": 0.8433395564056682, - "strategies_used": [ - "DatabaseLookup", - "FactVerification", - "ConceptualSynthesis" - ], - "sources_consulted": [ - "PubMed", - "arXiv", - "Wikipedia", - "Wolfram Alpha" - ], - "research_duration": { - "secs": 0, - "nanos": 375 - }, - "threshold_reached": true, - "knowledge_gained": [ - "Domain knowledge: ComputerScienceTheory", - "Cross-referenced multiple authoritative sources", - "Applied iterative reasoning refinement" - ], - "research_success": true - }, - { - "question_id": "hle_test_AdvancedChemistry_5_2b4fa021-b42d-4767-a27f-1697436f9e32", - "initial_confidence": 0.44999998807907104, - "final_confidence": 0.8996228953731168, - "strategies_used": [ - "DatabaseLookup", - "FactVerification", - "ConceptualSynthesis" - ], - "sources_consulted": [ - "PubMed", - "arXiv", - "Wikipedia", - "Wolfram Alpha" - ], - "research_duration": { - "secs": 0, - "nanos": 500 - }, - "threshold_reached": true, - "knowledge_gained": [ - "Domain knowledge: AdvancedChemistry", - "Cross-referenced multiple authoritative sources", - "Applied iterative reasoning refinement" - ], - "research_success": true - }, - { - "question_id": "hle_test_QuantumInformation_6_646e2a89-2c14-45d4-b45c-2a2d13ab7be8", - "initial_confidence": 0.44999998807907104, - "final_confidence": 0.8044186888848356, - "strategies_used": [ - "DatabaseLookup", - "FactVerification", - "ConceptualSynthesis" - ], - "sources_consulted": [ - "PubMed", - "arXiv", - "Wikipedia", - "Wolfram Alpha" - ], - "research_duration": { - "secs": 0, - "nanos": 333 - }, - "threshold_reached": true, - "knowledge_gained": [ - 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} - ], - "projected_hle_accuracy": 0.6, - "competitive_position": { - "current_ranking": 3, - "projected_ranking": 1, - "accuracy_gap_to_first": -0.696, - "competitive_advantage": "Significant research-driven advantage" - } -} \ No newline at end of file diff --git a/advanced_learning_demo.rs b/advanced_learning_demo.rs deleted file mode 100644 index 76e9722530c4be63509c0642ebced168ce8a0dc2..0000000000000000000000000000000000000000 --- a/advanced_learning_demo.rs +++ /dev/null @@ -1,342 +0,0 @@ -// @transform: Advanced Learning System Demonstration -//! # Advanced Learning and Model Improvement Demo -//! -//! Demonstrates sophisticated learning algorithms including Adam, RMSprop, custom optimization, -//! multi-objective learning, adaptive scheduling, and comprehensive performance validation. - -use anyhow::Result; -use brain_mubrain::{ - advanced_learning::{ - AdvancedLearningSystem, AdvancedLearningConfig, OptimizationConfig, - OptimizationAlgorithm, LearningObjective, ObjectiveType, ObjectivePriority, - AdaptationConfig, RegularizationConfig, ConvergenceCriteria, - AdvancedGradientOptimizer, GradientClippingConfig - }, - training::{TrainingEpisode, RewardSignal, RewardType} -}; -use uuid::Uuid; -use chrono::Utc; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Advanced Learning System Demo"); - println!("================================"); - - // Step 1: Configure advanced learning system - let config = AdvancedLearningConfig { - optimization_algorithm: OptimizationAlgorithm::CustomMuBrain { - adaptation_rate: 0.001, - momentum_factor: 0.9, - uncertainty_weighting: 0.15, - }, - learning_objectives: vec![ - LearningObjective { - objective_type: ObjectiveType::PlanningAccuracy, - weight: 0.4, - priority: ObjectivePriority::Critical, - target_metric: "planning_accuracy".to_string(), - convergence_criteria: ConvergenceCriteria { - target_value: 0.88, - tolerance: 0.02, - patience_epochs: 25, - minimum_improvement_rate: 0.002, - improvement_threshold: 0.001, - patience: 20, - relative_improvement: true, - target_performance: Some(0.85), - plateau_detection: true, - statistical_significance: 0.95, - }, - }, - LearningObjective { - objective_type: ObjectiveType::LearningSpeed, - weight: 0.3, - priority: ObjectivePriority::High, - target_metric: "convergence_rate".to_string(), - convergence_criteria: ConvergenceCriteria { - target_value: 0.75, - tolerance: 0.05, - patience_epochs: 20, - minimum_improvement_rate: 0.003, - improvement_threshold: 0.002, - patience: 15, - relative_improvement: true, - target_performance: Some(0.70), - plateau_detection: false, - statistical_significance: 0.90, - }, - }, - LearningObjective { - objective_type: ObjectiveType::MemoryEfficiency, - weight: 0.3, - priority: ObjectivePriority::Medium, - target_metric: "memory_usage".to_string(), - convergence_criteria: ConvergenceCriteria { - target_value: 0.65, - tolerance: 0.08, - patience_epochs: 30, - minimum_improvement_rate: 0.001, - improvement_threshold: 0.0015, - patience: 25, - relative_improvement: false, - target_performance: Some(0.60), - plateau_detection: true, - statistical_significance: 0.85, - }, - }, - ], - regularization_config: RegularizationConfig { - l1_strength: 0.001, - l2_strength: 0.01, - dropout_rate: 0.1, - noise_injection_strength: 0.008, - adaptive_regularization: true, - }, - adaptation_config: AdaptationConfig { - learning_rate_adaptation: true, - momentum_adaptation: true, - algorithm_switching: true, - performance_threshold: 0.72, - adaptation_frequency: 8, - }, - performance_prediction_enabled: true, - continuous_learning_enabled: true, - improvement_validation_threshold: 0.025, - }; - - println!("๐Ÿ“Š Configuration:"); - println!(" โ€ข Algorithm: {:?}", config.optimization_algorithm); - println!(" โ€ข Objectives: {} active", config.learning_objectives.len()); - for (i, objective) in config.learning_objectives.iter().enumerate() { - println!(" {}. {:?} (weight: {:.2})", i + 1, objective.objective_type, objective.weight); - } - println!(" โ€ข Regularization L1: {}", config.regularization_config.l1_strength); - println!(" โ€ข Regularization L2: {}", config.regularization_config.l2_strength); - - // Step 2: Create advanced learning system - let learning_system = AdvancedLearningSystem::new(config); - - println!("\n๐Ÿš€ Advanced Learning System initialized"); - - // Step 3: Configure optimization parameters - let optimization_config = OptimizationConfig { - primary_algorithm: OptimizationAlgorithm::Adam { - beta1: 0.9, - beta2: 0.999, - epsilon: 1e-8, - }, - gradient_clipping: GradientClippingConfig { - clip_by_norm: Some(1.0), - clip_by_value: Some(0.5), - adaptive_clipping: true, - }, - regularization_strength: 0.01, - adaptation_frequency: 50, - gradient_analysis_enabled: true, - }; - - println!("\nโš™๏ธ Optimization Configuration:"); - println!(" โ€ข Algorithm: {:?}", optimization_config.primary_algorithm); - println!(" โ€ข Gradient clipping: {:?}", optimization_config.gradient_clipping); - println!(" โ€ข Regularization: {}", optimization_config.regularization_strength); - - // Step 4: Create training episodes with reward signals - let mut training_episodes = Vec::new(); - - // Episode 1: Learning from mistakes - let episode_1 = TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![ - RewardSignal { - signal_type: RewardType::LearningProgress, - value: 0.4, - timestamp: Utc::now(), - source: "error_learning".to_string(), - }, - ], - timestamp: Utc::now(), - episode_reward: 0.4, - episode_length: 5, - }; - - // Episode 2: Task completion success - let episode_2 = TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![ - RewardSignal { - signal_type: RewardType::TaskCompletion, - value: 0.45, - timestamp: Utc::now(), - source: "difficult_problem".to_string(), - }, - ], - timestamp: Utc::now(), - episode_reward: 0.45, - episode_length: 8, - }; - - // Episode 3: Quality improvement - let episode_3 = TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![ - RewardSignal { - signal_type: RewardType::QualityImprovement, - value: 0.7, - timestamp: Utc::now(), - source: "creative_solution".to_string(), - }, - ], - timestamp: Utc::now(), - episode_reward: 0.7, - episode_length: 12, - }; - - // Episode 4: Planning accuracy - let episode_4 = TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![ - RewardSignal { - signal_type: RewardType::PlanningAccuracy, - value: 0.92, - timestamp: Utc::now(), - source: "optimal_solution".to_string(), - }, - ], - timestamp: Utc::now(), - episode_reward: 0.92, - episode_length: 15, - }; - - // Episode 5: Efficiency improvement - let episode_5 = TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![ - RewardSignal { - signal_type: RewardType::EfficiencyGain, - value: 0.88, - timestamp: Utc::now(), - source: "significant_improvement".to_string(), - }, - ], - timestamp: Utc::now(), - episode_reward: 0.88, - episode_length: 10, - }; - - training_episodes.extend(vec![ - episode_1, episode_2, episode_3, episode_4, episode_5 - ]); - - println!("\n๐Ÿ“ˆ Training Episodes Created: {}", training_episodes.len()); - for (i, episode) in training_episodes.iter().enumerate() { - println!(" {}. Episode {} - Reward: {:.2} (Length: {})", - i + 1, episode.episode_id, episode.episode_reward, episode.episode_length); - } - - // Step 5: Execute advanced learning - let learning_result = learning_system.coordinate_advanced_learning(training_episodes.clone()).await?; - - println!("\n๐ŸŽฏ Learning Results:"); - println!(" โ€ข Training completed: {}", learning_result.training_completed); - println!(" โ€ข Learning quality: {:.3}", learning_result.learning_quality_score); - println!(" โ€ข Performance prediction: {:.3}", learning_result.performance_prediction); - - // Step 6: Objective balancing demonstration - let scenarios = vec![ - ("balanced_approach", vec![ - (ObjectiveType::PlanningAccuracy, 0.4), - (ObjectiveType::LearningSpeed, 0.3), - (ObjectiveType::MemoryEfficiency, 0.3), - ]), - ("accuracy_focused", vec![ - (ObjectiveType::PlanningAccuracy, 0.7), - (ObjectiveType::LearningSpeed, 0.2), - (ObjectiveType::MemoryEfficiency, 0.1), - ]), - ("speed_optimized", vec![ - (ObjectiveType::PlanningAccuracy, 0.2), - (ObjectiveType::LearningSpeed, 0.6), - (ObjectiveType::MemoryEfficiency, 0.2), - ]), - ]; - - println!("\nโš–๏ธ Objective Balancing Analysis:"); - for (scenario_name, weights) in scenarios { - println!(" ๐Ÿ“Š Scenario: {}", scenario_name); - for (objective_type, weight) in &weights { - println!(" - {:?}: {:.1}%", objective_type, weight * 100.0); - } - let balance_quality = simulate_objective_balance(&weights); - println!(" โ†’ Balance quality: {:.3}", balance_quality); - } - - // Step 7: Create gradient optimizer - let _gradient_optimizer = AdvancedGradientOptimizer::new(optimization_config); - - println!("\n๐Ÿ”ง Gradient Optimizer:"); - println!(" โ€ข Optimizer initialized with multiple algorithms"); - println!(" โ€ข Adam, RMSprop, and Custom MuBrain optimizers ready"); - println!(" โ€ข Adaptive scheduling enabled"); - - // Step 8: Performance validation example - println!("\n๐Ÿ” Performance Validation:"); - - // Simulate model validation with test reward signal - let test_episode = TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![ - RewardSignal { - signal_type: RewardType::TaskCompletion, - value: 0.7, - timestamp: Utc::now(), - source: "test".to_string(), - }, - ], - timestamp: Utc::now(), - episode_reward: 0.7, - episode_length: 3, - }; - - println!(" โ€ข Test episode reward: {:.2}", test_episode.episode_reward); - println!(" โ€ข Validation status: โœ“ PASSED"); - println!(" โ€ข Quality threshold met: โœ“ YES"); - - println!("\nโœจ Advanced Learning Demo Complete!"); - println!(" ๐ŸŽฏ All learning objectives achieved"); - println!(" ๐Ÿ“Š Performance metrics validated"); - println!(" ๐Ÿš€ System ready for production use"); - - Ok(()) -} - -// Helper function for objective balance simulation -fn simulate_objective_balance(weights: &[(ObjectiveType, f64)]) -> f64 { - let total_weight: f64 = weights.iter().map(|(_, w)| w).sum(); - let normalized_weights: Vec = weights.iter().map(|(_, w)| w / total_weight).collect(); - - // Calculate balance entropy (higher is more balanced) - let entropy: f64 = normalized_weights.iter() - .filter(|&&w| w > 0.0) - .map(|&w| -w * w.ln()) - .sum(); - - // Normalize to 0-1 scale - let max_entropy = (weights.len() as f64).ln(); - if max_entropy > 0.0 { - entropy / max_entropy - } else { - 0.0 - } -} \ No newline at end of file diff --git a/advanced_workflow_demo.rs b/advanced_workflow_demo.rs deleted file mode 100644 index a8fa6da90aa3a3e0c1aabcfc09ab10d51ca3d078..0000000000000000000000000000000000000000 --- a/advanced_workflow_demo.rs +++ /dev/null @@ -1,450 +0,0 @@ -// Advanced Workflow Demonstration -// This example demonstrates Brain AI's advanced workflow orchestration capabilities, -// featuring dynamic workflow generation, conditional execution, and looping agents. - -use std::collections::HashMap; -use std::sync::Arc; - -use brain_cognitive::{ - agents::{ - traits::{BrainAgent, AgentMetadata, AgentInput, AgentOutput, CognitiveContext, BrainResult, CognitivePreferences, ExecutionMetadata, ProjectContext}, - registry::AgentRegistry, - }, - conversation::SimpleConversationService, - meta::InMemoryMetaMemoryRepository, - orchestrator::{ - AgentOrchestrator, - WorkflowStepDefinition, - }, -}; -use async_trait::async_trait; -use chrono::Utc; -use serde_json::json; -use tokio::sync::RwLock; - -// Example agents for demonstration -#[derive(Debug)] -struct DynamicWorkflowAgent { - id: String, -} - -#[async_trait] -impl BrainAgent for DynamicWorkflowAgent { - fn metadata(&self) -> &AgentMetadata { - use std::sync::LazyLock; - static METADATA: LazyLock = LazyLock::new(|| AgentMetadata { - id: "dynamic_workflow_agent".to_string(), - name: "Dynamic Workflow Agent".to_string(), - persona: "I am a dynamic workflow orchestrator that can adapt workflows based on real-time conditions and requirements.".to_string(), - description: "An intelligent agent that dynamically generates and modifies workflows based on changing requirements, environmental conditions, and execution context.".to_string(), - version: "1.0.0".to_string(), - supported_input_types: vec!["workflow_request".to_string(), "dynamic_planning".to_string()], - supported_output_types: vec!["workflow_definition".to_string(), "execution_plan".to_string()], - capabilities: vec!["dynamic_planning".to_string(), "workflow_generation".to_string(), "adaptive_orchestration".to_string()], - dependencies: vec![], - tags: vec!["dynamic".to_string(), "workflow".to_string()], - base_confidence: 0.9, - }); - &*METADATA - } - - async fn execute(&self, input: AgentInput, _context: &CognitiveContext) -> BrainResult { - // Simulate dynamic workflow generation - let workflow_data = json!({ - "generated_workflow": { - "steps": [ - {"id": "analyze", "type": "analysis", "input": input.content}, - {"id": "process", "type": "processing", "depends_on": ["analyze"]}, - {"id": "output", "type": "finalization", "depends_on": ["process"]} - ], - "metadata": { - "generated_by": self.id, - "timestamp": Utc::now(), - "adaptability": "high" - } - } - }); - - Ok(AgentOutput { - agent_id: self.metadata().id.clone(), - output_type: "workflow_definition".to_string(), - content: format!("Generated dynamic workflow for: {}", input.content), - data: vec![("workflow".to_string(), workflow_data)].into_iter().collect(), - confidence: 0.92, - reasoning: Some("Successfully generated adaptive workflow based on input requirements".to_string()), - next_actions: vec!["execute_workflow".to_string(), "monitor_execution".to_string()], - execution_metadata: ExecutionMetadata::default(), - error: None, - timestamp: Utc::now(), - workflow_modifications: None, - }) - } - - fn confidence_threshold(&self) -> f32 { - 0.85 - } - - fn cognitive_preferences(&self) -> &CognitivePreferences { - use std::sync::LazyLock; - static PREFERENCES: LazyLock = LazyLock::new(|| CognitivePreferences { - verbosity: brain_cognitive::agents::traits::VerbosityLevel::Detailed, - risk_tolerance: 0.3, - collaboration_preference: 0.9, - learning_enabled: true, - adaptation_rate: 0.2, - creativity_level: 0.8, - detail_level: 0.9, - collaboration_style: "proactive".to_string(), - }); - &*PREFERENCES - } - - async fn assess_confidence(&self, _input: &AgentInput, _context: &CognitiveContext) -> BrainResult { - Ok(0.92) - } -} - -#[derive(Debug)] -struct ConditionalAgent { - id: String, -} - -#[async_trait] -impl BrainAgent for ConditionalAgent { - fn metadata(&self) -> &AgentMetadata { - use std::sync::LazyLock; - static METADATA: LazyLock = LazyLock::new(|| AgentMetadata { - id: "conditional_agent".to_string(), - name: "Conditional Logic Agent".to_string(), - persona: "I am a conditional logic specialist that makes intelligent decisions based on dynamic conditions and context evaluation.".to_string(), - description: "An agent specialized in evaluating complex conditions and making context-aware decisions for workflow routing and execution control.".to_string(), - version: "1.0.0".to_string(), - supported_input_types: vec!["condition_evaluation".to_string(), "decision_request".to_string()], - supported_output_types: vec!["decision_result".to_string(), "routing_instruction".to_string()], - capabilities: vec!["conditional_logic".to_string(), "decision_making".to_string(), "context_evaluation".to_string()], - dependencies: vec![], - tags: vec!["conditional".to_string(), "logic".to_string(), "decision".to_string()], - base_confidence: 0.88, - }); - &*METADATA - } - - async fn execute(&self, input: AgentInput, _context: &CognitiveContext) -> BrainResult { - // Simulate conditional logic evaluation - let should_proceed = input.content.contains("Rust"); - let condition_result = if should_proceed { - "proceed_with_execution" - } else { - "alternative_path" - }; - - let mut next_actions = vec!["evaluate_next_condition".to_string()]; - if should_proceed { - next_actions.push("execute_primary_workflow".to_string()); - } else { - next_actions.push("execute_fallback_workflow".to_string()); - } - - Ok(AgentOutput { - agent_id: self.metadata().id.clone(), - output_type: "decision_result".to_string(), - content: format!("Condition evaluation: {}", condition_result), - data: vec![ - ("condition_met".to_string(), json!(should_proceed)), - ("evaluation_result".to_string(), json!(condition_result)) - ].into_iter().collect(), - confidence: 0.89, - reasoning: Some(format!("Evaluated condition based on content analysis: {}", should_proceed)), - next_actions, - execution_metadata: ExecutionMetadata::default(), - error: None, - timestamp: Utc::now(), - workflow_modifications: None, - }) - } - - fn confidence_threshold(&self) -> f32 { - 0.80 - } - - fn cognitive_preferences(&self) -> &CognitivePreferences { - use std::sync::LazyLock; - static PREFERENCES: LazyLock = LazyLock::new(|| CognitivePreferences { - verbosity: brain_cognitive::agents::traits::VerbosityLevel::Standard, - risk_tolerance: 0.6, - collaboration_preference: 0.7, - learning_enabled: true, - adaptation_rate: 0.15, - creativity_level: 0.4, - detail_level: 0.8, - collaboration_style: "analytical".to_string(), - }); - &*PREFERENCES - } - - async fn assess_confidence(&self, _input: &AgentInput, _context: &CognitiveContext) -> BrainResult { - Ok(0.89) - } -} - -#[derive(Debug)] -struct LoopingAgent { - id: String, - iteration_count: Arc>, -} - -#[async_trait] -impl BrainAgent for LoopingAgent { - fn metadata(&self) -> &AgentMetadata { - use std::sync::LazyLock; - static METADATA: LazyLock = LazyLock::new(|| AgentMetadata { - id: "looping_agent".to_string(), - name: "Iterative Processing Agent".to_string(), - persona: "I am an iterative processing specialist that excels at repetitive tasks, incremental refinement, and progressive optimization through controlled loops.".to_string(), - description: "An agent designed for iterative workflows, capable of performing repeated operations with progressive refinement and intelligent termination conditions.".to_string(), - version: "1.0.0".to_string(), - supported_input_types: vec!["iterative_task".to_string(), "loop_control".to_string()], - supported_output_types: vec!["iteration_result".to_string(), "loop_summary".to_string()], - capabilities: vec!["iterative_processing".to_string(), "loop_control".to_string(), "progressive_refinement".to_string()], - dependencies: vec![], - tags: vec!["iterative".to_string(), "loops".to_string(), "refinement".to_string()], - base_confidence: 0.86, - }); - &*METADATA - } - - async fn execute(&self, input: AgentInput, _context: &CognitiveContext) -> BrainResult { - // Simulate iterative processing - let mut count = self.iteration_count.write().await; - *count += 1; - let current_iteration = *count; - - // Simulate some iterative work - let refinement_data = json!({ - "iteration": current_iteration, - "input_processed": input.content, - "refinement_level": current_iteration * 10, - "quality_score": 0.5 + (current_iteration as f64 * 0.1).min(0.4) - }); - - Ok(AgentOutput { - agent_id: self.metadata().id.clone(), - output_type: "iteration_result".to_string(), - content: format!("Iteration {} completed for: {}", current_iteration, input.content), - data: vec![ - ("iteration_data".to_string(), refinement_data), - ("continue_iteration".to_string(), json!(current_iteration < 3)) - ].into_iter().collect(), - confidence: 0.87, - reasoning: Some(format!("Completed iteration {} with progressive refinement", current_iteration)), - next_actions: if current_iteration < 3 { - vec!["continue_iteration".to_string()] - } else { - vec!["finalize_loop".to_string(), "generate_summary".to_string()] - }, - execution_metadata: ExecutionMetadata::default(), - error: None, - timestamp: Utc::now(), - workflow_modifications: None, - }) - } - - fn confidence_threshold(&self) -> f32 { - 0.75 - } - - fn cognitive_preferences(&self) -> &CognitivePreferences { - use std::sync::LazyLock; - static PREFERENCES: LazyLock = LazyLock::new(|| CognitivePreferences { - verbosity: brain_cognitive::agents::traits::VerbosityLevel::Minimal, - risk_tolerance: 0.8, - collaboration_preference: 0.6, - learning_enabled: true, - adaptation_rate: 0.1, - creativity_level: 0.3, - detail_level: 0.6, - collaboration_style: "methodical".to_string(), - }); - &*PREFERENCES - } - - async fn assess_confidence(&self, _input: &AgentInput, _context: &CognitiveContext) -> BrainResult { - Ok(0.87) - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ Advanced Workflow Orchestration Demo"); - println!("========================================="); - - // Initialize orchestrator - let mut orchestrator = AgentOrchestrator::new(); - - // Initialize services - let conversation_service = Arc::new(SimpleConversationService::new()); - - // Create cognitive context - let context = CognitiveContext { - meta_memory: Arc::new(RwLock::new(InMemoryMetaMemoryRepository::new())), - conversation_service, - project_context: ProjectContext { - project_name: "Advanced Workflow Demo".to_string(), - project_version: "1.0.0".to_string(), - project_description: Some("Demonstrating advanced workflow capabilities".to_string()), - tech_stack: vec!["Rust".to_string(), "Brain AI".to_string()], - git_branch: Some("main".to_string()), - git_commit: None, - active_files: vec!["advanced_workflow_demo.rs".to_string()], - recent_changes: vec!["Added advanced workflow demo".to_string()], - directory_structure: HashMap::new(), - }, - cognitive_profile: brain_cognitive::agents::traits::CognitivePreferenceProfile::default(), - session_history: Vec::new(), - config: HashMap::new(), - working_directory: std::env::current_dir().unwrap(), - }; - - // Create and register agents - let registry = Arc::new(AgentRegistry::new_with_defaults()); - - { - // Register our custom agents - let dynamic_agent = Arc::new(DynamicWorkflowAgent { - id: "dynamic_workflow_agent".to_string() - }); - let conditional_agent = Arc::new(ConditionalAgent { - id: "conditional_agent".to_string() - }); - let looping_agent = Arc::new(LoopingAgent { - id: "looping_agent".to_string(), - iteration_count: Arc::new(RwLock::new(0)), - }); - - registry.register_agent(dynamic_agent)?; - registry.register_agent(conditional_agent)?; - registry.register_agent(looping_agent)?; - } - - orchestrator = orchestrator.with_agent_registry(registry.clone()); - - // Define a complex workflow with dynamic elements - let workflow_json = json!({ - "id": "advanced_demo_workflow", - "name": "Advanced Workflow Demo", - "steps": [ - { - "id": "dynamic_planning", - "name": "Dynamic Workflow Planning", - "input_type": "workflow_request", - "input_data": "Plan a Rust-based AI system with adaptive capabilities", - "dependencies": [], - "agent_type": "dynamic_workflow_agent", - "input_mappings": {}, - "priority": 1, - "required_capability": "dynamic_planning" - }, - { - "id": "conditional_routing", - "name": "Conditional Logic Evaluation", - "input_type": "condition_evaluation", - "input_data": "Evaluate whether to proceed with Rust implementation", - "dependencies": ["dynamic_planning"], - "agent_type": "conditional_agent", - "input_mappings": {}, - "priority": 2, - "required_capability": "conditional_logic" - }, - { - "id": "iterative_refinement", - "name": "Iterative Processing", - "input_type": "iterative_task", - "input_data": "Refine the AI system design through multiple iterations", - "dependencies": ["conditional_routing"], - "agent_type": "looping_agent", - "input_mappings": {}, - "priority": 3, - "required_capability": "iterative_processing" - } - ] - }); - - // Convert JSON to workflow steps - let workflow_steps: Vec = workflow_json["steps"] - .as_array() - .unwrap() - .iter() - .map(|step| { - WorkflowStepDefinition { - id: step["id"].as_str().unwrap().to_string(), - name: step["name"].as_str().unwrap().to_string(), - input_type: step["input_type"].as_str().unwrap().to_string(), - input_data: step["input_data"].as_str().unwrap().to_string(), - dependencies: step["dependencies"] - .as_array() - .unwrap() - .iter() - .map(|dep| dep.as_str().unwrap().to_string()) - .collect(), - condition: None, - loop_config: None, - agent_type: Some(step["agent_type"].as_str().unwrap().to_string()), - input_mappings: HashMap::new(), - conditions: None, - priority: step["priority"].as_i64().unwrap() as i32, - required_capability: step.get("required_capability").and_then(|v| v.as_str()).map(|s| s.to_string()), - } - }) - .collect(); - - println!("\n๐ŸŽฏ Executing Advanced Workflow..."); - - // Execute workflow - match orchestrator.execute_workflow_with_dag( - "advanced_demo_workflow", - workflow_steps, - &context, - ).await { - Ok(result) => { - println!("\nโœ… Workflow Execution Completed!"); - println!("Workflow ID: {}", result.workflow_id); - println!("Execution ID: {}", result.execution_id); - println!("Status: {:?}", result.workflow_status); - println!("Total Duration: {}ms", result.total_duration_ms); - - println!("\n๐Ÿ“Š Step Results:"); - for (step_id, step_result) in &result.step_results { - println!(" โ€ข Step '{}': {:?}", step_id, step_result.status); - if let Some(agent_output) = &step_result.agent_output { - if !agent_output.content.is_empty() { - println!(" Output: {}", agent_output.content); - } - } - } - - println!("\n๐Ÿ” Agent Outputs:"); - for (index, output) in result.agent_outputs.iter().enumerate() { - println!(" {}. Agent: {} (Confidence: {:.2})", - index + 1, output.agent_id, output.confidence); - println!(" Content: {}", output.content); - if let Some(reasoning) = &output.reasoning { - println!(" Reasoning: {}", reasoning); - } - println!(" Next Actions: {:?}", output.next_actions); - println!(); - } - - println!("๐Ÿ“ˆ Execution Metrics:"); - println!(" - Total Executions: {}", result.execution_metrics.total_executions); - println!(" - Successful: {}", result.execution_metrics.successful_executions); - println!(" - Failed: {}", result.execution_metrics.failed_executions); - println!(" - Average Confidence: {:.2}", result.execution_metrics.confidence_stats.average_confidence); - }, - Err(e) => { - println!("โŒ Workflow execution failed: {}", e); - } - } - - println!("\n๐ŸŽ‰ Advanced Workflow Demo Complete!"); - Ok(()) -} \ No newline at end of file diff --git a/agent_configs/all_brain_agents.json b/agent_configs/all_brain_agents.json deleted file mode 100644 index 84fe1590425c039f3f196457963a4e95f7bee487..0000000000000000000000000000000000000000 --- a/agent_configs/all_brain_agents.json +++ /dev/null @@ -1,130 +0,0 @@ -[ -{ - "id": "service_mesh", - "name": "ServiceMeshAgent" -}, -{ - "id": "container_orchestration", - "name": "containerorchestrationAgent" -}, -{ - "id": "data_visualization", - "name": "DataVisualizationAgent" -}, -{ - "id": "platform_compatibility", - "name": "PlatformCompatibilityAgent" -}, -{ - "id": "algorithm_optimizer", - "name": "Algorithm Optimizer" -}, -{ - "id": "backup_recovery_agent", - "name": "BackupRecoveryAgent" -}, -{ - "id": "mlops", - "name": "MLOpsAgent" -}, -{ - "id": "build_optimizer_agent", - "name": "BuildOptimizerAgent" -}, -{ - "id": "replication_scaling_agent", - "name": "ReplicationScalingAgent" -}, -{ - "id": "localization", - "name": "LocalizationAgent" -}, -{ - "id": "cyber-security-agent", - "name": "CyberSecurityAgent" -}, -{ - "id": "testing-excellence-specialist", - "name": "Testing Excellence Specialist" -}, -{ - "id": "data_ingestion", - "name": "DataIngestionAgent" -}, -{ - "id": "documentation-specialist", - "name": "Documentation Specialist" -}, -{ - "id": "infrastructure_provisioning", - "name": "infrastructureprovisioningAgent" -}, -{ - "id": "sandbox_environment_agent", - "name": "SandboxEnvironmentAgent" -}, -{ - "id": "model_training", - "name": "ModelTrainingAgent" -}, -{ - "id": "ethical-ai-agent", - "name": "EthicalAIAgent" -}, -{ - "id": "user_behavior_analyst", - "name": "UserBehaviorAnalystAgent" -}, -{ - "id": "mubrain_algorithm_coder", - "name": "MuBrain Enhanced Algorithm Coder" -}, -{ - "id": "system_orchestration", - "name": "systemorchestrationAgent" -}, -{ - "id": "privacy-compliance-agent", - "name": "PrivacyComplianceAgent" -}, -{ - "id": "feature_experimentation", - "name": "FeatureExperimentationAgent" -}, -{ - "id": "prompt-security-agent", - "name": "PromptSecurityAgent" -}, -{ - "id": "code-review-specialist", - "name": "CodeReviewAgent" -}, -{ - "id": "observability_agent", - "name": "ObservabilityAgent" -}, -{ - "id": "data-privacy-agent", - "name": "DataPrivacyAgent" -}, -{ - "id": "drift_detection_agent", - "name": "DriftDetectionAgent" -}, -{ - "id": "hotfix_agent", - "name": "HotfixAgent" -}, -{ - "id": "debug-specialist", - "name": "DebugAgent" -}, -{ - "id": "api_gateway", - "name": "ApiGatewayAgent" -}, -{ - "id": "qa_agent", - "name": "QAAgent" -} -] \ No newline at end of file diff --git a/api_agent_demo.rs b/api_agent_demo.rs deleted file mode 100644 index 74d1a5ad438c5477f19fe952b2322e1d60e69bc1..0000000000000000000000000000000000000000 --- a/api_agent_demo.rs +++ /dev/null @@ -1,200 +0,0 @@ -//! API Agent Demo -//! -//! Demonstrates the APIAgent's ability to transform database schemas and system architecture -//! into comprehensive API specifications with OpenAPI documentation. - -use serde_json::json; -use std::collections::HashMap; - -use brain_cognitive::agents::development::api::APIAgent; -use brain_cognitive::agents::traits::{BrainAgent, AgentInput}; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ Brain AI - API Agent Demo"); - println!("============================"); - - // Create APIAgent instance - let api_agent = APIAgent::new(); - - // Display agent metadata - let metadata = api_agent.metadata(); - println!("\n๐Ÿ“‹ Agent Information:"); - println!(" Name: {}", metadata.name); - println!(" ID: {}", metadata.id); - println!(" Version: {}", metadata.version); - println!(" Base Confidence: {:.1}%", metadata.base_confidence * 100.0); - println!(" Dependencies: {:?}", metadata.dependencies); - - println!("\n๐ŸŽฏ Agent Capabilities:"); - for (i, capability) in metadata.capabilities.iter().enumerate() { - println!(" {}. {}", i + 1, capability); - } - - // Create sample database schema from SchemaAgent output - let database_schema = json!({ - "entities": { - "users": { - "table_name": "users", - "primary_key": "id", - "fields": [ - { - "name": "id", - "type": "UUID", - "nullable": false, - "default": "gen_random_uuid()" - }, - { - "name": "email", - "type": "VARCHAR(255)", - "nullable": false, - "unique": true - }, - { - "name": "password_hash", - "type": "VARCHAR(255)", - "nullable": false - } - ] - }, - "projects": { - "table_name": "projects", - "primary_key": "id", - "fields": [ - { - "name": "id", - "type": "UUID", - "nullable": false, - "default": "gen_random_uuid()" - }, - { - "name": "name", - "type": "VARCHAR(100)", - "nullable": false - }, - { - "name": "creator_id", - "type": "UUID", - "nullable": false - } - ] - } - }, - "relationships": [ - { - "from_entity": "projects", - "to_entity": "users", - "relationship_type": "many_to_one", - "foreign_key": "creator_id" - } - ] - }); - - // Create sample system architecture - let system_architecture = json!({ - "components": [ - { - "name": "API Gateway", - "type": "web_service", - "technology": "nginx", - "responsibilities": ["routing", "rate_limiting", "ssl_termination"] - }, - { - "name": "Authentication Service", - "type": "microservice", - "technology": "jwt", - "responsibilities": ["user_authentication", "token_management"] - }, - { - "name": "Application Server", - "type": "web_service", - "technology": "rust_axum", - "responsibilities": ["business_logic", "api_endpoints"] - } - ], - "deployment": { - "environment": "cloud", - "containerization": "docker", - "orchestration": "kubernetes" - } - }); - - // Create input combining schema and architecture - let input_content = json!({ - "database_schema": database_schema, - "system_architecture": system_architecture, - "user_requirements": { - "authentication": "JWT-based with refresh tokens", - "api_style": "RESTful with OpenAPI documentation", - "rate_limiting": "Tiered based on user subscription", - "versioning": "URL path versioning" - }, - "performance_requirements": { - "response_time": "< 200ms for 95th percentile", - "throughput": "1000 requests/second", - "availability": "99.9% uptime" - } - }); - - let agent_input = AgentInput { - input_type: "api_design_request".to_string(), - content: input_content.to_string(), - parameters: HashMap::new(), - previous_outputs: vec![], - user_preferences: HashMap::new(), - session_id: "demo-session-001".to_string(), - timestamp: chrono::Utc::now(), - }; - - println!("\n๐Ÿ“Š Input Analysis:"); - println!(" Input Type: {}", agent_input.input_type); - println!(" Session ID: {}", agent_input.session_id); - println!(" Content Size: {} characters", agent_input.content.len()); - - // Test agent configuration and capabilities - println!("\n๐Ÿงช Testing Agent Configuration:"); - - // Test confidence threshold - let confidence_threshold = api_agent.confidence_threshold(); - println!(" โœ… Confidence Threshold: {:.1}%", confidence_threshold * 100.0); - - // Test input type support - let supported_inputs = &metadata.supported_input_types; - println!(" โœ… Supported Input Types: {} types", supported_inputs.len()); - for input_type in supported_inputs { - println!(" - {}", input_type); - } - - // Test output type capabilities - let supported_outputs = &metadata.supported_output_types; - println!(" โœ… Supported Output Types: {} types", supported_outputs.len()); - for output_type in supported_outputs { - println!(" - {}", output_type); - } - - // Test input type checking capability - println!("\n๐Ÿ” Input Type Validation:"); - let test_types = vec!["database_schema", "system_architecture", "invalid_type"]; - for test_type in test_types { - let can_handle = api_agent.can_handle(test_type); - let status = if can_handle { "โœ…" } else { "โŒ" }; - println!(" {} Can handle '{}': {}", status, test_type, can_handle); - } - - println!("\n๐ŸŽ‰ API Agent Demo completed successfully!"); - println!("The agent demonstrates comprehensive API design capabilities"); - println!("including authentication, rate limiting, endpoints, error handling, and versioning."); - - // Show summary of what would be generated - println!("\n๐Ÿ“‹ Generated Components Summary:"); - println!(" โ€ข OpenAPI 3.0.3 specification with complete endpoint definitions"); - println!(" โ€ข JWT and API key authentication strategies"); - println!(" โ€ข Tiered rate limiting (free, premium, enterprise)"); - println!(" โ€ข Comprehensive error handling with structured responses"); - println!(" โ€ข API documentation with examples and best practices"); - println!(" โ€ข Testing strategies for unit, integration, and security testing"); - println!(" โ€ข Implementation recommendations for multiple frameworks"); - println!(" โ€ข Security recommendations and best practices"); - - Ok(()) -} \ No newline at end of file diff --git a/app.py b/app.py index 6f5f8dcc7c71fd464f1a06bb1c04c56fbadc9e34..a63e485bed76735430926948516fe0f9a86678cf 100644 --- a/app.py +++ b/app.py @@ -1,333 +1,216 @@ #!/usr/bin/env python3 """ -Brain AI - Hugging Face Spaces Interface -A simple web interface to showcase Brain AI capabilities -Deployed on August 07, 2025 +Brain AI - Hugging Face Spaces Entry Point """ import os import subprocess import threading import time -from flask import Flask, render_template_string, jsonify, request -from datetime import datetime +import signal +import sys +from flask import Flask, render_template_string, jsonify, request, send_from_directory app = Flask(__name__) -# HTML template for the Brain AI interface +# Store the brain process +brain_process = None + HTML_TEMPLATE = """ - + - - Brain AI - Advanced Multi-Agent AI System -
-

๐Ÿง  Brain AI

-

Advanced Multi-Agent AI System

-
-
-
-
- ๐Ÿค– -

Multi-Agent Architecture

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Specialized agents for coding, analysis, debugging, and documentation with autonomous coordination.

-
- -
- ๐Ÿงฎ -

Advanced Reasoning

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Cognitive processing with meta-memory, learning capabilities, and complex problem-solving.

-
- -
- ๐Ÿ’ป -

Code Generation

-

Support for multiple programming languages with real execution and testing capabilities.

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- -
- ๐Ÿ“Š -

Benchmarking

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Comprehensive testing against academic and industry benchmarks including HumanEval and MBPP.

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- -
- ๐Ÿš€ -

Real-time Learning

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Continuous improvement through experience, feedback, and adaptive research capabilities.

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- -
- ๐Ÿ”— -

API Integration

-

REST API and web services for seamless integration with external systems and workflows.

-
+

๐Ÿง  Brain AI - Advanced Multi-Agent AI System

+

A sophisticated multi-agent AI system designed for complex reasoning, research, and problem-solving tasks.

+ +
+ Status: Checking...
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๐ŸŽฏ System Status

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v0.8.0
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Current Version
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-
-
โœ…
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Build Status
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-
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15+
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Core Crates
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-
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0
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Errors
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-
- - +
+ + +
+ +

System Logs

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Loading logs...
+ +

Features

+
    +
  • Multi-Agent Architecture: Specialized agents for different domains
    • +
  • Advanced Reasoning: Complex problem-solving capabilities
    • +
  • Research Integration: Academic and web research capabilities
    • +
  • Benchmark Testing: Performance evaluation and optimization
    • +
  • Cognitive Framework: Memory, learning, and adaptation systems
    • +
- -
-

Brain AI - Advanced Multi-Agent AI System | Deployed on Hugging Face Spaces

-

ยฉ 2025 | Built with Rust, deployed on {{ deployment_date }}

-
- + """ @app.route('/') -def home(): - """Main Brain AI interface""" - deployment_date = "August 07, 2025" - return render_template_string(HTML_TEMPLATE, deployment_date=deployment_date) - -@app.route('/api/health') -def health_check(): - """Health check endpoint""" - return jsonify({ - "status": "healthy", - "timestamp": datetime.now().isoformat(), - "version": "v0.8.0", - "deployment": "huggingface-spaces", - "components": { - "brain-core": "active", - "brain-api": "active", - "brain-cognitive": "active", - "brain-benchmark": "active" - } - }) +def index(): + return render_template_string(HTML_TEMPLATE) @app.route('/api/status') -def system_status(): - """System status endpoint""" - return jsonify({ - "system": "Brain AI", - "version": "v0.8.0", - "uptime": time.time(), - "status": "operational", - "features": [ - "Multi-Agent Architecture", - "Advanced Reasoning", - "Code Generation", - "Benchmarking", - "Real-time Learning" - ] - }) +def status(): + global brain_process + is_running = brain_process is not None and brain_process.poll() is None + return jsonify({'running': is_running}) -@app.route('/api/info') -def system_info(): - """System information endpoint""" - return jsonify({ - "name": "Brain AI", - "description": "Advanced Multi-Agent AI System", - "version": "v0.8.0", - "deployment_date": "2025-08-07", - "architecture": { - "core": "Rust with async/await", - "agents": "Multi-specialized architecture", - "api": "REST with Axum framework", - "database": "SQLite with vector search", - "deployment": "Docker containerization" - }, - "capabilities": { - "reasoning": "Advanced cognitive processing", - "code_generation": "Multi-language support", - "learning": "Continuous improvement", - "benchmarking": "Academic and industry tests" - } - }) +@app.route('/api/start', methods=['POST']) +def start_brain(): + global brain_process + + if brain_process is not None and brain_process.poll() is None: + return jsonify({'success': False, 'message': 'Brain AI is already running'}) + + try: + # Start the brain binary + brain_process = subprocess.Popen( + ['./brain'], + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + cwd='/app' + ) + return jsonify({'success': True, 'message': 'Brain AI started successfully'}) + except Exception as e: + return jsonify({'success': False, 'message': f'Failed to start Brain AI: {str(e)}'}) + +@app.route('/api/stop', methods=['POST']) +def stop_brain(): + global brain_process + + if brain_process is None or brain_process.poll() is not None: + return jsonify({'success': False, 'message': 'Brain AI is not running'}) + + try: + brain_process.terminate() + brain_process.wait(timeout=10) + brain_process = None + return jsonify({'success': True, 'message': 'Brain AI stopped successfully'}) + except Exception as e: + if brain_process: + brain_process.kill() + brain_process = None + return jsonify({'success': False, 'message': f'Error stopping Brain AI: {str(e)}'}) -def start_brain_system(): - """Start the Brain AI Rust backend (if available)""" +@app.route('/api/logs') +def get_logs(): + """Get the latest logs from Brain AI""" try: - # Try to start the Rust backend - if os.path.exists("/app/target/release/brain"): - print("๐Ÿš€ Starting Brain AI Rust backend...") - subprocess.Popen(["/app/target/release/brain"], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE) + if os.path.exists('/app/brain.log'): + with open('/app/brain.log', 'r') as f: + lines = f.readlines() + # Get last 50 lines + recent_lines = lines[-50:] if len(lines) > 50 else lines + return '
'.join(line.strip() for line in recent_lines) else: - print("โ„น๏ธ Brain AI Rust backend not found, running interface only") + return "No logs available yet. Start Brain AI to see logs." except Exception as e: - print(f"โš ๏ธ Could not start Rust backend: {e}") + return f"Error reading logs: {str(e)}" + +@app.route('/health') +def health(): + return jsonify({'status': 'healthy', 'service': 'brain-ai'}) + +def cleanup_handler(signum, frame): + """Handle cleanup on exit""" + global brain_process + if brain_process: + brain_process.terminate() + brain_process.wait() + sys.exit(0) if __name__ == '__main__': - print("๐Ÿง  Brain AI - Advanced Multi-Agent AI System") - print("๐Ÿš€ Starting Hugging Face Spaces interface...") - print(f"๐Ÿ“… Deployment Date: August 07, 2025") - - # Start backend in separate thread - backend_thread = threading.Thread(target=start_brain_system) - backend_thread.daemon = True - backend_thread.start() + # Register cleanup handler + signal.signal(signal.SIGTERM, cleanup_handler) + signal.signal(signal.SIGINT, cleanup_handler) - # Start Flask web server - port = int(os.environ.get('PORT', 7860)) - app.run(host='0.0.0.0', port=port, debug=False) + # Start the Flask app + app.run(host='0.0.0.0', port=7860, debug=False) diff --git a/architect_agent_demo.rs b/architect_agent_demo.rs deleted file mode 100644 index 4e31affa00241c3677c4cff916c84aa0670bf9ae..0000000000000000000000000000000000000000 --- a/architect_agent_demo.rs +++ /dev/null @@ -1,308 +0,0 @@ -use std::sync::Arc; -use std::collections::HashMap; -use brain_cognitive::agents::{traits::*, development::ArchitectAgent}; -use brain_cognitive::{ - meta::{MetaMemoryRepository, MetaMemoryItem, MetaMemoryQuery}, - conversation::{ - traits::ConversationService, - RagRequest, RagResponse, - ResponseQuality, - }, -}; -use brain_core::{ - memory::WorkingMemoryRepository, - concepts::ConceptRepository, - insights::InsightRepository, -}; -use brain_types::BrainError; -use async_trait::async_trait; -use uuid::Uuid; - -/// Mock implementation for MetaMemoryRepository -#[derive(Debug)] -struct MockMetaMemoryRepository; - -#[async_trait] -impl MetaMemoryRepository for MockMetaMemoryRepository { - async fn store_item(&mut self, _item: MetaMemoryItem) -> Result { - Ok(Uuid::new_v4()) - } - - async fn get_item(&self, _id: Uuid) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(None) - } - - async fn get_item_by_component(&self, _component_id: Uuid) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(None) - } - - async fn query_items(&self, _query: &MetaMemoryQuery) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(Vec::new()) - } - - async fn remove_item(&mut self, _id: Uuid) -> Result { - Ok(true) - } - - async fn batch_update(&mut self, _items: Vec) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(Vec::new()) - } - - async fn count_items(&self) -> Result { - Ok(0) - } - - async fn clear_all(&mut self) -> Result { - Ok(0) - } -} - -/// Mock implementation for ConversationService -#[derive(Debug)] -struct MockConversationService; - -#[async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: RagRequest, - _memory_repo: &mut dyn WorkingMemoryRepository, - _concept_repo: &mut dyn ConceptRepository, - _insight_repo: &mut dyn InsightRepository, - ) -> Result { - Ok(RagResponse { - response: "Mock response".to_string(), - conversation_id: "mock-conversation".to_string(), - context_used: Vec::new(), - confidence_score: 0.8, - response_quality: ResponseQuality { - factual_grounding: 0.8, - coherence: 0.9, - relevance: 0.8, - safety_score: 1.0, - source_attribution: 0.7, - consistency_score: 0.8, - completeness: 0.7, - clarity: 0.9, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.1, - confidence_calibration: 0.8, - }, - }) - } - - fn get_conversation_stats(&self) -> HashMap { - let mut stats = HashMap::new(); - stats.insert("total_conversations".to_string(), 1); - stats - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ—๏ธ ArchitectAgent Demo - System Architecture Design"); - println!("{}", "=".repeat(60)); - println!(); - - // Initialize infrastructure components (simplified) - let _config = brain_infra::config::BrainConfig::default(); - let _db_config = brain_infra::database::DatabaseConfig::default(); - - // Create mock dependencies - let meta_memory: Arc> = - Arc::new(tokio::sync::RwLock::new(MockMetaMemoryRepository)); - let conversation_service = Arc::new(MockConversationService); - - // Create project context - let project_context = ProjectContext { - project_name: "TaskFlow Pro".to_string(), - project_version: "2.0.0".to_string(), - project_description: Some("Advanced task management platform with real-time collaboration".to_string()), - tech_stack: vec!["React".to_string(), "Node.js".to_string(), "PostgreSQL".to_string(), "Redis".to_string()], - git_branch: Some("feature/architecture-redesign".to_string()), - git_commit: Some("abc123def".to_string()), - active_files: vec!["src/components/TaskBoard.tsx".to_string(), "src/api/tasks.ts".to_string()], - recent_changes: vec!["Added real-time sync functionality".to_string()], - directory_structure: { - let mut map = HashMap::new(); - map.insert("src".to_string(), vec!["components".to_string(), "api".to_string(), "utils".to_string()]); - map.insert("docs".to_string(), vec!["architecture.md".to_string(), "api.md".to_string()]); - map - }, - }; - - // Create cognitive preference profile - let cognitive_profile = CognitivePreferenceProfile { - interaction_mode: InteractionMode::Collaborative, - detail_level: DetailLevel::Detailed, - emotional_sensitivity: EmotionalSensitivity::Medium, - autonomy_level: AutonomyLevel::SemiAuto, - communication_style: brain_cognitive::agents::traits::CommunicationStyle::Technical, - cognitive_load_settings: CognitiveLoadSettings { - max_items_per_chunk: 7, - pacing_preference: PacingPreference::Medium, - progressive_disclosure: true, - }, - }; - - // Build cognitive context manually - let mut config = HashMap::new(); - config.insert("demo_mode".to_string(), serde_json::Value::Bool(true)); - - let context = CognitiveContext { - meta_memory, - conversation_service, - project_context, - cognitive_profile, - session_history: Vec::new(), - config, - working_directory: std::env::current_dir().unwrap_or_else(|_| std::path::PathBuf::from(".")), - }; - - println!("โœ… Cognitive context initialized"); - println!(" Project: {}", context.project_context.project_name); - println!(" Tech Stack: {:?}", context.project_context.tech_stack); - println!(" Interaction Mode: {:?}", context.cognitive_profile.interaction_mode); - println!(" Detail Level: {:?}", context.cognitive_profile.detail_level); - println!(); - - // Initialize ArchitectAgent - let architect_agent = ArchitectAgent::new(); - println!("๐Ÿ—๏ธ Initializing ArchitectAgent..."); - println!(" Agent: {}", architect_agent.metadata().name); - println!(" Persona: {}", architect_agent.metadata().persona); - println!(" Capabilities: {:?}", architect_agent.metadata().capabilities); - println!(" Base Confidence: {:.2}", architect_agent.metadata().base_confidence); - println!(); - - // Test Case 1: Project Requirements Analysis - println!("๐Ÿ“‹ Test Case 1: Project Requirements Analysis"); - println!("{}", "-".repeat(50)); - - let requirements_input = AgentInput::new( - "project_plan".to_string(), - r#" - We need to design a scalable task management system that supports: - - Real-time collaboration for teams of up to 100 users - - Advanced project analytics and reporting - - Integration with external tools (Slack, GitHub, Jira) - - Mobile app support for iOS and Android - - Enterprise-grade security and compliance - - Multi-tenant architecture for SaaS deployment - - Global deployment across multiple regions - - 99.9% uptime guarantee - "#.to_string(), - "architect-demo-session".to_string(), - ); - - let confidence = architect_agent.assess_confidence(&requirements_input, &context).await?; - println!("๐Ÿ“Š Confidence Assessment: {:.2}", confidence); - - if confidence >= architect_agent.confidence_threshold() { - println!("โœ… Confidence threshold met, proceeding with architecture design..."); - let result = architect_agent.execute(requirements_input, &context).await?; - - println!("๐Ÿ“ Architecture Design Result:"); - println!(" Output Type: {}", result.output_type); - println!(" Confidence: {:.2}", result.confidence); - println!(" Execution Time: {}ms", result.execution_metadata.execution_time_ms); - - if let Some(reasoning) = &result.reasoning { - println!(" Reasoning: {}", reasoning); - } - - println!(" Next Actions: {:?}", result.next_actions); - - // Parse and display key architecture components - if let Ok(arch_data) = serde_json::from_str::(&result.content) { - if let Some(system_arch) = arch_data.get("system_architecture") { - if let Some(pattern) = system_arch.get("architecture_overview").and_then(|o| o.get("pattern")) { - println!(" ๐Ÿ—๏ธ Recommended Pattern: {}", pattern.as_str().unwrap_or("N/A")); - } - if let Some(components) = system_arch.get("system_components") { - println!(" ๐Ÿงฉ Key Components: {}", components.get("microservices").map(|v| v.to_string()).unwrap_or("N/A".to_string())); - } - } - } - } else { - println!("โŒ Confidence too low ({:.2}), skipping execution", confidence); - } - println!(); - - // Test Case 2: Architecture Review - println!("๐Ÿ” Test Case 2: Architecture Review"); - println!("{}", "-".repeat(50)); - - let review_input = AgentInput::new( - "architecture_review".to_string(), - r#" - Current architecture uses: - - Monolithic Node.js application with Express - - Single PostgreSQL database - - Redis for session management - - React frontend served from same server - - Basic Docker deployment on single server - - Issues identified: - - Performance bottlenecks under high load - - Difficulty scaling individual components - - Single point of failure - - Manual deployment process - "#.to_string(), - "architect-demo-session".to_string(), - ); - - let review_result = architect_agent.execute(review_input, &context).await?; - println!("๐Ÿ” Architecture Review Result:"); - println!(" Output Type: {}", review_result.output_type); - println!(" Confidence: {:.2}", review_result.confidence); - println!(" Execution Time: {}ms", review_result.execution_metadata.execution_time_ms); - println!(); - - // Test Case 3: Scalability Analysis - println!("๐Ÿ“ˆ Test Case 3: Scalability Requirements"); - println!("{}", "-".repeat(50)); - - let scalability_input = AgentInput::new( - "scalability_requirements".to_string(), - r#" - Expected growth: - - 10,000 concurrent users within 6 months - - 1M+ tasks processed daily - - 100GB+ data storage requirements - - Global user base requiring low latency - - Peak loads during business hours (10x normal) - "#.to_string(), - "architect-demo-session".to_string(), - ); - - let scalability_result = architect_agent.execute(scalability_input, &context).await?; - println!("๐Ÿ“ˆ Scalability Analysis Result:"); - println!(" Output Type: {}", scalability_result.output_type); - println!(" Confidence: {:.2}", scalability_result.confidence); - println!(" Execution Time: {}ms", scalability_result.execution_metadata.execution_time_ms); - println!(); - - // Display agent capabilities summary - println!("๐ŸŽฏ ArchitectAgent Capabilities Summary"); - println!("{}", "-".repeat(50)); - println!("โœ… System architecture design and validation"); - println!("โœ… Technology stack recommendations"); - println!("โœ… Scalability and performance planning"); - println!("โœ… Security architecture guidance"); - println!("โœ… Deployment strategy design"); - println!("โœ… API specification design"); - println!("โœ… Data architecture planning"); - println!("โœ… Component relationship modeling"); - println!("โœ… Performance optimization strategies"); - println!("โœ… Architecture pattern recommendations"); - println!(); - - println!("๐ŸŽ‰ ArchitectAgent Demo completed successfully!"); - Ok(()) -} \ No newline at end of file diff --git a/auth_logging_demo.rs b/auth_logging_demo.rs deleted file mode 100644 index 003738e9bb5c84d6480a88d93d1e87a1584e194e..0000000000000000000000000000000000000000 --- a/auth_logging_demo.rs +++ /dev/null @@ -1,238 +0,0 @@ -use brain::{ - AuthManager, AuthConfig, UserRole, Permission, User, - RateLimitManager, RateLimitConfig, create_request_context, - LoggingManager, LoggingConfig, ErrorCategory, ErrorSeverity, - AuthenticationResult, -}; -use std::net::{IpAddr, Ipv4Addr}; -use std::collections::HashMap; -use anyhow::Result; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿ” Brain AI - Authentication, Logging & Rate Limiting Demo"); - println!("=========================================================\n"); - - // ================================ - // Phase 1: Authentication System - // ================================ - println!("๐Ÿ“‹ Phase 1: Authentication System"); - println!("----------------------------------"); - - let auth_config = AuthConfig::default(); - let mut auth_manager = AuthManager::new(auth_config)?; - - // Create users with different roles - let admin_user = User { - id: "admin_001".to_string(), - name: "Admin User".to_string(), - email: "admin@brain.ai".to_string(), - role: UserRole::Admin, - created_at: chrono::Utc::now(), - last_login: None, - active: true, - metadata: HashMap::new(), - }; - auth_manager.add_user(admin_user.clone())?; - println!("โœ… Created admin user: {}", admin_user.id); - - let developer_user = User { - id: "dev_001".to_string(), - name: "Developer User".to_string(), - email: "dev@brain.ai".to_string(), - role: UserRole::Developer, - created_at: chrono::Utc::now(), - last_login: None, - active: true, - metadata: HashMap::new(), - }; - auth_manager.add_user(developer_user.clone())?; - println!("โœ… Created developer user: {}", developer_user.id); - - // Generate API keys - let admin_api_key = auth_manager.generate_api_key(&admin_user.id, UserRole::Admin, "Demo admin key")?; - let _dev_api_key = auth_manager.generate_api_key(&developer_user.id, UserRole::Developer, "Demo dev key")?; - println!("๐Ÿ”‘ Generated API keys for admin and developer"); - - // Generate JWT tokens - let _admin_token = auth_manager.generate_token(&admin_user.id, UserRole::Admin)?; - let dev_token = auth_manager.generate_token(&developer_user.id, UserRole::Developer)?; - println!("๐ŸŽซ Generated JWT tokens for admin and developer"); - - // Test authentication methods - println!("\n๐Ÿ” Testing Authentication Methods:"); - - // Test API key authentication - let (api_user_id, api_role) = auth_manager.validate_api_key(&admin_api_key)?; - println!(" โœ… API Key Auth: User {} (Role: {:?})", api_user_id, api_role); - - // Test JWT authentication - let jwt_claims = auth_manager.validate_token(&dev_token)?; - println!(" โœ… JWT Auth: User {} (Role: {:?})", jwt_claims.sub, jwt_claims.role); - - // Test permission checking - let has_query_permission = UserRole::Admin.has_permission(&Permission::QueryMemory); - let has_manage_permission = UserRole::Analyst.has_permission(&Permission::ManageUsers); - println!(" โœ… Admin has query permission: {}", has_query_permission); - println!(" โŒ Analyst has manage permission: {}", has_manage_permission); - - // ================================ - // Phase 2: Rate Limiting System - // ================================ - println!("\n๐Ÿ“Š Phase 2: Rate Limiting System"); - println!("--------------------------------"); - - let rate_config = RateLimitConfig::default(); - let rate_manager = RateLimitManager::new(rate_config)?; - - // Test different rate limiting scenarios - let client_ip = IpAddr::V4(Ipv4Addr::new(192, 168, 1, 100)); - let admin_context = create_request_context( - Some(admin_user.id.clone()), - Some(UserRole::Admin), - client_ip, - "admin_endpoint".to_string() - ); - - println!("๐Ÿšฆ Testing Rate Limits by User Role:"); - - // Admin user (1000 req/min limit) - for i in 1..=5 { - let result = rate_manager.check_rate_limit(&admin_context)?; - println!(" Admin Request {}: {} (Remaining: {})", - i, if result.allowed { "โœ… ALLOWED" } else { "โŒ BLOCKED" }, result.remaining); - } - - // Test IP-based rate limiting - println!("\n๐ŸŒ Testing IP-based Rate Limiting:"); - let ip_context = create_request_context( - None, - None, - client_ip, - "guest_endpoint".to_string() - ); - for i in 1..=3 { - let result = rate_manager.check_rate_limit(&ip_context)?; - println!(" IP Request {}: {} (Remaining: {})", - i, if result.allowed { "โœ… ALLOWED" } else { "โŒ BLOCKED" }, result.remaining); - } - - // Get rate limiting statistics - let stats = rate_manager.get_stats()?; - println!("\n๐Ÿ“ˆ Rate Limiting Statistics:"); - println!(" Total Requests: {}", stats.total_requests); - println!(" Allowed Requests: {}", stats.allowed_requests); - println!(" Blocked Requests: {}", stats.blocked_requests); - if stats.total_requests > 0 { - println!(" Block Rate: {:.2}%", (stats.blocked_requests as f64 / stats.total_requests as f64) * 100.0); - } - - // ================================ - // Phase 3: Logging System - // ================================ - println!("\n๐Ÿ“ Phase 3: Logging System"); - println!("--------------------------"); - - let logging_config = LoggingConfig::default(); - let logging_manager = LoggingManager::new(logging_config)?; - - // Start tracking a request - let request_id = "req_001".to_string(); - logging_manager.start_request( - request_id.clone(), - "/api/memory/query".to_string(), - "POST".to_string(), - client_ip - ); - - // Complete the request - let auth_result = AuthenticationResult::new(api_user_id.clone(), api_role); - let mut metadata = HashMap::new(); - metadata.insert("query_type".to_string(), "concept_search".to_string()); - metadata.insert("result_count".to_string(), "25".to_string()); - - logging_manager.complete_request( - request_id, - 200, - Some(auth_result), - metadata, - ); - - // Log some errors - let mut error_context = HashMap::new(); - error_context.insert("query".to_string(), "SELECT * FROM concepts".to_string()); - - logging_manager.log_error( - ErrorCategory::Validation, - ErrorSeverity::Medium, - "Invalid query syntax".to_string(), - Some("Missing WHERE clause".to_string()), - error_context, - Some("req_001".to_string()), - Some(api_user_id.clone()), - ); - - logging_manager.log_error( - ErrorCategory::Authentication, - ErrorSeverity::High, - "JWT token expired".to_string(), - Some("Token issued too long ago".to_string()), - HashMap::new(), - None, - Some(api_user_id), - ); - - // Log an audit event - logging_manager.log_audit( - "user_action".to_string(), - admin_user.id.clone(), - UserRole::Admin, - "memory_query".to_string(), - Some("concept_search".to_string()), - client_ip, - true, - HashMap::new(), - ); - - // Get logging statistics - let log_stats = logging_manager.get_stats()?; - println!("\n๐Ÿ“ˆ Logging Statistics:"); - println!(" Total Requests: {}", log_stats.total_requests); - println!(" Successful Requests: {}", log_stats.successful_requests); - println!(" Failed Requests: {}", log_stats.failed_requests); - println!(" Average Response Time: {:.2}ms", log_stats.average_response_time_ms); - - // Get recent errors - let recent_errors = logging_manager.get_recent_errors(5)?; - println!("\n๐Ÿ“‹ Recent Errors:"); - for error in recent_errors { - println!(" {} - {}: {} ({})", - error.timestamp.format("%H:%M:%S"), - error.category, - error.message, - error.severity); - } - - // ================================ - // Phase 4: Integration Demo - // ================================ - println!("\n๐Ÿ”— Phase 4: Integration Demo"); - println!("----------------------------"); - - // Get authentication statistics - let auth_stats = auth_manager.get_stats(); - println!("๐Ÿ‘ฅ Authentication Statistics:"); - println!(" Total Users: {}", auth_stats.total_users); - println!(" Active Users: {}", auth_stats.active_users); - println!(" Total API Keys: {}", auth_stats.total_api_keys); - println!(" Active API Keys: {}", auth_stats.active_api_keys); - - println!("\n๐ŸŽ‰ Brain AI Authentication & Logging Demo Complete!"); - println!("===================================================="); - println!("โœ… Authentication: Users, API keys, JWT tokens"); - println!("โœ… Rate Limiting: Role-based and IP-based limits"); - println!("โœ… Logging: Request tracking, error logging, audit trails"); - println!("โœ… Integration: All systems working together"); - - Ok(()) -} \ No newline at end of file diff --git a/basic_keyword_search.rs b/basic_keyword_search.rs deleted file mode 100644 index 1c4d38b2065de4d900a33ecc849d4fdf25743fcb..0000000000000000000000000000000000000000 --- a/basic_keyword_search.rs +++ /dev/null @@ -1,133 +0,0 @@ -#!/usr/bin/env cargo run --example basic_keyword_search -//! Basic Keyword Search Demo -//! -//! Tests if simple keyword pattern matching can find the stored PocketFlow knowledge. - -use brain::{MemoryService, WorkingMemoryQuery, Priority, Result}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Basic Keyword Search Demo"); - println!("============================"); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| { - eprintln!("Failed to create data directory: {}", e); - brain::BrainError::InvalidInput { - message: "Failed to create data directory".to_string(), - context: None, - } - })?; - - // Initialize repositories - let working_repo = Box::new(WorkingMemoryRepository::new(100)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/memory.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let mut memory_service = MemoryService::new(working_repo, episodic_repo, semantic_repo); - - println!("\n๐Ÿง  Loading Simple Test Knowledge"); - - let simple_knowledge = vec![ - "PocketFlow is an efficient deep learning framework", - "It optimizes neural network models for mobile deployment", - "PocketFlow supports quantization and pruning techniques", - "The framework reduces model size while maintaining accuracy", - "Mobile deployment requires optimized neural networks", - "Quantization converts float32 to lower precision formats", - "Pruning removes unnecessary network connections", - "The goal is faster inference on mobile devices" - ]; - - for knowledge in simple_knowledge.iter() { - let _id = memory_service.learn(knowledge.to_string(), Priority::High).await?; - println!("โœ… Stored: {}", knowledge); - } - - println!("\n๐Ÿ” Testing Basic Keyword Searches"); - - let search_terms = vec!["PocketFlow", "mobile", "quantization", "pruning"]; - - for search_term in &search_terms { - println!("\n๐ŸŽฏ Searching for: '{}'", search_term); - - let query = WorkingMemoryQuery { - content_pattern: Some(search_term.to_string()), - limit: Some(5), - ..Default::default() - }; - - let results = memory_service.query_working(&query).await?; - - if !results.is_empty() { - println!(" โœ… Found {} items:", results.len()); - for (i, item) in results.iter().enumerate() { - println!(" {}. {} (Priority: {:?}, Score: {:.2})", - i + 1, item.content, item.priority, item.importance_score()); - } - } else { - println!(" โŒ No items found"); - } - } - - println!("\n๐Ÿ” Testing Phrase Searches"); - - let phrases = vec!["neural network", "deep learning", "model size"]; - - for phrase in &phrases { - println!("\n๐ŸŽฏ Searching for phrase: '{}'", phrase); - - let query = WorkingMemoryQuery { - content_pattern: Some(phrase.to_string()), - limit: Some(5), - ..Default::default() - }; - - let results = memory_service.query_working(&query).await?; - - if !results.is_empty() { - println!(" โœ… Found {} items:", results.len()); - for (i, item) in results.iter().enumerate() { - println!(" {}. {} (Priority: {:?}, Score: {:.2})", - i + 1, item.content, item.priority, item.importance_score()); - } - } else { - println!(" โŒ No items found"); - } - } - - println!("\n๐Ÿ”„ Testing Cross-Memory Search"); - - let search_terms = vec!["optimization", "framework", "accuracy"]; - - for search_term in &search_terms { - println!("\n๐ŸŽฏ Cross-memory search for: '{}'", search_term); - - let results = memory_service.query_all_memories(search_term).await?; - - let total = results.working_results.len() + results.episodic_results.len() + results.semantic_results.len(); - if total > 0 { - println!(" โœ… Found {} total memories:", total); - for (i, item) in results.working_results.iter().enumerate() { - println!(" {}. {} (Priority: {:?}, Score: {:.2})", - i + 1, item.content, item.priority, item.importance_score()); - } - } else { - println!(" โŒ No memories found"); - } - } - - println!("\n๐Ÿ“Š Memory Statistics Summary"); - println!("{}", "-".repeat(40)); - - // Since MemoryService doesn't have get_stats, we check individual repositories - println!("โœ… Search demo completed successfully!"); - println!(" - Stored {} knowledge items", simple_knowledge.len()); - println!(" - Tested keyword and phrase searches"); - println!(" - Demonstrated cross-memory queries"); - - Ok(()) -} \ No newline at end of file diff --git a/benchmarks/FINAL_VICTORY_100_PERCENT.jsonl b/benchmarks/FINAL_VICTORY_100_PERCENT.jsonl deleted file mode 100644 index 348354f1096e876d40adc11f53034c03ca6fc61d..0000000000000000000000000000000000000000 --- a/benchmarks/FINAL_VICTORY_100_PERCENT.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"task_id":"HumanEval/0","completion":" for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False"} -{"task_id":"HumanEval/1","completion":" result = []\n current_group = \"\"\n depth = 0\n \n for char in paren_string:\n if char == ' ':\n continue\n current_group += char\n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n if depth == 0:\n result.append(current_group)\n current_group = \"\"\n \n return result"} -{"task_id":"HumanEval/2","completion":" return number - int(number)"} -{"task_id":"HumanEval/3","completion":" balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False"} -{"task_id":"HumanEval/4","completion":" mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)"} -{"task_id":"HumanEval/5","completion":" if not numbers:\n return []\n result = []\n for i, num in enumerate(numbers):\n result.append(num)\n if i < len(numbers) - 1:\n result.append(delimeter)\n return result"} -{"task_id":"HumanEval/6","completion":" groups = paren_string.split()\n result = []\n for group in groups:\n max_depth = 0\n current_depth = 0\n for char in group:\n if char == '(':\n current_depth += 1\n max_depth = max(max_depth, current_depth)\n elif char == ')':\n current_depth -= 1\n result.append(max_depth)\n return result"} -{"task_id":"HumanEval/7","completion":" return [s for s in strings if substring in s]"} -{"task_id":"HumanEval/8","completion":" if not numbers:\n return (0, 1)\n total_sum = sum(numbers)\n total_product = 1\n for num in numbers:\n total_product *= num\n return (total_sum, total_product)"} -{"task_id":"HumanEval/9","completion":" if not numbers:\n return []\n result = []\n current_max = numbers[0]\n for num in numbers:\n current_max = max(current_max, num)\n result.append(current_max)\n return result"} \ No newline at end of file diff --git a/benchmarks/benchmark_results_20250703_114530/brain_humaneval_full_20250703_114530_full.jsonl b/benchmarks/benchmark_results_20250703_114530/brain_humaneval_full_20250703_114530_full.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/benchmarks/benchmark_results_20250703_114530/brain_humaneval_full_20250703_114530_pass1.jsonl b/benchmarks/benchmark_results_20250703_114530/brain_humaneval_full_20250703_114530_pass1.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/benchmarks/benchmark_results_20250703_114530/brain_humaneval_full_20250703_114530_pass10.jsonl b/benchmarks/benchmark_results_20250703_114530/brain_humaneval_full_20250703_114530_pass10.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/benchmarks/benchmark_retry_algo_direct.jsonl b/benchmarks/benchmark_retry_algo_direct.jsonl deleted file mode 100644 index b33298e09d60368db511a6a282f77d46650682c4..0000000000000000000000000000000000000000 --- a/benchmarks/benchmark_retry_algo_direct.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} \ No newline at end of file diff --git a/benchmarks/benchmark_retry_backend_orchestrated.jsonl b/benchmarks/benchmark_retry_backend_orchestrated.jsonl deleted file mode 100644 index 19b308aad0fdd6ca468883cf86c06d375ee71da7..0000000000000000000000000000000000000000 --- a/benchmarks/benchmark_retry_backend_orchestrated.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"completion":"def has_close_elements(numbers, threshold):\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"def separate_paren_groups(paren_string):\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n if char != ' ':\n current_string += char\n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n if depth == 0:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"def truncate_number(number):\n return number - int(number)","task_id":"HumanEval/2"} -{"completion":"def below_zero(operations):\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"def mean_absolute_deviation(numbers):\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} \ No newline at end of file diff --git a/benchmarks/benchmark_retry_qa_quality.jsonl b/benchmarks/benchmark_retry_qa_quality.jsonl deleted file mode 100644 index 9e92a71cbe7cdcaf2e1d2622401bc3251178750e..0000000000000000000000000000000000000000 --- a/benchmarks/benchmark_retry_qa_quality.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -{"completion":"","task_id":"HumanEval/0"} -{"completion":"","task_id":"HumanEval/1"} -{"completion":"","task_id":"HumanEval/2"} \ No newline at end of file diff --git a/benchmarks/brain_humaneval_full_164.jsonl b/benchmarks/brain_humaneval_full_164.jsonl deleted file mode 100644 index c9fc0f36cd8644bf6f01fd3e7ecbf3cfe527fbdc..0000000000000000000000000000000000000000 --- a/benchmarks/brain_humaneval_full_164.jsonl +++ /dev/null @@ -1,164 +0,0 @@ -{"task_id":"HumanEval/0","completion":" for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False"} -{"task_id":"HumanEval/1","completion":" result = []\n current_group = \"\"\n depth = 0\n \n for char in paren_string:\n if char == ' ':\n continue\n current_group += char\n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n if depth == 0:\n result.append(current_group)\n current_group = \"\"\n \n return result"} -{"task_id":"HumanEval/2","completion":" return number - int(number)"} -{"task_id":"HumanEval/3","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/4","completion":"# Learning template for mean_absolute_deviation - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n \"\"\" For a \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/5","completion":"# Learning template for intersperse - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n \"\"\" \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/6","completion":"# Learning template for parse_nested_parens - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n \"\"\" Input to \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/7","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/8","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/9","completion":"# Learning template for rolling_max - analyzing problem patterns\n # Problem: from typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n \"\"\" From a gi\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/10","completion":"# Learning: This should return a string\n return \"\""} -{"task_id":"HumanEval/11","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/12","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/13","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/14","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/15","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/16","completion":"# Learning template for count_distinct_characters - analyzing problem patterns\n # Problem: \n\ndef count_distinct_characters(string: str) -> int:\n \"\"\" Given a string, find out how many disti\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/17","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/18","completion":"# Learning template for how_many_times - analyzing problem patterns\n # Problem: \n\ndef how_many_times(string: str, substring: str) -> int:\n \"\"\" Find how many times a given substr\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/19","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/20","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/21","completion":"# Learning template for rescale_to_unit - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef rescale_to_unit(numbers: List[float]) -> List[float]:\n \"\"\" Given li\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/22","completion":"# Learning: This involves filtering\n return [x for x in lst if True] if 'lst' in locals() else []"} -{"task_id":"HumanEval/23","completion":" # String processing for strlen\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/24","completion":" # Mathematical calculation for largest_divisor\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/25","completion":" result = []\n for item in factorize_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/26","completion":" # Data structure operation for remove_duplicates\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/27","completion":" # String processing for flip_case\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/28","completion":" # Data structure operation for concatenate\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/29","completion":" # Data structure operation for filter_by_prefix\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/30","completion":" result = []\n for item in get_positive_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/31","completion":" # Mathematical calculation for is_prime\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/32","completion":" result = []\n for item in find_zero_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/33","completion":" result = []\n for item in sort_third_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/34","completion":" result = []\n for item in unique_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/35","completion":" result = []\n for item in max_element_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/36","completion":" # Data structure operation for fizz_buzz\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/37","completion":" result = []\n for item in sort_even_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/38","completion":" # String processing for decode_cyclic\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/39","completion":" # Mathematical calculation for prime_fib\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/40","completion":" result = []\n for item in triples_sum_to_zero_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/41","completion":" # Mathematical calculation for car_race_collision\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/42","completion":" result = []\n for item in incr_list_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/43","completion":" result = []\n for item in pairs_sum_to_zero_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/44","completion":" # String processing for change_base\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/45","completion":"# Learning template for triangle_area - analyzing problem patterns\n # Problem: \n\ndef triangle_area(a, h):\n \"\"\"Given length of a side and high return area for a triangle.\n >>\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/46","completion":" # Mathematical calculation for fib4\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/47","completion":" result = []\n for item in median_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/48","completion":" # String processing for is_palindrome\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/49","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/50","completion":" # String processing for decode_shift\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/51","completion":" # String processing for remove_vowels\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/52","completion":" result = []\n for item in below_threshold_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/53","completion":" # Mathematical calculation for add\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/54","completion":" # String processing for same_chars\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/55","completion":" # Mathematical calculation for fib\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/56","completion":" # String processing for correct_bracketing\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/57","completion":" result = []\n for item in monotonic_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/58","completion":" result = []\n for item in common_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/59","completion":" # Mathematical calculation for largest_prime_factor\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/60","completion":" # Mathematical calculation for sum_to_n\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/61","completion":" # String processing for correct_bracketing\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/62","completion":" result = []\n for item in derivative_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/63","completion":" # Mathematical calculation for fibfib\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/64","completion":" # String processing for vowels_count\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/65","completion":" # String processing for circular_shift\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/66","completion":" # String processing for digitSum\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/67","completion":" # Data structure operation for fruit_distribution\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/68","completion":" result = []\n for item in pluck_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/69","completion":" result = []\n for item in search_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/70","completion":" result = []\n for item in strange_sort_list_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/71","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/72","completion":" result = []\n for item in will_it_fly_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/73","completion":" # Data structure operation for smallest_change\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/74","completion":" result = []\n for item in total_match_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/75","completion":" # Mathematical calculation for is_multiply_prime\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/76","completion":" # Mathematical calculation for is_simple_power\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/77","completion":" # Mathematical calculation for iscube\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/78","completion":" # String processing for hex_key\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/79","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/80","completion":" # String processing for is_happy\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/81","completion":" # Data structure operation for numerical_letter_grade\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/82","completion":" # String processing for prime_length\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/83","completion":" # Mathematical calculation for starts_one_ends\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/84","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/85","completion":" # Data structure operation for add\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/86","completion":" # Data structure operation for anti_shuffle\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/87","completion":" result = []\n for item in get_row_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/88","completion":" # Data structure operation for sort_array\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/89","completion":" # String processing for encrypt\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/90","completion":" result = []\n for item in next_smallest_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/91","completion":" # String processing for is_bored\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/92","completion":" # Mathematical calculation for any_int\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/93","completion":" # Data structure operation for encode\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/94","completion":" result = []\n for item in skjkasdkd_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/95","completion":" # Data structure operation for check_dict_case\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/96","completion":" # Data structure operation for count_up_to\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/97","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/98","completion":" # String processing for count_upper\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/99","completion":" # String processing for closest_integer\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/100","completion":" result = []\n for item in make_a_pile_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/101","completion":" # Data structure operation for words_string\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/102","completion":" # Mathematical calculation for choose_num\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/103","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/104","completion":" result = []\n for item in unique_digits_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/105","completion":" # Data structure operation for by_length\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/106","completion":" result = []\n for item in f_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/107","completion":" # Mathematical calculation for even_odd_palindrome\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/108","completion":" # Data structure operation for count_nums\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/109","completion":" result = []\n for item in move_one_ball_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/110","completion":" result = []\n for item in exchange_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/111","completion":" # Data structure operation for histogram\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/112","completion":" # String processing for reverse_delete\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/113","completion":" result = []\n for item in odd_count_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/114","completion":" # Data structure operation for minSubArraySum\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/115","completion":" # Mathematical calculation for max_fill\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/116","completion":" # Data structure operation for sort_array\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/117","completion":" result = []\n for item in select_words_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/118","completion":" # String processing for get_closest_vowel\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/119","completion":" result = []\n for item in match_parens_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/120","completion":" result = []\n for item in maximum_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/121","completion":" result = []\n for item in solution_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/122","completion":" # Data structure operation for add_elements\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/123","completion":" result = []\n for item in get_odd_collatz_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/124","completion":" # String processing for valid_date\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/125","completion":" result = []\n for item in split_words_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/126","completion":" result = []\n for item in is_sorted_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/127","completion":" # Mathematical calculation for intersection\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/128","completion":" # Data structure operation for prod_signs\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/129","completion":" result = []\n for item in minPath_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/130","completion":" result = []\n for item in tri_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/131","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/132","completion":" # String processing for is_nested\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/133","completion":" result = []\n for item in sum_squares_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/134","completion":" # String processing for check_if_last_char_is_a_letter\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/135","completion":" # Data structure operation for can_arrange\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/136","completion":" result = []\n for item in largest_smallest_integers_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/137","completion":" # String processing for compare_one\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/138","completion":" # Mathematical calculation for is_equal_to_sum_even\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/139","completion":" # Mathematical calculation for special_factorial\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/140","completion":" # String processing for fix_spaces\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/141","completion":" # String processing for file_name_check\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/142","completion":" result = []\n for item in sum_squares_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/143","completion":" # String processing for words_in_sentence\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/144","completion":" # String processing for simplify\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/145","completion":" # Data structure operation for order_by_points\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/146","completion":" # Data structure operation for specialFilter\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/147","completion":" # Data structure operation for get_max_triples\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/148","completion":"# Learning: This should return a string\n return \"\""} -{"task_id":"HumanEval/149","completion":" result = []\n for item in sorted_list_sum_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/150","completion":" # Mathematical calculation for x_or_y\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/151","completion":" result = []\n for item in double_the_difference_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/152","completion":" # Data structure operation for compare\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/153","completion":" result = []\n for item in Strongest_Extension_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/154","completion":" # String processing for cycpattern_check\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/155","completion":" # Mathematical calculation for even_odd_count\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/156","completion":" # String processing for int_to_mini_roman\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/157","completion":"# Learning: This should return a boolean\n return False"} -{"task_id":"HumanEval/158","completion":" result = []\n for item in find_max_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/159","completion":" # Data structure operation for eat\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/160","completion":" result = []\n for item in do_algebra_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/161","completion":"# Learning: This should return a string\n return \"\""} -{"task_id":"HumanEval/162","completion":" # String processing for string_to_md5\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/163","completion":"# Learning: This should return a number\n return 0"} \ No newline at end of file diff --git a/benchmarks/brain_humaneval_industry_test.jsonl b/benchmarks/brain_humaneval_industry_test.jsonl deleted file mode 100644 index c02f634e999a35426e1bf0f58b442d0b8511d6e2..0000000000000000000000000000000000000000 --- a/benchmarks/brain_humaneval_industry_test.jsonl +++ /dev/null @@ -1 +0,0 @@ -{"task_id":"HumanEval/0","completion":" for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False"} \ No newline at end of file diff --git a/benchmarks/brain_humaneval_learning_iteration_1.jsonl b/benchmarks/brain_humaneval_learning_iteration_1.jsonl deleted file mode 100644 index cdb4619f5e43e6fbf71a2a394ac6cf76c97cd01b..0000000000000000000000000000000000000000 --- a/benchmarks/brain_humaneval_learning_iteration_1.jsonl +++ /dev/null @@ -1,164 +0,0 @@ -{"task_id":"HumanEval/0","completion":" for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False"} -{"task_id":"HumanEval/1","completion":" result = []\n current_group = \"\"\n depth = 0\n \n for char in paren_string:\n if char == ' ':\n continue\n current_group += char\n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n if depth == 0:\n result.append(current_group)\n current_group = \"\"\n \n return result"} -{"task_id":"HumanEval/2","completion":" return number - int(number)"} -{"task_id":"HumanEval/3","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/4","completion":"# Learning template for mean_absolute_deviation - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef mean_absolute_deviation(numbers: List[float]) -> float:\n \"\"\" For a \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/5","completion":"# Learning template for intersperse - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef intersperse(numbers: List[int], delimeter: int) -> List[int]:\n \"\"\" \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/6","completion":"# Learning template for parse_nested_parens - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef parse_nested_parens(paren_string: str) -> List[int]:\n \"\"\" Input to \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/7","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/8","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/9","completion":"# Learning template for rolling_max - analyzing problem patterns\n # Problem: from typing import List, Tuple\n\n\ndef rolling_max(numbers: List[int]) -> List[int]:\n \"\"\" From a gi\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/10","completion":"# Learning: This should return a string\n return \"\""} -{"task_id":"HumanEval/11","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/12","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/13","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/14","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/15","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/16","completion":"# Learning template for count_distinct_characters - analyzing problem patterns\n # Problem: \n\ndef count_distinct_characters(string: str) -> int:\n \"\"\" Given a string, find out how many disti\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/17","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/18","completion":"# Learning template for how_many_times - analyzing problem patterns\n # Problem: \n\ndef how_many_times(string: str, substring: str) -> int:\n \"\"\" Find how many times a given substr\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/19","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/20","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/21","completion":"# Learning template for rescale_to_unit - analyzing problem patterns\n # Problem: from typing import List\n\n\ndef rescale_to_unit(numbers: List[float]) -> List[float]:\n \"\"\" Given li\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/22","completion":"# Learning: This involves filtering\n return [x for x in lst if True] if 'lst' in locals() else []"} -{"task_id":"HumanEval/23","completion":" # String processing for strlen\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/24","completion":" # Mathematical calculation for largest_divisor\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/25","completion":" result = []\n for item in factorize_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/26","completion":" # Data structure operation for remove_duplicates\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/27","completion":" # String processing for flip_case\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/28","completion":" # Data structure operation for concatenate\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/29","completion":" # Data structure operation for filter_by_prefix\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/30","completion":" result = []\n for item in get_positive_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/31","completion":" # Mathematical calculation for is_prime\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/32","completion":" result = []\n for item in find_zero_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/33","completion":" result = []\n for item in sort_third_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/34","completion":" result = []\n for item in unique_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/35","completion":" result = []\n for item in max_element_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/36","completion":" # Data structure operation for fizz_buzz\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/37","completion":" result = []\n for item in sort_even_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/38","completion":" # String processing for decode_cyclic\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/39","completion":" # Mathematical calculation for prime_fib\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/40","completion":" result = []\n for item in triples_sum_to_zero_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/41","completion":" # Mathematical calculation for car_race_collision\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/42","completion":" result = []\n for item in incr_list_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/43","completion":" result = []\n for item in pairs_sum_to_zero_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/44","completion":" # String processing for change_base\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/45","completion":"# Learning template for triangle_area - analyzing problem patterns\n # Problem: \n\ndef triangle_area(a, h):\n \"\"\"Given length of a side and high return area for a triangle.\n >>\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/46","completion":" # Mathematical calculation for fib4\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/47","completion":" result = []\n for item in median_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/48","completion":" # String processing for is_palindrome\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/49","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/50","completion":" # String processing for decode_shift\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/51","completion":" # String processing for remove_vowels\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/52","completion":" result = []\n for item in below_threshold_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/53","completion":"# Learning template for add - analyzing problem patterns\n # Problem: \n\ndef add(x: int, y: int):\n \"\"\"Add two numbers x and y\n >>> add(2, 3)\n 5\n >>> add(5, 7)\n\n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/54","completion":" # String processing for same_chars\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/55","completion":" # Mathematical calculation for fib\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/56","completion":"# Learning: This should return a boolean\n return False"} -{"task_id":"HumanEval/57","completion":" result = []\n for item in monotonic_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/58","completion":" result = []\n for item in common_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/59","completion":" # Mathematical calculation for largest_prime_factor\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/60","completion":" # Mathematical calculation for sum_to_n\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/61","completion":"# Learning: This should return a boolean\n return False"} -{"task_id":"HumanEval/62","completion":" result = []\n for item in derivative_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/63","completion":" # Mathematical calculation for fibfib\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/64","completion":" # String processing for vowels_count\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/65","completion":" # String processing for circular_shift\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/66","completion":" # String processing for digitSum\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/67","completion":" # Data structure operation for fruit_distribution\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/68","completion":" result = []\n for item in pluck_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/69","completion":" result = []\n for item in search_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/70","completion":" result = []\n for item in strange_sort_list_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/71","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/72","completion":" result = []\n for item in will_it_fly_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/73","completion":" # Data structure operation for smallest_change\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/74","completion":" result = []\n for item in total_match_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/75","completion":" # Mathematical calculation for is_multiply_prime\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/76","completion":" # Mathematical calculation for is_simple_power\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/77","completion":" # Mathematical calculation for iscube\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/78","completion":" # String processing for hex_key\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/79","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/80","completion":" # String processing for is_happy\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/81","completion":" # Data structure operation for numerical_letter_grade\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/82","completion":" # String processing for prime_length\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/83","completion":" # Mathematical calculation for starts_one_ends\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/84","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/85","completion":"# Learning template for add - analyzing problem patterns\n # Problem: \ndef add(lst):\n \"\"\"Given a non-empty list of integers lst. add the even elements that are at odd \n # This will fail initially and become a learning opportunity\n pass"} -{"task_id":"HumanEval/86","completion":" # Data structure operation for anti_shuffle\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/87","completion":" result = []\n for item in get_row_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/88","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/89","completion":" # String processing for encrypt\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/90","completion":" result = []\n for item in next_smallest_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/91","completion":" # String processing for is_bored\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/92","completion":" # Mathematical calculation for any_int\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/93","completion":" # Data structure operation for encode\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/94","completion":" result = []\n for item in skjkasdkd_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/95","completion":" # Data structure operation for check_dict_case\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/96","completion":" # Data structure operation for count_up_to\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/97","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/98","completion":" # String processing for count_upper\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/99","completion":" # String processing for closest_integer\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/100","completion":" result = []\n for item in make_a_pile_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/101","completion":" # Data structure operation for words_string\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/102","completion":" # Mathematical calculation for choose_num\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/103","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/104","completion":" result = []\n for item in unique_digits_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/105","completion":" # Data structure operation for by_length\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/106","completion":" result = []\n for item in f_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/107","completion":" # Mathematical calculation for even_odd_palindrome\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/108","completion":" # Data structure operation for count_nums\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/109","completion":" result = []\n for item in move_one_ball_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/110","completion":" result = []\n for item in exchange_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/111","completion":" # Data structure operation for histogram\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/112","completion":" # String processing for reverse_delete\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/113","completion":" result = []\n for item in odd_count_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/114","completion":" # Data structure operation for minSubArraySum\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/115","completion":" # Mathematical calculation for max_fill\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/116","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/117","completion":" result = []\n for item in select_words_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/118","completion":" # String processing for get_closest_vowel\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/119","completion":" result = []\n for item in match_parens_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/120","completion":" result = []\n for item in maximum_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/121","completion":" result = []\n for item in solution_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/122","completion":" # Data structure operation for add_elements\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/123","completion":" result = []\n for item in get_odd_collatz_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/124","completion":" # String processing for valid_date\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/125","completion":" result = []\n for item in split_words_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/126","completion":" result = []\n for item in is_sorted_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/127","completion":" # Mathematical calculation for intersection\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/128","completion":" # Data structure operation for prod_signs\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/129","completion":" result = []\n for item in minPath_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/130","completion":" result = []\n for item in tri_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/131","completion":"# Learning: This should return a number\n return 0"} -{"task_id":"HumanEval/132","completion":" # String processing for is_nested\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/133","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/134","completion":" # String processing for check_if_last_char_is_a_letter\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/135","completion":" # Data structure operation for can_arrange\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/136","completion":" result = []\n for item in largest_smallest_integers_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/137","completion":" # String processing for compare_one\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/138","completion":" # Mathematical calculation for is_equal_to_sum_even\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/139","completion":" # Mathematical calculation for special_factorial\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/140","completion":" # String processing for fix_spaces\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/141","completion":" # String processing for file_name_check\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/142","completion":"# Learning: This should return a list\n return []"} -{"task_id":"HumanEval/143","completion":" # String processing for words_in_sentence\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/144","completion":" # String processing for simplify\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/145","completion":" # Data structure operation for order_by_points\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/146","completion":" # Data structure operation for specialFilter\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/147","completion":" # Data structure operation for get_max_triples\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/148","completion":"# Learning: This should return a string\n return \"\""} -{"task_id":"HumanEval/149","completion":" result = []\n for item in sorted_list_sum_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/150","completion":" # Mathematical calculation for x_or_y\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/151","completion":" result = []\n for item in double_the_difference_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/152","completion":" # Data structure operation for compare\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/153","completion":" result = []\n for item in Strongest_Extension_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/154","completion":" # String processing for cycpattern_check\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/155","completion":" # Mathematical calculation for even_odd_count\n # Implement calculation based on input parameters\n return 0"} -{"task_id":"HumanEval/156","completion":" # String processing for int_to_mini_roman\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/157","completion":"# Learning: This should return a boolean\n return False"} -{"task_id":"HumanEval/158","completion":" result = []\n for item in find_max_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/159","completion":" # Data structure operation for eat\n # Analyze input and return appropriate result\n return []"} -{"task_id":"HumanEval/160","completion":" result = []\n for item in do_algebra_input: # Process input data\n # Add processing logic here\n result.append(item)\n return result"} -{"task_id":"HumanEval/161","completion":"# Learning: This should return a string\n return \"\""} -{"task_id":"HumanEval/162","completion":" # String processing for string_to_md5\n result = \"\"\n # Process input string and return result\n return result"} -{"task_id":"HumanEval/163","completion":"# Learning: This should return a number\n return 0"} \ No newline at end of file diff --git a/benchmarks/brain_swe_bench_sota_20250728_122242.json b/benchmarks/brain_swe_bench_sota_20250728_122242.json deleted file mode 100644 index a168023baf9dd3f8fdff5cf9d696ac3c26850f4e..0000000000000000000000000000000000000000 --- a/benchmarks/brain_swe_bench_sota_20250728_122242.json +++ /dev/null @@ -1,305 +0,0 @@ -{ - "summary": { - "pass_rate": 20.0, - "average_score": 27.375, - "problems_solved": 1, - "total_problems": 5, - "rank_vs_sota": 7, - "beats_sota": false, - "difficulty_breakdown": { - "medium": { - "total": 6, - "passed": 1 - }, - "hard": { - "total": 4, - "passed": 0 - } - }, - "agent_performance": { - "maintainer-agent": { - "total": 5, - "passed": 0, - "avg_score": 0.0, - "pass_rate": 0.0 - }, - "mubrain_algorithm_coder": { - "total": 5, - "passed": 1, - "avg_score": 54.75, - "pass_rate": 20.0 - } - }, - "quality_metrics": { - "code_inclusion_rate": 50.0, - "file_mention_rate": 10.0, - "test_inclusion_rate": 10.0 - }, - "execution_stats": { - "average_time_ms": 4.639339447021484, - "total_time_seconds": 0.018468856811523438 - } - }, - "results": [ - { - "task_id": "swe_bench_requests_timeout", - "repository": "requests/requests", - "issue_number": 5248, - "difficulty": "medium", - "agent_used": "maintainer-agent", - "strategy_used": "quality", - "success": false, - "overall_score": 0.0, - "detailed_scores": { - "root_cause_analysis": 0, - "solution_quality": 0, - "technical_accuracy": 0, - "implementation_detail": 0 - }, - "execution_time_ms": 3.203868865966797, - "confidence": 0.0, - "response_length": 0, - "has_code": false, - "mentions_files": false, - "includes_tests": false, - "raw_response": "", - "timestamp": "2025-07-28T12:22:42.855838" - }, - { - "task_id": "swe_bench_django_subquery", - "repository": "django/django", - "issue_number": 32879, - "difficulty": "hard", - "agent_used": "maintainer-agent", - "strategy_used": "quality", - "success": false, - "overall_score": 0.0, - "detailed_scores": { - "root_cause_analysis": 0, - "solution_quality": 0, - "technical_accuracy": 0, - "implementation_detail": 0 - }, - "execution_time_ms": 3.5581588745117188, - "confidence": 0.0, - "response_length": 0, - "has_code": false, - "mentions_files": false, - "includes_tests": false, - "raw_response": "", - "timestamp": "2025-07-28T12:22:42.856701" - }, - { - "task_id": "swe_bench_numpy_linalg", - "repository": "numpy/numpy", - "issue_number": 18784, - "difficulty": "medium", - "agent_used": "maintainer-agent", - "strategy_used": "quality", - "success": false, - "overall_score": 0.0, - "detailed_scores": { - "root_cause_analysis": 0, - "solution_quality": 0, - "technical_accuracy": 0, - "implementation_detail": 0 - }, - "execution_time_ms": 1.542806625366211, - "confidence": 0.0, - "response_length": 0, - "has_code": false, - "mentions_files": false, - "includes_tests": false, - "raw_response": "", - "timestamp": "2025-07-28T12:22:42.858357" - }, - { - "task_id": "swe_bench_requests_timeout", - "repository": "requests/requests", - "issue_number": 5248, - "difficulty": "medium", - "agent_used": "mubrain_algorithm_coder", - "strategy_used": "quality", - "success": false, - "overall_score": 43.5, - "detailed_scores": { - "root_cause_analysis": 90, - "solution_quality": 60, - "technical_accuracy": 0, - "implementation_detail": 0 - }, - "execution_time_ms": 5.975008010864258, - "confidence": 0.95, - "response_length": 966, - "has_code": true, - "mentions_files": false, - "includes_tests": false, - "raw_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\nclass Locate:\n \"\"\"Neural-generated class using brain-core character predictor.\"\"\"\n\ndef __init__(self, neural_params=None):\n self.neural_params = neural_params or {}\n self.neural_state = self._initialize_neural_state()\n\ndef _initialize_neural_state(self):\n \"\"\"Initialize neural processing state.\"\"\"\n # High-attention return (pos: 9)\n return {\n 'activation_level': 0.7,\n 'learning_rate': 0.01,\n 'memory_consolida...", - "timestamp": "2025-07-28T12:22:42.859064" - }, - { - "task_id": "swe_bench_django_subquery", - "repository": "django/django", - "issue_number": 32879, - "difficulty": "hard", - "agent_used": "mubrain_algorithm_coder", - "strategy_used": "quality", - "success": false, - "overall_score": 47.0, - "detailed_scores": { - "root_cause_analysis": 91, - "solution_quality": 35, - "technical_accuracy": 30, - "implementation_detail": 30 - }, - "execution_time_ms": 3.3757686614990234, - "confidence": 0.95, - "response_length": 858, - "has_code": true, - "mentions_files": false, - "includes_tests": false, - "raw_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\ndef process_data(data):\n \"\"\"\n Process input data.\n\n # High-attention return (pos: 4)\n Generated from: Problem: Software Engineering Problem: QuerySet.aggregate() returns incorrect results with Subquery\n\n Args:\n data: Input data to process\n\n Returns:\n Processed data\n \"\"\"\n # Handle different data types appropriately\n if isinstance(data, str):\n # High-attention return (pos: 14)\n return data.strip()...", - "timestamp": "2025-07-28T12:22:42.859614" - }, - { - "task_id": "swe_bench_flask_session", - "repository": "flask/flask", - "issue_number": 4169, - "difficulty": "medium", - "agent_used": "maintainer-agent", - "strategy_used": "quality", - "success": false, - "overall_score": 0.0, - "detailed_scores": { - "root_cause_analysis": 0, - "solution_quality": 0, - "technical_accuracy": 0, - "implementation_detail": 0 - }, - "execution_time_ms": 5.018949508666992, - "confidence": 0.0, - "response_length": 0, - "has_code": false, - "mentions_files": false, - "includes_tests": false, - "raw_response": "", - "timestamp": "2025-07-28T12:22:42.864210" - }, - { - "task_id": "swe_bench_numpy_linalg", - "repository": "numpy/numpy", - "issue_number": 18784, - "difficulty": "medium", - "agent_used": "mubrain_algorithm_coder", - "strategy_used": "quality", - "success": false, - "overall_score": 42.25, - "detailed_scores": { - "root_cause_analysis": 90, - "solution_quality": 35, - "technical_accuracy": 0, - "implementation_detail": 50 - }, - "execution_time_ms": 6.696939468383789, - "confidence": 0.95, - "response_length": 268, - "has_code": true, - "mentions_files": false, - "includes_tests": false, - "raw_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\ndef process_data(a, b):\n \"\"\"\n Add two numbers together.\n\n Args:\n a: First number (int or float)\n b: Second number (int or float)\n\n Returns:\n The sum of a and b\n \"\"\"\n return a + b", - "timestamp": "2025-07-28T12:22:42.865702" - }, - { - "task_id": "swe_bench_react_performance", - "repository": "facebook/react", - "issue_number": 24476, - "difficulty": "hard", - "agent_used": "maintainer-agent", - "strategy_used": "quality", - "success": false, - "overall_score": 0.0, - "detailed_scores": { - "root_cause_analysis": 0, - "solution_quality": 0, - "technical_accuracy": 0, - "implementation_detail": 0 - }, - "execution_time_ms": 3.222942352294922, - "confidence": 0.0, - "response_length": 0, - "has_code": false, - "mentions_files": false, - "includes_tests": false, - "raw_response": "", - "timestamp": "2025-07-28T12:22:42.867579" - }, - { - "task_id": "swe_bench_flask_session", - "repository": "flask/flask", - "issue_number": 4169, - "difficulty": "medium", - "agent_used": "mubrain_algorithm_coder", - "strategy_used": "quality", - "success": true, - "overall_score": 94.0, - "detailed_scores": { - "root_cause_analysis": 94, - "solution_quality": 100, - "technical_accuracy": 100, - "implementation_detail": 70 - }, - "execution_time_ms": 9.000062942504883, - "confidence": 0.95, - "response_length": 1945, - "has_code": true, - "mentions_files": true, - "includes_tests": true, - "raw_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\nProblem: Software Engineering Problem: Session cookie not properly secured with SameSite attribute\n\n Repository: flask/flask\n Issue #4169\n Difficulty: medium\n Category: security_vulnerability\n\n Problem Description:\n\n Flask session cookies lack proper SameSite attribute configuration, creating CSRF vulnerability.\n\n Security issue:\n ```python\n from flask import Flask, session\n app = Flask(__name__)\n app...", - "timestamp": "2025-07-28T12:22:42.868789" - }, - { - "task_id": "swe_bench_react_performance", - "repository": "facebook/react", - "issue_number": 24476, - "difficulty": "hard", - "agent_used": "mubrain_algorithm_coder", - "strategy_used": "quality", - "success": false, - "overall_score": 47.0, - "detailed_scores": { - "root_cause_analysis": 91, - "solution_quality": 35, - "technical_accuracy": 30, - "implementation_detail": 30 - }, - "execution_time_ms": 4.79888916015625, - "confidence": 0.95, - "response_length": 815, - "has_code": true, - "mentions_files": false, - "includes_tests": false, - "raw_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\ndef process_data(data):\n \"\"\"\n Process input data.\n\n Generated from: Problem: Software Engineering Problem: useEffect dependency array causes excessive re-renders\n\n Args:\n data: Input data to process\n\n Returns:\n Processed data\n \"\"\"\n # Handle different data types appropriately\n if isinstance(data, str):\n # High-attention return (pos: 14)\n return data.strip()\n elif isinstance(data, (list, tuple)):\n...", - "timestamp": "2025-07-28T12:22:42.870665" - } - ], - "sota_baselines": { - "Claude 3.5 Sonnet": 70.3, - "GPT-4o": 33.2, - "GPT-4.1": 54.6, - "DeepSeek V3": 49.0, - "Claude 3 Opus": 38.2, - "GPT-4": 21.7, - "Gemini Pro": 16.4 - }, - "metadata": { - "timestamp": "2025-07-28T12:22:42.871518", - "total_time_seconds": 0.018468856811523438, - "agents_tested": [ - "maintainer-agent", - "mubrain_algorithm_coder", - "architect-agent" - ], - "problems_count": 5, - "parallel_execution": true - } -} \ No newline at end of file diff --git a/benchmarks/brain_swe_optimized_20250728_122419.json b/benchmarks/brain_swe_optimized_20250728_122419.json deleted file mode 100644 index 43e66995617267190410faac0f7a4c7f64beba20..0000000000000000000000000000000000000000 --- a/benchmarks/brain_swe_optimized_20250728_122419.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "summary": { - "pass_rate": 33.33333333333333, - "average_score": 73.33333309491475, - "problems_solved": 1, - "total_problems": 3, - "improvement_vs_standard": 13.333333333333329 - }, - "results": [ - { - "task_id": "swe_bench_algorithm_optimization", - "success": true, - "overall_score": 92.49999964237213, - "execution_time_ms": 6.779193878173828, - "detailed_scores": { - "algorithm_understanding": 100, - "code_quality": 80, - "problem_specific": 100, - "neural_processing": 89.99999761581421 - }, - "neural_confidence": 0.8999999761581421, - "full_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\nProblem: def fix_complex_norm_calculation(arr, axis):\n '''\n Algorithm optimization for complex array norm calculation\n\n Repository: numpy/numpy\n Issue #18784\n Difficulty: medium\n\n\ndef fix_complex_norm_calculation(arr, axis):\n \"\"\"AI-generated implementation using real algorithms.\"\"\"\n # Real implementation\n if not data:\n # High-attention return (pos: 13)\n return None\n # High-attention return (pos: 14)\n return data\n '''\n Fix numpy.linalg.norm for complex arrays with axis parameter.\n\n # High-attention return (pos: 18)\n Current implementation returns incorrect results for complex arrays:\n\n Example:\n import numpy as np\n arr = np.array([[1+2j, 3+4j], [5+6j, 7+8j]])\n result = np.linalg.norm(arr, axis=1)\n # Expected: [sqrt(1\u00b2+2\u00b2+3\u00b2+4\u00b2), sqrt(5\u00b2+6\u00b2+7\u00b2+8\u00b2)] = [5.477, 12.207]\n # Actual: incorrect values\n\n Root cause: Complex magnitude not properly calculated in axis operations\n\n Required algorithm:\n 1. For each complex number a+bj, compute magnitude = sqrt(a\u00b2 + b\u00b2)\n 2. Apply norm operation to the magnitude values\n 3. Return correct result array\n\n Implementation requirements:\n - Handle complex numbers correctly\n - Maintain performance for large arrays\n - Support all axis configurations\n - Preserve existing API\n '''\n # Return optimized implementation\n pass\n\n\n Expected approach: algorithmic_optimization\n\n Return: Working implementation with optimized algorithm\n '''\n Approach: Dynamic Programming (Break down into subproblems)\n Complexity: O(n)" - }, - { - "task_id": "swe_bench_data_structure_fix", - "success": false, - "overall_score": 66.24999982118607, - "execution_time_ms": 13.324737548828125, - "detailed_scores": { - "algorithm_understanding": 60, - "code_quality": 80, - "problem_specific": 40.0, - "neural_processing": 94.9999988079071 - }, - "neural_confidence": 0.949999988079071, - "full_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\ndef process_data(data):\n \"\"\"\n Process input data.\n\n Generated from: Problem: def optimize_ssl_timeout_handling():\n\n Args:\n data: Input data to process\n\n Returns:\n Processed data\n \"\"\"\n # Handle different data types appropriately\n if isinstance(data, str):\n # High-attention return (pos: 14)\n return data.strip()\n elif isinstance(data, (list, tuple)):\n # High-attention return (pos: 16)\n return [item for item in data if item is not None]\n elif isinstance(data, dict):\n return {k: v for k, v in data.items() if v is not None}\n elif isinstance(data, (int, float)):\n return abs(data)\n elif data is None:\n return \"\"\n else:\n return str(data)" - }, - { - "task_id": "swe_bench_pattern_optimization", - "success": false, - "overall_score": 61.249999821186066, - "execution_time_ms": 9.835958480834961, - "detailed_scores": { - "algorithm_understanding": 60, - "code_quality": 80, - "problem_specific": 20.0, - "neural_processing": 94.9999988079071 - }, - "neural_confidence": 0.949999988079071, - "full_response": "# Symbolic Reasoning Applied\n# Dynamic Programming Enhancement\ndef process_data(data):\n \"\"\"\n Process input data.\n\n Generated from: Problem: def optimize_subquery_aggregation_pattern():\n\n Args:\n data: Input data to process\n\n Returns:\n Processed data\n \"\"\"\n # Handle different data types appropriately\n if isinstance(data, str):\n # High-attention return (pos: 14)\n return data.strip()\n elif isinstance(data, (list, tuple)):\n # High-attention return (pos: 16)\n return [item for item in data if item is not None]\n elif isinstance(data, dict):\n return {k: v for k, v in data.items() if v is not None}\n elif isinstance(data, (int, float)):\n return abs(data)\n elif data is None:\n return \"\"\n else:\n return str(data)" - } - ], - "agent_used": "mubrain_algorithm_coder", - "optimization_approach": "algorithmic_focus", - "timestamp": "2025-07-28T12:24:19.596731" -} \ No newline at end of file diff --git a/benchmarks/extreme_scale_200_problems.jsonl b/benchmarks/extreme_scale_200_problems.jsonl deleted file mode 100644 index 6b60e9730f8f9cc95dcd4a0f86710743f835bd74..0000000000000000000000000000000000000000 --- a/benchmarks/extreme_scale_200_problems.jsonl +++ /dev/null @@ -1,164 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} -{"completion":"# Two pointer technique\n left = 0\n right = len(string) - 1\n \n while left < right:\n # Process current pair\n if string[left] + string[right] == target:\n return True\n elif string[left] + string[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/10"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/11"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/12"} -{"completion":"# Enhanced generic validation\n return len(a) > 0 and all(x is not None for x in a)","task_id":"HumanEval/13"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/14"} -{"completion":"# RNN-inspired: sequential processing\n return string_sequence(*args)","task_id":"HumanEval/15"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in string:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/16"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in music_string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/17"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(string)\n return result","task_id":"HumanEval/18"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(numbers)","task_id":"HumanEval/19"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in numbers:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/20"} -{"completion":"# Data transformation\n result = []\n for item in numbers:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/21"} -{"completion":"# CNN-inspired: spatial feature extraction\n return filter_integers(*args)","task_id":"HumanEval/22"} -{"completion":"# Intelligent single value computation\n return sum(string) if string else 0","task_id":"HumanEval/23"} -{"completion":"if not n:\n return []\n result = []\n running_max = n[0]\n for num in n:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/24"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/25"} -{"completion":"# Hash table operations\n table = {}\n \n for item in numbers:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/26"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/27"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/28"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in strings:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/29"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/30"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/31"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in xs:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/32"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/33"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/34"} -{"completion":"def max_element(l: list):\n \"\"\"return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/35"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/36"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/37"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/38"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/39"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in l:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/40"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/41"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/42"} -{"completion":"# Enhanced iterative comparison\n for i in range(len(l)):\n for j in range(i + 1, len(l)):\n if l[i] == l[j]: # Compare elements for equality\n return True\n return False","task_id":"HumanEval/43"} -{"completion":"# Data transformation\n result = []\n for item in x:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/44"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/45"} -{"completion":"# Recursive decomposition\n if len(n) <= 1:\n return n\n \n mid = len(n) // 2\n left = self.recursive_decomposition(n[:mid])\n right = self.recursive_decomposition(n[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/46"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/47"} -{"completion":"# Two pointer technique\n left = 0\n right = len(text) - 1\n \n while left < right:\n # Process current pair\n if text[left] + text[right] == target:\n return True\n elif text[left] + text[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/48"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/49"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in s:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/50"} -{"completion":"# Intelligent generic solution with context awareness\n return len(text) if text else None","task_id":"HumanEval/51"} -{"completion":"# Intelligent boolean analysis\n if not l:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in l)","task_id":"HumanEval/52"} -{"completion":"# Intelligent generic solution with context awareness\n return len(x) if x else None","task_id":"HumanEval/53"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s0:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/54"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/55"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in brackets:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/56"} -{"completion":"# Intelligent boolean analysis\n if not l:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in l)","task_id":"HumanEval/57"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l1:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/58"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/59"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in n:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/60"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in brackets:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/61"} -{"completion":"# Intelligent generic solution with context awareness\n return len(xs) if xs else None","task_id":"HumanEval/62"} -{"completion":"# RNN-inspired: sequential processing\n return fibfib(*args)","task_id":"HumanEval/63"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in s:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/64"} -{"completion":"# Two pointer technique\n left = 0\n right = len(x) - 1\n \n while left < right:\n # Process current pair\n if x[left] + x[right] == target:\n return True\n elif x[left] + x[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/65"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/66"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in s:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/67"} -{"completion":"# Intelligent generic solution with context awareness\n return len(arr) if arr else None","task_id":"HumanEval/68"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in lst:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/69"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(lst)","task_id":"HumanEval/70"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/71"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in q:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/72"} -{"completion":"def smallest_change(arr):\n \"\"\"\n given an array arr of integers, find the minimum number of elements that\n need to be changed to make the array palindromic. a palindromic array is an array that\n is read the same backwards and forwards. in one change, you can change one element to any other element.\n\n for example:\n smallest_change([1,2,3,5,4,7,9,6]) == 4\n smallest_change([1, 2, 3, 4, 3, 2, 2]) == 1\n smallest_change([1, 2, 3, 2, 1]) == 0\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/73"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst1:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/74"} -{"completion":"# Mathematical computation\n if not a:\n return 0\n \n return sum(a) / len(a)","task_id":"HumanEval/75"} -{"completion":"# Mathematical computation\n if not x:\n return 0\n \n return sum(x) / len(x)","task_id":"HumanEval/76"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/77"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(num)","task_id":"HumanEval/78"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in decimal:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/79"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(s)):\n current_sum += s[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= s[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/80"} -{"completion":"# Mathematical computation\n if not grades:\n return 0\n \n return sum(grades) / len(grades)","task_id":"HumanEval/81"} -{"completion":"# Mathematical computation\n if not string:\n return 0\n \n return sum(string) / len(string)","task_id":"HumanEval/82"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/83"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in N:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/84"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/85"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/86"} -{"completion":"# Recursive decomposition\n if len(lst) <= 1:\n return lst\n \n mid = len(lst) // 2\n left = self.recursive_decomposition(lst[:mid])\n right = self.recursive_decomposition(lst[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/87"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(array)","task_id":"HumanEval/88"} -{"completion":"# Data transformation\n result = []\n for item in s:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/89"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/90"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in S:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/91"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in x:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/92"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in message:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/93"} -{"completion":"# Mathematical computation\n if not lst:\n return 0\n \n return sum(lst) / len(lst)","task_id":"HumanEval/94"} -{"completion":"# Hash table operations\n table = {}\n \n for item in dict:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/95"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in n:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/96"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/97"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in s:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/98"} -{"completion":"# Enhanced generic aggregation\n result = 0\n for item in value:\n result += item # Default: sum aggregation\n return result","task_id":"HumanEval/99"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in n:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/100"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/101"} -{"completion":"# Intelligent generic solution with context awareness\n return len(x) if x else None","task_id":"HumanEval/102"} -{"completion":"# Enhanced generic aggregation\n result = 0\n for item in n:\n result += item # Default: sum aggregation\n return result","task_id":"HumanEval/103"} -{"completion":"# Hash table operations\n table = {}\n \n for item in x:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/104"} -{"completion":"# Two pointer technique\n left = 0\n right = len(arr) - 1\n \n while left < right:\n # Process current pair\n if arr[left] + arr[right] == target:\n return True\n elif arr[left] + arr[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/105"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/106"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/107"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in arr:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/108"} -{"completion":"# Hash table operations\n table = {}\n \n for item in arr:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/109"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst1:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/110"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in test:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/111"} -{"completion":"# Two pointer technique\n left = 0\n right = len(s) - 1\n \n while left < right:\n # Process current pair\n if s[left] + s[right] == target:\n return True\n elif s[left] + s[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/112"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in lst:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/113"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(nums)","task_id":"HumanEval/114"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in grid:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/115"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(arr)","task_id":"HumanEval/116"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/117"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in word:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/118"} -{"completion":"balance = 0\n for operation in lst:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/119"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(arr)):\n current_sum += arr[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= arr[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/120"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/121"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in arr:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/122"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(n)","task_id":"HumanEval/123"} -{"completion":"# Enhanced validation with comprehensive checking\n if not date:\n return True\n \n # Context-aware validation logic\n for item in date:\n if not isinstance(item, (int, float, str)):\n return False\n \n return True","task_id":"HumanEval/124"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in txt:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/125"} -{"completion":"# Hash table operations\n table = {}\n \n for item in lst:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/126"} -{"completion":"# Two pointer technique\n left = 0\n right = len(interval1) - 1\n \n while left < right:\n # Process current pair\n if interval1[left] + interval1[right] == target:\n return True\n elif interval1[left] + interval1[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/127"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in arr:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/128"} -{"completion":"# Hash table operations\n table = {}\n \n for item in grid:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/129"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/130"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/131"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(string)","task_id":"HumanEval/132"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in lst:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/133"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in txt:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/134"} -{"completion":"# Hash table operations\n table = {}\n \n for item in arr:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/135"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/136"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(a)","task_id":"HumanEval/137"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in n:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/138"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/139"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(text)):\n current_sum += text[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= text[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/140"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(file_name)\n return result","task_id":"HumanEval/141"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/142"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in sentence:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/143"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in x:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/144"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(nums)","task_id":"HumanEval/145"} -{"completion":"# CNN-inspired: spatial feature extraction\n return specialFilter(*args)","task_id":"HumanEval/146"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in n:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/147"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(planet1)","task_id":"HumanEval/148"} -{"completion":"# Hash table operations\n table = {}\n \n for item in lst:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/149"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/150"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/151"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(game)","task_id":"HumanEval/152"} -{"completion":"if not class_name:\n return []\n result = []\n running_max = class_name[0]\n for num in class_name:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/153"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(a)\n return result","task_id":"HumanEval/154"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in num:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/155"} -{"completion":"# Intelligent generic solution with context awareness\n return len(number) if number else None","task_id":"HumanEval/156"} -{"completion":"# Intelligent boolean analysis\n if not a:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in a)","task_id":"HumanEval/157"} -{"completion":"# Hash table operations\n table = {}\n \n for item in words:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/158"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in number:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/159"} -{"completion":"# Mathematical computation\n if not operator:\n return 0\n \n return sum(operator) / len(operator)","task_id":"HumanEval/160"} -{"completion":"# Two pointer technique\n left = 0\n right = len(s) - 1\n \n while left < right:\n # Process current pair\n if s[left] + s[right] == target:\n return True\n elif s[left] + s[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/161"} -{"completion":"# Hash table operations\n table = {}\n \n for item in text:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/162"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(a)","task_id":"HumanEval/163"} \ No newline at end of file diff --git a/benchmarks/full_humaneval_164_extreme_test.jsonl b/benchmarks/full_humaneval_164_extreme_test.jsonl deleted file mode 100644 index 6b60e9730f8f9cc95dcd4a0f86710743f835bd74..0000000000000000000000000000000000000000 --- a/benchmarks/full_humaneval_164_extreme_test.jsonl +++ /dev/null @@ -1,164 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} -{"completion":"# Two pointer technique\n left = 0\n right = len(string) - 1\n \n while left < right:\n # Process current pair\n if string[left] + string[right] == target:\n return True\n elif string[left] + string[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/10"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/11"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/12"} -{"completion":"# Enhanced generic validation\n return len(a) > 0 and all(x is not None for x in a)","task_id":"HumanEval/13"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/14"} -{"completion":"# RNN-inspired: sequential processing\n return string_sequence(*args)","task_id":"HumanEval/15"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in string:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/16"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in music_string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/17"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(string)\n return result","task_id":"HumanEval/18"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(numbers)","task_id":"HumanEval/19"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in numbers:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/20"} -{"completion":"# Data transformation\n result = []\n for item in numbers:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/21"} -{"completion":"# CNN-inspired: spatial feature extraction\n return filter_integers(*args)","task_id":"HumanEval/22"} -{"completion":"# Intelligent single value computation\n return sum(string) if string else 0","task_id":"HumanEval/23"} -{"completion":"if not n:\n return []\n result = []\n running_max = n[0]\n for num in n:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/24"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/25"} -{"completion":"# Hash table operations\n table = {}\n \n for item in numbers:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/26"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/27"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/28"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in strings:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/29"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/30"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/31"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in xs:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/32"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/33"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/34"} -{"completion":"def max_element(l: list):\n \"\"\"return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/35"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/36"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/37"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/38"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/39"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in l:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/40"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/41"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/42"} -{"completion":"# Enhanced iterative comparison\n for i in range(len(l)):\n for j in range(i + 1, len(l)):\n if l[i] == l[j]: # Compare elements for equality\n return True\n return False","task_id":"HumanEval/43"} -{"completion":"# Data transformation\n result = []\n for item in x:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/44"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/45"} -{"completion":"# Recursive decomposition\n if len(n) <= 1:\n return n\n \n mid = len(n) // 2\n left = self.recursive_decomposition(n[:mid])\n right = self.recursive_decomposition(n[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/46"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/47"} -{"completion":"# Two pointer technique\n left = 0\n right = len(text) - 1\n \n while left < right:\n # Process current pair\n if text[left] + text[right] == target:\n return True\n elif text[left] + text[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/48"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/49"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in s:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/50"} -{"completion":"# Intelligent generic solution with context awareness\n return len(text) if text else None","task_id":"HumanEval/51"} -{"completion":"# Intelligent boolean analysis\n if not l:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in l)","task_id":"HumanEval/52"} -{"completion":"# Intelligent generic solution with context awareness\n return len(x) if x else None","task_id":"HumanEval/53"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s0:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/54"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/55"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in brackets:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/56"} -{"completion":"# Intelligent boolean analysis\n if not l:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in l)","task_id":"HumanEval/57"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l1:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/58"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/59"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in n:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/60"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in brackets:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/61"} -{"completion":"# Intelligent generic solution with context awareness\n return len(xs) if xs else None","task_id":"HumanEval/62"} -{"completion":"# RNN-inspired: sequential processing\n return fibfib(*args)","task_id":"HumanEval/63"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in s:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/64"} -{"completion":"# Two pointer technique\n left = 0\n right = len(x) - 1\n \n while left < right:\n # Process current pair\n if x[left] + x[right] == target:\n return True\n elif x[left] + x[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/65"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/66"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in s:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/67"} -{"completion":"# Intelligent generic solution with context awareness\n return len(arr) if arr else None","task_id":"HumanEval/68"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in lst:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/69"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(lst)","task_id":"HumanEval/70"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/71"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in q:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/72"} -{"completion":"def smallest_change(arr):\n \"\"\"\n given an array arr of integers, find the minimum number of elements that\n need to be changed to make the array palindromic. a palindromic array is an array that\n is read the same backwards and forwards. in one change, you can change one element to any other element.\n\n for example:\n smallest_change([1,2,3,5,4,7,9,6]) == 4\n smallest_change([1, 2, 3, 4, 3, 2, 2]) == 1\n smallest_change([1, 2, 3, 2, 1]) == 0\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/73"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst1:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/74"} -{"completion":"# Mathematical computation\n if not a:\n return 0\n \n return sum(a) / len(a)","task_id":"HumanEval/75"} -{"completion":"# Mathematical computation\n if not x:\n return 0\n \n return sum(x) / len(x)","task_id":"HumanEval/76"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/77"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(num)","task_id":"HumanEval/78"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in decimal:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/79"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(s)):\n current_sum += s[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= s[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/80"} -{"completion":"# Mathematical computation\n if not grades:\n return 0\n \n return sum(grades) / len(grades)","task_id":"HumanEval/81"} -{"completion":"# Mathematical computation\n if not string:\n return 0\n \n return sum(string) / len(string)","task_id":"HumanEval/82"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/83"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in N:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/84"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/85"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/86"} -{"completion":"# Recursive decomposition\n if len(lst) <= 1:\n return lst\n \n mid = len(lst) // 2\n left = self.recursive_decomposition(lst[:mid])\n right = self.recursive_decomposition(lst[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/87"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(array)","task_id":"HumanEval/88"} -{"completion":"# Data transformation\n result = []\n for item in s:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/89"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/90"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in S:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/91"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in x:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/92"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in message:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/93"} -{"completion":"# Mathematical computation\n if not lst:\n return 0\n \n return sum(lst) / len(lst)","task_id":"HumanEval/94"} -{"completion":"# Hash table operations\n table = {}\n \n for item in dict:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/95"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in n:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/96"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/97"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in s:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/98"} -{"completion":"# Enhanced generic aggregation\n result = 0\n for item in value:\n result += item # Default: sum aggregation\n return result","task_id":"HumanEval/99"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in n:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/100"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/101"} -{"completion":"# Intelligent generic solution with context awareness\n return len(x) if x else None","task_id":"HumanEval/102"} -{"completion":"# Enhanced generic aggregation\n result = 0\n for item in n:\n result += item # Default: sum aggregation\n return result","task_id":"HumanEval/103"} -{"completion":"# Hash table operations\n table = {}\n \n for item in x:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/104"} -{"completion":"# Two pointer technique\n left = 0\n right = len(arr) - 1\n \n while left < right:\n # Process current pair\n if arr[left] + arr[right] == target:\n return True\n elif arr[left] + arr[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/105"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/106"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/107"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in arr:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/108"} -{"completion":"# Hash table operations\n table = {}\n \n for item in arr:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/109"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst1:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/110"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in test:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/111"} -{"completion":"# Two pointer technique\n left = 0\n right = len(s) - 1\n \n while left < right:\n # Process current pair\n if s[left] + s[right] == target:\n return True\n elif s[left] + s[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/112"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in lst:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/113"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(nums)","task_id":"HumanEval/114"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in grid:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/115"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(arr)","task_id":"HumanEval/116"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/117"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in word:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/118"} -{"completion":"balance = 0\n for operation in lst:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/119"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(arr)):\n current_sum += arr[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= arr[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/120"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/121"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in arr:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/122"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(n)","task_id":"HumanEval/123"} -{"completion":"# Enhanced validation with comprehensive checking\n if not date:\n return True\n \n # Context-aware validation logic\n for item in date:\n if not isinstance(item, (int, float, str)):\n return False\n \n return True","task_id":"HumanEval/124"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in txt:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/125"} -{"completion":"# Hash table operations\n table = {}\n \n for item in lst:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/126"} -{"completion":"# Two pointer technique\n left = 0\n right = len(interval1) - 1\n \n while left < right:\n # Process current pair\n if interval1[left] + interval1[right] == target:\n return True\n elif interval1[left] + interval1[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/127"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in arr:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/128"} -{"completion":"# Hash table operations\n table = {}\n \n for item in grid:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/129"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/130"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/131"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(string)","task_id":"HumanEval/132"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in lst:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/133"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in txt:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/134"} -{"completion":"# Hash table operations\n table = {}\n \n for item in arr:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/135"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/136"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(a)","task_id":"HumanEval/137"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in n:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/138"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/139"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(text)):\n current_sum += text[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= text[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/140"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(file_name)\n return result","task_id":"HumanEval/141"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/142"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in sentence:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/143"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in x:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/144"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(nums)","task_id":"HumanEval/145"} -{"completion":"# CNN-inspired: spatial feature extraction\n return specialFilter(*args)","task_id":"HumanEval/146"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in n:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/147"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(planet1)","task_id":"HumanEval/148"} -{"completion":"# Hash table operations\n table = {}\n \n for item in lst:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/149"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/150"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/151"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(game)","task_id":"HumanEval/152"} -{"completion":"if not class_name:\n return []\n result = []\n running_max = class_name[0]\n for num in class_name:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/153"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(a)\n return result","task_id":"HumanEval/154"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in num:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/155"} -{"completion":"# Intelligent generic solution with context awareness\n return len(number) if number else None","task_id":"HumanEval/156"} -{"completion":"# Intelligent boolean analysis\n if not a:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in a)","task_id":"HumanEval/157"} -{"completion":"# Hash table operations\n table = {}\n \n for item in words:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/158"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in number:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/159"} -{"completion":"# Mathematical computation\n if not operator:\n return 0\n \n return sum(operator) / len(operator)","task_id":"HumanEval/160"} -{"completion":"# Two pointer technique\n left = 0\n right = len(s) - 1\n \n while left < right:\n # Process current pair\n if s[left] + s[right] == target:\n return True\n elif s[left] + s[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/161"} -{"completion":"# Hash table operations\n table = {}\n \n for item in text:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/162"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(a)","task_id":"HumanEval/163"} \ No newline at end of file diff --git a/benchmarks/mbpp_results_20250719_002636.json b/benchmarks/mbpp_results_20250719_002636.json deleted file mode 100644 index 624717d48d594f6f44f04b3f3af5b6299763a757..0000000000000000000000000000000000000000 --- a/benchmarks/mbpp_results_20250719_002636.json +++ /dev/null @@ -1,68 +0,0 @@ -{ - "summary": { - "total_problems": 5, - "successful": 5, - "pass_rate": 100.0, - "avg_quality_score": 100.0, - "avg_execution_time_ms": 2846.815586090088, - "total_time_seconds": 14.252975940704346, - "timestamp": "2025-07-19T00:26:36.138327" - }, - "results": [ - { - "task_id": "mbpp_1", - "description": "Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix...", - "difficulty": "medium", - "category": "dynamic_programming", - "agent_used": "algorithm-coder", - "success": true, - "quality_score": 100, - "execution_time_ms": 3880.542039871216, - "timestamp": "2025-07-19T00:26:25.776750" - }, - { - "task_id": "mbpp_2", - "description": "Write a function to find the similar elements from the given two tuple lists....", - "difficulty": "easy", - "category": "data_structures", - "agent_used": "algorithm-coder", - "success": true, - "quality_score": 100, - "execution_time_ms": 2438.4281635284424, - "timestamp": "2025-07-19T00:26:28.217503" - }, - { - "task_id": "mbpp_3", - "description": "Write a function to find the n largest integers from a given list of numbers, returned in descending...", - "difficulty": "easy", - "category": "algorithms", - "agent_used": "algorithm-coder", - "success": true, - "quality_score": 100, - "execution_time_ms": 3170.7217693328857, - "timestamp": "2025-07-19T00:26:31.390242" - }, - { - "task_id": "mbpp_4", - "description": "Write a function to find the maximum difference between the number of 0s and number of 1s in any sub...", - "difficulty": "hard", - "category": "arrays", - "agent_used": "algorithm-coder", - "success": true, - "quality_score": 100, - "execution_time_ms": 2368.858814239502, - "timestamp": "2025-07-19T00:26:33.761499" - }, - { - "task_id": "mbpp_5", - "description": "Write a function to calculate the harmonic sum of n-1....", - "difficulty": "medium", - "category": "mathematics", - "agent_used": "algorithm-coder", - "success": true, - "quality_score": 100, - "execution_time_ms": 2375.5271434783936, - "timestamp": "2025-07-19T00:26:36.137958" - } - ] -} \ No newline at end of file diff --git a/benchmarks/mbpp_results_AlgorithmCoder_1752891895.jsonl b/benchmarks/mbpp_results_AlgorithmCoder_1752891895.jsonl deleted file mode 100644 index 670f3ba6d43fc4b0f45eef7b03bcfaf937a9ac4d..0000000000000000000000000000000000000000 --- a/benchmarks/mbpp_results_AlgorithmCoder_1752891895.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"task_id": "mbpp_1", "passed": true, "generated_code": "def find_min(numbers):\n return min(numbers)", "execution_time_ms": 0.1647472381591797} -{"task_id": "mbpp_2", "passed": true, "generated_code": "def is_even(n):\n return n % 2 == 0", "execution_time_ms": 0.07772445678710938} -{"task_id": "mbpp_3", "passed": true, "generated_code": "def reverse_string(s):\n return s[::-1]", "execution_time_ms": 0.06389617919921875} -{"task_id": "mbpp_4", "passed": true, "generated_code": "def factorial(n):\n if n <= 1:\n return 1\n return n * factorial(n-1)", "execution_time_ms": 0.17213821411132812} -{"task_id": "mbpp_5", "passed": true, "generated_code": "def sum_even(numbers):\n return sum(n for n in numbers if n % 2 == 0)", "execution_time_ms": 0.2460479736328125} diff --git a/benchmarks/orchestrated_backend_test.jsonl b/benchmarks/orchestrated_backend_test.jsonl deleted file mode 100644 index 929ef910ab46b50ddd3f43c3672ca27990257c8e..0000000000000000000000000000000000000000 --- a/benchmarks/orchestrated_backend_test.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"completion":"def has_close_elements(numbers, threshold):\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"def separate_paren_groups(paren_string):\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n if char != ' ':\n current_string += char\n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n if depth == 0:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"def truncate_number(number):\n return number - int(number)","task_id":"HumanEval/2"} -{"completion":"def below_zero(operations):\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"def mean_absolute_deviation(numbers):\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/5"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/6"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/7"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/8"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/9"} \ No newline at end of file diff --git a/benchmarks/qa_agent_input.json b/benchmarks/qa_agent_input.json deleted file mode 100644 index eb29066573ed0f75e4b81a2239a4c60c32a19155..0000000000000000000000000000000000000000 --- a/benchmarks/qa_agent_input.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "input_type": "qa_request", - "content": { - "project_context": { - "project_name": "Brain-AI", - "project_path": "/Users/diego/Documents/DEV/Brain", - "language": "Rust", - "framework": "Tokio", - "dependencies": [ - "tokio", - "serde", - "async-trait", - "chrono", - "brain-types", - "brain-cognitive", - "brain-api" - ] - }, - "test_request": { - "test_types": [ - "Unit", - "Integration", - "Performance", - "Security" - ], - "target_coverage": 85.0, - "performance_requirements": { - "max_response_time_ms": 1000, - "max_memory_usage_mb": 512, - "min_throughput_rps": 100, - "error_rate_threshold": 1.0 - }, - "custom_test_commands": [ - "cargo test --lib", - "cargo test --test integration_tests", - "cargo clippy -- -D warnings" - ] - }, - "target_environment": "development" - }, - "metadata": { - "timestamp": "2025-07-18T22:44:42.380678", - "test_scenario": "comprehensive_qa_validation", - "expected_outputs": [ - "test_results", - "quality_assessment", - "qa_report", - "recommendations" - ] - } -} \ No newline at end of file diff --git a/benchmarks/qa_quality_test.jsonl b/benchmarks/qa_quality_test.jsonl deleted file mode 100644 index 959f2f704c72bf5ef5986e66e352ab3ad2ed9db0..0000000000000000000000000000000000000000 --- a/benchmarks/qa_quality_test.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"completion":"","task_id":"HumanEval/0"} -{"completion":"","task_id":"HumanEval/1"} -{"completion":"","task_id":"HumanEval/2"} -{"completion":"","task_id":"HumanEval/3"} -{"completion":"","task_id":"HumanEval/4"} \ No newline at end of file diff --git a/benchmarks/qa_working_test.jsonl b/benchmarks/qa_working_test.jsonl deleted file mode 100644 index 959f2f704c72bf5ef5986e66e352ab3ad2ed9db0..0000000000000000000000000000000000000000 --- a/benchmarks/qa_working_test.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"completion":"","task_id":"HumanEval/0"} -{"completion":"","task_id":"HumanEval/1"} -{"completion":"","task_id":"HumanEval/2"} -{"completion":"","task_id":"HumanEval/3"} -{"completion":"","task_id":"HumanEval/4"} \ No newline at end of file diff --git a/benchmarks/stress_test_algo_direct.jsonl b/benchmarks/stress_test_algo_direct.jsonl deleted file mode 100644 index cf6b99080b10019f991c37e43bf5c1d11b834d5a..0000000000000000000000000000000000000000 --- a/benchmarks/stress_test_algo_direct.jsonl +++ /dev/null @@ -1,50 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} -{"completion":"# Two pointer technique\n left = 0\n right = len(string) - 1\n \n while left < right:\n # Process current pair\n if string[left] + string[right] == target:\n return True\n elif string[left] + string[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/10"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/11"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/12"} -{"completion":"# Enhanced generic validation\n return len(a) > 0 and all(x is not None for x in a)","task_id":"HumanEval/13"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/14"} -{"completion":"# RNN-inspired: sequential processing\n return string_sequence(*args)","task_id":"HumanEval/15"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in string:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/16"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in music_string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/17"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(string)\n return result","task_id":"HumanEval/18"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(numbers)","task_id":"HumanEval/19"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in numbers:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/20"} -{"completion":"# Data transformation\n result = []\n for item in numbers:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/21"} -{"completion":"# CNN-inspired: spatial feature extraction\n return filter_integers(*args)","task_id":"HumanEval/22"} -{"completion":"# Intelligent single value computation\n return sum(string) if string else 0","task_id":"HumanEval/23"} -{"completion":"if not n:\n return []\n result = []\n running_max = n[0]\n for num in n:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/24"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/25"} -{"completion":"# Hash table operations\n table = {}\n \n for item in numbers:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/26"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/27"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/28"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in strings:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/29"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/30"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/31"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in xs:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/32"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/33"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/34"} -{"completion":"def max_element(l: list):\n \"\"\"return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/35"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/36"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/37"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/38"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/39"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in l:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/40"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/41"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/42"} -{"completion":"# Enhanced iterative comparison\n for i in range(len(l)):\n for j in range(i + 1, len(l)):\n if l[i] == l[j]: # Compare elements for equality\n return True\n return False","task_id":"HumanEval/43"} -{"completion":"# Data transformation\n result = []\n for item in x:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/44"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/45"} -{"completion":"# Recursive decomposition\n if len(n) <= 1:\n return n\n \n mid = len(n) // 2\n left = self.recursive_decomposition(n[:mid])\n right = self.recursive_decomposition(n[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/46"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/47"} -{"completion":"# Two pointer technique\n left = 0\n right = len(text) - 1\n \n while left < right:\n # Process current pair\n if text[left] + text[right] == target:\n return True\n elif text[left] + text[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/48"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/49"} \ No newline at end of file diff --git a/benchmarks/stress_test_backend_orchestrated.jsonl b/benchmarks/stress_test_backend_orchestrated.jsonl deleted file mode 100644 index 198d215bd7e438a5e6add1a86c91bfbf8305f9d2..0000000000000000000000000000000000000000 --- a/benchmarks/stress_test_backend_orchestrated.jsonl +++ /dev/null @@ -1,50 +0,0 @@ -{"completion":"def has_close_elements(numbers, threshold):\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"def separate_paren_groups(paren_string):\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n if char != ' ':\n current_string += char\n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n if depth == 0:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"def truncate_number(number):\n return number - int(number)","task_id":"HumanEval/2"} -{"completion":"def below_zero(operations):\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"def mean_absolute_deviation(numbers):\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/5"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/6"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/7"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/8"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/9"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/10"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/11"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/12"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/13"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/14"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/15"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/16"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/17"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/18"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/19"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/20"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/21"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/22"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/23"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/24"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/25"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/26"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/27"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/28"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/29"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/30"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/31"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/32"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/33"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/34"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/35"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/36"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/37"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/38"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/39"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/40"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/41"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/42"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/43"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/44"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/45"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/46"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/47"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/48"} -{"completion":"def solution():\n # Generic implementation\n return None","task_id":"HumanEval/49"} \ No newline at end of file diff --git a/benchmarks/stress_test_qa_quality.jsonl b/benchmarks/stress_test_qa_quality.jsonl deleted file mode 100644 index 079f45f193eb4fe62ab8ac830a75230c1e9eea80..0000000000000000000000000000000000000000 --- a/benchmarks/stress_test_qa_quality.jsonl +++ /dev/null @@ -1,50 +0,0 @@ -{"completion":"","task_id":"HumanEval/0"} -{"completion":"","task_id":"HumanEval/1"} -{"completion":"","task_id":"HumanEval/2"} -{"completion":"","task_id":"HumanEval/3"} -{"completion":"","task_id":"HumanEval/4"} -{"completion":"","task_id":"HumanEval/5"} -{"completion":"","task_id":"HumanEval/6"} -{"completion":"","task_id":"HumanEval/7"} -{"completion":"","task_id":"HumanEval/8"} -{"completion":"","task_id":"HumanEval/9"} -{"completion":"","task_id":"HumanEval/10"} -{"completion":"","task_id":"HumanEval/11"} -{"completion":"","task_id":"HumanEval/12"} -{"completion":"","task_id":"HumanEval/13"} -{"completion":"","task_id":"HumanEval/14"} -{"completion":"","task_id":"HumanEval/15"} -{"completion":"","task_id":"HumanEval/16"} -{"completion":"","task_id":"HumanEval/17"} -{"completion":"","task_id":"HumanEval/18"} -{"completion":"","task_id":"HumanEval/19"} -{"completion":"","task_id":"HumanEval/20"} -{"completion":"","task_id":"HumanEval/21"} -{"completion":"","task_id":"HumanEval/22"} -{"completion":"","task_id":"HumanEval/23"} -{"completion":"","task_id":"HumanEval/24"} -{"completion":"","task_id":"HumanEval/25"} -{"completion":"","task_id":"HumanEval/26"} -{"completion":"","task_id":"HumanEval/27"} -{"completion":"","task_id":"HumanEval/28"} -{"completion":"","task_id":"HumanEval/29"} -{"completion":"","task_id":"HumanEval/30"} -{"completion":"","task_id":"HumanEval/31"} -{"completion":"","task_id":"HumanEval/32"} -{"completion":"","task_id":"HumanEval/33"} -{"completion":"","task_id":"HumanEval/34"} -{"completion":"","task_id":"HumanEval/35"} -{"completion":"","task_id":"HumanEval/36"} -{"completion":"","task_id":"HumanEval/37"} -{"completion":"","task_id":"HumanEval/38"} -{"completion":"","task_id":"HumanEval/39"} -{"completion":"","task_id":"HumanEval/40"} -{"completion":"","task_id":"HumanEval/41"} -{"completion":"","task_id":"HumanEval/42"} -{"completion":"","task_id":"HumanEval/43"} -{"completion":"","task_id":"HumanEval/44"} -{"completion":"","task_id":"HumanEval/45"} -{"completion":"","task_id":"HumanEval/46"} -{"completion":"","task_id":"HumanEval/47"} -{"completion":"","task_id":"HumanEval/48"} -{"completion":"","task_id":"HumanEval/49"} \ No newline at end of file diff --git a/benchmarks/swe_bench_results_20250718_230931.json b/benchmarks/swe_bench_results_20250718_230931.json deleted file mode 100644 index 28e1b09bcea5c384078bd3128a447e79f1bae2d6..0000000000000000000000000000000000000000 --- a/benchmarks/swe_bench_results_20250718_230931.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "summary": { - "total_problems": 3, - "successful": 0, - "pass_rate": 0.0, - "avg_quality_score": 0.0, - "avg_execution_time_ms": 1208.6908022562664, - "total_time_seconds": 3.6279568672180176, - "timestamp": "2025-07-18T23:09:31.953553" - }, - "results": [ - { - "task_id": "swe_bench_1", - "repository": "requests/requests", - "issue_number": 5248, - "difficulty": "medium", - "requires_multi_file": true, - "agent_used": "backend-coder", - "strategy_used": "quality", - "success": false, - "quality_score": 0, - "execution_time_ms": 1760.70237159729, - "timestamp": "2025-07-18T23:09:30.086827" - }, - { - "task_id": "swe_bench_2", - "repository": "django/django", - "issue_number": 32879, - "difficulty": "hard", - "requires_multi_file": true, - "agent_used": "architect-agent", - "strategy_used": "orchestrated", - "success": false, - "quality_score": 0, - "execution_time_ms": 849.4760990142822, - "timestamp": "2025-07-18T23:09:30.936797" - }, - { - "task_id": "swe_bench_3", - "repository": "numpy/numpy", - "issue_number": 18784, - "difficulty": "medium", - "requires_multi_file": false, - "agent_used": "backend-coder", - "strategy_used": "quality", - "success": false, - "quality_score": 0, - "execution_time_ms": 1015.8939361572266, - "timestamp": "2025-07-18T23:09:31.953199" - } - ] -} \ No newline at end of file diff --git a/benchmarks/swe_bench_results_20250718_231034.json b/benchmarks/swe_bench_results_20250718_231034.json deleted file mode 100644 index 7dc1d3415effd9123f1eb56394569838fc6d58f8..0000000000000000000000000000000000000000 --- a/benchmarks/swe_bench_results_20250718_231034.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "summary": { - "total_problems": 3, - "successful": 3, - "pass_rate": 100.0, - "avg_quality_score": 100.0, - "avg_execution_time_ms": 2731.2126954396567, - "total_time_seconds": 8.200797080993652, - "timestamp": "2025-07-18T23:10:34.669238" - }, - "results": [ - { - "task_id": "swe_bench_1", - "repository": "requests/requests", - "issue_number": 5248, - "difficulty": "medium", - "requires_multi_file": true, - "agent_used": "backend-coder", - "strategy_used": "quality", - "success": true, - "quality_score": 100, - "execution_time_ms": 3266.4551734924316, - "timestamp": "2025-07-18T23:10:29.735522" - }, - { - "task_id": "swe_bench_2", - "repository": "django/django", - "issue_number": 32879, - "difficulty": "hard", - "requires_multi_file": true, - "agent_used": "architect-agent", - "strategy_used": "orchestrated", - "success": true, - "quality_score": 100, - "execution_time_ms": 2370.3670501708984, - "timestamp": "2025-07-18T23:10:32.108005" - }, - { - "task_id": "swe_bench_3", - "repository": "numpy/numpy", - "issue_number": 18784, - "difficulty": "medium", - "requires_multi_file": false, - "agent_used": "backend-coder", - "strategy_used": "quality", - "success": true, - "quality_score": 100, - "execution_time_ms": 2556.8158626556396, - "timestamp": "2025-07-18T23:10:34.668106" - } - ] -} \ No newline at end of file diff --git a/benchmarks/test_100_problems.jsonl b/benchmarks/test_100_problems.jsonl deleted file mode 100644 index 5f2260724e2391bd8424ea050118b6c2471bd31e..0000000000000000000000000000000000000000 --- a/benchmarks/test_100_problems.jsonl +++ /dev/null @@ -1,100 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} -{"completion":"# Two pointer technique\n left = 0\n right = len(string) - 1\n \n while left < right:\n # Process current pair\n if string[left] + string[right] == target:\n return True\n elif string[left] + string[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/10"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/11"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/12"} -{"completion":"# Enhanced generic validation\n return len(a) > 0 and all(x is not None for x in a)","task_id":"HumanEval/13"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/14"} -{"completion":"# RNN-inspired: sequential processing\n return string_sequence(*args)","task_id":"HumanEval/15"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in string:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/16"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in music_string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/17"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(string)\n return result","task_id":"HumanEval/18"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(numbers)","task_id":"HumanEval/19"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in numbers:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/20"} -{"completion":"# Data transformation\n result = []\n for item in numbers:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/21"} -{"completion":"# CNN-inspired: spatial feature extraction\n return filter_integers(*args)","task_id":"HumanEval/22"} -{"completion":"# Intelligent single value computation\n return sum(string) if string else 0","task_id":"HumanEval/23"} -{"completion":"if not n:\n return []\n result = []\n running_max = n[0]\n for num in n:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/24"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/25"} -{"completion":"# Hash table operations\n table = {}\n \n for item in numbers:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/26"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/27"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/28"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in strings:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/29"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/30"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/31"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in xs:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/32"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/33"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/34"} -{"completion":"def max_element(l: list):\n \"\"\"return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/35"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/36"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/37"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/38"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/39"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in l:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/40"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/41"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/42"} -{"completion":"# Enhanced iterative comparison\n for i in range(len(l)):\n for j in range(i + 1, len(l)):\n if l[i] == l[j]: # Compare elements for equality\n return True\n return False","task_id":"HumanEval/43"} -{"completion":"# Data transformation\n result = []\n for item in x:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/44"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/45"} -{"completion":"# Recursive decomposition\n if len(n) <= 1:\n return n\n \n mid = len(n) // 2\n left = self.recursive_decomposition(n[:mid])\n right = self.recursive_decomposition(n[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/46"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/47"} -{"completion":"# Two pointer technique\n left = 0\n right = len(text) - 1\n \n while left < right:\n # Process current pair\n if text[left] + text[right] == target:\n return True\n elif text[left] + text[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/48"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/49"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in s:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/50"} -{"completion":"# Intelligent generic solution with context awareness\n return len(text) if text else None","task_id":"HumanEval/51"} -{"completion":"# Intelligent boolean analysis\n if not l:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in l)","task_id":"HumanEval/52"} -{"completion":"# Intelligent generic solution with context awareness\n return len(x) if x else None","task_id":"HumanEval/53"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s0:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/54"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/55"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in brackets:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/56"} -{"completion":"# Intelligent boolean analysis\n if not l:\n return False\n \n # Apply intelligent decision logic\n return all(isinstance(x, (int, float)) for x in l)","task_id":"HumanEval/57"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l1:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/58"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/59"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in n:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/60"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in brackets:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/61"} -{"completion":"# Intelligent generic solution with context awareness\n return len(xs) if xs else None","task_id":"HumanEval/62"} -{"completion":"# RNN-inspired: sequential processing\n return fibfib(*args)","task_id":"HumanEval/63"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in s:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/64"} -{"completion":"# Two pointer technique\n left = 0\n right = len(x) - 1\n \n while left < right:\n # Process current pair\n if x[left] + x[right] == target:\n return True\n elif x[left] + x[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/65"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/66"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in s:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/67"} -{"completion":"# Intelligent generic solution with context awareness\n return len(arr) if arr else None","task_id":"HumanEval/68"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in lst:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/69"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(lst)","task_id":"HumanEval/70"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/71"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in q:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/72"} -{"completion":"def smallest_change(arr):\n \"\"\"\n given an array arr of integers, find the minimum number of elements that\n need to be changed to make the array palindromic. a palindromic array is an array that\n is read the same backwards and forwards. in one change, you can change one element to any other element.\n\n for example:\n smallest_change([1,2,3,5,4,7,9,6]) == 4\n smallest_change([1, 2, 3, 4, 3, 2, 2]) == 1\n smallest_change([1, 2, 3, 2, 1]) == 0\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/73"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in lst1:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/74"} -{"completion":"# Mathematical computation\n if not a:\n return 0\n \n return sum(a) / len(a)","task_id":"HumanEval/75"} -{"completion":"# Mathematical computation\n if not x:\n return 0\n \n return sum(x) / len(x)","task_id":"HumanEval/76"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/77"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(num)","task_id":"HumanEval/78"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in decimal:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/79"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(s)):\n current_sum += s[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= s[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/80"} -{"completion":"# Mathematical computation\n if not grades:\n return 0\n \n return sum(grades) / len(grades)","task_id":"HumanEval/81"} -{"completion":"# Mathematical computation\n if not string:\n return 0\n \n return sum(string) / len(string)","task_id":"HumanEval/82"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/83"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in N:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/84"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/85"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/86"} -{"completion":"# Recursive decomposition\n if len(lst) <= 1:\n return lst\n \n mid = len(lst) // 2\n left = self.recursive_decomposition(lst[:mid])\n right = self.recursive_decomposition(lst[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/87"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(array)","task_id":"HumanEval/88"} -{"completion":"# Data transformation\n result = []\n for item in s:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/89"} -{"completion":"# Intelligent generic solution with context awareness\n return len(lst) if lst else None","task_id":"HumanEval/90"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in S:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/91"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in x:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/92"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in message:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/93"} -{"completion":"# Mathematical computation\n if not lst:\n return 0\n \n return sum(lst) / len(lst)","task_id":"HumanEval/94"} -{"completion":"# Hash table operations\n table = {}\n \n for item in dict:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/95"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in n:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/96"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in a:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/97"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in s:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/98"} -{"completion":"# Enhanced generic aggregation\n result = 0\n for item in value:\n result += item # Default: sum aggregation\n return result","task_id":"HumanEval/99"} \ No newline at end of file diff --git a/benchmarks/test_10_problems.jsonl b/benchmarks/test_10_problems.jsonl deleted file mode 100644 index 50a8a3105270603b81f83a6f919a939c4d5df6ba..0000000000000000000000000000000000000000 --- a/benchmarks/test_10_problems.jsonl +++ /dev/null @@ -1,10 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} \ No newline at end of file diff --git a/benchmarks/test_3_problems.jsonl b/benchmarks/test_3_problems.jsonl deleted file mode 100644 index f7afbf14010316de342403d79dd86f5add93b0a2..0000000000000000000000000000000000000000 --- a/benchmarks/test_3_problems.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} \ No newline at end of file diff --git a/benchmarks/test_50_problems.jsonl b/benchmarks/test_50_problems.jsonl deleted file mode 100644 index cf6b99080b10019f991c37e43bf5c1d11b834d5a..0000000000000000000000000000000000000000 --- a/benchmarks/test_50_problems.jsonl +++ /dev/null @@ -1,50 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} -{"completion":"# Two pointer technique\n left = 0\n right = len(string) - 1\n \n while left < right:\n # Process current pair\n if string[left] + string[right] == target:\n return True\n elif string[left] + string[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/10"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/11"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/12"} -{"completion":"# Enhanced generic validation\n return len(a) > 0 and all(x is not None for x in a)","task_id":"HumanEval/13"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/14"} -{"completion":"# RNN-inspired: sequential processing\n return string_sequence(*args)","task_id":"HumanEval/15"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in string:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/16"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in music_string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/17"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(string)\n return result","task_id":"HumanEval/18"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(numbers)","task_id":"HumanEval/19"} -{"completion":"# Enhanced sequential processing\n result = []\n \n for item in numbers:\n # Process each item with appropriate logic\n processed_item = item # Default: pass through\n if processed_item is not None:\n result.append(processed_item)\n \n return result","task_id":"HumanEval/20"} -{"completion":"# Data transformation\n result = []\n for item in numbers:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/21"} -{"completion":"# CNN-inspired: spatial feature extraction\n return filter_integers(*args)","task_id":"HumanEval/22"} -{"completion":"# Intelligent single value computation\n return sum(string) if string else 0","task_id":"HumanEval/23"} -{"completion":"if not n:\n return []\n result = []\n running_max = n[0]\n for num in n:\n running_max = max(running_max, num)\n result.append(running_max)\n return result","task_id":"HumanEval/24"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/25"} -{"completion":"# Hash table operations\n table = {}\n \n for item in numbers:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/26"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/27"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/28"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in strings:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/29"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/30"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/31"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in xs:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/32"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/33"} -{"completion":"# Hash table operations\n table = {}\n \n for item in l:\n # Create mapping or count\n table[item] = table.get(item, 0) + 1\n \n # Process based on requirements\n return table","task_id":"HumanEval/34"} -{"completion":"def max_element(l: list):\n \"\"\"return maximum element in the list.\n >>> max_element([1, 2, 3])\n 3\n >>> max_element([5, 3, -5, 2, -3, 3, 9, 0, 123, 1, -10])\n 123\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/35"} -{"completion":"# Intelligent generic solution with context awareness\n return len(n) if n else None","task_id":"HumanEval/36"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(l)","task_id":"HumanEval/37"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in s:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/38"} -{"completion":"def solve(state):\n if state in dp:\n return dp[state]\n \n # Base case\n if not state:\n return 0\n \n # Recurrence relation\n result = solve(state[1:]) + 1\n dp[state] = result\n return result\n \n return solve(n)","task_id":"HumanEval/39"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in l:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/40"} -{"completion":"# Two pointer technique\n left = 0\n right = len(n) - 1\n \n while left < right:\n # Process current pair\n if n[left] + n[right] == target:\n return True\n elif n[left] + n[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/41"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/42"} -{"completion":"# Enhanced iterative comparison\n for i in range(len(l)):\n for j in range(i + 1, len(l)):\n if l[i] == l[j]: # Compare elements for equality\n return True\n return False","task_id":"HumanEval/43"} -{"completion":"# Data transformation\n result = []\n for item in x:\n # Apply transformation (modify based on problem requirements)\n transformed = str(item).upper() if isinstance(item, str) else item * 2\n result.append(transformed)\n return result","task_id":"HumanEval/44"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/45"} -{"completion":"# Recursive decomposition\n if len(n) <= 1:\n return n\n \n mid = len(n) // 2\n left = self.recursive_decomposition(n[:mid])\n right = self.recursive_decomposition(n[mid:])\n \n return self.combine(left, right)","task_id":"HumanEval/46"} -{"completion":"# Intelligent generic solution with context awareness\n return len(l) if l else None","task_id":"HumanEval/47"} -{"completion":"# Two pointer technique\n left = 0\n right = len(text) - 1\n \n while left < right:\n # Process current pair\n if text[left] + text[right] == target:\n return True\n elif text[left] + text[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/48"} -{"completion":"# Mathematical computation\n if not n:\n return 0\n \n return sum(n) / len(n)","task_id":"HumanEval/49"} \ No newline at end of file diff --git a/benchmarks/ultimate_challenge_20250718_225554.jsonl b/benchmarks/ultimate_challenge_20250718_225554.jsonl deleted file mode 100644 index a31eb107d79187ed3cbfde73c360665a119f6e35..0000000000000000000000000000000000000000 --- a/benchmarks/ultimate_challenge_20250718_225554.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"task_id": "ultimate_1", "problem": "\ndef maximum_weighted_independent_set_tree(tree_nodes, weights):\n '''\n ULTIMATE CHALLENGE: Maximum Weighted Independent Set in Trees\n \n Given a tree with weighted nodes, find the maximum weight independent set.\n An independent set contains no two adjacent nodes.\n \n This is a classic dynamic programming problem on trees requiring:\n - Tree traversal with memoization\n - Optimal substructure analysis\n - Bottom-up dynamic programming\n \n Input: tree_nodes (adjacency list), weights (list of node weights)\n Output: Maximum possible weight of independent set\n \n Example:\n tree_nodes = {0: [1, 2], 1: [0, 3], 2: [0], 3: [1]}\n weights = [4, 1, 3, 2]\n Expected: 7 (nodes 0 and 3: 4+3=7 or nodes 2 and 3: 3+2=5, choose max)\n '''\n pass\n", "difficulty": "PhD-level", "algorithms_required": ["Dynamic Programming", "Tree Traversal", "Optimal Substructure"]} -{"task_id": "ultimate_2", "problem": "\ndef minimum_vertex_cover_approximation(graph):\n '''\n ULTIMATE CHALLENGE: Minimum Vertex Cover (NP-Complete Problem)\n \n Find the smallest set of vertices such that every edge has at least one endpoint in the set.\n Since this is NP-Complete, implement a 2-approximation algorithm.\n \n This requires:\n - Understanding of NP-completeness\n - Greedy approximation algorithms\n - Graph theory fundamentals\n - Optimality analysis\n \n Input: graph (adjacency list representation)\n Output: Vertex cover (approximately minimum)\n \n Example:\n graph = {0: [1, 2], 1: [0, 2, 3], 2: [0, 1], 3: [1]}\n Expected: A vertex cover like [1, 2] (covers all edges)\n '''\n pass\n", "difficulty": "PhD-level", "algorithms_required": ["Approximation Algorithms", "Graph Theory", "NP-Completeness"]} -{"task_id": "ultimate_3", "problem": "\ndef longest_common_subsequence_k_sequences(sequences):\n '''\n ULTIMATE CHALLENGE: Longest Common Subsequence of K Sequences\n \n Generalization of LCS to multiple sequences. Find the longest subsequence\n that appears in ALL given sequences.\n \n This is computationally intensive and requires:\n - Multi-dimensional dynamic programming\n - Exponential space/time complexity management\n - Advanced memoization techniques\n \n Input: List of sequences (strings or lists)\n Output: Length of longest common subsequence across ALL sequences\n \n Example:\n sequences = [\"ABCDGH\", \"AEDFHR\", \"ABDFHR\"]\n Expected: 3 (subsequence \"ADH\" appears in all)\n '''\n pass\n", "difficulty": "PhD-level", "algorithms_required": ["Multi-dimensional DP", "Sequence Algorithms", "Complexity Analysis"]} -{"task_id": "ultimate_4", "problem": "\ndef traveling_salesman_branch_bound(cities, distances):\n '''\n ULTIMATE CHALLENGE: Traveling Salesman Problem (Branch & Bound)\n \n Solve TSP exactly using branch and bound optimization.\n Visit all cities exactly once and return to start with minimum cost.\n \n This is the classic NP-hard problem requiring:\n - Branch and bound algorithm design\n - Pruning strategies for optimization\n - Lower bound computation\n - State space tree traversal\n \n Input: cities (list), distances (2D matrix)\n Output: (minimum_cost, optimal_path)\n \n Example:\n cities = [0, 1, 2, 3]\n distances = [[0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]]\n Expected: (80, [0, 1, 3, 2, 0]) or similar optimal tour\n '''\n pass\n", "difficulty": "PhD-level", "algorithms_required": ["Branch & Bound", "Optimization", "State Space Search"]} -{"task_id": "ultimate_5", "problem": "\ndef maximum_flow_min_cut_theorem(graph, source, sink):\n '''\n ULTIMATE CHALLENGE: Maximum Flow with Min-Cut Verification\n \n Implement Ford-Fulkerson algorithm and verify the Max-Flow Min-Cut theorem:\n The maximum flow equals the minimum cut capacity.\n \n This requires deep understanding of:\n - Network flow algorithms\n - Augmenting path algorithms\n - Graph theory theorems\n - Algorithm correctness proofs\n \n Input: graph (capacity matrix), source, sink nodes \n Output: (max_flow_value, min_cut_edges, verification_result)\n \n Example:\n graph = [[0, 16, 13, 0, 0, 0], [0, 0, 10, 12, 0, 0], ...]\n source = 0, sink = 5\n Expected: Max flow value and corresponding minimum cut\n '''\n pass\n", "difficulty": "PhD-level", "algorithms_required": ["Network Flow", "Ford-Fulkerson", "Graph Theory Theorems"]} diff --git a/benchmarks/ultimate_experimental_test.jsonl b/benchmarks/ultimate_experimental_test.jsonl deleted file mode 100644 index c661d4a3d9c08ea5411ad884e2b11ad1a1a96c44..0000000000000000000000000000000000000000 --- a/benchmarks/ultimate_experimental_test.jsonl +++ /dev/null @@ -1,20 +0,0 @@ -{"completion":"# Enhanced proximity detection with early termination\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False","task_id":"HumanEval/0"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/1"} -{"completion":"# Intelligent single value computation\n return sum(number) if number else 0","task_id":"HumanEval/2"} -{"completion":"# Track if balance goes below zero\n balance = 0\n for operation in operations:\n balance += operation\n if balance < 0:\n return True\n return False","task_id":"HumanEval/3"} -{"completion":"# Calculate mean absolute deviation\n if not numbers:\n return 0.0\n \n mean = sum(numbers) / len(numbers)\n return sum(abs(x - mean) for x in numbers) / len(numbers)","task_id":"HumanEval/4"} -{"completion":"# Sliding window approach\n window_start = 0\n max_sum = float('-inf')\n current_sum = 0\n \n for window_end in range(len(numbers)):\n current_sum += numbers[window_end]\n \n # Shrink window if needed\n while window_end - window_start + 1 > window_size:\n current_sum -= numbers[window_start]\n window_start += 1\n \n max_sum = max(max_sum, current_sum)\n \n return max_sum","task_id":"HumanEval/5"} -{"completion":"# Separate parentheses groups\n result = []\n current_string = \"\"\n depth = 0\n \n for char in paren_string:\n current_string += char\n \n if char == '(':\n depth += 1\n elif char == ')':\n depth -= 1\n \n if depth == 0 and current_string:\n result.append(current_string)\n current_string = \"\"\n \n return result","task_id":"HumanEval/6"} -{"completion":"# Filter strings containing substring\n result = []\n for string in strings:\n if substring in string:\n result.append(string)\n return result","task_id":"HumanEval/7"} -{"completion":"# Enhanced aggregation with overflow protection\n result = 0\n for item in numbers:\n result += item\n # Handle potential overflow\n if result > 1e15:\n return float('inf')\n return result","task_id":"HumanEval/8"} -{"completion":"def rolling_max(numbers: list[int]) -> list[int]:\n \"\"\" from a given list of integers, generate a list of rolling maximum element found until given moment\n in the sequence.\n >>> rolling_max([1, 2, 3, 2, 3, 4, 2])\n [1, 2, 3, 3, 3, 4, 4]\n \"\"\"\n' in 'maximum' else min(result, item)\n \n return result","task_id":"HumanEval/9"} -{"completion":"# Two pointer technique\n left = 0\n right = len(string) - 1\n \n while left < right:\n # Process current pair\n if string[left] + string[right] == target:\n return True\n elif string[left] + string[right] < target:\n left += 1\n else:\n right -= 1\n \n return False","task_id":"HumanEval/10"} -{"completion":"# Intelligent generic solution with context awareness\n return len(a) if a else None","task_id":"HumanEval/11"} -{"completion":"# Intelligent generic solution with context awareness\n return len(strings) if strings else None","task_id":"HumanEval/12"} -{"completion":"# Enhanced generic validation\n return len(a) > 0 and all(x is not None for x in a)","task_id":"HumanEval/13"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/14"} -{"completion":"# RNN-inspired: sequential processing\n return string_sequence(*args)","task_id":"HumanEval/15"} -{"completion":"# Count frequency using hash table\n count = {}\n for item in string:\n count[item] = count.get(item, 0) + 1\n return count","task_id":"HumanEval/16"} -{"completion":"# String parsing solution\n result = []\n current_token = ''\n \n for char in music_string:\n if char.isspace():\n if current_token:\n result.append(current_token)\n current_token = ''\n else:\n current_token += char\n \n if current_token:\n result.append(current_token)\n \n return result","task_id":"HumanEval/17"} -{"completion":"def traverse(node):\n if node:\n result.append(node.value)\n for child in node.children:\n traverse(child)\n \n traverse(string)\n return result","task_id":"HumanEval/18"} -{"completion":"def quicksort(arr):\n if len(arr) <= 1:\n return arr\n \n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n \n return quicksort(left) + middle + quicksort(right)\n \n return quicksort(numbers)","task_id":"HumanEval/19"} \ No newline at end of file diff --git a/bpe_demo.rs b/bpe_demo.rs deleted file mode 100644 index 6bd1ed485a61d67e7c8fcc2b687c6ca38ed340e4..0000000000000000000000000000000000000000 --- a/bpe_demo.rs +++ /dev/null @@ -1,167 +0,0 @@ -//! BPE Segmentation Demo -//! -//! Demonstrates the Brain AI BPE (Byte Pair Encoding) segmentation capabilities - -use brain::{Result, segment_discovery::{BpeSegmenter, BpeConfig}}; - -fn main() -> Result<()> { - println!("๐Ÿง  Brain Project - BPE Segmentation Demo"); - println!("========================================"); - - // Sample text for demonstration - let text = "the quick brown fox jumps over the lazy dog the quick brown fox jumps again"; - println!("\n๐Ÿ“„ Input text:"); - println!("\"{}\"", text); - - // Create BPE segmenter with configuration - let config = BpeConfig { - min_frequency: 2, - max_vocab_size: 50, - num_merges: 10, - include_chars: true, - min_entropy_threshold: 0.3, - context_window_size: 4, - min_confidence: 0.2, - enable_advanced_heuristics: true, - }; - - println!("\nโš™๏ธ Configuration:"); - println!(" Min frequency: {}", config.min_frequency); - println!(" Max vocab size: {}", config.max_vocab_size); - println!(" Num merges: {}", config.num_merges); - println!(" Entropy threshold: {:.1}", config.min_entropy_threshold); - println!(" Context window: {}", config.context_window_size); - println!(" Min confidence: {:.1}", config.min_confidence); - println!(" Advanced heuristics: {}", config.enable_advanced_heuristics); - - let mut bpe = BpeSegmenter::new(config.clone()); - - // Initialize and train - println!("\n๐Ÿ” Initializing and training BPE..."); - bpe.initialize_from_text(text)?; - - println!(" Initial vocabulary size: {}", bpe.vocab_size()); - - bpe.train()?; - - // Get final statistics - let stats = bpe.get_stats(); - println!("\n๐Ÿ“Š Training Results:"); - println!(" Final vocabulary size: {}", stats.total_segments); - println!(" Character segments: {}", stats.character_segments); - println!(" Merged segments: {}", stats.merged_segments); - println!(" Merges performed: {}", stats.merges_performed); - println!(" Max segment length: {}", stats.max_segment_length); - println!(" High confidence segments: {}", stats.high_confidence_segments); - println!(" Average confidence: {:.3}", stats.average_confidence); - println!(" Average entropy: {:.3}", stats.average_entropy); - println!(" Context observations: {}", stats.context_observations); - - // Display discovered segments by frequency - println!("\n๐Ÿ”ค Discovered Segments (by frequency):"); - let segments_by_freq = bpe.get_segments_by_frequency(); - for (i, segment) in segments_by_freq.iter().take(15).enumerate() { - println!(" {}: '{}' (freq: {}, len: {}, conf: {:.2}, ent: {:.2}, stab: {:.2})", - i + 1, - segment.segment, - segment.frequency, - segment.length, - segment.confidence, - segment.entropy, - segment.context_stability); - } - - // Display high-confidence segments - println!("\nโญ High-Confidence Segments:"); - let high_conf_segments = bpe.get_high_confidence_segments(); - if high_conf_segments.is_empty() { - println!(" (No segments above confidence threshold)"); - } else { - for segment in &high_conf_segments { - println!(" '{}' (conf: {:.3}, freq: {}, ent: {:.2})", - segment.segment, - segment.confidence, - segment.frequency, - segment.entropy); - } - } - - // Display segments by confidence - println!("\n๐ŸŽฏ Segments by Confidence Score:"); - let segments_by_conf = bpe.get_segments_by_confidence(); - for segment in segments_by_conf.iter().take(10) { - if segment.confidence > 0.0 { - println!(" '{}': {:.3} (freq: {}, len: {})", - segment.segment, - segment.confidence, - segment.frequency, - segment.length); - } - } - - // Show merged segments with formation history - println!("\n๐Ÿ”— Merged Segments (formation history):"); - let all_segments = bpe.get_segments_by_frequency(); - let merged_segments: Vec<_> = all_segments.iter() - .filter(|s| s.formed_from.is_some()) - .collect(); - - for segment in &merged_segments { - if let Some(ref pair) = segment.formed_from { - println!(" '{}' <- '{}' + '{}' (step: {}, conf: {:.2})", - segment.segment, - pair.left, - pair.right, - segment.merge_step.unwrap_or(0), - segment.confidence); - } - } - - // Demonstrate text segmentation - println!("\nโœ‚๏ธ Text Segmentation:"); - let test_texts = vec![ - "the quick brown", - "fox jumps over", - "lazy dog again", - ]; - - for test_text in test_texts { - let segments = bpe.segment_text(test_text); - println!(" '{}' -> {:?}", test_text, segments); - } - - // Compare with basic BPE (without advanced heuristics) - println!("\n๐Ÿ“ˆ Comparison with Basic BPE:"); - let basic_config = BpeConfig { - enable_advanced_heuristics: false, - ..config - }; - - let mut basic_bpe = BpeSegmenter::new(basic_config); - basic_bpe.initialize_from_text(text)?; - basic_bpe.train()?; - - let basic_stats = basic_bpe.get_stats(); - - println!(" Advanced BPE:"); - println!(" Vocabulary: {}, Merges: {}, High-conf: {}", - stats.total_segments, stats.merges_performed, stats.high_confidence_segments); - println!(" Avg confidence: {:.3}, Avg entropy: {:.3}", - stats.average_confidence, stats.average_entropy); - - println!(" Basic BPE:"); - println!(" Vocabulary: {}, Merges: {}, High-conf: {}", - basic_stats.total_segments, basic_stats.merges_performed, basic_stats.high_confidence_segments); - println!(" Avg confidence: {:.3}, Avg entropy: {:.3}", - basic_stats.average_confidence, basic_stats.average_entropy); - - println!("\nโœจ Advanced heuristics provide enhanced segmentation quality!"); - println!(" โ€ข Entropy analysis identifies natural boundaries"); - println!(" โ€ข Confidence scoring ranks segment reliability"); - println!(" โ€ข Context tracking captures co-occurrence patterns"); - println!(" โ€ข Segment splitting prevents over-segmentation"); - - println!("\nโœ… BPE Demo completed successfully!"); - - Ok(()) -} \ No newline at end of file diff --git a/brain-interface.html b/brain-interface.html deleted file mode 100644 index 99b35c459b47e3fa6797e1f0bf05497fe2629682..0000000000000000000000000000000000000000 --- a/brain-interface.html +++ /dev/null @@ -1,1450 +0,0 @@ - - - - - - - - - Brain AI - Cognitive Interface v2.2-DEBUG - - - -
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๐Ÿง  Brain AI

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Advanced Cognitive Intelligence Interface

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Online
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2.1GB
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- ๐ŸŽฎ - Control Center -

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- ๐Ÿ’ฌ - Chat with Brain -

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- ๐Ÿ’ญ - Brain Response -

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Welcome to Brain AI! ๐Ÿง  - -This is your cognitive intelligence interface. You can: -โ€ข Learn from new text and store it in memory -โ€ข Query the brain's knowledge base -โ€ข Segment text into meaningful patterns -โ€ข Run simulations based on learned concepts -โ€ข Analyze conceptual relationships - -Select an operation mode and enter your text to get started.
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Concept Graph

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Explore the interconnected web of concepts and their relationships

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Memory Timeline

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View chronological memory events and learning progression

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Simulation Dashboard

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Interactive dashboard for simulation results and insights

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System Health

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Comprehensive system diagnostics and performance metrics

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Export Data

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Export knowledge graphs, memories, and insights

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Documentation

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Comprehensive guides and API documentation

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- - - - \ No newline at end of file diff --git a/brain_ai_orchestrator_test.rs b/brain_ai_orchestrator_test.rs deleted file mode 100644 index d9467a7669e37dbe52c6efd3a678acd75984154f..0000000000000000000000000000000000000000 --- a/brain_ai_orchestrator_test.rs +++ /dev/null @@ -1,142 +0,0 @@ -//! Brain AI Orchestrator Test -//! -//! This example demonstrates Brain AI orchestration capabilities with the -//! updated conversation module API. -//! -//! This example uses the new service architecture with: -//! - MemoryService instead of MemorySystem -//! - ConceptGraphService instead of ConceptGraphManager -//! - RagOrchestrator with updated API parameters -//! -//! For other conversation examples, see: -//! - simple_pocketflow_chat.rs -//! - openai_brain_test.rs -//! - independent_intelligence_demo.rs - -use brain::*; -use brain::services::*; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain AI Orchestrator Test - True AI Delegation"); - println!("================================================="); - - // Check for OpenAI API key - let _openai_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| { - println!("โš ๏ธ OPENAI_API_KEY not set. Please set it to use this demo."); - println!(" export OPENAI_API_KEY=your_key_here"); - std::process::exit(1); - }); - - println!("โœ… OpenAI API key found"); - - // Initialize Brain AI components using new service architecture - println!("\n๐Ÿ”ง Initializing Brain AI Services..."); - let mut memory_service = create_memory_service_with_capacity(2000).await?; - let mut concept_graph_service = create_concept_graph_service_default().await?; - - println!("โœ… MemoryService initialized with SQLite persistence"); - println!("โœ… ConceptGraphService initialized with in-memory storage"); - - // Load some orchestration-specific knowledge - println!("\n๐Ÿ“š Loading Orchestration Knowledge..."); - let orchestration_knowledge = vec![ - "Brain AI orchestration coordinates multiple AI systems for optimal performance", - "The orchestrator delegates tasks based on capability and specialization", - "Orchestration involves resource allocation, task routing, and result aggregation", - "Complex queries may be decomposed into subtasks for parallel processing", - "The Brain AI orchestrator monitors performance and adapts routing strategies", - "Delegation decisions consider model strengths, response quality, and latency", - "Orchestration enables seamless integration of specialized AI capabilities", - "The system uses feedback loops to improve delegation decisions over time", - ]; - - for (i, knowledge) in orchestration_knowledge.iter().enumerate() { - memory_service.learn(knowledge.to_string(), Priority::High).await?; - println!("โœ… Loaded knowledge chunk {}", i + 1); - } - - println!("โœ… Knowledge base loaded with {} items", orchestration_knowledge.len()); - - // Create RAG orchestrator - println!("\n๐Ÿค– Initializing RAG Orchestrator..."); - let mut rag_orchestrator = RagOrchestrator::new()?; - println!("โœ… RAG Orchestrator initialized"); - - // Test orchestration capabilities - let test_queries = vec![ - "What is Brain AI orchestration?", - "How does the orchestrator delegate tasks?", - "What factors influence delegation decisions?", - "How does orchestration handle complex queries?", - "How does the system improve over time?", - ]; - - println!("\n๐Ÿงช Testing Brain AI Orchestration"); - println!("================================="); - - for (i, query) in test_queries.iter().enumerate() { - println!("\n๐Ÿ“ Test Query {}: {}", i + 1, query); - - // Create request - let request = RagRequest { - message: query.to_string(), - conversation_id: Some("orchestration_test".to_string()), - context_limit: Some(5), - retrieval_threshold: Some(0.3), - }; - - // Process with Brain AI Orchestrator - match rag_orchestrator.process_conversation( - request, - &mut memory_service, - &mut concept_graph_service, - ).await { - Ok(response) => { - println!("๐Ÿค– Response: {}", response.response); - println!("๐Ÿ“Š Confidence: {:.1}%", response.confidence_score * 100.0); - println!("๐Ÿ“š Knowledge sources: {}", response.context_used.len()); - - // Store interaction for learning - let interaction = format!("Q: {} | A: {}", query, response.response); - memory_service.learn(interaction, Priority::Medium).await?; - } - Err(e) => { - println!("โŒ Error: {}", e); - println!(" This might be due to missing OpenAI API key or network issues."); - } - } - - // Brief pause between queries - tokio::time::sleep(tokio::time::Duration::from_millis(500)).await; - } - - // Display orchestration statistics - println!("\n๐Ÿ“Š Orchestration Statistics"); - println!("=========================="); - let stats = rag_orchestrator.get_conversation_stats(); - for (key, value) in stats { - println!(" {}: {}", key, value); - } - - // Test memory consolidation - println!("\n๐Ÿง  Testing Memory Consolidation..."); - match memory_service.consolidate().await { - Ok(result) => { - println!("โœ… Memory consolidation successful:"); - println!(" Working to Episodic: {} items", result.working_to_episodic); - println!(" Episodic to Semantic: {} items", result.episodic_to_semantic); - println!(" Forgotten items: {} items", result.forgotten_events); - } - Err(e) => { - println!("โš ๏ธ Memory consolidation warning: {}", e); - } - } - - println!("\nโœ… Brain AI Orchestrator Test Complete!"); - println!(" The orchestration system is functioning properly with the new service architecture."); - - Ok(()) -} \ No newline at end of file diff --git a/brain_chat_demo.rs b/brain_chat_demo.rs deleted file mode 100644 index 59943af0f3bcdaa7884254040938bef79472deb6..0000000000000000000000000000000000000000 --- a/brain_chat_demo.rs +++ /dev/null @@ -1,138 +0,0 @@ -//! Brain Chat Demo -//! -//! A simple demonstration of the brain-chat conversational AI system. -//! This tests the core ConversationEngine functionality with a basic -//! command-line interface. - -use std::io::{self, Write}; -use brain_chat::*; - -#[tokio::main] -async fn main() -> Result<(), Box> { - // Initialize logging - env_logger::Builder::from_env(env_logger::Env::default().default_filter_or("info")) - .init(); - - println!("๐Ÿง  Brain Chat Demo"); - println!("=================="); - println!("Testing the Brain AI conversational system...\n"); - - // Create conversation engine with default configuration - println!("๐Ÿ”ง Initializing Brain Chat Engine..."); - let config = ConversationEngineConfig::default(); - let engine = match ConversationEngine::new(config).await { - Ok(engine) => { - println!("โœ… Brain Chat Engine initialized successfully!"); - engine - } - Err(e) => { - println!("โŒ Failed to initialize Brain Chat Engine: {}", e); - println!("This might be because some dependencies are not available."); - println!("The brain-chat system is implemented but may need additional setup."); - return Ok(()); - } - }; - - // Start a conversation session - println!("\n๐Ÿš€ Starting conversation session..."); - let session_id = match engine.start_conversation( - Some("demo_user".to_string()), - Platform::CLI - ).await { - Ok(session_id) => { - println!("โœ… Session started: {}", session_id); - session_id - } - Err(e) => { - println!("โŒ Failed to start session: {}", e); - return Ok(()); - } - }; - - // Get initial session state - if let Ok(state) = engine.get_conversation_state(&session_id).await { - println!("๐Ÿ“Š Initial conversation state: {:?}", state); - } - - println!("\n๐Ÿ’ฌ Chat Interface"); - println!("Type your messages below. Type 'quit' to exit, 'stats' for statistics.\n"); - - // Chat loop - loop { - // Get user input - print!("You: "); - io::stdout().flush().unwrap(); - - let mut input = String::new(); - io::stdin().read_line(&mut input).expect("Failed to read line"); - let message = input.trim(); - - if message.is_empty() { - continue; - } - - if message.to_lowercase() == "quit" { - break; - } - - if message.to_lowercase() == "stats" { - let stats = engine.get_statistics().await; - println!("๐Ÿ“Š Engine Statistics:"); - println!(" Active sessions: {}", stats.active_sessions); - println!(" Messages processed: {}", stats.total_messages_processed); - println!(" Avg response time: {}ms", stats.average_response_time_ms); - println!(" Intent accuracy: {:.1}%", stats.intent_classification_accuracy * 100.0); - println!(" Learning enabled: {}", stats.learning_enabled); - println!(" Personality adaptation: {}", stats.personality_adaptation_enabled); - continue; - } - - // Process message - print!("๐Ÿค– Processing... "); - io::stdout().flush().unwrap(); - - let start_time = std::time::Instant::now(); - match engine.process_message(&session_id, message.to_string()).await { - Ok(response) => { - let processing_time = start_time.elapsed(); - - println!("\r๐Ÿค– Bot: {}", response.content); - println!("๐Ÿ“‹ Intent: {:?} (confidence: {:.1}%)", - response.intent, response.confidence * 100.0); - println!("โšก Response time: {:.2}ms", processing_time.as_millis()); - println!("๐Ÿง  State: {:?}", response.state); - - if !response.sources.is_empty() { - println!("๐Ÿ“š Sources: {}", response.sources.join(", ")); - } - - if let Some(adaptation) = &response.personality_adaptation { - println!("๐ŸŽญ Personality update: {:?}", adaptation); - } - - println!(); // Empty line for readability - } - Err(e) => { - println!("\rโŒ Error processing message: {}", e); - } - } - } - - // End conversation - println!("\n๐Ÿ›‘ Ending conversation..."); - if let Err(e) = engine.end_conversation(&session_id).await { - println!("โš ๏ธ Warning: Failed to properly end conversation: {}", e); - } else { - println!("โœ… Conversation ended successfully"); - } - - // Final statistics - let final_stats = engine.get_statistics().await; - println!("\n๐Ÿ“Š Final Statistics:"); - println!(" Messages processed: {}", final_stats.total_messages_processed); - println!(" Average response time: {}ms", final_stats.average_response_time_ms); - - println!("\n๐Ÿ‘‹ Thanks for testing Brain Chat!"); - - Ok(()) -} \ No newline at end of file diff --git a/brain_integration_day2_demo.rs b/brain_integration_day2_demo.rs deleted file mode 100644 index 7be910832920ada69f951efa175364aacfdea93c..0000000000000000000000000000000000000000 --- a/brain_integration_day2_demo.rs +++ /dev/null @@ -1,556 +0,0 @@ -//! Day 2 Brain AI Integration Demo -//! -//! Comprehensive demonstration of brain-chat Phase 4 features: -//! - PostgreSQL + pgvector integration -//! - Redis caching performance optimization -//! - Brain AI vector bridge and learning system -//! - Real-time conversation embedding pipeline -//! - Metrics monitoring and health checks - -use std::sync::Arc; -use std::collections::{VecDeque, HashMap}; -use std::time::Duration; -use tokio::time::sleep; -use uuid::Uuid; -use chrono::Utc; - -use brain_chat::persistence::{ - VectorPersistence, VectorDatabaseConfig, - RedisCache, RedisCacheConfig, - BrainVectorBridge, - brain_vector_bridge::ConversationEmbeddings, - ConversationEmbeddingPipeline, PipelineConfig, - ConversationSuccessMetrics, -}; - -use brain_csm::types::*; - -// TODO [phase-4]: Replace with actual Brain AI components when available -use brain_core::memory::MemoryService; -use brain_core::concepts::ConceptGraphService; -use brain_cognitive::meta_memory::MetaMemorySystem; -use brain_cognitive::conversation::RagOrchestrator; - -/// Day 2 comprehensive demo -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ Starting Day 2 Brain AI Integration Demo"); - println!("{}", "=".repeat(60)); - - // Initialize all components - let (_vector_db, redis_cache, brain_bridge, pipeline) = initialize_systems().await?; - - // Demo 1: Complete conversation processing pipeline - demo_conversation_processing(&pipeline).await?; - - // Demo 2: Brain AI learning and pattern extraction - demo_brain_learning(&brain_bridge).await?; - - // Demo 3: Redis caching performance optimization - demo_redis_caching(&redis_cache).await?; - - // Demo 4: Vector similarity and recommendations - demo_vector_similarity(&pipeline).await?; - - // Demo 5: Real-time metrics and monitoring - demo_metrics_monitoring(&pipeline, &redis_cache).await?; - - // Demo 6: Health monitoring and error recovery - demo_health_monitoring(&pipeline).await?; - - println!("\n๐ŸŽ‰ Day 2 Demo completed successfully!"); - println!("โœ… Brain AI vector bridge fully operational"); - println!("โœ… Redis caching optimized for performance"); - println!("โœ… Conversation embedding pipeline integrated"); - println!("โœ… Real-time learning and pattern extraction"); - - Ok(()) -} - -async fn initialize_systems() -> Result<(Arc, Arc, Arc, ConversationEmbeddingPipeline), Box> { - println!("\n๐Ÿ”ง Initializing Brain AI Integration Systems..."); - - // 1. Initialize vector database - let vector_config = VectorDatabaseConfig { - host: "localhost".to_string(), - port: 5432, - database: "brain_chat".to_string(), - username: "postgres".to_string(), - password: "password".to_string(), - max_connections: 10, - min_connections: 2, - acquire_timeout_seconds: 30, - idle_timeout_seconds: 600, - vector_dimensions: 768, - similarity_threshold: 0.7, - max_vector_results: 100, - }; - - let vector_db = Arc::new( - VectorPersistence::new(vector_config).await - .expect("Failed to initialize vector database") - ); - println!("โœ… Vector database connected"); - - // 2. Initialize Redis cache - let redis_config = RedisCacheConfig { - url: "redis://localhost:6379".to_string(), - session_ttl_seconds: 1800, - conversation_cache_size: 1000, - embedding_cache_ttl_seconds: 3600, - pattern_cache_ttl_seconds: 21600, - similarity_cache_ttl_seconds: 1800, - max_retries: 3, - connection_timeout_ms: 5000, - enable_compression: false, - enable_metrics: true, - }; - - let redis_cache = Arc::new( - RedisCache::new(redis_config).await - .expect("Failed to initialize Redis cache") - ); - println!("โœ… Redis cache connected"); - - // 3. Initialize Brain AI memory systems (simplified for demo) - let memory_service = create_demo_memory_service().await; - let concept_graph = create_demo_concept_graph().await; - let meta_memory = create_demo_meta_memory().await; - let rag_orchestrator = create_demo_rag_orchestrator().await; - - // 4. Initialize Brain AI vector bridge - let brain_bridge = Arc::new( - BrainVectorBridge::new( - vector_db.clone(), - memory_service, - concept_graph, - meta_memory, - rag_orchestrator, - ).await? - ); - println!("โœ… Brain AI vector bridge initialized"); - - // 5. Initialize conversation embedding pipeline - let pipeline_config = PipelineConfig { - enable_caching: true, - enable_brain_integration: true, - cache_embedding_threshold: 0.7, - learning_enabled: true, - similarity_threshold: 0.75, - max_cache_size: 10000, - embedding_dimensions: 768, - enable_metrics: true, - }; - - let pipeline = ConversationEmbeddingPipeline::new( - vector_db.clone(), - redis_cache.clone(), - brain_bridge.clone(), - pipeline_config, - ).await?; - println!("โœ… Conversation embedding pipeline ready"); - - Ok((vector_db, redis_cache, brain_bridge, pipeline)) -} - -async fn demo_conversation_processing(pipeline: &ConversationEmbeddingPipeline) -> Result<(), Box> { - println!("\n๐Ÿ“‹ Demo 1: Complete Conversation Processing Pipeline"); - println!("{}", "-".repeat(50)); - - // Create test conversation context - let session_id = Uuid::new_v4().to_string(); - let mut conversation_context = create_test_conversation_context(&session_id); - - let test_messages = vec![ - "Hello, I'm looking for help with machine learning", - "Can you explain neural networks?", - "What about transformers and attention mechanisms?", - "How do I implement this in practice?", - ]; - - for (i, message) in test_messages.iter().enumerate() { - println!("\n๐Ÿ“ Processing message {}: {}", i + 1, message); - - // Add message to conversation context - let msg = Message { - id: Uuid::new_v4().to_string(), - session_id: session_id.clone(), - role: MessageRole::User, - content: message.to_string(), - timestamp: Utc::now(), - metadata: MessageMetadata::default(), - state_when_created: ConversationState::Active, - }; - conversation_context.conversation_history.push_back(msg); - - // Process through pipeline - let start_time = std::time::Instant::now(); - let result = pipeline.process_conversation( - &session_id, - &conversation_context, - message, - ).await?; - - let processing_time = start_time.elapsed(); - - println!(" โšก Processing time: {:.2}ms", processing_time.as_millis()); - println!(" ๐Ÿง  Embeddings computed: {} dimensions", result.embeddings.context_embedding.len()); - println!(" ๐Ÿ’ก Recommendations: {}", result.recommendations.len()); - println!(" ๐Ÿ“Š Cache hit: {}", result.cache_hit); - - if let Some(learning) = &result.learning_result { - println!(" ๐ŸŽฏ Success metrics: satisfaction={:.2}, relevance={:.2}", - learning.user_satisfaction_score, learning.response_relevance); - } - - // Short delay for demonstration - sleep(Duration::from_millis(100)).await; - } - - println!("\nโœ… Conversation processing pipeline demo completed"); - Ok(()) -} - -async fn demo_brain_learning(brain_bridge: &BrainVectorBridge) -> Result<(), Box> { - println!("\n๐Ÿง  Demo 2: Brain AI Learning and Pattern Extraction"); - println!("{}", "-".repeat(50)); - - let session_id = Uuid::new_v4().to_string(); - let conversation_context = create_test_conversation_context(&session_id); - - // Simulate different success scenarios - let success_scenarios = vec![ - ConversationSuccessMetrics { - user_satisfaction_score: 0.95, - response_relevance: 0.90, - conversation_completion: true, - learning_occurred: true, - intent_accuracy: 0.92, - context_utilization: 0.88, - response_time_ms: 120, - follow_up_questions: 1, - }, - ConversationSuccessMetrics { - user_satisfaction_score: 0.75, - response_relevance: 0.80, - conversation_completion: false, - learning_occurred: true, - intent_accuracy: 0.85, - context_utilization: 0.70, - response_time_ms: 200, - follow_up_questions: 3, - }, - ConversationSuccessMetrics { - user_satisfaction_score: 0.60, - response_relevance: 0.65, - conversation_completion: true, - learning_occurred: false, - intent_accuracy: 0.70, - context_utilization: 0.60, - response_time_ms: 300, - follow_up_questions: 2, - }, - ]; - - for (i, metrics) in success_scenarios.iter().enumerate() { - println!("\n๐Ÿ“š Learning session {}: satisfaction={:.2}, relevance={:.2}", - i + 1, metrics.user_satisfaction_score, metrics.response_relevance); - - let learning_start = std::time::Instant::now(); - - brain_bridge.learn_from_conversation( - &session_id, - &conversation_context, - metrics, - ).await?; - - let learning_time = learning_start.elapsed(); - println!(" โฑ๏ธ Learning completed in {:.2}ms", learning_time.as_millis()); - - // Demonstrate knowledge retrieval - let query_embedding = vec![0.1; 768]; // Demo embedding - let retrieved_knowledge = brain_bridge.retrieve_relevant_knowledge( - &conversation_context, - &query_embedding, - 5, - ).await?; - - println!(" ๐Ÿ” Retrieved {} relevant knowledge items", retrieved_knowledge.len()); - for (j, knowledge) in retrieved_knowledge.iter().take(3).enumerate() { - println!(" {}. {} (relevance: {:.2})", - j + 1, knowledge.content, knowledge.relevance_score); - } - } - - println!("\nโœ… Brain AI learning demo completed"); - Ok(()) -} - -async fn demo_redis_caching(redis_cache: &RedisCache) -> Result<(), Box> { - println!("\nโšก Demo 3: Redis Caching Performance Optimization"); - println!("{}", "-".repeat(50)); - - let session_id = Uuid::new_v4().to_string(); - - // Demo embedding caching - println!("\n๐Ÿ’พ Testing embedding cache performance..."); - - let test_embedding = ConversationEmbeddings { - conversation_embedding: vec![0.1; 768], - intent_embedding: vec![0.2; 768], - context_embedding: vec![0.3; 768], - response_quality: 0.85, - confidence_score: 0.90, - }; - - // Cache miss scenario - let cache_start = std::time::Instant::now(); - let cached_result = redis_cache.get_conversation_embeddings(&session_id).await?; - let cache_miss_time = cache_start.elapsed(); - - println!(" ๐Ÿ“Š Cache miss time: {:.2}ms", cache_miss_time.as_millis()); - assert!(cached_result.is_none()); - - // Store in cache - let store_start = std::time::Instant::now(); - redis_cache.cache_conversation_embeddings( - &session_id, - &test_embedding, - 0.85, - 5, - ).await?; - let store_time = store_start.elapsed(); - - println!(" ๐Ÿ’พ Cache store time: {:.2}ms", store_time.as_millis()); - - // Cache hit scenario - let hit_start = std::time::Instant::now(); - let cached_result = redis_cache.get_conversation_embeddings(&session_id).await?; - let cache_hit_time = hit_start.elapsed(); - - println!(" โšก Cache hit time: {:.2}ms", cache_hit_time.as_millis()); - assert!(cached_result.is_some()); - - let cached_data = cached_result.unwrap(); - println!(" ๐Ÿ“ˆ Quality score: {:.2}", cached_data.quality_score); - println!(" ๐Ÿ“Š Message count: {}", cached_data.message_count); - - // Demo hot conversation data caching - println!("\n๐Ÿ”ฅ Testing hot conversation data cache..."); - - let intent_scores = HashMap::from([ - ("question".to_string(), 0.8), - ("request".to_string(), 0.6), - ("greeting".to_string(), 0.2), - ]); - - let personality_vector = vec![0.5; 50]; - - redis_cache.cache_hot_conversation_data( - &session_id, - "Can you help me with machine learning?", - "Technical discussion about ML concepts", - &intent_scores, - &personality_vector, - ).await?; - - let hot_data = redis_cache.get_hot_conversation_data(&session_id).await?; - assert!(hot_data.is_some()); - - let hot_conversation = hot_data.unwrap(); - println!(" ๐ŸŽฏ Intent scores: {:?}", hot_conversation.intent_scores); - println!(" ๐Ÿ‘ค Personality vector dimensions: {}", hot_conversation.personality_vector.len()); - - println!("\nโœ… Redis caching demo completed"); - Ok(()) -} - -async fn demo_vector_similarity(pipeline: &ConversationEmbeddingPipeline) -> Result<(), Box> { - println!("\n๐Ÿ” Demo 4: Vector Similarity and Recommendations"); - println!("{}", "-".repeat(50)); - - // Create multiple test conversations - let conversation_topics = vec![ - ("machine_learning", "I want to learn about neural networks and deep learning"), - ("web_development", "How do I build a modern web application with React?"), - ("data_science", "What's the best approach for data analysis and visualization?"), - ("machine_learning", "Can you explain transformer architectures in detail?"), - ]; - - let mut session_ids = Vec::new(); - - // Process conversations to build similarity index - println!("\n๐Ÿ“š Building conversation similarity index..."); - for (topic, content) in &conversation_topics { - let session_id = Uuid::new_v4().to_string(); - let conversation_context = create_test_conversation_context(&session_id); - - println!(" ๐Ÿ“ Processing: {} - {}", topic, content); - - let result = pipeline.process_conversation( - &session_id, - &conversation_context, - content, - ).await?; - - println!(" โšก Processed in {}ms", result.processing_time_ms); - session_ids.push((session_id, topic.to_string())); - - sleep(Duration::from_millis(50)).await; - } - - // Test similarity search - println!("\n๐Ÿ”Ž Testing similarity search..."); - let query_session = &session_ids[0].0; // First ML conversation - let query_context = create_test_conversation_context(query_session); - let query_embedding = vec![0.1; 768]; // Demo embedding - - let similar_conversations = pipeline.find_similar_conversations( - &query_embedding, - &query_context, - 3, - ).await?; - - println!(" ๐ŸŽฏ Found {} similar conversations:", similar_conversations.len()); - for (i, similar) in similar_conversations.iter().enumerate() { - println!(" {}. Session {} (similarity: {:.2})", - i + 1, similar.session_id, similar.similarity_score); - } - - // Test recommendations - println!("\n๐Ÿ’ก Testing intelligent recommendations..."); - let recommendations = pipeline.get_conversation_recommendations( - query_session, - &query_context, - ).await?; - - println!(" ๐Ÿ“‹ Generated {} recommendations:", recommendations.len()); - for (i, rec) in recommendations.iter().take(3).enumerate() { - println!(" {}. {} (relevance: {:.2})", - i + 1, rec.content, rec.relevance_score); - } - - println!("\nโœ… Vector similarity demo completed"); - Ok(()) -} - -async fn demo_metrics_monitoring( - pipeline: &ConversationEmbeddingPipeline, - redis_cache: &RedisCache, -) -> Result<(), Box> { - println!("\n๐Ÿ“Š Demo 5: Real-time Metrics and Monitoring"); - println!("{}", "-".repeat(50)); - - // Get pipeline metrics - let pipeline_metrics = pipeline.get_metrics().await; - println!("\n๐Ÿ”„ Pipeline Metrics:"); - println!(" ๐Ÿ“ˆ Conversations processed: {}", pipeline_metrics.total_conversations_processed); - println!(" ๐Ÿง  Embeddings computed: {}", pipeline_metrics.embeddings_computed); - println!(" โšก Cache hit rate: {:.1}%", pipeline_metrics.cache_hit_rate() * 100.0); - println!(" ๐Ÿ”— Brain integrations: {}", pipeline_metrics.brain_integrations); - println!(" โฑ๏ธ Average processing time: {:.2}ms", pipeline_metrics.average_processing_time_ms); - - // Get Redis cache metrics - let cache_metrics = redis_cache.get_metrics().await; - println!("\n๐Ÿ’พ Redis Cache Metrics:"); - println!(" ๐ŸŽฏ Total operations: {}", cache_metrics.total_operations); - println!(" โœ… Hit rate: {:.1}%", cache_metrics.hit_rate() * 100.0); - println!(" ๐Ÿง  Embedding cache hits: {}", cache_metrics.embedding_cache_hits); - println!(" ๐Ÿ” Similarity cache hits: {}", cache_metrics.similarity_cache_hits); - println!(" โฑ๏ธ Average response time: {:.2}ms", cache_metrics.average_response_time_ms); - - // Simulate continuous monitoring - println!("\n๐Ÿ” Simulating real-time monitoring (5 seconds)..."); - for i in 1..=5 { - sleep(Duration::from_secs(1)).await; - - // Simulate some activity - let session_id = Uuid::new_v4().to_string(); - let conversation_context = create_test_conversation_context(&session_id); - - let _ = pipeline.process_conversation( - &session_id, - &conversation_context, - &format!("Monitoring test message {}", i), - ).await; - - let current_metrics = pipeline.get_metrics().await; - println!(" ๐Ÿ“Š Second {}: {} conversations processed", - i, current_metrics.total_conversations_processed); - } - - println!("\nโœ… Metrics monitoring demo completed"); - Ok(()) -} - -async fn demo_health_monitoring(pipeline: &ConversationEmbeddingPipeline) -> Result<(), Box> { - println!("\n๐Ÿฅ Demo 6: Health Monitoring and Error Recovery"); - println!("{}", "-".repeat(50)); - - // Perform health check - println!("\n๐Ÿ” Performing system health check..."); - - let health_status = pipeline.health_check().await?; - - println!(" ๐Ÿ’พ Vector Database: {}", if health_status.vector_db_healthy { "โœ… Healthy" } else { "โŒ Unhealthy" }); - println!(" โšก Redis Cache: {}", if health_status.redis_healthy { "โœ… Healthy" } else { "โŒ Unhealthy" }); - println!(" ๐Ÿง  Brain Integration: {}", if health_status.brain_integration_healthy { "โœ… Healthy" } else { "โŒ Unhealthy" }); - - if !health_status.errors.is_empty() { - println!(" โš ๏ธ Errors detected:"); - for error in &health_status.errors { - println!(" - {}", error); - } - } - - println!(" ๐Ÿ•’ Last check: {}", health_status.last_check.format("%H:%M:%S")); - println!(" ๐ŸŽฏ Overall status: {}", if health_status.is_healthy() { "โœ… All systems operational" } else { "โš ๏ธ Issues detected" }); - - // Simulate error recovery - if !health_status.is_healthy() { - println!("\n๐Ÿ”ง Initiating error recovery procedures..."); - sleep(Duration::from_millis(500)).await; - println!(" โœ… Recovery completed"); - } - - println!("\nโœ… Health monitoring demo completed"); - Ok(()) -} - -// Helper functions for demo setup - -fn create_test_conversation_context(session_id: &str) -> ConversationContext { - ConversationContext { - session_id: session_id.to_string(), - user_id: Some("demo_user".to_string()), - conversation_history: VecDeque::new(), - current_topic: Some("AI and Machine Learning".to_string()), - user_preferences: UserPreferences::default(), - emotional_state: EmotionalState::default(), - intent_history: Vec::new(), - confidence_scores: Vec::new(), - context_window_size: 50, - } -} - -// Simplified Brain AI component creation for demo -async fn create_demo_memory_service() -> Arc> { - // In a real implementation, this would create actual memory repositories - // For demo purposes, we'll create simplified versions - - // This is a placeholder - in practice you'd initialize with real repositories - // Arc::new(RwLock::new(MemoryService::new(...))) - todo!("Demo memory service - requires actual repository implementations") -} - -async fn create_demo_concept_graph() -> Arc> { - todo!("Demo concept graph - requires actual graph implementation") -} - -async fn create_demo_meta_memory() -> Arc> { - todo!("Demo meta memory - requires actual meta memory implementation") -} - -async fn create_demo_rag_orchestrator() -> Arc> { - todo!("Demo RAG orchestrator - requires actual orchestrator implementation") -} \ No newline at end of file diff --git a/brain_server.rs b/brain_server.rs deleted file mode 100644 index c9cc6600e2dfe9738f5927232d77cbfc3ef40697..0000000000000000000000000000000000000000 --- a/brain_server.rs +++ /dev/null @@ -1,58 +0,0 @@ -#![recursion_limit = "1024"] - -use brain_api::start_web_server; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Brain AI Server - Starting..."); - println!("========================================="); - println!(""); - println!("๐Ÿš€ Initializing Brain AI API Server..."); - println!("๐ŸŒ Server will be available at: http://localhost:8080"); - println!(""); - println!("๐Ÿ“š Available API Endpoints:"); - println!(" โ”Œโ”€ Health & Status"); - println!(" โ”‚ โ€ข GET /health - Health check"); - println!(" โ”‚ โ€ข GET /status - System status"); - println!(" โ”‚ โ€ข GET /stats - Performance statistics"); - println!(" โ”‚"); - println!(" โ”Œโ”€ Memory & Learning"); - println!(" โ”‚ โ€ข POST /learn - Add content to memory"); - println!(" โ”‚ โ€ข POST /query - Query memory system"); - println!(" โ”‚"); - println!(" โ”Œโ”€ Chat & Conversation"); - println!(" โ”‚ โ€ข POST /chat - Chat with AI"); - println!(" โ”‚ โ€ข POST /chat/learn - Simple chat learning"); - println!(" โ”‚ โ€ข POST /chat/converse - Simple conversation"); - println!(" โ”‚"); - println!(" โ”Œโ”€ Code Analysis"); - println!(" โ”‚ โ€ข POST /code/analyze - Code pattern analysis"); - println!(" โ”‚"); - println!(" โ””โ”€ Development Context"); - println!(" โ€ข POST /development/context - Create dev context"); - println!(" โ€ข GET /development/context/:id - Get dev context"); - println!(""); - println!("๐Ÿ”ง Example API Usage:"); - println!(" curl http://localhost:8080/health"); - println!(" curl -X POST http://localhost:8080/learn \\"); - println!(" -H 'Content-Type: application/json' \\"); - println!(" -d '{{\"text\": \"Hello Brain AI!\"}}'"); - println!(""); - println!("๐Ÿ“– For web interface, open: web/brain-interface.html"); - println!(""); - println!("๐ŸŽฏ Press Ctrl+C to stop the server"); - println!("========================================="); - println!(""); - - match start_web_server(8080).await { - Ok(_) => { - println!("โœ… Server started successfully!"); - } - Err(e) => { - eprintln!("โŒ Failed to start server: {}", e); - std::process::exit(1); - } - } - - Ok(()) -} \ No newline at end of file diff --git a/branching_simulation_demo.rs b/branching_simulation_demo.rs deleted file mode 100644 index dc6c90c38df92c4cbf7c5d6cae0ebf7e4a5d5452..0000000000000000000000000000000000000000 --- a/branching_simulation_demo.rs +++ /dev/null @@ -1,392 +0,0 @@ -//! Branching Simulation Demo - Task 6.3 Showcase -//! -//! This demo showcases the advanced branching simulation capabilities -//! implemented in Task 6.3, including: -//! - Tree-based branching structure -//! - Confidence scoring algorithms -//! - Pruning mechanisms for complexity management -//! - Constraint injection for guided exploration -//! - Comprehensive result analysis - -use anyhow::Result; -use std::collections::HashMap; -use std::sync::Arc; -use tokio::sync::RwLock; -use uuid::Uuid; - -use brain::simulation_engine::{ - SimulationEngine, SimulationState, StateProperty, PropertyType, - Action, ActionPriority, Effect, EffectType, Condition, ConditionType, ComparisonOperator, - BranchingConfig, SimulationConstraint, ConstraintType, BranchingResult, -}; -// Import ConfidenceConfig from brain_infra since it exists there -use brain_infra::simulation_engine::ConfidenceConfig; -use brain::concept_graph::{ConceptGraphManager, ConceptNode, ConceptType}; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain AI - Branching Simulation Demo (Task 6.3)"); - println!("==================================================\n"); - - // Initialize the simulation engine - let config = brain::ConceptGraphConfig::default(); - let concept_graph = Arc::new(RwLock::new(ConceptGraphManager::new(config).await?)); - let mut engine = SimulationEngine::new(concept_graph); - - // Configure branching parameters for demonstration - let branching_config = BranchingConfig { - max_branches_per_step: 3, - max_branching_depth: 4, - min_branch_confidence: 0.3, - max_active_branches: 15, - pruning_threshold: 0.2, - enable_aggressive_pruning: true, - max_simulation_time_seconds: 60, - }; - engine.set_branching_config(branching_config); - - // Configure confidence scoring - let confidence_config = ConfidenceConfig { - rule_confidence_weight: 0.4, - path_likelihood_weight: 0.3, - state_consistency_weight: 0.2, - historical_accuracy_weight: 0.1, - confidence_decay_factor: 0.95, - constraint_satisfaction_bonus: 0.1, - }; - engine.set_confidence_config(confidence_config); - - // Demo 1: Basic Branching Simulation - println!("๐Ÿ“Š Demo 1: Basic Branching Simulation"); - println!("-------------------------------------"); - - let _initial_state = create_demo_scenario().await?; - engine.reset(); - - // Set initial state (simulating initialization from text) - let state_id = engine.initialize_from_text("A person stands in a room with a door and a window").await?; - println!("โœ… Initialized simulation with state ID: {}", state_id); - - // Add some demo actions - add_demo_actions(&mut engine)?; - println!("โœ… Added {} demo actions", engine.get_available_actions().len()); - - // Run branching simulation - println!("\n๐ŸŒณ Running branching simulation..."); - let result = engine.run_branching_simulation(5).await?; - - println!("๐Ÿ“ˆ Simulation Results:"); - println!(" โ€ข Total branches explored: {}", result.total_branches_explored); - println!(" โ€ข Total branches pruned: {}", result.total_branches_pruned); - println!(" โ€ข Overall confidence: {:.3}", result.overall_confidence); - println!(" โ€ข Execution time: {}ms", result.execution_time_ms); - println!(" โ€ข Most likely outcomes: {} branches", result.most_likely_outcomes.len()); - - // Demo 2: Constraint-Guided Simulation - println!("\n๐Ÿ“Š Demo 2: Constraint-Guided Simulation"); - println!("---------------------------------------"); - - // Add constraints to guide the simulation - add_demo_constraints(&mut engine)?; - println!("โœ… Added {} simulation constraints", engine.get_constraints().len()); - - let constrained_result = engine.run_branching_simulation(4).await?; - - println!("๐Ÿ“ˆ Constrained Simulation Results:"); - println!(" โ€ข Total branches explored: {}", constrained_result.total_branches_explored); - println!(" โ€ข Total branches pruned: {}", constrained_result.total_branches_pruned); - println!(" โ€ข Overall confidence: {:.3}", constrained_result.overall_confidence); - println!(" โ€ข Execution time: {}ms", constrained_result.execution_time_ms); - - // Demo 3: Detailed Branch Analysis - println!("\n๐Ÿ“Š Demo 3: Detailed Branch Analysis"); - println!("-----------------------------------"); - - analyze_branching_results(&constrained_result)?; - - // Demo 4: Confidence Scoring Analysis - println!("\n๐Ÿ“Š Demo 4: Confidence Scoring Analysis"); - println!("--------------------------------------"); - - analyze_confidence_scoring(&constrained_result)?; - - // Demo 5: Pruning Mechanism Demonstration - println!("\n๐Ÿ“Š Demo 5: Pruning Mechanism Analysis"); - println!("-------------------------------------"); - - demonstrate_pruning_mechanisms(&constrained_result)?; - - println!("\n๐ŸŽ‰ Branching Simulation Demo Complete!"); - println!("Task 6.3 successfully demonstrates:"); - println!(" โœ… Tree-based branching structure"); - println!(" โœ… Advanced confidence scoring"); - println!(" โœ… Intelligent pruning mechanisms"); - println!(" โœ… Constraint-guided exploration"); - println!(" โœ… Comprehensive result analysis"); - - Ok(()) -} - -/// Create a demo scenario for simulation -async fn create_demo_scenario() -> Result { - let mut state = SimulationState::new(); - state.set_description("Demo scenario: Person in a room with multiple interaction possibilities".to_string()); - - // Add demo entities (these would normally come from concept graph) - let person_concept = ConceptNode::new( - ConceptType::Entity, - "person".to_string(), - 0.95, - Some("demo_scenario".to_string()), - ); - - let person_properties = vec![ - StateProperty { - name: "position".to_string(), - value: "center".to_string(), - property_type: PropertyType::Location, - confidence: 0.9, - source: "initial_state".to_string(), - }, - StateProperty { - name: "energy".to_string(), - value: "high".to_string(), - property_type: PropertyType::State, - confidence: 0.8, - source: "initial_state".to_string(), - }, - ]; - - state.add_entity(person_concept, person_properties); - - Ok(state) -} - -/// Add demonstration actions to the simulation engine -fn add_demo_actions(engine: &mut SimulationEngine) -> Result<()> { - // Action 1: Move to door - let move_to_door = Action { - id: Uuid::new_v4(), - name: "move_to_door".to_string(), - description: "Move towards the door".to_string(), - preconditions: vec![ - Condition { - condition_type: ConditionType::PropertyEquals, - entity_id: None, - property_name: Some("position".to_string()), - expected_value: "center".to_string(), - operator: ComparisonOperator::Equals, - required_confidence: 0.7, - } - ], - effects: vec![ - Effect { - effect_type: EffectType::SetProperty, - entity_id: None, - property_name: Some("position".to_string()), - new_value: Some("near_door".to_string()), - probability: 0.9, - delay_ms: 1000, - } - ], - confidence: 0.85, - duration_ms: 2000, - priority: ActionPriority::Medium, - context: HashMap::new(), - }; - - // Action 2: Move to window - let move_to_window = Action { - id: Uuid::new_v4(), - name: "move_to_window".to_string(), - description: "Move towards the window".to_string(), - preconditions: vec![ - Condition { - condition_type: ConditionType::PropertyEquals, - entity_id: None, - property_name: Some("position".to_string()), - expected_value: "center".to_string(), - operator: ComparisonOperator::Equals, - required_confidence: 0.7, - } - ], - effects: vec![ - Effect { - effect_type: EffectType::SetProperty, - entity_id: None, - property_name: Some("position".to_string()), - new_value: Some("near_window".to_string()), - probability: 0.8, - delay_ms: 1500, - } - ], - confidence: 0.75, - duration_ms: 2500, - priority: ActionPriority::Low, - context: HashMap::new(), - }; - - // Action 3: Rest (available from any position) - let rest_action = Action { - id: Uuid::new_v4(), - name: "rest".to_string(), - description: "Rest and recover energy".to_string(), - preconditions: vec![ - Condition { - condition_type: ConditionType::PropertyEquals, - entity_id: None, - property_name: Some("energy".to_string()), - expected_value: "low".to_string(), - operator: ComparisonOperator::Equals, - required_confidence: 0.6, - } - ], - effects: vec![ - Effect { - effect_type: EffectType::SetProperty, - entity_id: None, - property_name: Some("energy".to_string()), - new_value: Some("high".to_string()), - probability: 0.95, - delay_ms: 500, - } - ], - confidence: 0.9, - duration_ms: 3000, - priority: ActionPriority::High, - context: HashMap::new(), - }; - - engine.add_action(move_to_door); - engine.add_action(move_to_window); - engine.add_action(rest_action); - - Ok(()) -} - -/// Add demonstration constraints -fn add_demo_constraints(engine: &mut SimulationEngine) -> Result<()> { - // Constraint 1: Avoid staying in center too long - let avoid_center = SimulationConstraint { - id: Uuid::new_v4(), - constraint_type: ConstraintType::Avoidance, - target_entity: None, - target_property: Some("position".to_string()), - target_value: Some("center".to_string()), - weight: 0.7, - priority: ActionPriority::Medium, - description: "Avoid staying in center position for too long".to_string(), - }; - - // Constraint 2: Maintain high energy - let maintain_energy = SimulationConstraint { - id: Uuid::new_v4(), - constraint_type: ConstraintType::Maintenance, - target_entity: None, - target_property: Some("energy".to_string()), - target_value: Some("high".to_string()), - weight: 0.8, - priority: ActionPriority::High, - description: "Try to maintain high energy levels".to_string(), - }; - - engine.add_constraint(avoid_center); - engine.add_constraint(maintain_energy); - - Ok(()) -} - -/// Analyze branching results in detail -fn analyze_branching_results(result: &BranchingResult) -> Result<()> { - println!("๐ŸŒณ Branch Tree Analysis:"); - println!(" โ€ข Total branches explored: {}", result.total_branches_explored); - println!(" โ€ข Total branches pruned: {}", result.total_branches_pruned); - println!(" โ€ข Final states: {}", result.final_states.len()); - - // Analyze branch depths from most likely outcomes - let depths: Vec = result.most_likely_outcomes.iter().map(|b| b.depth).collect(); - let max_depth = depths.iter().max().unwrap_or(&0); - let avg_depth = if !depths.is_empty() { - depths.iter().sum::() as f64 / depths.len() as f64 - } else { 0.0 }; - - println!(" โ€ข Maximum depth reached: {}", max_depth); - println!(" โ€ข Average branch depth: {:.2}", avg_depth); - - // Analyze pruning statistics - println!(" โ€ข Pruning breakdown:"); - println!(" - Low confidence: {}", result.pruning_statistics.low_confidence_pruned); - println!(" - Resource limit: {}", result.pruning_statistics.resource_limit_pruned); - println!(" - Constraint violation: {}", result.pruning_statistics.constraint_violation_pruned); - println!(" - Time limit: {}", result.pruning_statistics.time_limit_pruned); - println!(" - Aggressive pruning: {}", result.pruning_statistics.aggressive_pruned); - - Ok(()) -} - -/// Analyze confidence scoring in detail -fn analyze_confidence_scoring(result: &BranchingResult) -> Result<()> { - println!("๐Ÿ“Š Confidence Scoring Analysis:"); - - // Collect confidence scores from most likely outcomes - let confidences: Vec = result.most_likely_outcomes - .iter() - .map(|b| b.confidence) - .collect(); - - if !confidences.is_empty() { - let max_conf = confidences.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b)); - let min_conf = confidences.iter().fold(f64::INFINITY, |a, &b| a.min(b)); - let avg_conf = confidences.iter().sum::() / confidences.len() as f64; - - println!(" โ€ข Confidence range: {:.3} - {:.3}", min_conf, max_conf); - println!(" โ€ข Average confidence: {:.3}", avg_conf); - println!(" โ€ข Overall simulation confidence: {:.3}", result.overall_confidence); - println!(" โ€ข Constraint satisfaction: {:.3}", result.constraint_satisfaction_score); - } - - // Analyze most likely outcomes - println!(" โ€ข Most likely outcomes: {} branches", result.most_likely_outcomes.len()); - for (i, branch) in result.most_likely_outcomes.iter().take(3).enumerate() { - println!(" {}. Branch {:?} (confidence: {:.3}, depth: {})", - i + 1, branch.id, branch.confidence, branch.depth); - } - - Ok(()) -} - -/// Demonstrate pruning mechanisms -fn demonstrate_pruning_mechanisms(result: &BranchingResult) -> Result<()> { - println!("โœ‚๏ธ Pruning Mechanism Analysis:"); - - let pruning_ratio = if result.total_branches_explored > 0 { - result.total_branches_pruned as f64 / result.total_branches_explored as f64 - } else { - 0.0 - }; - - println!(" โ€ข Pruning efficiency: {:.1}% ({}/{} branches pruned)", - pruning_ratio * 100.0, result.total_branches_pruned, result.total_branches_explored); - - // Analyze available statistics - println!(" โ€ข Available metrics:"); - println!(" - Overall confidence: {:.3}", result.overall_confidence); - println!(" - Constraint satisfaction: {:.3}", result.constraint_satisfaction_score); - println!(" - Most likely outcomes: {} branches", result.most_likely_outcomes.len()); - println!(" - Final states: {} states", result.final_states.len()); - println!(" - Execution time: {}ms", result.execution_time_ms); - - // Detailed pruning breakdown - let stats = &result.pruning_statistics; - println!(" โ€ข Detailed pruning breakdown:"); - println!(" - Low confidence pruned: {}", stats.low_confidence_pruned); - println!(" - Resource limit pruned: {}", stats.resource_limit_pruned); - println!(" - Constraint violation pruned: {}", stats.constraint_violation_pruned); - println!(" - Time limit pruned: {}", stats.time_limit_pruned); - println!(" - Aggressive pruned: {}", stats.aggressive_pruned); - - println!("\n๐Ÿ’ก Pruning helps manage computational complexity while preserving"); - println!(" the most promising simulation paths for exploration."); - - Ok(()) -} \ No newline at end of file diff --git a/chat.css b/chat.css deleted file mode 100644 index 17248c873eb2058d1c4d9457f3d181f59b7dea51..0000000000000000000000000000000000000000 --- a/chat.css +++ /dev/null @@ -1,822 +0,0 @@ -* { - margin: 0; - padding: 0; - box-sizing: border-box; -} - -body { - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; - background: #f8f8f8; - height: 100vh; - display: flex; - justify-content: center; - align-items: center; -} - -.chat-container { - width: 90%; - max-width: 800px; - height: 90vh; - background: white; - border: 1px solid #e0e0e0; - border-radius: 8px; - box-shadow: 0 4px 12px rgba(0,0,0,0.08); - display: flex; - flex-direction: column; - overflow: hidden; -} - -.chat-header { - background: #000000; - color: white; - padding: 20px; - text-align: center; - border-bottom: 1px solid #e0e0e0; -} - -.chat-header h1 { - font-size: 24px; - margin-bottom: 8px; - font-weight: 600; -} - -.chat-header p { - opacity: 0.8; - font-size: 14px; - font-weight: 400; -} - -.chat-messages { - flex: 1; - overflow-y: auto; - padding: 20px; - background: #ffffff; -} - -.message { - margin-bottom: 16px; - display: flex; - align-items: flex-start; - gap: 12px; -} - -.message.user { - flex-direction: row-reverse; -} - -.message-avatar { - width: 40px; - height: 40px; - border-radius: 50%; - display: flex; - align-items: center; - justify-content: center; - font-weight: bold; - color: white; - font-size: 16px; - border: 1px solid #e0e0e0; -} - -.message.user .message-avatar { - background: #333333; -} - -.message.assistant .message-avatar { - background: #666666; -} - -.message.system .message-avatar { - background: #999999; -} - -.message-content { - background: #f5f5f5; - padding: 16px 20px; - border-radius: 12px; - max-width: 75%; - border: 1px solid #e8e8e8; - word-wrap: break-word; - line-height: 1.6; - font-size: 14px; - color: #2c2c2c; - box-shadow: 0 1px 3px rgba(0,0,0,0.1); -} - -.message.user .message-content { - background: #ffffff; - color: #2c2c2c; - border: 1px solid #e0e0e0; - box-shadow: 0 1px 3px rgba(0,0,0,0.1); -} - -.message.assistant .message-content { - background: #f8f9fa; - color: #2c2c2c; - border: 1px solid #e9ecef; -} - -/* Better text formatting within messages */ -.message-content p { - margin: 0 0 12px 0; - line-height: 1.6; -} - -.message-content p:last-child { - margin-bottom: 0; -} - -/* Enhanced list styling */ -.message-content ul, .message-content ol { - margin: 12px 0; - padding-left: 20px; -} - -.message-content li { - margin-bottom: 6px; - line-height: 1.5; -} - -/* Better heading styles */ -.message-content h1, .message-content h2, .message-content h3, -.message-content h4, .message-content h5, .message-content h6 { - margin: 16px 0 8px 0; - font-weight: 600; - color: #1a1a1a; -} - -.message-content h1 { font-size: 18px; } -.message-content h2 { font-size: 16px; } -.message-content h3 { font-size: 15px; } - -/* Better emphasis styling */ -.message-content strong, .message-content b { - font-weight: 600; - color: #1a1a1a; -} - -.message-content em, .message-content i { - font-style: italic; - color: #444; -} - -/* Quote styling */ -.message-content blockquote { - border-left: 3px solid #ddd; - margin: 12px 0; - padding: 8px 16px; - background: #fafafa; - font-style: italic; -} - -/* Numbered lists styling */ -.message-content .insight-list { - counter-reset: insight-counter; - list-style: none; - padding-left: 0; -} - -.message-content .insight-list li { - counter-increment: insight-counter; - margin-bottom: 12px; - padding-left: 30px; - position: relative; -} - -.message-content .insight-list li::before { - content: counter(insight-counter); - position: absolute; - left: 0; - top: 0; - background: #333; - color: white; - border-radius: 50%; - width: 20px; - height: 20px; - display: flex; - align-items: center; - justify-content: center; - font-size: 12px; - font-weight: 600; -} - -/* Style for the welcome message list */ -.welcome-list { - margin: 8px 0; - padding-left: 20px; -} - -.message-content pre { - background: #f0f0f0; - padding: 8px; - border-radius: 4px; - overflow-x: auto; - font-size: 12px; - margin: 8px 0; - border: 1px solid #e0e0e0; -} - -.message.user .message-content pre { - background: #333333; - border: 1px solid #555555; -} - -.chat-input-container { - padding: 20px; - background: white; - border-top: 1px solid #e0e0e0; -} - -.chat-input { - display: flex; - gap: 12px; - align-items: flex-end; -} - -.input-wrapper { - flex: 1; - position: relative; -} - -.chat-textarea { - width: 100%; - min-height: 44px; - max-height: 120px; - padding: 12px 16px; - border: 1px solid #d0d0d0; - border-radius: 4px; - font-size: 14px; - resize: none; - outline: none; - transition: all 0.2s ease; - font-family: inherit; - background: white; -} - -.chat-textarea:focus { - border-color: #000000; - box-shadow: 0 0 0 2px rgba(0, 0, 0, 0.1); -} - -.send-button { - padding: 12px 20px; - background: #000000; - color: white; - border: none; - border-radius: 4px; - cursor: pointer; - font-weight: 600; - transition: all 0.2s ease; - height: 44px; -} - -.send-button:hover:not(:disabled) { - background: #333333; -} - -.send-button:disabled { - background: #cccccc; - cursor: not-allowed; -} - -.learn-button { - padding: 8px 16px; - background: #666666; - color: white; - border: none; - border-radius: 4px; - cursor: pointer; - font-size: 12px; - font-weight: 600; - margin-top: 8px; - transition: all 0.2s ease; -} - -.learn-button:hover:not(:disabled) { - background: #333333; -} - -.learn-button:disabled { - background: #cccccc; - cursor: not-allowed; -} - -.quick-actions { - display: flex; - gap: 8px; - margin-top: 12px; - flex-wrap: wrap; -} - -.quick-action-btn { - padding: 6px 12px; - background: #f5f5f5; - color: #333333; - border: 1px solid #d0d0d0; - border-radius: 4px; - cursor: pointer; - font-size: 12px; - transition: all 0.2s ease; -} - -.quick-action-btn:hover { - background: #e8e8e8; - border-color: #999999; -} - -.typing-indicator { - display: none; - align-items: center; - gap: 8px; - padding: 12px 16px; - background: #f5f5f5; - border-radius: 8px; - margin-bottom: 16px; - border: 1px solid #e8e8e8; -} - -.typing-dots { - display: flex; - gap: 4px; -} - -.typing-dot { - width: 6px; - height: 6px; - background: #999999; - border-radius: 50%; - animation: typing 1.4s infinite ease-in-out; -} - -.typing-dot:nth-child(1) { animation-delay: -0.32s; } -.typing-dot:nth-child(2) { animation-delay: -0.16s; } - -@keyframes typing { - 0%, 80%, 100% { transform: scale(0.8); opacity: 0.5; } - 40% { transform: scale(1); opacity: 1; } -} - -.status-indicator { - position: fixed; - top: 20px; - right: 20px; - padding: 8px 16px; - background: #000000; - color: white; - border-radius: 4px; - font-size: 12px; - font-weight: 600; - opacity: 0; - transition: opacity 0.3s ease; - z-index: 1000; -} - -.status-indicator.show { - opacity: 1; -} - -.status-indicator.error { - background: #666666; -} - -.status-indicator.success { - background: #333333; -} - -/* Mobile responsive */ -@media (max-width: 768px) { - .chat-container { - width: 100%; - height: 100vh; - border-radius: 0; - border: none; - } - - .message-content { - max-width: 85%; - } - - .chat-header { - padding: 16px; - } - - .chat-messages { - padding: 16px; - } - - .chat-input-container { - padding: 16px; - } - - .quick-actions { - margin-top: 8px; - } -} - -/* Scrollbar styling */ -.chat-messages::-webkit-scrollbar { - width: 6px; -} - -.chat-messages::-webkit-scrollbar-track { - background: #f5f5f5; -} - -.chat-messages::-webkit-scrollbar-thumb { - background: #d0d0d0; - border-radius: 3px; -} - -.chat-messages::-webkit-scrollbar-thumb:hover { - background: #999999; -} - -/* Enhanced text formatting styles for better readability and scanning */ -.text-block { - margin: 8px 0; - line-height: 1.6; -} - -.numbered-item { - display: flex; - align-items: flex-start; - margin: 8px 0; - padding: 6px 0; -} - -.numbered-item .number { - color: #000; - font-weight: bold; - margin-right: 8px; - min-width: 20px; -} - -.numbered-item .content { - flex: 1; - line-height: 1.5; -} - -.bullet-item { - margin: 6px 0; - padding-left: 16px; - line-height: 1.5; -} - -/* Analysis section styles with different colors for better scanning */ -.analysis-section, -.pattern-section, -.web-section, -.code-section, -.document-section, -.text-section, -.tech-section { - margin: 12px 0; - padding: 8px 12px; - border-radius: 6px; - border-left: 4px solid; - background: rgba(0,0,0,0.02); -} - -.analysis-section { - border-left-color: #2196F3; - background: rgba(33, 150, 243, 0.05); -} - -.pattern-section { - border-left-color: #9C27B0; - background: rgba(156, 39, 176, 0.05); -} - -.web-section { - border-left-color: #FF9800; - background: rgba(255, 152, 0, 0.05); -} - -.code-section { - border-left-color: #4CAF50; - background: rgba(76, 175, 80, 0.05); -} - -.document-section { - border-left-color: #795548; - background: rgba(121, 85, 72, 0.05); -} - -.text-section { - border-left-color: #607D8B; - background: rgba(96, 125, 139, 0.05); -} - -.tech-section { - border-left-color: #E91E63; - background: rgba(233, 30, 99, 0.05); -} - -/* Enhanced Markdown Styling */ -.message-content h1 { - font-size: 24px; - font-weight: 700; - margin: 16px 0 12px 0; - color: #1a1a1a; - border-bottom: 2px solid #e0e0e0; - padding-bottom: 8px; -} - -.message-content h2 { - font-size: 20px; - font-weight: 600; - margin: 14px 0 10px 0; - color: #2c2c2c; - border-bottom: 1px solid #e8e8e8; - padding-bottom: 6px; -} - -.message-content h3 { - font-size: 18px; - font-weight: 600; - margin: 12px 0 8px 0; - color: #3c3c3c; -} - -.message-content h4 { - font-size: 16px; - font-weight: 600; - margin: 10px 0 6px 0; - color: #4c4c4c; -} - -.message-content h5 { - font-size: 14px; - font-weight: 600; - margin: 8px 0 4px 0; - color: #5c5c5c; -} - -.message-content h6 { - font-size: 13px; - font-weight: 600; - margin: 6px 0 4px 0; - color: #6c6c6c; -} - -/* Enhanced Lists */ -.message-content ul { - margin: 12px 0; - padding-left: 20px; - list-style-type: none; -} - -.message-content ul li { - margin: 6px 0; - position: relative; - line-height: 1.6; -} - -.message-content ul li::before { - content: "โ€ข"; - color: #666; - font-weight: bold; - position: absolute; - left: -15px; -} - -.message-content ol { - margin: 12px 0; - padding-left: 20px; - counter-reset: list-counter; -} - -.message-content ol li { - margin: 6px 0; - position: relative; - line-height: 1.6; - counter-increment: list-counter; -} - -.message-content ol li::before { - content: counter(list-counter) "."; - color: #000; - font-weight: bold; - position: absolute; - left: -20px; - min-width: 15px; -} - -/* Enhanced Code Blocks */ -.message-content pre { - background: #f8f9fa; - border: 1px solid #e9ecef; - border-radius: 8px; - padding: 16px; - margin: 16px 0; - overflow-x: auto; - font-family: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace; - font-size: 13px; - line-height: 1.5; - box-shadow: 0 2px 4px rgba(0,0,0,0.1); -} - -.message-content code { - background: #f1f3f4; - padding: 2px 6px; - border-radius: 4px; - font-family: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace; - font-size: 13px; - color: #d73a49; - border: 1px solid #e1e4e8; -} - -.message-content pre code { - background: none; - padding: 0; - border: none; - color: inherit; - border-radius: 0; -} - -/* Enhanced Blockquotes */ -.message-content blockquote { - margin: 16px 0; - padding: 12px 20px; - border-left: 4px solid #dfe2e5; - background: #f6f8fa; - border-radius: 0 6px 6px 0; - color: #6a737d; - font-style: italic; -} - -.message-content blockquote p { - margin: 0; -} - -.message-content blockquote cite { - display: block; - margin-top: 8px; - font-size: 12px; - color: #959da5; - font-style: normal; -} - -/* Tables */ -.message-content table { - border-collapse: collapse; - margin: 16px 0; - width: 100%; - font-size: 14px; -} - -.message-content th, -.message-content td { - border: 1px solid #d0d7de; - padding: 8px 12px; - text-align: left; -} - -.message-content th { - background: #f6f8fa; - font-weight: 600; -} - -.message-content tr:nth-child(even) { - background: #f6f8fa; -} - -/* Links */ -.message-content a { - color: #0366d6; - text-decoration: none; - border-bottom: 1px solid transparent; - transition: all 0.2s ease; -} - -.message-content a:hover { - color: #0366d6; - border-bottom-color: #0366d6; -} - -/* Horizontal Rules */ -.message-content hr { - height: 2px; - background: #e1e4e8; - border: none; - margin: 24px 0; - border-radius: 1px; -} - -/* Mermaid Diagrams */ -.mermaid-container { - margin: 20px 0; - padding: 16px; - background: #fafbfc; - border: 1px solid #e1e4e8; - border-radius: 8px; - text-align: center; - min-height: 60px; - display: flex; - align-items: center; - justify-content: center; -} - -.mermaid-rendered { - background: white; - padding: 20px; - border-radius: 8px; - box-shadow: 0 2px 8px rgba(0,0,0,0.1); -} - -.mermaid-error { - color: #d73a49; - font-style: italic; - padding: 20px; -} - -/* Enhanced Text Emphasis */ -.message-content strong { - font-weight: 700; - color: #1a1a1a; -} - -.message-content em { - font-style: italic; - color: #2c2c2c; -} - -.message-content mark { - background: #fff3cd; - padding: 2px 4px; - border-radius: 3px; -} - -/* Keyboard Keys */ -.message-content kbd { - background: #fafbfc; - border: 1px solid #d1d5da; - border-bottom-color: #c6cbd1; - border-radius: 3px; - box-shadow: inset 0 -1px 0 #c6cbd1; - color: #444d56; - display: inline-block; - font-family: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace; - font-size: 11px; - line-height: 10px; - padding: 3px 5px; - vertical-align: middle; -} - -/* Task Lists */ -.message-content .task-list-item { - list-style-type: none; - margin-left: -20px; -} - -.message-content .task-list-item input[type="checkbox"] { - margin: 0 6px 0 0; -} - -/* Footnotes */ -.message-content .footnote { - font-size: 12px; - color: #6a737d; - border-top: 1px solid #e1e4e8; - margin-top: 24px; - padding-top: 16px; -} - -/* Enhanced paragraphs */ -.message-content p { - margin: 12px 0; - line-height: 1.6; - color: #24292e; -} - -.message-content p:first-child { - margin-top: 0; -} - -.message-content p:last-child { - margin-bottom: 0; -} - -/* Additional list fixes */ -.message-content ol li, -.message-content ul li { - margin-bottom: 4px; - line-height: 1.5; -} - -.message-content ol { - counter-reset: item; -} - -.message-content ol > li { - display: block; - position: relative; -} - -.message-content ol > li:before { - content: counter(item) "."; - counter-increment: item; - font-weight: bold; - color: #2563eb; - position: absolute; - left: -20px; -} \ No newline at end of file diff --git a/chat.html b/chat.html deleted file mode 100644 index ce10c14a516641538edc67c732ae2b9a27594530..0000000000000000000000000000000000000000 --- a/chat.html +++ /dev/null @@ -1,445 +0,0 @@ - - - - - - Brain AI Chat - - - - - - - - - - -
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๐Ÿง  Brain AI Chat

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Paste content to learn from, then chat about it โ€ข Powered by LLM Orchestrator

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Hi! I'm your Brain AI assistant. You can:

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Just type or paste something to get started!

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- - - - \ No newline at end of file diff --git a/cognitive_agents_demo_simple.rs b/cognitive_agents_demo_simple.rs deleted file mode 100644 index 6751fff01675efa0af1ff160f3943cbcf62bf68b..0000000000000000000000000000000000000000 --- a/cognitive_agents_demo_simple.rs +++ /dev/null @@ -1,440 +0,0 @@ -use std::sync::Arc; -use brain_cognitive::{ - agents::{ - traits::{ - BrainAgent, AgentMetadata, AgentInput, AgentOutput, CognitivePreferences, - CognitivePreferenceProfile, ProjectContext, VerbosityLevel, InteractionMode, - DetailLevel, EmotionalSensitivity, AutonomyLevel, CommunicationStyle, - CognitiveLoadSettings, PacingPreference, ExecutionMetadata, ExecutionStatus, - CognitiveContext - }, - registry::{AgentRegistry, AgentQuery}, - }, - conversation::ConversationService, - meta::MetaMemoryRepository, - RagRequest, RagResponse, -}; -use brain_infra::{ - config::BrainConfig, - database::DatabaseConfig, -}; -use brain::{WorkingMemoryRepository, ConceptRepository, InsightRepository}; -use async_trait::async_trait; -use brain_types::error::BrainError; -use std::collections::HashMap; -use tokio; -use tokio::sync::RwLock; -use uuid::Uuid; - -/// Simple meta-memory repository implementation for demo -#[derive(Debug)] -pub struct SimpleMetaMemoryRepository { - items: Arc>>, - component_to_meta: Arc>>, -} - -impl SimpleMetaMemoryRepository { - pub fn new() -> Self { - Self { - items: Arc::new(RwLock::new(HashMap::new())), - component_to_meta: Arc::new(RwLock::new(HashMap::new())), - } - } -} - -#[async_trait] -impl MetaMemoryRepository for SimpleMetaMemoryRepository { - async fn store_item(&mut self, item: brain_cognitive::meta::MetaMemoryItem) -> brain_cognitive::meta::MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_meta.write().await; - - let item_id = item.id; - let component_id = item.component_id; - - items.insert(item_id, item); - component_map.insert(component_id, item_id); - - Ok(item_id) - } - - async fn get_item(&self, id: Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - let items = self.items.read().await; - Ok(items.get(&id).cloned()) - } - - async fn get_item_by_component(&self, component_id: Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - let component_map = self.component_to_meta.read().await; - if let Some(&meta_id) = component_map.get(&component_id) { - self.get_item(meta_id).await - } else { - Ok(None) - } - } - - async fn query_items(&self, _query: &brain_cognitive::meta::MetaMemoryQuery) -> brain_cognitive::meta::MetaMemoryResult> { - let items = self.items.read().await; - Ok(items.values().cloned().collect()) - } - - async fn remove_item(&mut self, id: Uuid) -> brain_cognitive::meta::MetaMemoryResult { - let mut items = self.items.write().await; - Ok(items.remove(&id).is_some()) - } - - async fn batch_update(&mut self, items_to_update: Vec) -> brain_cognitive::meta::MetaMemoryResult> { - let mut ids = Vec::new(); - for item in items_to_update { - let id = self.store_item(item).await?; - ids.push(id); - } - Ok(ids) - } - - async fn count_items(&self) -> brain_cognitive::meta::MetaMemoryResult { - let items = self.items.read().await; - Ok(items.len()) - } - - async fn clear_all(&mut self) -> brain_cognitive::meta::MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_meta.write().await; - let count = items.len(); - items.clear(); - component_map.clear(); - Ok(count) - } -} - -/// Example agent that processes code requests -#[derive(Clone)] -#[derive(Debug)] -pub struct DemoCodeAgent { - metadata: AgentMetadata, - preferences: CognitivePreferences, -} - -impl DemoCodeAgent { - pub fn new() -> Self { - let metadata = AgentMetadata { - id: "demo-code-agent".to_string(), - name: "Demo Code Agent".to_string(), - persona: "A helpful coding assistant that can analyze and generate code".to_string(), - description: "A demonstration agent that showcases code generation and analysis capabilities".to_string(), - version: "1.0.0".to_string(), - supported_input_types: vec!["code_request".to_string(), "code_analysis".to_string()], - supported_output_types: vec!["code_response".to_string(), "analysis_report".to_string()], - capabilities: vec![ - "code_generation".to_string(), - "code_analysis".to_string(), - "refactoring".to_string(), - ], - dependencies: vec![], - tags: vec!["development".to_string(), "coding".to_string()], - base_confidence: 0.8, - }; - - let preferences = CognitivePreferences { - verbosity: VerbosityLevel::Standard, - risk_tolerance: 0.7, - collaboration_preference: 0.8, - learning_enabled: true, - adaptation_rate: 0.1, - creativity_level: 0.6, - detail_level: 0.8, - collaboration_style: "cooperative".to_string(), - }; - - Self { metadata, preferences } - } -} - -#[async_trait] -impl BrainAgent for DemoCodeAgent { - async fn execute( - &self, - input: AgentInput, - _context: &CognitiveContext, - ) -> Result { - println!("๐Ÿค– Demo Code Agent executing with input: {}", input.input_type); - - let start_time = std::time::Instant::now(); - - // Simulate some processing based on the input type - let (content, confidence) = match input.input_type.as_str() { - "code_request" => { - let response = format!( - "Generated code for: {}\n\n```rust\nfn example() {{\n println!(\"Hello, Brain AI!\");\n}}\n```", - input.content - ); - (response, 0.85) - } - "code_analysis" => { - let analysis = format!( - "Code analysis for: {}\n\nThe code appears to be well-structured with good practices.", - input.content - ); - (analysis, 0.9) - } - _ => { - return Err(BrainError::InvalidInput { - message: format!("Unsupported input type: {}", input.input_type), - context: None - }); - } - }; - - let execution_time = start_time.elapsed().as_millis() as u64; - - let output = AgentOutput::new( - self.metadata.id.clone(), - "code_response".to_string(), - content, - confidence, - ) - .with_reasoning("Processed code request using demo logic".to_string()) - .with_next_actions(vec!["test_code".to_string(), "review_code".to_string()]); - - // Update execution metadata - let mut output = output; - output.execution_metadata = ExecutionMetadata { - execution_time_ms: execution_time, - memory_usage_mb: 1.2, - api_calls: 0, - status: ExecutionStatus::Success, - warnings: vec![], - }; - - println!("โœ… Agent completed execution in {}ms", execution_time); - Ok(output) - } - - fn metadata(&self) -> &AgentMetadata { - &self.metadata - } - - fn confidence_threshold(&self) -> f32 { - 0.6 - } - - fn cognitive_preferences(&self) -> &CognitivePreferences { - &self.preferences - } - - async fn assess_confidence( - &self, - input: &AgentInput, - _context: &CognitiveContext, - ) -> Result { - // Simple confidence assessment based on input type - let confidence = match input.input_type.as_str() { - "code_request" => 0.85, - "code_analysis" => 0.9, - _ => 0.5, - }; - Ok(confidence) - } -} - -/// Mock conversation service for demo -#[derive(Clone, Debug)] -pub struct MockConversationService; - -#[async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: RagRequest, - _working_memory: &mut dyn WorkingMemoryRepository, - _concept_repo: &mut dyn ConceptRepository, - _insight_repo: &mut dyn InsightRepository, - ) -> Result { - Ok(RagResponse { - response: "Mock conversation response".to_string(), - conversation_id: "mock".to_string(), - context_used: vec![], - confidence_score: 0.8, - response_quality: brain_cognitive::ResponseQuality { - factual_grounding: 0.8, - coherence: 0.85, - relevance: 0.9, - safety_score: 0.95, - source_attribution: 0.7, - consistency_score: 0.8, - completeness: 0.8, - clarity: 0.85, - toxicity_score: 0.05, - bias_score: 0.1, - hallucination_risk: 0.1, - confidence_calibration: 0.8, - }, - }) - } - - fn get_conversation_stats(&self) -> HashMap { - let mut stats = HashMap::new(); - stats.insert("total_conversations".to_string(), 1); - stats - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -#[tokio::main] -async fn main() -> Result<(), BrainError> { - println!("๐Ÿง  Brain AI Cognitive Agents Demo (Simplified)"); - println!("===============================================\n"); - - // Initialize infrastructure components (simplified) - let _config = BrainConfig::default(); - let _db_config = DatabaseConfig::default(); - let meta_memory: Arc> = - Arc::new(tokio::sync::RwLock::new(SimpleMetaMemoryRepository::new())); - let conversation_service: Arc = - Arc::new(MockConversationService); - - // Create project context - let project_context = ProjectContext::rust_project( - "Brain AI".to_string(), - "0.8.0".to_string(), - ) - .with_technology("Tokio".to_string()) - .with_technology("Brain AI".to_string()) - .with_description("Advanced cognitive AI system with agent-based architecture".to_string()); - - // Create cognitive preference profile - let cognitive_profile = CognitivePreferenceProfile { - interaction_mode: InteractionMode::Collaborative, - detail_level: DetailLevel::Standard, - emotional_sensitivity: EmotionalSensitivity::Medium, - autonomy_level: AutonomyLevel::SemiAuto, - communication_style: CommunicationStyle::Technical, - cognitive_load_settings: CognitiveLoadSettings { - max_items_per_chunk: 5, - pacing_preference: PacingPreference::Medium, - progressive_disclosure: true, - }, - }; - - // Build cognitive context manually - let mut config = HashMap::new(); - config.insert("demo_mode".to_string(), serde_json::Value::Bool(true)); - - let context = CognitiveContext { - meta_memory, - conversation_service, - project_context, - cognitive_profile, - session_history: Vec::new(), - config, - working_directory: std::env::current_dir().unwrap_or_else(|_| std::path::PathBuf::from(".")), - }; - - println!("โœ… Cognitive context initialized"); - println!(" Project: {}", context.project_context.project_name); - println!(" Tech Stack: {:?}", context.project_context.tech_stack); - println!(" Interaction Mode: {:?}", context.cognitive_profile.interaction_mode); - println!(" Detail Level: {:?}", context.cognitive_profile.detail_level); - println!(); - - // Create agent registry - let registry = AgentRegistry::new(); - - // Create and register demo agent - let demo_agent = Arc::new(DemoCodeAgent::new()) as Arc; - registry.register_agent(demo_agent.clone())?; - - println!("โœ… Registered demo agent: {}", demo_agent.metadata().name); - println!(" Capabilities: {:?}", demo_agent.metadata().capabilities); - println!(" Confidence Threshold: {}", demo_agent.confidence_threshold()); - println!(); - - // Demonstrate agent discovery - println!("๐Ÿ” Agent Discovery Demo"); - println!("-----------------------"); - - let query = AgentQuery::new() - .with_input_type("code_request".to_string()) - .with_capability("code_generation".to_string()); - - let discovered_agents = registry.discover_agents(&query)?; - println!("Found {} agent(s) for code_request with code_generation capability", - discovered_agents.len()); - - for agent in &discovered_agents { - println!(" - {}: {}", agent.metadata().name, agent.metadata().persona); - } - println!(); - - // Demonstrate agent execution - println!("๐Ÿš€ Agent Execution Demo"); - println!("-----------------------"); - - let input = AgentInput::new( - "code_request".to_string(), - "Create a function to calculate fibonacci numbers".to_string(), - "demo-session".to_string(), - ); - - println!("๐Ÿ“ Input: {} - {}", input.input_type, input.content); - - // Execute the agent - if let Some(agent) = discovered_agents.first() { - // Check confidence first - let confidence = agent.assess_confidence(&input, &context).await?; - println!("๐ŸŽฏ Agent confidence: {:.2}", confidence); - - if confidence >= agent.confidence_threshold() { - let output = agent.execute(input.clone(), &context).await?; - - println!("๐Ÿ“ค Output:"); - println!(" Type: {}", output.output_type); - println!(" Confidence: {:.2}", output.confidence); - println!(" Execution Time: {}ms", output.execution_metadata.execution_time_ms); - println!(" Content Preview: {}", - output.content.chars().take(100).collect::()); - - if let Some(reasoning) = &output.reasoning { - println!(" Reasoning: {}", reasoning); - } - - if !output.next_actions.is_empty() { - println!(" Suggested Next Actions: {:?}", output.next_actions); - } - } else { - println!("โŒ Agent confidence ({:.2}) below threshold ({:.2})", - confidence, agent.confidence_threshold()); - } - } - println!(); - - // Demonstrate registry statistics - println!("๐Ÿ“Š Registry Statistics"); - println!("---------------------"); - let stats = registry.get_statistics()?; - println!("Total Agents: {}", stats.total_agents); - println!("Total Capabilities: {}", stats.total_capabilities); - println!("Total Input Types: {}", stats.total_input_types); - println!("Agents by Category: {:?}", stats.agents_by_category); - println!(); - - println!("๐ŸŽ‰ Demo completed successfully!"); - println!("\n๐Ÿš€ Phase 1 Complete: Core Agent Infrastructure"); - println!("=============================================="); - println!("โœ… Agent trait system with async execution"); - println!("โœ… Agent metadata and capability system"); - println!("โœ… Cognitive preference profiles (CPP)"); - println!("โœ… Agent registry with discovery"); - println!("โœ… Cognitive context for shared execution environment"); - println!("โœ… Integration with existing Brain AI infrastructure"); - - println!("\n๐Ÿ“‹ Next Implementation Steps:"); - println!("1. Phase 2: Implement specialized agents (PlannerAgent, ArchitectAgent, etc.)"); - println!("2. Phase 3: Add agent orchestration with DAG execution engine"); - println!("3. Phase 4: Implement agent-specific memory and learning integration"); - println!("4. Phase 5: Add self-evolution and meta-agent capabilities"); - println!("5. Phase 6: Full cognitive preference adaptation and personalization"); - - Ok(()) -} \ No newline at end of file diff --git a/cognitive_integration_test.rs b/cognitive_integration_test.rs deleted file mode 100644 index c7b584f26561ff3806883adb6c2d1fa06907ef31..0000000000000000000000000000000000000000 --- a/cognitive_integration_test.rs +++ /dev/null @@ -1,167 +0,0 @@ -//! # Cognitive Integration Test -//! -//! This test verifies the end-to-end integration of cognitive capabilities -//! (brain-sast, brain-dota-rag) with the brain-chat system. - -use anyhow::Result; -use brain_chat::{ - ConversationEngine, ConversationEngineConfig, - ResponseGeneratorConfig, ConversationManagerConfig, -}; -use brain_csm::Platform; -use brain_benchmark::{ - BenchmarkOrchestrator, BenchmarkOrchestratorConfig, ExecutionEngine, - ExecutionEngineConfig, ResultAnalyzer, ResultAnalyzerConfig, -}; -use brain_cognitive::AgentOrchestrator; -use std::sync::Arc; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Cognitive Integration Test"); - println!("============================="); - println!(); - - // Test 1: Basic Component Creation - println!("๐Ÿ”ง Test 1: Basic Component Creation"); - println!("----------------------------------"); - - let basic_test_passed = test_basic_component_creation().await?; - if basic_test_passed { - println!("โœ… Basic component creation works correctly"); - } else { - println!("โŒ Basic component creation has issues"); - } - println!(); - - // Test 2: Brain-Chat Integration - println!("๐Ÿ”ง Test 2: Brain-Chat Integration"); - println!("--------------------------------"); - - let chat_test_passed = test_brain_chat_integration().await?; - if chat_test_passed { - println!("โœ… Brain-Chat integration works correctly"); - } else { - println!("โŒ Brain-Chat integration has issues"); - } - println!(); - - // Test 3: Brain-Benchmark Integration - println!("๐Ÿ”ง Test 3: Brain-Benchmark Integration"); - println!("------------------------------------"); - - let benchmark_test_passed = test_brain_benchmark_integration().await?; - if benchmark_test_passed { - println!("โœ… Brain-Benchmark integration works correctly"); - } else { - println!("โŒ Brain-Benchmark integration has issues"); - } - println!(); - - // Summary - println!("๐Ÿ“Š Integration Test Summary"); - println!("========================="); - println!("Basic Components: {}", if basic_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Brain-Chat: {}", if chat_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Brain-Benchmark: {}", if benchmark_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - - let all_tests_passed = basic_test_passed && chat_test_passed && benchmark_test_passed; - println!(); - println!("๐ŸŽฏ Overall Result: {}", if all_tests_passed { "โœ… ALL TESTS PASSED" } else { "โŒ SOME TESTS FAILED" }); - - if all_tests_passed { - println!("๐Ÿš€ Cognitive capabilities are successfully integrated!"); - println!(" Ready for Phase 1 Validation completion."); - } else { - println!("๐Ÿ”ง Some integration issues remain."); - println!(" Please check the test output above for details."); - } - - Ok(()) -} - -/// Test basic component creation -async fn test_basic_component_creation() -> Result { - println!(" Testing basic component creation..."); - - // Test AgentOrchestrator creation - let _agent_orchestrator = AgentOrchestrator::new(); - println!(" โœ“ AgentOrchestrator created successfully"); - - // Test ExecutionEngine creation - let exec_engine_config = ExecutionEngineConfig::default(); - let _execution_engine = ExecutionEngine::new(exec_engine_config).await?; - println!(" โœ“ ExecutionEngine created successfully"); - - // Test ResultAnalyzer creation - let analyzer_config = ResultAnalyzerConfig::default(); - let _result_analyzer = ResultAnalyzer::new(analyzer_config); - println!(" โœ“ ResultAnalyzer created successfully"); - - Ok(true) -} - -/// Test brain-chat integration -async fn test_brain_chat_integration() -> Result { - println!(" Testing brain-chat components..."); - - // Create conversation engine config with simpler setup - let config = ConversationEngineConfig { - response_generator: ResponseGeneratorConfig::default(), - conversation_manager: ConversationManagerConfig::default(), - enable_learning: true, - enable_personality_adaptation: true, - response_timeout_ms: 10000, - max_context_tokens: 2000, - }; - - // Create conversation engine - let engine = ConversationEngine::new(config).await?; - println!(" โœ“ ConversationEngine created successfully"); - - // Start conversation - let session_id = engine.start_conversation( - Some("test_user_cognitive".to_string()), - Platform::CLI, - ).await?; - println!(" โœ“ Conversation session started: {}", session_id); - - // Test basic message processing - let response = engine.process_message( - &session_id, - "Hello, can you help me with coding?".to_string(), - ).await?; - - println!(" ๐Ÿ“ Response received: {}", response.content.chars().take(100).collect::()); - println!(" ๐Ÿ”ฅ Confidence: {:.2}", response.confidence); - - // Check if response is reasonable - let has_reasonable_response = response.content.len() > 10 && response.confidence > 0.0; - - Ok(has_reasonable_response) -} - -/// Test brain-benchmark integration -async fn test_brain_benchmark_integration() -> Result { - println!(" Testing brain-benchmark components..."); - - // Create benchmark components - let exec_engine_config = ExecutionEngineConfig::default(); - let execution_engine = Arc::new(ExecutionEngine::new(exec_engine_config).await?); - - let analyzer_config = ResultAnalyzerConfig::default(); - let result_analyzer = Arc::new(ResultAnalyzer::new(analyzer_config)); - - let orchestrator_config = BenchmarkOrchestratorConfig::default(); - let _orchestrator = BenchmarkOrchestrator::new( - orchestrator_config, - execution_engine, - result_analyzer, - ); - println!(" โœ“ BenchmarkOrchestrator created successfully"); - - // Test that components can be created and basic operations work - println!(" โœ“ All benchmark components operational"); - - Ok(true) -} \ No newline at end of file diff --git a/concept_graph.html b/concept_graph.html deleted file mode 100644 index 3d3c37d4a6bd48d863aa33093039002d3759247b..0000000000000000000000000000000000000000 --- a/concept_graph.html +++ /dev/null @@ -1,717 +0,0 @@ - - - - - - Brain AI - Concept Graph Visualization - - - - -
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- - - - \ No newline at end of file diff --git a/concept_graph_demo.rs b/concept_graph_demo.rs deleted file mode 100644 index 2c586778b17820544148edd67b20ebe3ef24069d..0000000000000000000000000000000000000000 --- a/concept_graph_demo.rs +++ /dev/null @@ -1,504 +0,0 @@ -use anyhow::Result; -use brain::concept_graph::{ - ConceptGraphManager, ConceptGraphConfig, ConceptNode, ConceptType, ConceptQuery, - RelationshipType, RelationshipQuery, HebbianConfig, ConceptRepository, - RelationshipRepository, ConceptRelationship -}; -use tokio; -use uuid::Uuid; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain Concept Graph Engine Demo - Task 4.1"); - println!("==============================================="); - println!(); - - // Initialize the concept graph manager - let config = ConceptGraphConfig { - uri: "neo4j://localhost:7687".to_string(), - username: "neo4j".to_string(), - password: "password".to_string(), - database: Some("brain_demo".to_string()), - pool_size: 5, - timeout_seconds: 30, - }; - - println!("๐Ÿ“ก Attempting to connect to Neo4j database..."); - println!(" URI: {}", config.uri); - println!(" Database: {:?}", config.database); - - // Try to connect to Neo4j - match ConceptGraphManager::new(config).await { - Ok(manager) => { - println!("โœ… Successfully connected to Neo4j!"); - println!(); - - // Run the full demonstration - run_concept_graph_demo(manager).await?; - } - Err(e) => { - println!("โŒ Failed to connect to Neo4j: {}", e); - println!(); - println!("๐Ÿ”ง To run this demo, you need:"); - println!(" 1. Neo4j database running on localhost:7687"); - println!(" 2. Username: neo4j"); - println!(" 3. Password: password"); - println!(" 4. Optional: Create a database named 'brain_demo'"); - println!(); - println!("๐Ÿ“š Neo4j Installation:"); - println!(" โ€ข Download from: https://neo4j.com/download/"); - println!(" โ€ข Or use Docker: docker run -p 7474:7474 -p 7687:7687 neo4j"); - println!(); - println!("๐Ÿ”„ Running offline demonstration instead..."); - println!(); - - // Run offline demo showing data structures - run_offline_demo()?; - } - } - - Ok(()) -} - -async fn run_concept_graph_demo(mut manager: ConceptGraphManager) -> Result<()> { - println!("๐ŸŽฏ Phase 1: Creating Concept Nodes"); - println!("====================================="); - - // Create various types of concept nodes - let concepts = vec![ - ConceptNode::new( - ConceptType::Entity, - "user".to_string(), - 0.95, - Some("segment_discovery".to_string()), - ), - ConceptNode::new( - ConceptType::Action, - "learns".to_string(), - 0.88, - Some("memory_consolidation".to_string()), - ), - ConceptNode::new( - ConceptType::Attribute, - "intelligent".to_string(), - 0.82, - Some("semantic_memory".to_string()), - ), - ConceptNode::new( - ConceptType::Abstract, - "knowledge".to_string(), - 0.90, - Some("episodic_memory".to_string()), - ), - ConceptNode::new( - ConceptType::Relation, - "belongs_to".to_string(), - 0.75, - Some("concept_formation".to_string()), - ), - ]; - - let mut created_ids = Vec::new(); - - for (i, mut concept) in concepts.into_iter().enumerate() { - // Add some metadata - concept.set_metadata("demo_phase".to_string(), "1".to_string()); - concept.set_metadata("creation_order".to_string(), i.to_string()); - - let id = manager.create_concept(concept.clone()).await?; - created_ids.push(id); - - println!(" โœ… Created {} concept: '{}' (ID: {})", - concept.concept_type, concept.content, id); - println!(" Confidence: {:.2}, Source: {:?}", - concept.confidence_score, concept.source_reference); - } - - println!("๐Ÿ“Š Created {} concept nodes successfully!", created_ids.len()); - println!(); - - println!("๐Ÿ” Phase 2: Retrieving and Updating Concepts"); - println!("=============================================="); - - // Retrieve and update concepts - for (i, &id) in created_ids.iter().enumerate() { - if let Some(mut concept) = manager.get_concept(id).await? { - println!(" ๐Ÿ“– Retrieved: '{}' ({})", concept.content, concept.concept_type); - - // Mark as accessed multiple times to simulate usage - for _ in 0..=i { - manager.mark_concept_accessed(id).await?; - } - - // Update confidence based on usage - let new_confidence = (concept.confidence_score + 0.05).min(1.0); - concept.update_confidence(new_confidence); - - manager.update_concept(&concept).await?; - println!(" ๐Ÿ”„ Updated confidence to {:.2}", new_confidence); - } - } - println!(); - - println!("๐Ÿ“ˆ Phase 3: Querying and Filtering Concepts"); - println!("============================================="); - - // Query high-confidence entity concepts - let entity_query = ConceptQuery { - concept_type: Some(ConceptType::Entity), - min_confidence: Some(0.9), - limit: Some(10), - sort_by: Some("confidence_score".to_string()), - descending: true, - ..Default::default() - }; - - println!("๐Ÿ”Ž Querying Entity concepts with confidence >= 0.9:"); - println!(" Query: type={:?}, min_confidence={:?}, limit={:?}", - entity_query.concept_type, entity_query.min_confidence, entity_query.limit); - - // Try to execute the query - match manager.query_concepts(&entity_query).await { - Ok(results) => { - println!(" โœ… Found {} matching concepts", results.len()); - for (i, concept) in results.iter().take(3).enumerate() { - println!(" {}. '{}' (confidence: {:.2})", i + 1, concept.content, concept.confidence_score); - } - } - Err(e) => { - println!(" โš ๏ธ Query failed (expected with in-memory storage): {}", e); - } - } - println!(); - - println!("๐Ÿ“Š Phase 4: Graph Statistics and Analysis"); - println!("=========================================="); - - let stats = manager.get_statistics().await?; - println!(" ๐Ÿ“ˆ Total Concepts: {}", stats.total_concepts); - println!(" ๐ŸŽฏ Average Confidence: {:.3}", stats.average_confidence); - println!(" โญ High Confidence Concepts: {}", stats.high_confidence_concepts); - println!(" ๐Ÿ”— Total Relationships: {}", stats.total_relationships); - - println!(" ๐Ÿ“‹ Concepts by Type:"); - for (concept_type, count) in &stats.concepts_by_type { - println!(" โ€ข {}: {}", concept_type, count); - } - - if let Some(age) = stats.newest_concept_age_seconds { - println!(" ๐Ÿ•’ Newest concept age: {} seconds", age); - } - - if let Some(age) = stats.last_access_age_seconds { - println!(" ๐Ÿ‘๏ธ Last access age: {} seconds", age); - } - println!(); - - // Run relationship demo with the created concepts - run_relationship_demo(&mut manager, &created_ids).await?; - - println!("๐Ÿงน Phase 8: Cleanup"); - println!("===================="); - - println!(" ๐Ÿ“Š Current concept count: {}", manager.concept_count()); - - // Clean up demo data - for &id in &created_ids { - if manager.delete_concept(id).await? { - println!(" โœ… Deleted concept: {}", id); - } - } - - println!("๐ŸŽ‰ Concept Graph Demo completed successfully!"); - println!(); - - Ok(()) -} - -async fn run_relationship_demo(manager: &mut ConceptGraphManager, concept_ids: &[Uuid]) -> Result<()> { - println!("๐Ÿ”— Phase 5: Relationship Management & Hebbian Learning"); - println!("====================================================="); - - if concept_ids.len() < 3 { - println!("โš ๏ธ Need at least 3 concepts for relationship demo"); - return Ok(()); - } - - // Create various types of relationships - println!(" ๐Ÿ—๏ธ Creating Relationships:"); - - let relationships = vec![ - (concept_ids[0], concept_ids[3], RelationshipType::IsA, 0.8, "user IS_A knowledge entity"), - (concept_ids[1], concept_ids[0], RelationshipType::Uses, 0.7, "learns USES user"), - (concept_ids[2], concept_ids[3], RelationshipType::PartOf, 0.6, "intelligent PART_OF knowledge"), - (concept_ids[0], concept_ids[2], RelationshipType::Has, 0.75, "user HAS intelligent"), - (concept_ids[1], concept_ids[2], RelationshipType::Causes, 0.65, "learns CAUSES intelligent"), - ]; - - let mut relationship_ids = Vec::new(); - - for (source, target, rel_type, weight, description) in relationships { - let relationship = ConceptRelationship::new(source, target, rel_type.clone(), weight); - let rel_id = manager.create_relationship(relationship).await?; - relationship_ids.push(rel_id); - println!(" โœ… {}", description); - println!(" Weight: {:.2}, ID: {}", weight, rel_id); - } - - println!(" ๐Ÿ”— Created {} relationships successfully!", relationship_ids.len()); - println!(); - - println!("๐Ÿง  Phase 6: Hebbian Learning Simulation"); - println!("========================================="); - - // Simulate co-activation and learning - println!(" โšก Simulating concept co-activations:"); - - // Activate some relationships multiple times to simulate learning - for (i, &rel_id) in relationship_ids.iter().enumerate() { - let activations = (i + 1) * 2; // Different activation patterns - - for _ in 0..activations { - manager.activate_relationship(rel_id).await?; - } - - if let Some(relationship) = manager.get_relationship(rel_id).await? { - println!(" ๐Ÿ”ฅ Relationship {} activated {} times, weight: {:.3} โ†’ {:.3}", - i + 1, activations, 0.6 + (i as f64 * 0.05), relationship.weight); - } - } - - // Test co-activation between concepts - let co_activations = manager.co_activate_concepts(concept_ids[0], concept_ids[3]).await?; - println!(" ๐Ÿค Co-activated {} concepts related to key concepts", co_activations.len()); - println!(); - - println!("๐Ÿ“Š Phase 7: Network Analysis & Metrics"); - println!("======================================"); - - // Get network metrics - let metrics = manager.get_network_metrics().await?; - - println!(" ๐Ÿ“ˆ Network Statistics:"); - println!(" โ€ข Total Relationships: {}", metrics.total_relationships); - println!(" โ€ข Average Weight: {:.3}", metrics.average_weight); - println!(" โ€ข Strong Relationships (โ‰ฅ0.7): {}", metrics.strong_relationships); - println!(" โ€ข Weak Relationships (<0.3): {}", metrics.weak_relationships); - println!(" โ€ข Isolated Concepts: {}", metrics.isolated_concepts); - println!(" โ€ข Average Degree: {:.2}", metrics.average_degree); - println!(" โ€ข Clustering Coefficient: {:.3}", metrics.clustering_coefficient); - - println!(" ๐Ÿ“‹ Relationships by Type:"); - for (rel_type, count) in &metrics.relationships_by_type { - println!(" โ€ข {}: {}", rel_type, count); - } - - if !metrics.most_connected_concepts.is_empty() { - println!(" ๐Ÿ† Most Connected Concepts:"); - for (concept_id, degree) in metrics.most_connected_concepts.iter().take(3) { - println!(" โ€ข {}: {} connections", concept_id, degree); - } - } - println!(); - - // Demonstrate relationship querying - println!("๐Ÿ” Relationship Query Examples:"); - println!("-------------------------------"); - - // Query by relationship type - let is_a_query = RelationshipQuery { - relationship_type: Some(RelationshipType::IsA), - ..Default::default() - }; - - let is_a_rels = manager.query_relationships(&is_a_query).await?; - println!(" ๐Ÿ”Ž IS_A relationships: {}", is_a_rels.len()); - - // Query strong relationships - let strong_query = RelationshipQuery { - min_weight: Some(0.7), - sort_by: Some("weight".to_string()), - descending: true, - ..Default::default() - }; - - let strong_rels = manager.query_relationships(&strong_query).await?; - println!(" ๐Ÿ’ช Strong relationships (โ‰ฅ0.7): {}", strong_rels.len()); - - for rel in strong_rels.iter().take(3) { - println!(" โ€ข {} โ†’ {} ({}, weight: {:.3})", - rel.source_id, rel.target_id, rel.relationship_type, rel.weight); - } - println!(); - - // Demonstrate decay and pruning - println!("๐Ÿ•’ Decay & Pruning Simulation:"); - println!("------------------------------"); - - println!(" โฐ Applying 24-hour decay to all relationships..."); - let decayed = manager.apply_decay_to_all_relationships(24.0).await?; - println!(" ๐Ÿ“‰ {} relationships affected by decay", decayed); - - println!(" โœ‚๏ธ Pruning weak relationships (threshold: 0.1)..."); - let pruned = manager.prune_weak_relationships().await?; - println!(" ๐Ÿ—‘๏ธ Pruned {} weak relationships", pruned); - - println!(" ๐Ÿ“Š Relationships remaining: {}", manager.relationship_count()); - println!(); - - // Demonstrate Hebbian configuration - println!("โš™๏ธ Hebbian Learning Configuration:"); - println!("------------------------------------"); - - let hebbian_config = manager.hebbian_config(); - println!(" ๐ŸŽ›๏ธ Current Settings:"); - println!(" โ€ข Learning Rate: {:.3}", hebbian_config.default_learning_rate); - println!(" โ€ข Decay Rate: {:.3}", hebbian_config.default_decay_rate); - println!(" โ€ข Pruning Threshold: {:.3}", hebbian_config.default_pruning_threshold); - println!(" โ€ข Max Relationships/Concept: {}", hebbian_config.max_relationships_per_concept); - println!(" โ€ข Co-activation Window: {} minutes", hebbian_config.co_activation_window_minutes); - - // Update configuration - let mut new_config = HebbianConfig::default(); - new_config.default_learning_rate = 0.15; - new_config.default_decay_rate = 0.005; - manager.set_hebbian_config(new_config); - - println!(" ๐Ÿ”„ Updated learning rate to 0.15 and decay rate to 0.005"); - println!(); - - Ok(()) -} - -fn run_offline_demo() -> Result<()> { - println!("๐Ÿ’ป Offline Concept Graph Structure Demo"); - println!("========================================"); - println!(); - - println!("๐Ÿ—๏ธ Core Data Structures:"); - println!("---------------------------"); - - // Demonstrate ConceptNode creation and usage - let mut concept = ConceptNode::new( - ConceptType::Entity, - "artificial_intelligence".to_string(), - 0.92, - Some("semantic_memory_consolidation".to_string()), - ); - - println!("โœ… Created ConceptNode:"); - println!(" ID: {}", concept.id); - println!(" Type: {}", concept.concept_type); - println!(" Content: '{}'", concept.content); - println!(" Confidence: {:.2}", concept.confidence_score); - println!(" Source: {:?}", concept.source_reference); - println!(" Created: {}", concept.created_at.format("%Y-%m-%d %H:%M:%S UTC")); - println!(" Usage Count: {}", concept.usage_count); - println!(); - - // Demonstrate metadata management - println!("๐Ÿท๏ธ Metadata Management:"); - println!("-------------------------"); - concept.set_metadata("domain".to_string(), "computer_science".to_string()); - concept.set_metadata("complexity".to_string(), "high".to_string()); - concept.set_metadata("relevance".to_string(), "core".to_string()); - - println!(" Added metadata:"); - for (key, value) in &concept.metadata { - println!(" โ€ข {}: {}", key, value); - } - println!(); - - // Demonstrate access tracking - println!("๐Ÿ“Š Access Tracking:"); - println!("-------------------"); - println!(" Before access - Usage: {}, Last accessed: {}", - concept.usage_count, concept.last_accessed_at.format("%H:%M:%S")); - - std::thread::sleep(std::time::Duration::from_millis(100)); - concept.mark_accessed(); - - println!(" After access - Usage: {}, Last accessed: {}", - concept.usage_count, concept.last_accessed_at.format("%H:%M:%S")); - println!(); - - // Demonstrate confidence updates - println!("๐ŸŽฏ Confidence Management:"); - println!("-------------------------"); - println!(" Initial confidence: {:.2}", concept.confidence_score); - - concept.update_confidence(1.2); // Should clamp to 1.0 - println!(" After setting to 1.2: {:.2} (clamped)", concept.confidence_score); - - concept.update_confidence(-0.1); // Should clamp to 0.0 - println!(" After setting to -0.1: {:.2} (clamped)", concept.confidence_score); - - concept.update_confidence(0.85); // Normal update - println!(" After setting to 0.85: {:.2}", concept.confidence_score); - println!(); - - // Demonstrate different concept types - println!("๐ŸŽญ Concept Types:"); - println!("-----------------"); - let types = vec![ - (ConceptType::Entity, "Object, person, place, or thing"), - (ConceptType::Action, "Verb, process, or behavior"), - (ConceptType::Attribute, "Property, quality, or characteristic"), - (ConceptType::Abstract, "Idea, emotion, or state"), - (ConceptType::Relation, "Connection or relationship"), - ]; - - for (concept_type, description) in types { - println!(" โ€ข {}: {}", concept_type, description); - } - println!(); - - // Demonstrate configuration - println!("โš™๏ธ Configuration Options:"); - println!("--------------------------"); - let config = ConceptGraphConfig::default(); - println!(" Default Neo4j URI: {}", config.uri); - println!(" Default Username: {}", config.username); - println!(" Default Pool Size: {}", config.pool_size); - println!(" Default Timeout: {} seconds", config.timeout_seconds); - println!(); - - // Demonstrate query parameters - println!("๐Ÿ” Query Capabilities:"); - println!("----------------------"); - let query = ConceptQuery { - concept_type: Some(ConceptType::Entity), - min_confidence: Some(0.8), - max_confidence: Some(1.0), - content_pattern: Some("intelligence".to_string()), - min_usage_count: Some(5), - limit: Some(20), - sort_by: Some("confidence".to_string()), - descending: true, - embedding: None, - min_similarity: None, - }; - - println!(" Example Query Parameters:"); - println!(" โ€ข Type filter: {:?}", query.concept_type); - println!(" โ€ข Confidence range: {:.1} - {:.1}", - query.min_confidence.unwrap(), query.max_confidence.unwrap()); - println!(" โ€ข Content pattern: '{}'", query.content_pattern.as_ref().unwrap()); - println!(" โ€ข Min usage count: {}", query.min_usage_count.unwrap()); - println!(" โ€ข Result limit: {}", query.limit.unwrap()); - println!(" โ€ข Sort by: {} ({})", - query.sort_by.as_ref().unwrap(), - if query.descending { "DESC" } else { "ASC" }); - println!(); - - println!("๐ŸŽฏ Next Steps:"); - println!("--------------"); - println!(" 1. Set up Neo4j database to enable full functionality"); - println!(" 2. Implement Task 4.2: Relationship management and Hebbian learning"); - println!(" 3. Create Task 4.3: Graph traversal algorithms and concept formation"); - println!(); - - println!("โœจ Concept Graph foundation is ready for Neo4j integration!"); - - Ok(()) -} \ No newline at end of file diff --git a/context_matrix.json b/context_matrix.json deleted file mode 100644 index d3ac66c3194f69d585a125edc2240e181b0ad279..0000000000000000000000000000000000000000 --- a/context_matrix.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "co_occurrence": { - "s|t": 3, - "e|s": 2, - "k|u": 2, - "c|k": 2, - " |b": 2, - "c|u": 1, - "b|t": 1, - "k|p": 1, - "p|u": 1, - "e|t": 2, - "b|c": 1, - "a|k": 1, - " |a": 1, - "a|c": 2, - " |t": 2, - " |s": 1, - "a|b": 2 - }, - "total_observations": 27 -} \ No newline at end of file diff --git a/crates/.DS_Store b/crates/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..baf232293439154900784ecde5189e9f53bffdfd Binary files /dev/null and b/crates/.DS_Store differ diff --git a/brain-analysis/Cargo.toml 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index 100% rename from brain-benchmark/src/domain/results.rs rename to crates/brain-benchmark/src/domain/results.rs diff --git a/brain-benchmark/src/domain/solution.rs b/crates/brain-benchmark/src/domain/solution.rs similarity index 100% rename from brain-benchmark/src/domain/solution.rs rename to crates/brain-benchmark/src/domain/solution.rs diff --git a/brain-benchmark/src/infrastructure/cognitive_repository.rs b/crates/brain-benchmark/src/infrastructure/cognitive_repository.rs similarity index 100% rename from brain-benchmark/src/infrastructure/cognitive_repository.rs rename to crates/brain-benchmark/src/infrastructure/cognitive_repository.rs diff --git a/brain-benchmark/src/infrastructure/meta_memory_repository.rs b/crates/brain-benchmark/src/infrastructure/meta_memory_repository.rs similarity index 100% rename from brain-benchmark/src/infrastructure/meta_memory_repository.rs rename to crates/brain-benchmark/src/infrastructure/meta_memory_repository.rs diff --git a/brain-benchmark/src/infrastructure/mod.rs b/crates/brain-benchmark/src/infrastructure/mod.rs similarity index 100% rename from brain-benchmark/src/infrastructure/mod.rs rename to crates/brain-benchmark/src/infrastructure/mod.rs diff --git a/brain-benchmark/src/infrastructure/quality_repository.rs b/crates/brain-benchmark/src/infrastructure/quality_repository.rs similarity index 100% rename from brain-benchmark/src/infrastructure/quality_repository.rs rename to crates/brain-benchmark/src/infrastructure/quality_repository.rs diff --git a/brain-benchmark/src/lib.rs b/crates/brain-benchmark/src/lib.rs similarity index 100% rename from brain-benchmark/src/lib.rs rename to crates/brain-benchmark/src/lib.rs diff --git a/brain-benchmark/src/validation_demo.rs b/crates/brain-benchmark/src/validation_demo.rs similarity index 100% rename from brain-benchmark/src/validation_demo.rs rename to crates/brain-benchmark/src/validation_demo.rs diff --git a/brain-benchmark/tests/integration_tests.rs b/crates/brain-benchmark/tests/integration_tests.rs similarity index 100% rename from brain-benchmark/tests/integration_tests.rs rename to crates/brain-benchmark/tests/integration_tests.rs diff --git a/brain-chat/Cargo.toml b/crates/brain-chat/Cargo.toml similarity index 100% rename from brain-chat/Cargo.toml rename to crates/brain-chat/Cargo.toml diff --git a/brain-chat/src/chat_interface.rs b/crates/brain-chat/src/chat_interface.rs similarity index 100% rename from brain-chat/src/chat_interface.rs rename to crates/brain-chat/src/chat_interface.rs diff --git a/brain-chat/src/conversation_manager.rs b/crates/brain-chat/src/conversation_manager.rs similarity index 100% rename from brain-chat/src/conversation_manager.rs rename to crates/brain-chat/src/conversation_manager.rs diff --git a/brain-chat/src/dialogue_agents/active_agent.rs b/crates/brain-chat/src/dialogue_agents/active_agent.rs similarity index 100% rename from brain-chat/src/dialogue_agents/active_agent.rs rename to crates/brain-chat/src/dialogue_agents/active_agent.rs diff --git a/brain-chat/src/dialogue_agents/agent_coordinator.rs b/crates/brain-chat/src/dialogue_agents/agent_coordinator.rs similarity index 100% rename from brain-chat/src/dialogue_agents/agent_coordinator.rs rename to crates/brain-chat/src/dialogue_agents/agent_coordinator.rs diff --git a/brain-chat/src/dialogue_agents/ended_agent.rs b/crates/brain-chat/src/dialogue_agents/ended_agent.rs similarity index 100% rename from brain-chat/src/dialogue_agents/ended_agent.rs rename to crates/brain-chat/src/dialogue_agents/ended_agent.rs diff --git a/brain-chat/src/dialogue_agents/error_recovery_agent.rs b/crates/brain-chat/src/dialogue_agents/error_recovery_agent.rs similarity index 100% rename from brain-chat/src/dialogue_agents/error_recovery_agent.rs rename to crates/brain-chat/src/dialogue_agents/error_recovery_agent.rs diff --git a/brain-chat/src/dialogue_agents/initial_agent.rs b/crates/brain-chat/src/dialogue_agents/initial_agent.rs similarity index 100% rename from brain-chat/src/dialogue_agents/initial_agent.rs rename to crates/brain-chat/src/dialogue_agents/initial_agent.rs diff --git a/brain-chat/src/dialogue_agents/mod.rs b/crates/brain-chat/src/dialogue_agents/mod.rs similarity index 100% rename from brain-chat/src/dialogue_agents/mod.rs rename to crates/brain-chat/src/dialogue_agents/mod.rs diff --git a/brain-chat/src/dialogue_agents/processing_agent.rs b/crates/brain-chat/src/dialogue_agents/processing_agent.rs similarity index 100% rename from brain-chat/src/dialogue_agents/processing_agent.rs rename to crates/brain-chat/src/dialogue_agents/processing_agent.rs diff --git a/brain-chat/src/dialogue_agents/waiting_agent.rs b/crates/brain-chat/src/dialogue_agents/waiting_agent.rs similarity index 100% rename from brain-chat/src/dialogue_agents/waiting_agent.rs rename to crates/brain-chat/src/dialogue_agents/waiting_agent.rs diff --git a/brain-chat/src/intent_classifier.rs b/crates/brain-chat/src/intent_classifier.rs similarity index 100% rename from brain-chat/src/intent_classifier.rs rename to crates/brain-chat/src/intent_classifier.rs diff --git a/brain-chat/src/knowledge_base.rs b/crates/brain-chat/src/knowledge_base.rs similarity index 100% rename from brain-chat/src/knowledge_base.rs rename to crates/brain-chat/src/knowledge_base.rs diff --git a/brain-chat/src/lib.rs b/crates/brain-chat/src/lib.rs similarity index 100% rename from brain-chat/src/lib.rs rename to crates/brain-chat/src/lib.rs diff --git a/brain-chat/src/memory_integration.rs b/crates/brain-chat/src/memory_integration.rs similarity index 100% rename from brain-chat/src/memory_integration.rs rename to crates/brain-chat/src/memory_integration.rs diff --git a/brain-chat/src/persistence/brain_vector_bridge.rs b/crates/brain-chat/src/persistence/brain_vector_bridge.rs similarity index 100% rename from brain-chat/src/persistence/brain_vector_bridge.rs rename to crates/brain-chat/src/persistence/brain_vector_bridge.rs diff --git a/brain-chat/src/persistence/migrations.rs b/crates/brain-chat/src/persistence/migrations.rs similarity index 100% rename from brain-chat/src/persistence/migrations.rs rename to crates/brain-chat/src/persistence/migrations.rs diff --git a/brain-chat/src/persistence/mod.rs b/crates/brain-chat/src/persistence/mod.rs similarity index 100% rename from brain-chat/src/persistence/mod.rs rename to crates/brain-chat/src/persistence/mod.rs diff --git a/brain-chat/src/persistence/redis_cache.rs b/crates/brain-chat/src/persistence/redis_cache.rs similarity index 100% rename from brain-chat/src/persistence/redis_cache.rs rename to crates/brain-chat/src/persistence/redis_cache.rs diff --git a/brain-chat/src/persistence/vector_database.rs b/crates/brain-chat/src/persistence/vector_database.rs similarity index 100% rename from brain-chat/src/persistence/vector_database.rs rename to crates/brain-chat/src/persistence/vector_database.rs diff --git a/brain-chat/src/personality_engine.rs b/crates/brain-chat/src/personality_engine.rs similarity index 100% rename from brain-chat/src/personality_engine.rs rename to crates/brain-chat/src/personality_engine.rs diff --git a/brain-chat/src/response_generator.rs b/crates/brain-chat/src/response_generator.rs similarity index 100% rename from brain-chat/src/response_generator.rs rename to crates/brain-chat/src/response_generator.rs diff --git a/brain-cli/Cargo.toml b/crates/brain-cli/Cargo.toml similarity index 100% rename from brain-cli/Cargo.toml rename to crates/brain-cli/Cargo.toml diff --git a/brain-cli/complete_fix_test.json b/crates/brain-cli/complete_fix_test.json similarity index 100% rename from brain-cli/complete_fix_test.json rename to crates/brain-cli/complete_fix_test.json diff --git a/brain-cli/complete_pattern_fix.json b/crates/brain-cli/complete_pattern_fix.json similarity index 100% rename from brain-cli/complete_pattern_fix.json rename to crates/brain-cli/complete_pattern_fix.json diff --git a/brain-cli/comprehensive_100_percent_final.json b/crates/brain-cli/comprehensive_100_percent_final.json similarity index 100% rename from brain-cli/comprehensive_100_percent_final.json rename to crates/brain-cli/comprehensive_100_percent_final.json diff --git a/brain-cli/comprehensive_100_problems_phase2.json b/crates/brain-cli/comprehensive_100_problems_phase2.json similarity index 100% rename from brain-cli/comprehensive_100_problems_phase2.json rename to crates/brain-cli/comprehensive_100_problems_phase2.json diff --git a/brain-cli/comprehensive_final_validation_50.json b/crates/brain-cli/comprehensive_final_validation_50.json similarity index 100% rename from brain-cli/comprehensive_final_validation_50.json rename to crates/brain-cli/comprehensive_final_validation_50.json diff --git a/brain-cli/comprehensive_routing_check.json b/crates/brain-cli/comprehensive_routing_check.json similarity index 100% rename from brain-cli/comprehensive_routing_check.json rename to crates/brain-cli/comprehensive_routing_check.json diff --git a/brain-cli/comprehensive_test_50.json b/crates/brain-cli/comprehensive_test_50.json similarity index 100% rename from brain-cli/comprehensive_test_50.json rename to crates/brain-cli/comprehensive_test_50.json diff --git a/brain-cli/comprehensive_verification_75.json b/crates/brain-cli/comprehensive_verification_75.json similarity index 100% rename from brain-cli/comprehensive_verification_75.json rename to crates/brain-cli/comprehensive_verification_75.json diff --git a/brain-cli/critical_fixes_test.json b/crates/brain-cli/critical_fixes_test.json similarity index 100% rename from brain-cli/critical_fixes_test.json rename to crates/brain-cli/critical_fixes_test.json diff --git a/brain-cli/data/benchmark_test.json b/crates/brain-cli/data/benchmark_test.json similarity index 100% rename from brain-cli/data/benchmark_test.json rename to crates/brain-cli/data/benchmark_test.json diff --git a/brain-cli/data/humaneval_test_10_backend.jsonl b/crates/brain-cli/data/humaneval_test_10_backend.jsonl similarity index 100% rename from brain-cli/data/humaneval_test_10_backend.jsonl rename to crates/brain-cli/data/humaneval_test_10_backend.jsonl diff --git a/brain-cli/data/humaneval_test_3_cognitive.jsonl b/crates/brain-cli/data/humaneval_test_3_cognitive.jsonl similarity index 100% rename from brain-cli/data/humaneval_test_3_cognitive.jsonl rename to crates/brain-cli/data/humaneval_test_3_cognitive.jsonl diff --git a/brain-cli/data/humaneval_test_3_cognitive_enabled.jsonl b/crates/brain-cli/data/humaneval_test_3_cognitive_enabled.jsonl similarity index 100% rename from brain-cli/data/humaneval_test_3_cognitive_enabled.jsonl rename to crates/brain-cli/data/humaneval_test_3_cognitive_enabled.jsonl diff --git a/brain-cli/data/humaneval_test_3_frontend.jsonl b/crates/brain-cli/data/humaneval_test_3_frontend.jsonl similarity index 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b/crates/brain-cli/debug_specific_problems.json similarity index 100% rename from brain-cli/debug_specific_problems.json rename to crates/brain-cli/debug_specific_problems.json diff --git a/brain-cli/expanded_test_25.json b/crates/brain-cli/expanded_test_25.json similarity index 100% rename from brain-cli/expanded_test_25.json rename to crates/brain-cli/expanded_test_25.json diff --git a/brain-cli/final_complete_fix.json b/crates/brain-cli/final_complete_fix.json similarity index 100% rename from brain-cli/final_complete_fix.json rename to crates/brain-cli/final_complete_fix.json diff --git a/brain-cli/final_comprehensive_fix.json b/crates/brain-cli/final_comprehensive_fix.json similarity index 100% rename from brain-cli/final_comprehensive_fix.json rename to crates/brain-cli/final_comprehensive_fix.json diff --git a/brain-cli/final_comprehensive_improvement_test.json b/crates/brain-cli/final_comprehensive_improvement_test.json similarity index 100% rename from brain-cli/final_comprehensive_improvement_test.json rename to crates/brain-cli/final_comprehensive_improvement_test.json diff --git a/brain-cli/final_derivative_test.json b/crates/brain-cli/final_derivative_test.json similarity index 100% rename from brain-cli/final_derivative_test.json rename to crates/brain-cli/final_derivative_test.json diff --git a/brain-cli/final_fixed_test.json b/crates/brain-cli/final_fixed_test.json similarity index 100% rename from brain-cli/final_fixed_test.json rename to crates/brain-cli/final_fixed_test.json diff --git a/brain-cli/final_routing_fixes_test.json b/crates/brain-cli/final_routing_fixes_test.json similarity index 100% rename from brain-cli/final_routing_fixes_test.json rename to crates/brain-cli/final_routing_fixes_test.json diff --git a/brain-cli/final_verification_20_problems.json b/crates/brain-cli/final_verification_20_problems.json similarity index 100% rename from brain-cli/final_verification_20_problems.json rename to crates/brain-cli/final_verification_20_problems.json diff --git a/brain-cli/fixed_100_percent_test.json b/crates/brain-cli/fixed_100_percent_test.json similarity index 100% rename from brain-cli/fixed_100_percent_test.json rename to crates/brain-cli/fixed_100_percent_test.json diff --git a/brain-cli/fixed_template_issues_test.json b/crates/brain-cli/fixed_template_issues_test.json similarity index 100% rename from brain-cli/fixed_template_issues_test.json rename to crates/brain-cli/fixed_template_issues_test.json diff --git a/brain-cli/fixed_template_test.json b/crates/brain-cli/fixed_template_test.json similarity index 100% rename from brain-cli/fixed_template_test.json rename to crates/brain-cli/fixed_template_test.json diff --git a/brain-cli/fixed_test_verification.json b/crates/brain-cli/fixed_test_verification.json similarity index 100% rename from brain-cli/fixed_test_verification.json rename to crates/brain-cli/fixed_test_verification.json diff --git a/brain-cli/gradual_test_5_problems.json b/crates/brain-cli/gradual_test_5_problems.json similarity index 100% rename from brain-cli/gradual_test_5_problems.json rename to crates/brain-cli/gradual_test_5_problems.json diff --git a/brain-cli/humaneval_10_problems_FIXED.json b/crates/brain-cli/humaneval_10_problems_FIXED.json similarity index 100% rename from brain-cli/humaneval_10_problems_FIXED.json rename to crates/brain-cli/humaneval_10_problems_FIXED.json diff --git a/brain-cli/humaneval_50_problems_consistency_test.json b/crates/brain-cli/humaneval_50_problems_consistency_test.json similarity index 100% rename from brain-cli/humaneval_50_problems_consistency_test.json rename to crates/brain-cli/humaneval_50_problems_consistency_test.json diff --git a/brain-cli/logs/learning_records_20250706.jsonl b/crates/brain-cli/logs/learning_records_20250706.jsonl similarity index 100% rename from brain-cli/logs/learning_records_20250706.jsonl rename to crates/brain-cli/logs/learning_records_20250706.jsonl diff --git a/brain-cli/major_fixes_test.json b/crates/brain-cli/major_fixes_test.json similarity index 100% rename from brain-cli/major_fixes_test.json rename to crates/brain-cli/major_fixes_test.json diff --git a/brain-cli/mbpp_benchmark_test.json b/crates/brain-cli/mbpp_benchmark_test.json similarity index 100% rename from brain-cli/mbpp_benchmark_test.json rename to crates/brain-cli/mbpp_benchmark_test.json diff --git a/brain-cli/perfect_100_percent_test.json b/crates/brain-cli/perfect_100_percent_test.json similarity index 100% rename from brain-cli/perfect_100_percent_test.json rename to crates/brain-cli/perfect_100_percent_test.json diff --git a/brain-cli/real_test_verification.json b/crates/brain-cli/real_test_verification.json similarity index 100% rename from brain-cli/real_test_verification.json rename to crates/brain-cli/real_test_verification.json diff --git a/brain-cli/routing_fix_test.json b/crates/brain-cli/routing_fix_test.json similarity index 100% rename from brain-cli/routing_fix_test.json rename to crates/brain-cli/routing_fix_test.json diff --git a/brain-cli/searchoptimization_fix_test.json b/crates/brain-cli/searchoptimization_fix_test.json similarity index 100% rename from brain-cli/searchoptimization_fix_test.json rename to crates/brain-cli/searchoptimization_fix_test.json diff --git a/brain-cli/single_debug.json b/crates/brain-cli/single_debug.json similarity index 100% rename from brain-cli/single_debug.json rename to crates/brain-cli/single_debug.json diff --git a/brain-cli/single_test_verification.json b/crates/brain-cli/single_test_verification.json similarity index 100% rename from brain-cli/single_test_verification.json rename to crates/brain-cli/single_test_verification.json diff --git a/brain-cli/src/benchmark_integration.rs b/crates/brain-cli/src/benchmark_integration.rs similarity index 100% rename from brain-cli/src/benchmark_integration.rs rename to crates/brain-cli/src/benchmark_integration.rs diff --git a/brain-cli/src/benchmark_integration_test.rs b/crates/brain-cli/src/benchmark_integration_test.rs similarity index 100% rename from brain-cli/src/benchmark_integration_test.rs rename to crates/brain-cli/src/benchmark_integration_test.rs diff --git a/brain-cli/src/concierge.rs b/crates/brain-cli/src/concierge.rs similarity index 100% rename from brain-cli/src/concierge.rs rename to crates/brain-cli/src/concierge.rs diff --git a/brain-cli/src/humaneval.rs b/crates/brain-cli/src/humaneval.rs similarity index 100% rename from brain-cli/src/humaneval.rs rename to crates/brain-cli/src/humaneval.rs diff --git a/brain-cli/src/humaneval_cognitive.rs b/crates/brain-cli/src/humaneval_cognitive.rs similarity index 100% rename from brain-cli/src/humaneval_cognitive.rs rename to crates/brain-cli/src/humaneval_cognitive.rs diff --git a/brain-cli/src/lib.rs b/crates/brain-cli/src/lib.rs similarity index 100% rename from brain-cli/src/lib.rs rename to crates/brain-cli/src/lib.rs diff --git a/brain-cli/src/main.rs b/crates/brain-cli/src/main.rs similarity index 100% rename from brain-cli/src/main.rs rename to crates/brain-cli/src/main.rs diff --git a/brain-cli/src/soma_cli.rs b/crates/brain-cli/src/soma_cli.rs similarity index 100% rename from brain-cli/src/soma_cli.rs rename to crates/brain-cli/src/soma_cli.rs diff --git a/brain-cli/table b/crates/brain-cli/table similarity index 100% rename from brain-cli/table rename to crates/brain-cli/table diff --git a/brain-cli/targeted_problematic_test.json b/crates/brain-cli/targeted_problematic_test.json similarity index 100% rename from brain-cli/targeted_problematic_test.json rename to crates/brain-cli/targeted_problematic_test.json diff --git a/brain-cli/temp/test_HumanEval_0.py b/crates/brain-cli/temp/test_HumanEval_0.py similarity index 100% rename from brain-cli/temp/test_HumanEval_0.py rename to crates/brain-cli/temp/test_HumanEval_0.py diff --git a/brain-cli/temp/test_HumanEval_1.py b/crates/brain-cli/temp/test_HumanEval_1.py similarity index 100% rename from brain-cli/temp/test_HumanEval_1.py rename to crates/brain-cli/temp/test_HumanEval_1.py diff --git a/brain-cli/temp/test_HumanEval_2.py b/crates/brain-cli/temp/test_HumanEval_2.py similarity index 100% rename from brain-cli/temp/test_HumanEval_2.py rename to crates/brain-cli/temp/test_HumanEval_2.py diff --git a/brain-cli/temp/test_HumanEval_3.py b/crates/brain-cli/temp/test_HumanEval_3.py similarity index 100% rename from brain-cli/temp/test_HumanEval_3.py rename to crates/brain-cli/temp/test_HumanEval_3.py diff --git a/brain-cli/temp/test_HumanEval_4.py b/crates/brain-cli/temp/test_HumanEval_4.py similarity index 100% rename from brain-cli/temp/test_HumanEval_4.py rename to crates/brain-cli/temp/test_HumanEval_4.py diff --git a/brain-cli/temp/test_HumanEval_5.py b/crates/brain-cli/temp/test_HumanEval_5.py similarity index 100% rename from brain-cli/temp/test_HumanEval_5.py rename to crates/brain-cli/temp/test_HumanEval_5.py diff --git a/brain-cli/temp/test_HumanEval_6.py b/crates/brain-cli/temp/test_HumanEval_6.py similarity index 100% rename from brain-cli/temp/test_HumanEval_6.py rename to crates/brain-cli/temp/test_HumanEval_6.py diff --git a/brain-cli/temp/test_HumanEval_7.py b/crates/brain-cli/temp/test_HumanEval_7.py similarity index 100% rename from brain-cli/temp/test_HumanEval_7.py rename to crates/brain-cli/temp/test_HumanEval_7.py diff --git a/brain-cli/temp/test_HumanEval_8.py b/crates/brain-cli/temp/test_HumanEval_8.py similarity index 100% rename from brain-cli/temp/test_HumanEval_8.py rename to crates/brain-cli/temp/test_HumanEval_8.py diff --git a/brain-cli/temp/test_HumanEval_9.py b/crates/brain-cli/temp/test_HumanEval_9.py similarity index 100% rename from brain-cli/temp/test_HumanEval_9.py rename to crates/brain-cli/temp/test_HumanEval_9.py diff --git a/brain-cli/template_fixes_test.json 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brain-cognitive/src/agents/development/algorithm_coder.rs rename to crates/brain-cognitive/src/agents/development/algorithm_coder.rs diff --git a/brain-cognitive/src/agents/development/algorithm_optimizer.rs b/crates/brain-cognitive/src/agents/development/algorithm_optimizer.rs similarity index 100% rename from brain-cognitive/src/agents/development/algorithm_optimizer.rs rename to crates/brain-cognitive/src/agents/development/algorithm_optimizer.rs diff --git a/brain-cognitive/src/agents/development/api.rs b/crates/brain-cognitive/src/agents/development/api.rs similarity index 100% rename from brain-cognitive/src/agents/development/api.rs rename to crates/brain-cognitive/src/agents/development/api.rs diff --git a/brain-cognitive/src/agents/development/architect.rs b/crates/brain-cognitive/src/agents/development/architect.rs similarity index 100% rename from brain-cognitive/src/agents/development/architect.rs rename to crates/brain-cognitive/src/agents/development/architect.rs diff --git a/brain-cognitive/src/agents/development/backend_coder.rs b/crates/brain-cognitive/src/agents/development/backend_coder.rs similarity index 100% rename from brain-cognitive/src/agents/development/backend_coder.rs rename to crates/brain-cognitive/src/agents/development/backend_coder.rs diff --git a/brain-cognitive/src/agents/development/code_review.rs b/crates/brain-cognitive/src/agents/development/code_review.rs similarity index 100% rename from brain-cognitive/src/agents/development/code_review.rs rename to crates/brain-cognitive/src/agents/development/code_review.rs diff --git a/brain-cognitive/src/agents/development/debug.rs b/crates/brain-cognitive/src/agents/development/debug.rs similarity index 100% rename from brain-cognitive/src/agents/development/debug.rs rename to crates/brain-cognitive/src/agents/development/debug.rs diff --git a/brain-cognitive/src/agents/development/deployer.rs b/crates/brain-cognitive/src/agents/development/deployer.rs similarity index 100% rename from brain-cognitive/src/agents/development/deployer.rs rename to crates/brain-cognitive/src/agents/development/deployer.rs diff --git a/brain-cognitive/src/agents/development/designer.rs b/crates/brain-cognitive/src/agents/development/designer.rs similarity index 100% rename from brain-cognitive/src/agents/development/designer.rs rename to crates/brain-cognitive/src/agents/development/designer.rs diff --git a/brain-cognitive/src/agents/development/doc.rs b/crates/brain-cognitive/src/agents/development/doc.rs similarity index 100% rename from brain-cognitive/src/agents/development/doc.rs rename to crates/brain-cognitive/src/agents/development/doc.rs diff --git a/brain-cognitive/src/agents/development/documentation_specialist.rs b/crates/brain-cognitive/src/agents/development/documentation_specialist.rs similarity index 100% rename from brain-cognitive/src/agents/development/documentation_specialist.rs rename to crates/brain-cognitive/src/agents/development/documentation_specialist.rs diff --git a/brain-cognitive/src/agents/development/engine.rs b/crates/brain-cognitive/src/agents/development/engine.rs similarity index 100% rename from brain-cognitive/src/agents/development/engine.rs rename to crates/brain-cognitive/src/agents/development/engine.rs diff --git a/brain-cognitive/src/agents/development/frontend_coder.rs b/crates/brain-cognitive/src/agents/development/frontend_coder.rs similarity index 100% rename from brain-cognitive/src/agents/development/frontend_coder.rs rename to crates/brain-cognitive/src/agents/development/frontend_coder.rs diff --git a/brain-cognitive/src/agents/development/maintainer.rs b/crates/brain-cognitive/src/agents/development/maintainer.rs similarity index 100% rename from brain-cognitive/src/agents/development/maintainer.rs rename to crates/brain-cognitive/src/agents/development/maintainer.rs diff --git a/brain-cognitive/src/agents/development/mod.rs b/crates/brain-cognitive/src/agents/development/mod.rs similarity index 100% rename from brain-cognitive/src/agents/development/mod.rs rename to crates/brain-cognitive/src/agents/development/mod.rs diff --git a/brain-cognitive/src/agents/development/mubrain_algorithm_coder.rs b/crates/brain-cognitive/src/agents/development/mubrain_algorithm_coder.rs similarity index 100% rename from brain-cognitive/src/agents/development/mubrain_algorithm_coder.rs rename to crates/brain-cognitive/src/agents/development/mubrain_algorithm_coder.rs diff --git a/brain-cognitive/src/agents/development/mubrain_integration.rs b/crates/brain-cognitive/src/agents/development/mubrain_integration.rs similarity index 100% rename from brain-cognitive/src/agents/development/mubrain_integration.rs rename to crates/brain-cognitive/src/agents/development/mubrain_integration.rs diff --git a/brain-cognitive/src/agents/development/planner.rs b/crates/brain-cognitive/src/agents/development/planner.rs similarity index 100% rename from brain-cognitive/src/agents/development/planner.rs rename to crates/brain-cognitive/src/agents/development/planner.rs diff --git a/brain-cognitive/src/agents/development/refactor.rs b/crates/brain-cognitive/src/agents/development/refactor.rs similarity index 100% rename from brain-cognitive/src/agents/development/refactor.rs rename to crates/brain-cognitive/src/agents/development/refactor.rs diff --git a/brain-cognitive/src/agents/development/schema.rs b/crates/brain-cognitive/src/agents/development/schema.rs similarity index 100% rename from brain-cognitive/src/agents/development/schema.rs rename to crates/brain-cognitive/src/agents/development/schema.rs diff --git a/brain-cognitive/src/agents/development/testing_excellence.rs b/crates/brain-cognitive/src/agents/development/testing_excellence.rs similarity index 100% rename from brain-cognitive/src/agents/development/testing_excellence.rs rename to crates/brain-cognitive/src/agents/development/testing_excellence.rs diff --git a/brain-cognitive/src/agents/intelligence/academic_knowledge_base.rs b/crates/brain-cognitive/src/agents/intelligence/academic_knowledge_base.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/academic_knowledge_base.rs rename to crates/brain-cognitive/src/agents/intelligence/academic_knowledge_base.rs diff --git a/brain-cognitive/src/agents/intelligence/academic_learning_integration.rs b/crates/brain-cognitive/src/agents/intelligence/academic_learning_integration.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/academic_learning_integration.rs rename to crates/brain-cognitive/src/agents/intelligence/academic_learning_integration.rs diff --git a/brain-cognitive/src/agents/intelligence/academic_performance_monitor.rs b/crates/brain-cognitive/src/agents/intelligence/academic_performance_monitor.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/academic_performance_monitor.rs rename to crates/brain-cognitive/src/agents/intelligence/academic_performance_monitor.rs diff --git a/brain-cognitive/src/agents/intelligence/academic_reasoning.rs b/crates/brain-cognitive/src/agents/intelligence/academic_reasoning.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/academic_reasoning.rs rename to crates/brain-cognitive/src/agents/intelligence/academic_reasoning.rs diff --git a/brain-cognitive/src/agents/intelligence/adaptive_research_engine.rs b/crates/brain-cognitive/src/agents/intelligence/adaptive_research_engine.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/adaptive_research_engine.rs rename to crates/brain-cognitive/src/agents/intelligence/adaptive_research_engine.rs diff --git a/brain-cognitive/src/agents/intelligence/advanced_chemistry_expert.rs b/crates/brain-cognitive/src/agents/intelligence/advanced_chemistry_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/advanced_chemistry_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/advanced_chemistry_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/computer_science_theory_expert.rs b/crates/brain-cognitive/src/agents/intelligence/computer_science_theory_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/computer_science_theory_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/computer_science_theory_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/cross_domain_synthesis_engine.rs b/crates/brain-cognitive/src/agents/intelligence/cross_domain_synthesis_engine.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/cross_domain_synthesis_engine.rs rename to crates/brain-cognitive/src/agents/intelligence/cross_domain_synthesis_engine.rs diff --git a/brain-cognitive/src/agents/intelligence/data_ingestion.rs b/crates/brain-cognitive/src/agents/intelligence/data_ingestion.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/data_ingestion.rs rename to crates/brain-cognitive/src/agents/intelligence/data_ingestion.rs diff --git a/brain-cognitive/src/agents/intelligence/feature_experimentation.rs b/crates/brain-cognitive/src/agents/intelligence/feature_experimentation.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/feature_experimentation.rs rename to crates/brain-cognitive/src/agents/intelligence/feature_experimentation.rs diff --git a/brain-cognitive/src/agents/intelligence/knowledge_base.rs b/crates/brain-cognitive/src/agents/intelligence/knowledge_base.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/knowledge_base.rs rename to crates/brain-cognitive/src/agents/intelligence/knowledge_base.rs diff --git a/brain-cognitive/src/agents/intelligence/linguistics_expert.rs b/crates/brain-cognitive/src/agents/intelligence/linguistics_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/linguistics_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/linguistics_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/materials_science_expert.rs b/crates/brain-cognitive/src/agents/intelligence/materials_science_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/materials_science_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/materials_science_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/mlops.rs b/crates/brain-cognitive/src/agents/intelligence/mlops.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/mlops.rs rename to crates/brain-cognitive/src/agents/intelligence/mlops.rs diff --git a/brain-cognitive/src/agents/intelligence/mod.rs b/crates/brain-cognitive/src/agents/intelligence/mod.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/mod.rs rename to crates/brain-cognitive/src/agents/intelligence/mod.rs diff --git a/brain-cognitive/src/agents/intelligence/model_training.rs b/crates/brain-cognitive/src/agents/intelligence/model_training.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/model_training.rs rename to crates/brain-cognitive/src/agents/intelligence/model_training.rs diff --git a/brain-cognitive/src/agents/intelligence/molecular_biology_expert.rs b/crates/brain-cognitive/src/agents/intelligence/molecular_biology_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/molecular_biology_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/molecular_biology_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/mubrain_integration.rs b/crates/brain-cognitive/src/agents/intelligence/mubrain_integration.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/mubrain_integration.rs rename to crates/brain-cognitive/src/agents/intelligence/mubrain_integration.rs diff --git a/brain-cognitive/src/agents/intelligence/multiple_choice_processor.rs b/crates/brain-cognitive/src/agents/intelligence/multiple_choice_processor.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/multiple_choice_processor.rs rename to crates/brain-cognitive/src/agents/intelligence/multiple_choice_processor.rs diff --git a/brain-cognitive/src/agents/intelligence/philosophy_expert.rs b/crates/brain-cognitive/src/agents/intelligence/philosophy_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/philosophy_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/philosophy_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/pure_mathematics_expert.rs b/crates/brain-cognitive/src/agents/intelligence/pure_mathematics_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/pure_mathematics_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/pure_mathematics_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/tests/academic_agent_tests.rs b/crates/brain-cognitive/src/agents/intelligence/tests/academic_agent_tests.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/tests/academic_agent_tests.rs rename to crates/brain-cognitive/src/agents/intelligence/tests/academic_agent_tests.rs diff --git a/brain-cognitive/src/agents/intelligence/tests/domain_expertise_tests.rs b/crates/brain-cognitive/src/agents/intelligence/tests/domain_expertise_tests.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/tests/domain_expertise_tests.rs rename to crates/brain-cognitive/src/agents/intelligence/tests/domain_expertise_tests.rs diff --git a/brain-cognitive/src/agents/intelligence/tests/hle_question_tests.rs b/crates/brain-cognitive/src/agents/intelligence/tests/hle_question_tests.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/tests/hle_question_tests.rs rename to crates/brain-cognitive/src/agents/intelligence/tests/hle_question_tests.rs diff --git a/brain-cognitive/src/agents/intelligence/tests/mod.rs b/crates/brain-cognitive/src/agents/intelligence/tests/mod.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/tests/mod.rs rename to crates/brain-cognitive/src/agents/intelligence/tests/mod.rs diff --git a/brain-cognitive/src/agents/intelligence/tests/multiple_choice_tests.rs b/crates/brain-cognitive/src/agents/intelligence/tests/multiple_choice_tests.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/tests/multiple_choice_tests.rs rename to crates/brain-cognitive/src/agents/intelligence/tests/multiple_choice_tests.rs diff --git a/brain-cognitive/src/agents/intelligence/theoretical_physics_expert.rs b/crates/brain-cognitive/src/agents/intelligence/theoretical_physics_expert.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/theoretical_physics_expert.rs rename to crates/brain-cognitive/src/agents/intelligence/theoretical_physics_expert.rs diff --git a/brain-cognitive/src/agents/intelligence/user_behavior_analyst.rs b/crates/brain-cognitive/src/agents/intelligence/user_behavior_analyst.rs similarity index 100% rename from brain-cognitive/src/agents/intelligence/user_behavior_analyst.rs rename to crates/brain-cognitive/src/agents/intelligence/user_behavior_analyst.rs diff --git a/brain-cognitive/src/agents/mathematics/mod.rs b/crates/brain-cognitive/src/agents/mathematics/mod.rs similarity index 100% rename from brain-cognitive/src/agents/mathematics/mod.rs rename to crates/brain-cognitive/src/agents/mathematics/mod.rs diff --git a/brain-cognitive/src/agents/mathematics/symbolic_math_agent.rs b/crates/brain-cognitive/src/agents/mathematics/symbolic_math_agent.rs similarity index 100% rename from brain-cognitive/src/agents/mathematics/symbolic_math_agent.rs rename to crates/brain-cognitive/src/agents/mathematics/symbolic_math_agent.rs diff --git a/brain-cognitive/src/agents/mod.rs b/crates/brain-cognitive/src/agents/mod.rs similarity index 100% rename from brain-cognitive/src/agents/mod.rs rename to crates/brain-cognitive/src/agents/mod.rs diff --git a/brain-cognitive/src/agents/monitoring/academic_performance_monitor.rs b/crates/brain-cognitive/src/agents/monitoring/academic_performance_monitor.rs similarity index 100% rename from brain-cognitive/src/agents/monitoring/academic_performance_monitor.rs rename to crates/brain-cognitive/src/agents/monitoring/academic_performance_monitor.rs diff --git a/brain-cognitive/src/agents/monitoring/mod.rs b/crates/brain-cognitive/src/agents/monitoring/mod.rs similarity index 100% rename from brain-cognitive/src/agents/monitoring/mod.rs rename to crates/brain-cognitive/src/agents/monitoring/mod.rs diff --git a/brain-cognitive/src/agents/nlp/google_translate.rs b/crates/brain-cognitive/src/agents/nlp/google_translate.rs similarity index 100% rename from brain-cognitive/src/agents/nlp/google_translate.rs rename to crates/brain-cognitive/src/agents/nlp/google_translate.rs diff --git a/brain-cognitive/src/agents/nlp/mod.rs b/crates/brain-cognitive/src/agents/nlp/mod.rs similarity index 100% rename from brain-cognitive/src/agents/nlp/mod.rs rename to crates/brain-cognitive/src/agents/nlp/mod.rs diff --git a/brain-cognitive/src/agents/nlp/openai_intent.rs b/crates/brain-cognitive/src/agents/nlp/openai_intent.rs similarity index 100% rename from brain-cognitive/src/agents/nlp/openai_intent.rs rename to crates/brain-cognitive/src/agents/nlp/openai_intent.rs diff --git a/brain-cognitive/src/agents/ops/backup_recovery.rs b/crates/brain-cognitive/src/agents/ops/backup_recovery.rs similarity index 100% rename from brain-cognitive/src/agents/ops/backup_recovery.rs rename to crates/brain-cognitive/src/agents/ops/backup_recovery.rs diff --git a/brain-cognitive/src/agents/ops/build_optimizer.rs b/crates/brain-cognitive/src/agents/ops/build_optimizer.rs similarity index 100% rename from brain-cognitive/src/agents/ops/build_optimizer.rs rename to crates/brain-cognitive/src/agents/ops/build_optimizer.rs diff --git a/brain-cognitive/src/agents/ops/drift_detection.rs b/crates/brain-cognitive/src/agents/ops/drift_detection.rs similarity index 100% rename from brain-cognitive/src/agents/ops/drift_detection.rs rename to crates/brain-cognitive/src/agents/ops/drift_detection.rs diff --git a/brain-cognitive/src/agents/ops/hotfix.rs b/crates/brain-cognitive/src/agents/ops/hotfix.rs similarity index 100% rename from brain-cognitive/src/agents/ops/hotfix.rs rename to crates/brain-cognitive/src/agents/ops/hotfix.rs diff --git a/brain-cognitive/src/agents/ops/mod.rs b/crates/brain-cognitive/src/agents/ops/mod.rs similarity index 100% rename from brain-cognitive/src/agents/ops/mod.rs rename to crates/brain-cognitive/src/agents/ops/mod.rs diff --git a/brain-cognitive/src/agents/ops/mubrain_integration.rs b/crates/brain-cognitive/src/agents/ops/mubrain_integration.rs similarity index 100% rename from brain-cognitive/src/agents/ops/mubrain_integration.rs rename to crates/brain-cognitive/src/agents/ops/mubrain_integration.rs diff --git a/brain-cognitive/src/agents/ops/observability.rs b/crates/brain-cognitive/src/agents/ops/observability.rs similarity index 100% rename from brain-cognitive/src/agents/ops/observability.rs rename to crates/brain-cognitive/src/agents/ops/observability.rs diff --git a/brain-cognitive/src/agents/ops/replication_scaling.rs b/crates/brain-cognitive/src/agents/ops/replication_scaling.rs similarity index 100% rename from brain-cognitive/src/agents/ops/replication_scaling.rs rename to crates/brain-cognitive/src/agents/ops/replication_scaling.rs diff --git a/brain-cognitive/src/agents/orchestration/adaptive_planning.rs b/crates/brain-cognitive/src/agents/orchestration/adaptive_planning.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/adaptive_planning.rs rename to crates/brain-cognitive/src/agents/orchestration/adaptive_planning.rs diff --git a/brain-cognitive/src/agents/orchestration/agent_orchestration.rs b/crates/brain-cognitive/src/agents/orchestration/agent_orchestration.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/agent_orchestration.rs rename to crates/brain-cognitive/src/agents/orchestration/agent_orchestration.rs diff --git a/brain-cognitive/src/agents/orchestration/conversation_persistence.rs b/crates/brain-cognitive/src/agents/orchestration/conversation_persistence.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/conversation_persistence.rs rename to crates/brain-cognitive/src/agents/orchestration/conversation_persistence.rs diff --git a/brain-cognitive/src/agents/orchestration/cto.rs b/crates/brain-cognitive/src/agents/orchestration/cto.rs similarity index 100% rename from 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crates/brain-cognitive/src/agents/orchestration/project_decomposition.rs diff --git a/brain-cognitive/src/agents/orchestration/stakeholder_communication.rs b/crates/brain-cognitive/src/agents/orchestration/stakeholder_communication.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/stakeholder_communication.rs rename to crates/brain-cognitive/src/agents/orchestration/stakeholder_communication.rs diff --git a/brain-cognitive/src/agents/orchestration/strategic_analysis.rs b/crates/brain-cognitive/src/agents/orchestration/strategic_analysis.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/strategic_analysis.rs rename to crates/brain-cognitive/src/agents/orchestration/strategic_analysis.rs diff --git a/brain-cognitive/src/agents/orchestration/universal_cto_agent.rs b/crates/brain-cognitive/src/agents/orchestration/universal_cto_agent.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/universal_cto_agent.rs rename to crates/brain-cognitive/src/agents/orchestration/universal_cto_agent.rs diff --git a/brain-cognitive/src/agents/orchestration/universal_cto_agent_test.rs b/crates/brain-cognitive/src/agents/orchestration/universal_cto_agent_test.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/universal_cto_agent_test.rs rename to crates/brain-cognitive/src/agents/orchestration/universal_cto_agent_test.rs diff --git a/brain-cognitive/src/agents/orchestration/universal_input.rs b/crates/brain-cognitive/src/agents/orchestration/universal_input.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/universal_input.rs rename to crates/brain-cognitive/src/agents/orchestration/universal_input.rs diff --git a/brain-cognitive/src/agents/orchestration/workflow_orchestration.rs b/crates/brain-cognitive/src/agents/orchestration/workflow_orchestration.rs similarity index 100% rename from brain-cognitive/src/agents/orchestration/workflow_orchestration.rs rename to crates/brain-cognitive/src/agents/orchestration/workflow_orchestration.rs diff --git a/brain-cognitive/src/agents/platform/api_gateway.rs b/crates/brain-cognitive/src/agents/platform/api_gateway.rs similarity index 100% rename from brain-cognitive/src/agents/platform/api_gateway.rs rename to crates/brain-cognitive/src/agents/platform/api_gateway.rs diff --git a/brain-cognitive/src/agents/platform/container_orchestration.rs b/crates/brain-cognitive/src/agents/platform/container_orchestration.rs similarity index 100% rename from brain-cognitive/src/agents/platform/container_orchestration.rs rename to crates/brain-cognitive/src/agents/platform/container_orchestration.rs diff --git a/brain-cognitive/src/agents/platform/data_visualization.rs b/crates/brain-cognitive/src/agents/platform/data_visualization.rs similarity index 100% rename from brain-cognitive/src/agents/platform/data_visualization.rs rename to crates/brain-cognitive/src/agents/platform/data_visualization.rs diff --git a/brain-cognitive/src/agents/platform/infrastructure_provisioning.rs b/crates/brain-cognitive/src/agents/platform/infrastructure_provisioning.rs similarity index 100% rename from brain-cognitive/src/agents/platform/infrastructure_provisioning.rs rename to crates/brain-cognitive/src/agents/platform/infrastructure_provisioning.rs diff --git a/brain-cognitive/src/agents/platform/localization.rs b/crates/brain-cognitive/src/agents/platform/localization.rs similarity index 100% rename from brain-cognitive/src/agents/platform/localization.rs rename to crates/brain-cognitive/src/agents/platform/localization.rs diff --git a/brain-cognitive/src/agents/platform/mod.rs b/crates/brain-cognitive/src/agents/platform/mod.rs similarity index 100% rename from brain-cognitive/src/agents/platform/mod.rs rename to crates/brain-cognitive/src/agents/platform/mod.rs diff --git a/brain-cognitive/src/agents/platform/mubrain_integration.rs b/crates/brain-cognitive/src/agents/platform/mubrain_integration.rs similarity index 100% rename from brain-cognitive/src/agents/platform/mubrain_integration.rs rename to crates/brain-cognitive/src/agents/platform/mubrain_integration.rs diff --git a/brain-cognitive/src/agents/platform/platform_compatibility.rs b/crates/brain-cognitive/src/agents/platform/platform_compatibility.rs similarity index 100% rename from brain-cognitive/src/agents/platform/platform_compatibility.rs rename to crates/brain-cognitive/src/agents/platform/platform_compatibility.rs diff --git a/brain-cognitive/src/agents/platform/service_mesh.rs b/crates/brain-cognitive/src/agents/platform/service_mesh.rs similarity index 100% rename from brain-cognitive/src/agents/platform/service_mesh.rs rename to crates/brain-cognitive/src/agents/platform/service_mesh.rs diff --git a/brain-cognitive/src/agents/platform/system_orchestration.rs b/crates/brain-cognitive/src/agents/platform/system_orchestration.rs similarity index 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crates/brain-types/src/common.rs diff --git a/brain-types/src/config.rs b/crates/brain-types/src/config.rs similarity index 100% rename from brain-types/src/config.rs rename to crates/brain-types/src/config.rs diff --git a/brain-types/src/error.rs b/crates/brain-types/src/error.rs similarity index 100% rename from brain-types/src/error.rs rename to crates/brain-types/src/error.rs diff --git a/brain-types/src/events.rs b/crates/brain-types/src/events.rs similarity index 100% rename from brain-types/src/events.rs rename to crates/brain-types/src/events.rs diff --git a/brain-types/src/lib.rs b/crates/brain-types/src/lib.rs similarity index 100% rename from brain-types/src/lib.rs rename to crates/brain-types/src/lib.rs diff --git a/brain-types/src/logging.rs b/crates/brain-types/src/logging.rs similarity index 100% rename from brain-types/src/logging.rs rename to crates/brain-types/src/logging.rs diff --git a/brain-types/src/soma/agent_bridge.rs b/crates/brain-types/src/soma/agent_bridge.rs similarity index 100% rename from brain-types/src/soma/agent_bridge.rs rename to crates/brain-types/src/soma/agent_bridge.rs diff --git a/brain-types/src/soma/builtin_operators.rs b/crates/brain-types/src/soma/builtin_operators.rs similarity index 100% rename from brain-types/src/soma/builtin_operators.rs rename to crates/brain-types/src/soma/builtin_operators.rs diff --git a/brain-types/src/soma/cognitive_connector.rs b/crates/brain-types/src/soma/cognitive_connector.rs similarity index 100% rename from brain-types/src/soma/cognitive_connector.rs rename to crates/brain-types/src/soma/cognitive_connector.rs diff --git a/brain-types/src/soma/dag_scheduler.rs b/crates/brain-types/src/soma/dag_scheduler.rs similarity index 100% rename from brain-types/src/soma/dag_scheduler.rs rename to crates/brain-types/src/soma/dag_scheduler.rs diff --git a/brain-types/src/soma/dsl_copilot.rs b/crates/brain-types/src/soma/dsl_copilot.rs similarity index 100% rename from brain-types/src/soma/dsl_copilot.rs rename to crates/brain-types/src/soma/dsl_copilot.rs diff --git a/brain-types/src/soma/execution.rs b/crates/brain-types/src/soma/execution.rs similarity index 100% rename from brain-types/src/soma/execution.rs rename to crates/brain-types/src/soma/execution.rs diff --git a/brain-types/src/soma/feedback_loops.rs b/crates/brain-types/src/soma/feedback_loops.rs similarity index 100% rename from brain-types/src/soma/feedback_loops.rs rename to crates/brain-types/src/soma/feedback_loops.rs diff --git a/brain-types/src/soma/lsp_server.rs b/crates/brain-types/src/soma/lsp_server.rs similarity index 100% rename from brain-types/src/soma/lsp_server.rs rename to crates/brain-types/src/soma/lsp_server.rs diff --git a/brain-types/src/soma/memory.rs b/crates/brain-types/src/soma/memory.rs similarity index 100% rename from brain-types/src/soma/memory.rs rename to crates/brain-types/src/soma/memory.rs diff --git a/brain-types/src/soma/mod.rs b/crates/brain-types/src/soma/mod.rs similarity index 100% rename from brain-types/src/soma/mod.rs rename to crates/brain-types/src/soma/mod.rs diff --git a/brain-types/src/soma/mubrain_connector.rs b/crates/brain-types/src/soma/mubrain_connector.rs similarity index 100% rename from brain-types/src/soma/mubrain_connector.rs rename to crates/brain-types/src/soma/mubrain_connector.rs diff --git a/brain-types/src/soma/operators.rs b/crates/brain-types/src/soma/operators.rs similarity index 100% rename from brain-types/src/soma/operators.rs rename to crates/brain-types/src/soma/operators.rs diff --git a/brain-types/src/soma/parallel_execution.rs b/crates/brain-types/src/soma/parallel_execution.rs similarity index 100% rename from brain-types/src/soma/parallel_execution.rs rename to crates/brain-types/src/soma/parallel_execution.rs diff --git a/brain-types/src/soma/parser.rs b/crates/brain-types/src/soma/parser.rs similarity index 100% rename from brain-types/src/soma/parser.rs rename to crates/brain-types/src/soma/parser.rs diff --git a/brain-types/src/soma/phase_engine.rs b/crates/brain-types/src/soma/phase_engine.rs similarity index 100% rename from brain-types/src/soma/phase_engine.rs rename to crates/brain-types/src/soma/phase_engine.rs diff --git a/brain-types/src/soma/plugins.rs b/crates/brain-types/src/soma/plugins.rs similarity index 100% rename from brain-types/src/soma/plugins.rs rename to crates/brain-types/src/soma/plugins.rs diff --git a/brain-types/src/soma/production.rs b/crates/brain-types/src/soma/production.rs similarity index 100% rename from brain-types/src/soma/production.rs rename to crates/brain-types/src/soma/production.rs diff --git a/brain-types/src/soma/profiling_dashboard.rs b/crates/brain-types/src/soma/profiling_dashboard.rs similarity index 100% rename from brain-types/src/soma/profiling_dashboard.rs rename to crates/brain-types/src/soma/profiling_dashboard.rs diff --git a/brain-types/src/soma/soma.pest b/crates/brain-types/src/soma/soma.pest similarity index 100% rename from brain-types/src/soma/soma.pest rename to crates/brain-types/src/soma/soma.pest diff --git a/brain-types/src/soma/temporal_planner.rs b/crates/brain-types/src/soma/temporal_planner.rs similarity index 100% rename from brain-types/src/soma/temporal_planner.rs rename to crates/brain-types/src/soma/temporal_planner.rs diff --git a/brain-types/src/soma/tests.rs b/crates/brain-types/src/soma/tests.rs similarity index 100% rename from brain-types/src/soma/tests.rs rename to crates/brain-types/src/soma/tests.rs diff --git a/cross_domain_synthesis_engine_demo.rs b/cross_domain_synthesis_engine_demo.rs deleted file mode 100644 index 3ae76d23397d64c9dd42a8a14a80dcee13be59f6..0000000000000000000000000000000000000000 --- a/cross_domain_synthesis_engine_demo.rs +++ /dev/null @@ -1,155 +0,0 @@ -/// Cross-Domain Synthesis Engine Demo -/// -/// This demonstrates TASK 3.1: Cross-Domain Synthesis Engine that combines insights -/// from multiple academic domains to solve complex interdisciplinary questions. -/// Shows how the engine identifies cross-domain connections, synthesizes knowledge, -/// and generates unified reasoning for sophisticated academic problems. - -use std::time::Instant; -use brain_cognitive::agents::intelligence::cross_domain_synthesis_engine::{ - CrossDomainSynthesisEngine -}; -use brain_cognitive::agents::traits::{ - AcademicQuestion, AcademicDomain, QuestionType -}; -use brain_cognitive::agents::CognitiveContext; -use uuid::Uuid; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  CROSS-DOMAIN SYNTHESIS ENGINE DEMO"); - println!("===================================="); - println!("Demonstrating TASK 3.1: Revolutionary interdisciplinary AI that"); - println!("combines insights across theoretical physics, advanced chemistry,"); - println!("pure mathematics, molecular biology, and computer science to solve"); - println!("complex academic questions requiring deep cross-domain understanding.\n"); - - // Initialize the Cross-Domain Synthesis Engine - println!("๐Ÿ”ง Initializing Cross-Domain Synthesis Engine..."); - let start_time = Instant::now(); - let engine = CrossDomainSynthesisEngine::new().await?; - let init_duration = start_time.elapsed(); - println!("โœ… Engine initialized in {:?}\n", init_duration); - - // Create interdisciplinary academic scenarios - let scenarios = create_interdisciplinary_scenarios(); - - println!("๐Ÿงช Testing Cross-Domain Synthesis with {} scenarios:\n", scenarios.len()); - - for (i, (question, description)) in scenarios.iter().enumerate() { - println!("๐Ÿ“‹ Scenario {}: {}", i + 1, description); - println!("โ“ Question: {}", question.question); - println!("๐ŸŽฏ Primary Domain: {:?}", question.domain); - println!("๐Ÿ” Question Type: {:?}", question.question_type); - println!("โšก Difficulty: {}", question.metadata.get("difficulty").unwrap_or(&"unknown".to_string())); - - let synthesis_start = Instant::now(); - - // Create cognitive context for synthesis - let context = CognitiveContext::default(); - - // Perform cross-domain synthesis - match engine.synthesize_interdisciplinary_response(question, &context).await { - Ok(response) => { - let synthesis_duration = synthesis_start.elapsed(); - - println!("๐ŸŽฏ Cross-Domain Analysis Complete in {:?}", synthesis_duration); - println!("โœ… Interdisciplinary synthesis successful!"); - println!("๐Ÿง  Response ID: {}", response.response_id); - println!("๐ŸŽฏ Primary Domain: {:?}", response.primary_domain); - println!("๐Ÿ”„ Contributing Domains: {} domains", response.contributing_domains.len()); - println!("๐Ÿ’ก Domain Insights: {} insights gathered", response.domain_insights.len()); - println!("๐ŸŒ‰ Cross-Domain Connections: {} connections identified", response.cross_domain_connections.len()); - println!("๐Ÿ“Š Overall Confidence: {:.2}", response.confidence); - println!("๐Ÿ’ญ Synthesized Reasoning: {}", response.synthesized_reasoning); - println!("โœ… Recommended Answer: {:?}", response.recommended_answer); - - println!("โœ… Scenario {} completed successfully\n", i + 1); - } - Err(e) => { - println!("โŒ Synthesis failed for scenario {}: {}\n", i + 1, e); - } - } - - // Add a brief pause between scenarios for readability - if i < scenarios.len() - 1 { - println!("{}", "=".repeat(60)); - } - } - - println!("\n๐ŸŽŠ Cross-Domain Synthesis Engine Demo Completed!"); - println!("โœจ TASK 3.1: Cross-Domain Synthesis Engine successfully demonstrates"); - println!(" revolutionary interdisciplinary AI capabilities combining multiple"); - println!(" domain expertise to solve complex academic problems."); - - Ok(()) -} - -/// Create interdisciplinary academic scenarios for testing -fn create_interdisciplinary_scenarios() -> Vec<(AcademicQuestion, String)> { - vec![ - ( - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "How do quantum mechanical principles in photosynthetic light-harvesting complexes inform the design of more efficient organic photovoltaic cells?".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question_type: QuestionType::ConceptualExplanation, - metadata: [ - ("context".to_string(), "Interdisciplinary question requiring physics, chemistry, and biology knowledge".to_string()), - ("difficulty".to_string(), "9".to_string()), - ("expected_time_minutes".to_string(), "20".to_string()), - ("keywords".to_string(), "quantum mechanics, photosynthesis, photovoltaics".to_string()), - ].iter().cloned().collect(), - options: Some(vec![ - "A) Quantum coherence effects are irrelevant to solar cell efficiency".to_string(), - "B) Quantum coherence enables efficient energy transfer that can be mimicked".to_string(), - "C) Only classical physics applies to photovoltaic design".to_string(), - "D) Biological systems cannot inform artificial energy systems".to_string(), - ]), - }, -"Physics-Chemistry-Biology Integration: Quantum biophysics applications".to_string() - ), - ( - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "What mathematical topology concepts can be applied to understand protein folding dynamics and inform machine learning architectures for predicting protein structures?".to_string(), - domain: AcademicDomain::AdvancedMathematics, - question_type: QuestionType::ConceptualExplanation, - metadata: [ - ("context".to_string(), "Cross-domain topology, biology, and computer science".to_string()), - ("difficulty".to_string(), "10".to_string()), - ("expected_time_minutes".to_string(), "25".to_string()), - ("keywords".to_string(), "topology, protein folding, machine learning".to_string()), - ].iter().cloned().collect(), - options: Some(vec![ - "A) Topology is irrelevant to protein folding".to_string(), - "B) Topological invariants can characterize folding pathways".to_string(), - "C) Only statistical mechanics applies to protein folding".to_string(), - "D) Machine learning cannot benefit from mathematical topology".to_string(), - ]), - }, -"Mathematics-Biology-CS Integration: Topological approaches to biological systems".to_string() - ), - ( - AcademicQuestion { - id: Uuid::new_v4().to_string(), - question: "How can insights from information theory and thermodynamics be combined to optimize both the chemical synthesis pathways and computational algorithms for drug discovery?".to_string(), - domain: AcademicDomain::AdvancedChemistry, - question_type: QuestionType::ConceptualExplanation, - metadata: [ - ("context".to_string(), "Information theory, thermodynamics, chemistry, and computer science convergence".to_string()), - ("difficulty".to_string(), "8".to_string()), - ("expected_time_minutes".to_string(), "18".to_string()), - ("keywords".to_string(), "information theory, thermodynamics, drug discovery".to_string()), - ].iter().cloned().collect(), - options: Some(vec![ - "A) Information theory has no relevance to chemical synthesis".to_string(), - "B) Thermodynamic principles can guide both synthesis and computation".to_string(), - "C) Drug discovery is purely empirical with no theoretical basis".to_string(), - "D) Computational and chemical optimization are unrelated".to_string(), - ]), - }, -"Chemistry-Physics-CS Integration: Information-theoretic optimization".to_string() - ), - ] -} \ No newline at end of file diff --git a/curiosity_learning_demo.rs b/curiosity_learning_demo.rs deleted file mode 100644 index 37d9778d5788345224bebb6e3f7e3cf7d83e85ac..0000000000000000000000000000000000000000 --- a/curiosity_learning_demo.rs +++ /dev/null @@ -1,528 +0,0 @@ -//! Curiosity-Driven Learning Demo -//! -//! This example demonstrates the complete curiosity-driven learning system, -//! showcasing how it integrates with meta-memory and novelty detection to -//! create intelligent learning priorities and adaptive exploration behavior. - -use anyhow::Result; -use brain_cognitive::learning::{ - CuriosityLearningEngine, CuriosityConfig, LearningEvent, - CuriosityDrive, NoveltyDetector, NoveltyAssessment, - NoveltyLevel, CuriosityLearningService, -}; -use brain_cognitive::meta::{ - MetaMemoryService, MetaMemoryRepository, MetaMemoryAnalytics, - MetaMemoryMaintenance, MetaMemoryConfig, KnowledgeType, -}; -use brain_types::BrainError; -use std::collections::HashMap; -use std::sync::Arc; -use tokio::sync::RwLock; -use uuid::Uuid; -use rand::Rng; - -pub struct SimpleNoveltyDetector { - novelty_threshold: f64, - known_patterns: Arc>>, -} - -impl std::fmt::Debug for SimpleNoveltyDetector { - fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { - f.debug_struct("SimpleNoveltyDetector") - .field("novelty_threshold", &self.novelty_threshold) - .field("pattern_count", &"") - .finish() - } -} - -impl SimpleNoveltyDetector { - pub fn new() -> Self { - Self { - novelty_threshold: 0.8, - known_patterns: Arc::new(RwLock::new(HashMap::new())), - } - } - - /// Add some known patterns to reduce novelty scores - pub async fn seed_with_patterns(&self, patterns: &[&str]) { - let mut known = self.known_patterns.write().await; - for pattern in patterns { - known.insert(pattern.to_string(), 0.9); // High familiarity - } - } -} - -#[async_trait::async_trait] -impl NoveltyDetector for SimpleNoveltyDetector { - async fn assess_novelty(&self, input: &str) -> Result { - let known = self.known_patterns.read().await; - - // Simple novelty assessment based on pattern familiarity - let words: Vec<&str> = input.split_whitespace().collect(); - let mut novelty_score = 1.0; // Start with high novelty - let mut familiar_count = 0; - - // Check for familiar patterns - for word in &words { - if known.contains_key(&word.to_lowercase()) { - familiar_count += 1; - } - } - - if !words.is_empty() { - let familiarity = familiar_count as f64 / words.len() as f64; - novelty_score = 1.0 - familiarity; // High familiarity = low novelty - } - - // Add some randomness for interesting results - let mut rng = rand::thread_rng(); - novelty_score = (novelty_score + rng.gen_range(-0.1..0.1)).clamp(0.0, 1.0); - - let novelty_level = if novelty_score > 0.8 { - NoveltyLevel::VeryHigh - } else if novelty_score > 0.6 { - NoveltyLevel::High - } else if novelty_score > 0.4 { - NoveltyLevel::Medium - } else if novelty_score > 0.2 { - NoveltyLevel::Low - } else { - NoveltyLevel::VeryLow - }; - - Ok(NoveltyAssessment { - novelty_score, - novelty_level, - novelty_factors: vec![ - format!("Word familiarity: {:.2}", 1.0 - novelty_score), - format!("Pattern complexity: {:.2}", words.len() as f64 / 10.0), - ], - assessment_confidence: 0.8, - }) - } - - async fn update_models(&mut self, input: &str) -> Result<(), BrainError> { - let mut known = self.known_patterns.write().await; - let words: Vec<&str> = input.split_whitespace().collect(); - - // Add new words to known patterns with moderate familiarity - for word in words { - let current_familiarity = known.get(&word.to_lowercase()).unwrap_or(&0.0); - let new_familiarity = (current_familiarity + 0.1).min(1.0); - known.insert(word.to_lowercase(), new_familiarity); - } - - Ok(()) - } -} - -/// Simple meta-memory repository implementation for demo -#[derive(Debug)] -pub struct SimpleMetaMemoryRepository { - items: Arc>>, - component_to_meta: Arc>>, -} - -impl SimpleMetaMemoryRepository { - pub fn new() -> Self { - Self { - items: Arc::new(RwLock::new(HashMap::new())), - component_to_meta: Arc::new(RwLock::new(HashMap::new())), - } - } -} - -#[async_trait::async_trait] -impl MetaMemoryRepository for SimpleMetaMemoryRepository { - async fn store_item(&mut self, item: brain_cognitive::meta::MetaMemoryItem) -> brain_cognitive::meta::MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_meta.write().await; - - let item_id = item.id; - let component_id = item.component_id; - - items.insert(item_id, item); - component_map.insert(component_id, item_id); - - Ok(item_id) - } - - async fn get_item(&self, id: Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - let items = self.items.read().await; - Ok(items.get(&id).cloned()) - } - - async fn get_item_by_component(&self, component_id: Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - let component_map = self.component_to_meta.read().await; - if let Some(&meta_id) = component_map.get(&component_id) { - self.get_item(meta_id).await - } else { - Ok(None) - } - } - - async fn query_items(&self, _query: &brain_cognitive::meta::MetaMemoryQuery) -> brain_cognitive::meta::MetaMemoryResult> { - let items = self.items.read().await; - Ok(items.values().cloned().collect()) - } - - async fn remove_item(&mut self, id: Uuid) -> brain_cognitive::meta::MetaMemoryResult { - let mut items = self.items.write().await; - Ok(items.remove(&id).is_some()) - } - - async fn batch_update(&mut self, items_to_update: Vec) -> brain_cognitive::meta::MetaMemoryResult> { - let mut ids = Vec::new(); - for item in items_to_update { - let id = self.store_item(item).await?; - ids.push(id); - } - Ok(ids) - } - - async fn count_items(&self) -> brain_cognitive::meta::MetaMemoryResult { - let items = self.items.read().await; - Ok(items.len()) - } - - async fn clear_all(&mut self) -> brain_cognitive::meta::MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_meta.write().await; - let count = items.len(); - items.clear(); - component_map.clear(); - Ok(count) - } -} - -// Simple implementations for analytics and maintenance -#[derive(Debug)] -pub struct SimpleMetaMemoryAnalytics; - -#[async_trait::async_trait] -impl MetaMemoryAnalytics for SimpleMetaMemoryAnalytics { - async fn calculate_stats(&self) -> brain_cognitive::meta::MetaMemoryResult { - Ok(brain_cognitive::meta::MetaMemoryStats::default()) - } - - async fn get_confidence_distribution(&self) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(HashMap::new()) - } - - async fn get_quality_distribution(&self) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(HashMap::new()) - } - - async fn get_knowledge_type_distribution(&self) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(HashMap::new()) - } - - async fn get_trending_components(&self, _limit: usize) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(Vec::new()) - } - - async fn get_performance_metrics(&self, _hours_back: f64) -> brain_cognitive::meta::MetaMemoryResult { - Ok(brain_cognitive::meta::PerformanceMetrics { - time_period_hours: 24.0, - items_added: 0, - items_updated: 0, - items_accessed: 0, - avg_confidence_change: 0.0, - avg_quality_improvement: 0.0, - validation_success_rate: 0.8, - storage_efficiency: 0.9, - }) - } -} - -#[derive(Debug)] -pub struct SimpleMetaMemoryMaintenance; - -#[async_trait::async_trait] -impl MetaMemoryMaintenance for SimpleMetaMemoryMaintenance { - async fn cleanup_stale_components(&mut self, _config: &MetaMemoryConfig) -> brain_cognitive::meta::MetaMemoryResult { - Ok(0) - } - - async fn optimize_storage(&mut self) -> brain_cognitive::meta::MetaMemoryResult<()> { - Ok(()) - } - - async fn backup_data(&self, _backup_path: &str) -> brain_cognitive::meta::MetaMemoryResult<()> { - Ok(()) - } - - async fn restore_data(&mut self, _backup_path: &str) -> brain_cognitive::meta::MetaMemoryResult { - Ok(0) - } - - async fn validate_integrity(&self) -> brain_cognitive::meta::MetaMemoryResult { - Ok(brain_cognitive::meta::IntegrityReport { - total_items: 0, - corrupted_items: 0, - missing_metadata: 0, - invalid_confidence: 0, - timestamp_issues: 0, - integrity_score: 1.0, - issues: Vec::new(), - }) - } -} - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿง  Brain AI - Curiosity-Driven Learning System Demo"); - println!("==================================================="); - println!(); - - // Phase 1: System Initialization - println!("๐Ÿ“‹ Phase 1: System Initialization"); - println!("----------------------------------"); - - // Create novelty detector - let novelty_detector = Arc::new(SimpleNoveltyDetector::new()); - - // Seed with some common patterns to make results more interesting - novelty_detector.seed_with_patterns(&[ - "the", "and", "a", "to", "of", "in", "is", "for", "with", "on", - "machine", "learning", "artificial", "intelligence", "computer", "data" - ]).await; - - println!("โœ… Novelty detector initialized with basic patterns"); - - // Create meta-memory service - let meta_memory_repo = Arc::new(RwLock::new(SimpleMetaMemoryRepository::new())); - let meta_memory_analytics = Arc::new(SimpleMetaMemoryAnalytics); - let meta_memory_maintenance = Arc::new(SimpleMetaMemoryMaintenance); - let meta_memory_config = MetaMemoryConfig::default(); - - let meta_memory = Arc::new(MetaMemoryService::new( - meta_memory_repo, - meta_memory_analytics, - meta_memory_maintenance, - meta_memory_config, - )); - - println!("โœ… Meta-memory system initialized"); - - // Create curiosity learning engine - let curiosity_config = CuriosityConfig { - novelty_weight: 0.4, - uncertainty_weight: 0.3, - progress_weight: 0.3, - learning_threshold: 0.25, - exploration_rate: 0.7, - ..Default::default() - }; - - let mut curiosity_engine = CuriosityLearningEngine::new( - curiosity_config.clone(), - meta_memory.clone(), - novelty_detector.clone(), - ); - - println!("โœ… Curiosity-driven learning engine initialized"); - println!(); - - // Phase 2: Populate Meta-Memory with Sample Knowledge - println!("๐Ÿ“Š Phase 2: Populating Meta-Memory with Sample Knowledge"); - println!("--------------------------------------------------------"); - - // Add some sample knowledge components - for (i, (knowledge_type, confidence, source)) in [ - (KnowledgeType::ConceptNode, 0.9, "Core concept: 'learning'"), - (KnowledgeType::ConceptNode, 0.85, "Important concept: 'intelligence'"), - (KnowledgeType::ConceptNode, 0.8, "Key concept: 'knowledge'"), - (KnowledgeType::Rule, 0.7, "Rule: if curious then explore"), - (KnowledgeType::Rule, 0.75, "Rule: if learning then remember"), - (KnowledgeType::Pattern, 0.65, "Pattern: question -> research -> answer"), - ].iter().enumerate() { - let component_id = Uuid::new_v4(); - match meta_memory.track_component( - component_id, - knowledge_type.clone(), - *confidence, - source.to_string(), - ).await { - Ok(_) => println!(" โœ… Added knowledge component {}: {}", i + 1, source), - Err(e) => println!(" โŒ Failed to add component: {}", e), - } - } - - println!(); - - // Phase 3: Test Curiosity Assessment - println!("๐Ÿ” Phase 3: Curiosity Assessment Tests"); - println!("--------------------------------------"); - - let test_inputs = vec![ - ("The quantum nature of reality suggests that observation affects outcome", "physics"), - ("Machine learning algorithms can exhibit emergent behavior", "AI"), - ("Economic systems show fractal patterns at multiple scales", "economics"), - ("Biological networks demonstrate small-world properties", "biology"), - ("Language evolution follows power law distributions", "linguistics"), - ("Consciousness may emerge from complex information integration", "neuroscience"), - ("Social networks exhibit preferential attachment dynamics", "sociology"), - ("Climate systems display chaotic behavior patterns", "climate"), - ("Mathematical proofs can be verified automatically", "mathematics"), - ("Artistic creativity involves pattern recognition and innovation", "art"), - ]; - - let mut curiosity_scores = Vec::new(); - - for (i, (input, domain)) in test_inputs.iter().enumerate() { - println!("๐Ÿงช Test {}: {} Domain", i + 1, domain.to_uppercase()); - - match curiosity_engine.assess_curiosity(input).await { - Ok(curiosity_score) => { - curiosity_scores.push(curiosity_score); - - println!(" Input: \"{}\"", input); - println!(" Curiosity Score: {:.3}", curiosity_score); - - if curiosity_score >= curiosity_config.learning_threshold { - println!(" ๐ŸŽฏ Learning priority created!"); - } else { - println!(" โ„น๏ธ Below learning threshold"); - } - } - Err(e) => { - println!(" โŒ Error assessing curiosity: {}", e); - } - } - println!(); - } - - let avg_curiosity = if !curiosity_scores.is_empty() { - curiosity_scores.iter().sum::() / curiosity_scores.len() as f64 - } else { - 0.0 - }; - println!("๐Ÿ“Š Average curiosity score across all tests: {:.3}", avg_curiosity); - println!(); - - // Phase 4: Learning Priorities Analysis - println!("๐ŸŽฏ Phase 4: Learning Priorities Analysis"); - println!("----------------------------------------"); - - match curiosity_engine.get_top_priorities(5).await { - Ok(top_priorities) => { - println!("๐Ÿ† Top {} Learning Priorities:", top_priorities.len()); - - for (i, priority) in top_priorities.iter().enumerate() { - println!(" {}. ID: {}", i + 1, priority.id); - println!(" Content: \"{}\"", priority.content); - println!(" Curiosity Score: {:.3}", priority.curiosity_score); - println!(" Drive: {:?}", priority.primary_drive); - println!(" Expected Value: {:.3}", priority.expected_value); - println!(" Knowledge Gaps: {}", priority.knowledge_gaps.len()); - println!(); - } - } - Err(e) => { - println!("โŒ Error getting learning priorities: {}", e); - } - } - - // Phase 5: Simulate Learning Events - println!("๐Ÿ“š Phase 5: Simulating Learning Events"); - println!("--------------------------------------"); - - // Simulate some learning events - for i in 0..3 { - let priority_id = Uuid::new_v4(); - let content = format!("Learning topic {}: Advanced concepts in curiosity-driven systems", i + 1); - - println!("๐ŸŽ“ Simulating learning event for: \"{}\"", content); - - let mut event = LearningEvent::new( - priority_id, - content.clone(), - CuriosityDrive::NoveltySeeker, - KnowledgeType::ConceptNode, - ); - - // Simulate learning outcomes - let mut rng = rand::thread_rng(); - let success = rng.gen_bool(0.7); // 70% success rate - - event.success = success; - event.progress_gained = if success { - rng.gen_range(0.3..0.7) // 30-70% progress - } else { - rng.gen_range(0.1..0.3) // 10-30% progress - }; - event.duration_minutes = rng.gen_range(15.0..45.0); // 15-45 minutes - event.satisfaction = if success { - rng.gen_range(0.6..1.0) // 60-100% satisfaction - } else { - rng.gen_range(0.2..0.6) // 20-60% satisfaction - }; - - // Store event details before moving - let progress_gained = event.progress_gained; - let duration_minutes = event.duration_minutes; - let satisfaction = event.satisfaction; - - match curiosity_engine.record_learning_event(event).await { - Ok(_) => { - println!(" โœ… Success: {}", if success { "Yes" } else { "No" }); - println!(" ๐Ÿ“ˆ Progress Gained: {:.1}%", progress_gained * 100.0); - println!(" โฑ๏ธ Duration: {:.1} minutes", duration_minutes); - println!(" ๐Ÿ˜Š Satisfaction: {:.1}%", satisfaction * 100.0); - } - Err(e) => { - println!(" โŒ Error recording learning event: {}", e); - } - } - println!(); - } - - // Get updated statistics - match curiosity_engine.get_stats().await { - Ok(updated_stats) => { - println!("๐Ÿ“ˆ Updated curiosity system statistics:"); - println!(" โ€ข Total priorities: {}", updated_stats.total_priorities); - println!(" โ€ข Active priorities: {}", updated_stats.active_priorities); - println!(" โ€ข Completed priorities: {}", updated_stats.completed_priorities); - println!(" โ€ข Overall success rate: {:.3}", updated_stats.overall_success_rate); - println!(" โ€ข Average progress: {:.3}", updated_stats.average_progress); - println!(); - - println!("๐ŸŽญ Drive Distribution:"); - for (drive, count) in &updated_stats.drive_distribution { - println!(" โ€ข {:?}: {} priorities", drive, count); - } - } - Err(e) => { - println!("โŒ Error getting statistics: {}", e); - } - } - println!(); - - // Phase 6: Summary - println!("๐ŸŽ‰ Phase 6: Demonstration Summary"); - println!("================================="); - println!("โœ… Curiosity-driven learning system operational"); - println!("โœ… Novelty detection for curiosity assessment"); - println!("โœ… Meta-memory integration for knowledge tracking"); - println!("โœ… Learning priority creation and management"); - println!("โœ… Learning event simulation and recording"); - println!("โœ… Statistical analysis and reporting"); - println!(); - - println!("๐ŸŽฏ The curiosity learning system successfully demonstrated:"); - println!(" โ€ข Adaptive novelty assessment"); - println!(" โ€ข Knowledge gap identification"); - println!(" โ€ข Learning priority generation"); - println!(" โ€ข Progress tracking and statistics"); - println!(" โ€ข Integration with meta-memory system"); - println!(); - - println!("๐Ÿ’ก Ready for integration with other Brain AI components!"); - - Ok(()) -} \ No newline at end of file diff --git a/dataset_20250623_230720.jsonl b/dataset_20250623_230720.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/dataset_20250623_231007.jsonl b/dataset_20250623_231007.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/dataset_20250623_232224.jsonl b/dataset_20250623_232224.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/dataset_20250629_134914.jsonl b/dataset_20250629_134914.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/dataset_20250629_134954.jsonl b/dataset_20250629_134954.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/dataset_20250629_135056.jsonl b/dataset_20250629_135056.jsonl deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/debug_academic_agent.rs b/debug_academic_agent.rs deleted file mode 100644 index b45854068547b540eb3adad2829e4e5e72a8cb43..0000000000000000000000000000000000000000 --- a/debug_academic_agent.rs +++ /dev/null @@ -1,100 +0,0 @@ -//! Debug Academic Agent Response Format -//! -//! Simple test to see what the UniversalAcademicAgent returns for a multiple choice question -//! and diagnose the answer extraction issue in the global HLE validation framework. - -use std::collections::HashMap; -use brain_cognitive::agents::{ - intelligence::UniversalAcademicAgent, - traits::{BrainAgent, AgentInput, CognitiveContext}, -}; -use brain_types::error::BrainError; - -#[tokio::main] -async fn main() -> Result<(), BrainError> { - println!("๐Ÿ” DEBUGGING ACADEMIC AGENT RESPONSE FORMAT"); - println!("Testing a simple multiple choice question to see output format\n"); - - // Create agent - let agent = UniversalAcademicAgent::new().await?; - let context = CognitiveContext::default(); - - // Create a simple test question - let options_str = "A) Oxidative addition, transmetalation, reductive elimination\nB) Nucleophilic substitution followed by elimination\nC) Radical chain mechanism\nD) Concerted cycloaddition"; - let full_question = format!("{}\n\nOptions:\n{}", - "What is the mechanism of the Suzuki-Miyaura cross-coupling reaction?", - options_str); - - let input = AgentInput { - input_type: "multiple_choice_question".to_string(), - content: full_question, - parameters: { - let mut params = HashMap::new(); - params.insert("options".to_string(), serde_json::Value::String(options_str.to_string())); - params.insert("domain".to_string(), serde_json::Value::String("AdvancedChemistry".to_string())); - params - }, - previous_outputs: Vec::new(), - session_id: "debug_test".to_string(), - timestamp: chrono::Utc::now(), - user_preferences: HashMap::new(), - }; - - // Execute agent - println!("๐Ÿ“ QUESTION:"); - println!("{}", input.content); - println!("\n๐Ÿค– EXECUTING ACADEMIC AGENT...\n"); - - let output = agent.execute(input, &context).await?; - - // Debug the output - println!("๐Ÿ” DEBUG RESULTS:"); - println!("================"); - println!("Agent ID: {}", output.agent_id); - println!("Output Type: {}", output.output_type); - println!("Confidence: {}", output.confidence); - println!("\n๐Ÿ“„ FULL CONTENT:"); - println!("\"{}\"", output.content); - println!("\n๐Ÿ” CONTENT LINES:"); - for (i, line) in output.content.lines().enumerate() { - println!("Line {}: \"{}\"", i, line); - if line.starts_with("Answer:") { - println!(" โ†ณ FOUND ANSWER LINE!"); - if let Some(answer_part) = line.split(':').nth(1) { - println!(" โ†ณ EXTRACTED: \"{}\"", answer_part.trim()); - } - } - } - - // Test current extraction logic - let selected_answer = output.content - .lines() - .find(|line| line.starts_with("Answer:")) - .and_then(|line| line.split(':').nth(1)) - .map(|s| { - // Extract just the letter (A, B, C, D) from responses like "A - Strong candidate" - s.trim() - .chars() - .next() - .filter(|c| ['A', 'B', 'C', 'D'].contains(c)) - .map(|c| c.to_string()) - .unwrap_or_else(|| "A".to_string()) - }) - .unwrap_or_else(|| "A".to_string()); - - println!("\nโœ… EXTRACTED ANSWER: \"{}\"", selected_answer); - println!("๐Ÿ“Š CORRECT ANSWER: \"A\""); - println!("๐ŸŽฏ MATCH: {}", if selected_answer == "A" { "โœ… YES" } else { "โŒ NO" }); - - // Show metadata - if let Some(reasoning) = output.reasoning { - println!("\n๐Ÿง  REASONING: {}", reasoning); - } - - println!("\n๐Ÿ“Š DATA FIELDS:"); - for (key, value) in output.data { - println!(" {}: {}", key, serde_json::to_string_pretty(&value).unwrap_or("N/A".to_string())); - } - - Ok(()) -} \ No newline at end of file diff --git a/debug_memory.db b/debug_memory.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/debug_memory.db and /dev/null differ diff --git a/debug_memory_content.rs b/debug_memory_content.rs deleted file mode 100644 index e5904aa2ebce824854b4f389e82dcc0c27bd06f1..0000000000000000000000000000000000000000 --- a/debug_memory_content.rs +++ /dev/null @@ -1,240 +0,0 @@ -#!/usr/bin/env cargo run --example debug_memory_content -//! Debug Memory Content -//! -//! This debug example shows what's actually stored in Brain AI's memory -//! after learning from PocketFlow to understand why insights aren't being generated. - -use brain::{MemoryService, WorkingMemoryQuery, Result, SemanticQuery, GitHubLearningEngine, GitHubLearningConfig}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Debug Memory Content Demo"); - println!("============================"); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| { - eprintln!("Failed to create data directory: {}", e); - brain::BrainError::InvalidInput { - message: "Failed to create data directory".to_string(), - context: None, - } - })?; - - // Initialize repositories - let mut working_repo = WorkingMemoryRepository::new(100); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/debug_memory.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service with a separate working repo for queries - let working_repo_for_service = Box::new(WorkingMemoryRepository::new(100)); - let memory_service = MemoryService::new(working_repo_for_service, episodic_repo, semantic_repo); - - // Get GitHub token - let github_token = env::var("GITHUB_TOKEN").ok(); - - // Create GitHub learning configuration - let config = GitHubLearningConfig { - max_files: 50, - max_file_size: 50_000, - include_code: true, - include_docs: true, - include_config: true, - ..Default::default() - }; - - let github_engine = GitHubLearningEngine::new(github_token.clone(), Some(config)); - - println!("\n๐Ÿš€ Learning from PocketFlow Repository"); - println!("{}", "-".repeat(40)); - - // Learn from PocketFlow repository (pass working_repo directly) - let pocketflow_url = "https://github.com/The-Pocket/PocketFlow"; - match github_engine.learn_from_repository(&mut working_repo, pocketflow_url).await { - Ok(result) => { - println!("โœ… Learning completed!"); - println!(" Files processed: {}", result.files_processed); - println!(" Concepts discovered: {}", result.concepts_discovered); - println!(" Memory entries: {}", result.memory_entries_created); - } - Err(e) => { - println!("โŒ Learning failed: {}", e); - return Err(e); - } - } - - println!("\n๐Ÿ” Analyzing Working Memory Content"); - println!("{}", "-".repeat(40)); - - // Query working memory to see what was stored - let query = WorkingMemoryQuery { - content_pattern: None, - priority: None, - min_importance: None, - created_after: None, - limit: Some(20), // Show first 20 items - }; - - match memory_service.query_working(&query).await { - Ok(items) => { - println!("Found {} working memory items:", items.len()); - for (i, item) in items.iter().enumerate() { - println!("\n๐Ÿ“ Item {}: (Priority: {:?}, Importance: {:.3})", - i + 1, item.priority, item.importance_score()); - - // Show first 200 characters of content - let content_preview = if item.content.len() > 200 { - format!("{}...", &item.content[..200]) - } else { - item.content.clone() - }; - println!(" Content: {}", content_preview); - } - } - Err(e) => { - println!("โŒ Failed to query working memory: {}", e); - } - } - - println!("\n๐Ÿ” Searching for Specific Patterns"); - println!("{}", "-".repeat(40)); - - // Search for specific architectural terms - let search_terms = vec![ - "agent", "workflow", "flow", "orchestration", "llm", "framework", - "pattern", "architecture", "design", "component", "class", "function" - ]; - - for term in search_terms { - let query = WorkingMemoryQuery { - content_pattern: Some(term.to_string()), - priority: None, - min_importance: None, - created_after: None, - limit: Some(5), - }; - - match memory_service.query_working(&query).await { - Ok(items) => { - if !items.is_empty() { - println!("\n๐ŸŽฏ Found {} items containing '{}':", items.len(), term); - for (i, item) in items.iter().enumerate() { - let content_preview = if item.content.len() > 150 { - format!("{}...", &item.content[..150]) - } else { - item.content.clone() - }; - println!(" {}. {}", i + 1, content_preview); - } - } - } - Err(e) => { - println!("โŒ Failed to search for '{}': {}", term, e); - } - } - } - - println!("\n๐Ÿ” Analyzing Semantic Memory"); - println!("{}", "-".repeat(40)); - - let semantic_query = SemanticQuery { - name_pattern: None, - embedding: None, - min_confidence: Some(0.1), - min_similarity: None, - limit: Some(10), - }; - - match memory_service.query_semantic(&semantic_query).await { - Ok(concepts) => { - if !concepts.is_empty() { - println!("Found {} semantic concepts:", concepts.len()); - for (i, concept) in concepts.iter().enumerate() { - println!(" {}. {} (confidence: {:.3})", - i + 1, concept.name, concept.confidence); - println!(" Description: {}", concept.description); - } - } else { - println!("No semantic concepts found"); - } - } - Err(e) => { - println!("โŒ Failed to query semantic memory: {}", e); - } - } - - println!("\n๐Ÿ” Cross-Memory Search for Architecture Terms"); - println!("{}", "-".repeat(40)); - - let architecture_terms = vec![ - "PocketFlow", "agent", "workflow", "orchestration", "framework" - ]; - - for term in architecture_terms { - match memory_service.query_all_memories(term).await { - Ok(results) => { - let total_results = results.working_results.len() + - results.episodic_results.len() + - results.semantic_results.len(); - - if total_results > 0 { - println!("\n๐ŸŽฏ Cross-memory search for '{}' found {} results:", term, total_results); - - for (i, item) in results.working_results.iter().enumerate() { - let preview = if item.content.len() > 100 { - format!("{}...", &item.content[..100]) - } else { - item.content.clone() - }; - println!(" Working {}: {}", i + 1, preview); - } - - for (i, event) in results.episodic_results.iter().enumerate() { - let preview = if event.content.len() > 100 { - format!("{}...", &event.content[..100]) - } else { - event.content.clone() - }; - println!(" Episodic {}: {}", i + 1, preview); - } - - for (i, concept) in results.semantic_results.iter().enumerate() { - println!(" Semantic {}: {} - {}", i + 1, concept.name, concept.description); - } - } - } - Err(e) => { - println!("โŒ Cross-memory search for '{}' failed: {}", term, e); - } - } - } - - println!("\n๐Ÿ“Š Memory Summary"); - println!("{}", "-".repeat(40)); - - // Get a summary of what's in memory - let all_query = WorkingMemoryQuery::default(); - match memory_service.query_working(&all_query).await { - Ok(items) => { - let total_items = items.len(); - let total_size: usize = items.iter().map(|item| item.content.len()).sum(); - println!("Working Memory Summary:"); - println!(" โ€ข Total items: {}", total_items); - println!(" โ€ข Total content size: {} bytes", total_size); - if total_items > 0 { - let avg_size = total_size / total_items; - println!(" โ€ข Average item size: {} bytes", avg_size); - } - } - Err(e) => { - println!("Failed to get memory summary: {}", e); - } - } - - println!("\nโœ… Memory Content Debug Complete!"); - println!("This should help identify what's being stored and why insights might not be generated."); - - Ok(()) -} \ No newline at end of file diff --git a/debug_rag.db b/debug_rag.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/debug_rag.db and /dev/null differ diff --git a/debug_rag_retrieval.rs b/debug_rag_retrieval.rs deleted file mode 100644 index b6850de897a1b92801b33af9b7b7c713f9da14ce..0000000000000000000000000000000000000000 --- a/debug_rag_retrieval.rs +++ /dev/null @@ -1,184 +0,0 @@ -#!/usr/bin/env cargo run --example debug_rag_retrieval -//! Debug RAG Retrieval -//! -//! This debug example tests RAG retrieval directly to understand why -//! the stored architectural knowledge isn't being found by the Brain AI. - -use brain::{MemoryService, WorkingMemoryQuery, Priority, Result}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿง  Debug RAG Retrieval Demo"); - println!("==========================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| brain::BrainError::from(e).with_context(brain::ErrorContext::new("Failed to create data directory")))?; - - // Initialize repositories - let working_repo = Box::new(WorkingMemoryRepository::new(100)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/debug_rag.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let mut memory_service = MemoryService::new(working_repo, episodic_repo, semantic_repo); - - println!("\n๐Ÿง  Loading Test Knowledge"); - println!("{}", "-".repeat(30)); - - // Load some test architectural knowledge - let test_knowledge = vec![ - "PocketFlow implements three unique architecture patterns: Node-Flow Architecture, Async Parallel Processing, and Batch Optimization Framework.", - "The Node-Flow pattern in PocketFlow separates processing logic (Nodes) from execution orchestration (Flows). BaseNode is the fundamental abstraction.", - "PocketFlow uses BatchNode and ParallelBatchNode for optimizing LLM API costs by grouping multiple requests together.", - "PocketFlow enables agent-based workflows through its 'Agents build Agents' design philosophy.", - "PocketFlow is a 100-line framework that provides essential LLM orchestration capabilities in a compact codebase.", - ]; - - for (i, knowledge) in test_knowledge.iter().enumerate() { - match memory_service.learn(knowledge.to_string(), Priority::High).await { - Ok(_) => println!("โœ… Stored knowledge {}", i + 1), - Err(e) => println!("โŒ Failed to store knowledge {}: {}", i + 1, e), - } - } - - println!("\n๐Ÿ” Testing Direct Memory Queries"); - println!("{}", "-".repeat(30)); - - // Test direct memory queries - let test_queries = vec![ - "architecture patterns", - "Node-Flow", - "BatchNode", - "agent-based", - "100-line framework", - "PocketFlow", - ]; - - for query in test_queries { - println!("\n๐ŸŽฏ Testing query: '{}'", query); - - // Test working memory query - let working_query = WorkingMemoryQuery { - content_pattern: Some(query.to_string()), - priority: None, - min_importance: None, - created_after: None, - limit: Some(5), - }; - - match memory_service.query_working(&working_query).await { - Ok(items) => { - println!(" Working memory: {} items found", items.len()); - for (i, item) in items.iter().take(2).enumerate() { - let preview = if item.content.len() > 80 { - format!("{}...", &item.content[..80]) - } else { - item.content.clone() - }; - println!(" {}. {} (importance: {:.3})", i + 1, preview, item.importance_score()); - } - } - Err(e) => { - println!(" Working memory query failed: {}", e); - } - } - - // Test cross-memory search - match memory_service.query_all_memories(query).await { - Ok(results) => { - let total = results.working_results.len() + results.episodic_results.len() + results.semantic_results.len(); - println!(" Cross-memory search: {} total results", total); - for (i, item) in results.working_results.iter().take(2).enumerate() { - let preview = if item.content.len() > 80 { - format!("{}...", &item.content[..80]) - } else { - item.content.clone() - }; - println!(" {}. {}", i + 1, preview); - } - } - Err(e) => { - println!(" Cross-memory search failed: {}", e); - } - } - } - - println!("\n๐Ÿค– Testing Similarity Calculations"); - println!("{}", "-".repeat(30)); - - // Test RAG retrieval with different thresholds - let test_questions = vec![ - "What are the 3 unique architecture patterns in PocketFlow?", - "How does the Node-Flow pattern work?", - "What is BatchNode used for?", - ]; - - for question in test_questions { - println!("\n๐Ÿ“ Question: '{}'", question); - - // Test simple text similarity - for (i, knowledge) in test_knowledge.iter().enumerate() { - let similarity = calculate_simple_similarity(question, knowledge); - println!(" Knowledge {}: similarity {:.3}", i + 1, similarity); - if similarity > 0.1 { - println!(" โœ… Would be retrieved (above threshold)"); - } else { - println!(" โŒ Below threshold"); - } - } - } - - println!("\n๐Ÿ“Š Memory System State"); - println!("{}", "-".repeat(30)); - - // Since MemoryService doesn't have get_stats, let's check working memory - let all_query = WorkingMemoryQuery::default(); - match memory_service.query_working(&all_query).await { - Ok(items) => { - let total_items = items.len(); - let total_size: usize = items.iter().map(|item| item.content.len()).sum(); - println!("Working Memory: {} items, {} bytes", total_items, total_size); - } - Err(e) => { - println!("Failed to get memory stats: {}", e); - } - } - - println!("\nโœ… RAG Retrieval Debug Complete!"); - println!("This should help identify why the RAG system isn't finding the stored knowledge."); - - Ok(()) -} - -// Simple similarity calculation for debugging -fn calculate_simple_similarity(query: &str, content: &str) -> f64 { - let query_lower = query.to_lowercase(); - let content_lower = content.to_lowercase(); - - let query_words: std::collections::HashSet<&str> = query_lower - .split_whitespace() - .collect(); - - let content_words: std::collections::HashSet<&str> = content_lower - .split_whitespace() - .collect(); - - let intersection: std::collections::HashSet<_> = query_words - .intersection(&content_words) - .collect(); - - let union: std::collections::HashSet<_> = query_words - .union(&content_words) - .collect(); - - if union.is_empty() { - 0.0 - } else { - intersection.len() as f64 / union.len() as f64 - } -} \ No newline at end of file diff --git a/demo_memory.db b/demo_memory.db deleted file mode 100644 index d55b4d4bea1867480ad962050a374feb4b88e976..0000000000000000000000000000000000000000 Binary files a/demo_memory.db and /dev/null differ diff --git a/demonstrate_brain_ai.py b/demonstrate_brain_ai.py deleted file mode 100644 index 65b2751a7e0084f181f68241cae9611d68b97a23..0000000000000000000000000000000000000000 --- a/demonstrate_brain_ai.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -""" -๐Ÿš€ BRAIN AI SYSTEM DEMONSTRATION -================================ - -Complete demonstration of the integrated Brain AI system with: -- 38 specialized AI agents across 4 categories -- Real cognitive processing and agent orchestration -- Authentic HumanEval integration with Python execution -- Native Brain AI intelligence (no external LLMs) - -TESTED & VERIFIED: -- 3 Problems: 100% Pass@1 (3/3) - 28ms avg -- 10 Problems: 90% Pass@1 (9/10) - 17.9ms avg -- 25 Problems: 68% Pass@1 (17/25) - 20ms avg - -AGENT CATEGORIES: -- Development Agents: 12 specialized coding and design agents -- Security Agents: 5 cybersecurity and compliance agents -- Testing & Operations: 8 QA and infrastructure agents -- Intelligence & Platform: 13 ML and platform agents - -Copyright ยฉ 2025 Brain AI. All Rights Reserved. -""" - -import subprocess -import time - - -def print_banner(): - """Print the main demonstration banner""" - print("๐Ÿง " + "="*70 + "๐Ÿง ") - print("๐Ÿš€ BRAIN AI INTEGRATED SYSTEM DEMONSTRATION") - print("๐Ÿง " + "="*70 + "๐Ÿง ") - print() - print("โœจ REAL AI AGENTS & COGNITIVE PROCESSING") - print("๐ŸŽฏ 38 Specialized Agents Across 4 Categories") - print("๐Ÿงช Authentic HumanEval Integration (164 Problems)") - print("โšก Native Brain AI Intelligence (No External LLMs)") - print("๐Ÿ”„ Real Python Execution & Validation") - print() - - -def print_agent_architecture(): - """Display the sophisticated agent architecture""" - print("๐Ÿ—๏ธ SOPHISTICATED AGENT ARCHITECTURE") - print("="*50) - print() - - print("๐Ÿ”ง DEVELOPMENT AGENTS (12):") - agents = [ - "PlannerAgent - Strategic project planning & roadmaps", - "ArchitectAgent - System architecture & scalability design", - "DesignerAgent - UI/UX design & user experience", - "SchemaAgent - Database design & optimization", - "APIAgent - REST/GraphQL API design & implementation", - "FrontendCoder - React/Vue frontend development", - "BackendCoder - Server-side logic & microservices", - "AlgorithmCoder - Advanced algorithm implementation", - "RefactorAgent - Code refactoring & optimization", - "DocAgent - Technical documentation generation", - "DeployerAgent - CI/CD & deployment automation", - "MaintainerAgent - Code maintenance & updates" - ] - for agent in agents: - print(f" โœ… {agent}") - print() - - print("๐Ÿ”’ SECURITY AGENTS (5):") - security_agents = [ - "CyberSecurityAgent - Vulnerability scanning & threat detection", - "PromptSecurityAgent - AI prompt injection protection", - "PrivacyComplianceAgent - GDPR/CCPA compliance validation", - "DataPrivacyAgent - Data governance & protection", - "EthicalAIAgent - AI ethics & bias detection" - ] - for agent in security_agents: - print(f" ๐Ÿ”’ {agent}") - print() - - print("๐Ÿงช TESTING & OPERATIONS AGENTS (8):") - testing_agents = [ - "QAAgent - Quality assurance & testing automation", - "SandboxEnvironmentAgent - Isolated testing environments", - "ObservabilityAgent - Monitoring & performance analysis", - "BuildOptimizerAgent - Build process optimization", - "DriftDetectionAgent - Configuration drift monitoring", - "HotfixAgent - Emergency bug fixes & patches", - "BackupRecoveryAgent - Data backup & disaster recovery", - "ReplicationScalingAgent - Auto-scaling & replication" - ] - for agent in testing_agents: - print(f" ๐Ÿงช {agent}") - print() - - print("๐ŸŽฏ INTELLIGENCE & PLATFORM AGENTS (13):") - intel_agents = [ - "UserBehaviorAnalystAgent - User pattern analysis", - "FeatureExperimentationAgent - A/B testing & experiments", - "MLOpsAgent - Machine learning operations", - "ModelTrainingAgent - AI model training & optimization", - "DataIngestionAgent - Data pipeline & ETL processes", - "LocalizationAgent - Multi-language & i18n support", - "PlatformCompatibilityAgent - Cross-platform compatibility", - "DataVisualizationAgent - Charts & analytics dashboards", - "ApiGatewayAgent - API gateway & rate limiting", - "ServiceMeshAgent - Service mesh management", - "ContainerOrchestrationAgent - Kubernetes & Docker", - "InfrastructureProvisioningAgent - Cloud infrastructure", - "SystemOrchestrationAgent - Distributed system coordination" - ] - for agent in intel_agents: - print(f" ๐ŸŽฏ {agent}") - print() - - -def print_cognitive_features(): - """Display cognitive processing features""" - print("๐Ÿง  COGNITIVE PROCESSING FEATURES") - print("="*40) - print() - print("๐Ÿ”„ Agent Orchestration:") - print(" โ€ข Dynamic agent discovery & selection") - print(" โ€ข Multi-agent workflow coordination") - print(" โ€ข Capability-based routing") - print(" โ€ข Real-time confidence scoring") - print() - print("๐Ÿ’ญ Meta-Memory Integration:") - print(" โ€ข Persistent learning storage") - print(" โ€ข Context-aware retrieval") - print(" โ€ข Pattern recognition") - print(" โ€ข Experience accumulation") - print() - print("๐ŸŽฏ Specialized Input Processing:") - print(" โ€ข algorithmic_challenge (for AlgorithmCoder)") - print(" โ€ข api_specifications (for BackendCoder)") - print(" โ€ข technical_requirements (for ArchitectAgent)") - print(" โ€ข security_assessment (for CyberSecurityAgent)") - print() - - -def run_cognitive_test(test_name, problems, agent_type="algorithm-coder"): - """Run a cognitive processing test""" - print("๐Ÿงช COGNITIVE TEST: {}".format(test_name)) - print("-" * 50) - print(f"๐ŸŽฏ Agent: {agent_type}") - print(f"๐Ÿ“Š Problems: {problems}") - print("๐Ÿง  Real AI Processing: Native Brain Intelligence") - print("โšก Executing...") - - start_time = time.time() - - try: - # Map test configurations - test_functions = { - 1: "test_real_humaneval_single_problem", - 3: "test_real_humaneval_three_problems", - 5: "test_real_humaneval_five_problems", - 10: "test_real_humaneval_ten_problems", - 25: "test_real_humaneval_twenty_five_problems", - 50: "test_real_humaneval_fifty_problems" - } - - if problems not in test_functions: - print(f"โŒ No test available for {problems} problems") - return False - - test_function = test_functions[problems] - - # Run the test - result = subprocess.run([ - "cargo", "test", test_function, "--", "--nocapture" - ], cwd="crates/brain-benchmark", capture_output=True, text=True, - timeout=120) - - end_time = time.time() - execution_time = end_time - start_time - - if result.returncode == 0: - # Extract results from output - output_lines = result.stdout.split('\n') - pass_rate = None - avg_time = None - - for line in output_lines: - if "Pass Rate:" in line and "%" in line: - try: - rate_str = line.split("Pass Rate: ")[1].split("%")[0] - pass_rate = float(rate_str) - except (IndexError, ValueError): - pass_rate = "Unknown" - elif "Avg Time:" in line and "ms" in line: - try: - time_str = line.split("Avg Time: ")[1].split("ms")[0] - avg_time = float(time_str) - except (IndexError, ValueError): - avg_time = "Unknown" - - print("โœ… SUCCESS!") - print(f"๐Ÿ“Š Pass Rate: {pass_rate}%") - print(f"โฑ๏ธ Avg Execution: {avg_time}ms per problem") - print(f"๐Ÿ•’ Total Time: {execution_time:.2f}s") - print("๐Ÿง  Real AI cognitive processing completed!") - return True - else: - print("โŒ Test failed!") - # Last 200 chars - print("Error:", result.stderr[-200:]) - return False - - except subprocess.TimeoutExpired: - print("โฐ Test timed out") - return False - except Exception as e: - print(f"โŒ Error: {e}") - return False - - -def demonstrate_system(): - """Main demonstration function""" - print_banner() - print_agent_architecture() - print_cognitive_features() - - print("๐Ÿš€ LIVE COGNITIVE PROCESSING DEMONSTRATION") - print("="*60) - print() - - tests = [ - ("Small-Scale Cognitive Test", 3, - "Real AI processing with 3 HumanEval problems"), - ("Medium-Scale Orchestration", 10, - "Multi-agent coordination with 10 problems"), - ("Comprehensive Final Test", 25, - "Full cognitive capabilities with 25 problems") - ] - - results = [] - - for test_name, problems, description in tests: - print(f"๐Ÿ”ฅ {description}") - success = run_cognitive_test(test_name, problems) - results.append((test_name, problems, success)) - print() - - # Summary - print("๐Ÿ† DEMONSTRATION SUMMARY") - print("="*40) - print("โœจ Brain AI System Features Demonstrated:") - print(" โœ… 38 Specialized AI Agents") - print(" โœ… Real Cognitive Processing") - print(" โœ… Authentic HumanEval Integration") - print(" โœ… Native Brain Intelligence") - print(" โœ… Real Python Execution") - print(" โœ… Multi-Agent Orchestration") - print(" โœ… Fast Sub-25ms Performance") - print() - - print("๐Ÿ“Š Test Results:") - for test_name, problems, success in results: - status = "โœ… PASSED" if success else "โŒ FAILED" - print(f" {status} {test_name} ({problems} problems)") - print() - - print("๐ŸŽฏ REAL AI ACHIEVEMENTS:") - print(" โ€ข Zero external LLM dependencies") - print(" โ€ข Authentic test validation") - print(" โ€ข Realistic performance metrics") - print(" โ€ข Transparent success/failure rates") - print(" โ€ข Scalable concurrent execution") - print(" โ€ข Production-ready architecture") - print() - - print("๐Ÿš€ The Brain AI system demonstrates authentic coding intelligence") - print(" with sophisticated agent orchestration and real cognitive", - "processing!") - - -if __name__ == "__main__": - demonstrate_system() \ No newline at end of file diff --git a/deployer_agent_demo.rs b/deployer_agent_demo.rs deleted file mode 100644 index adc0016a252c3e4c48d2f639bbb5fd38578f365b..0000000000000000000000000000000000000000 --- a/deployer_agent_demo.rs +++ /dev/null @@ -1,295 +0,0 @@ -//! DeployerAgent Demo - Deployment Orchestration and Infrastructure Management -//! -//! This example demonstrates the comprehensive deployment capabilities of the DeployerAgent, -//! including deployment strategy design, infrastructure automation, CI/CD pipeline creation, -//! and operational excellence frameworks. - -use brain_cognitive::agents::{ - development::DeployerAgent, - traits::BrainAgent, -}; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ DeployerAgent Demo - Deployment Orchestration and Infrastructure Management"); - println!("================================================================================"); - - // Initialize the DeployerAgent - let deployer = DeployerAgent::new(); - - // Display agent metadata - display_agent_metadata(&deployer); - - // Demonstrate deployment strategy analysis - demonstrate_deployment_strategies(&deployer); - - // Show deployment automation capabilities - demonstrate_automation_capabilities(&deployer); - - // Display operational excellence framework - demonstrate_operational_excellence(&deployer); - - // Show development pipeline integration - demonstrate_pipeline_integration(&deployer); - - println!("\nโœจ DeployerAgent Demo Complete!"); - println!("The DeployerAgent provides comprehensive deployment orchestration with:"); - println!("โ€ข Zero-downtime progressive deployment strategies"); - println!("โ€ข Infrastructure as Code automation"); - println!("โ€ข Comprehensive CI/CD pipeline creation"); - println!("โ€ข Multi-cloud and container orchestration"); - println!("โ€ข Operational excellence and monitoring frameworks"); - println!("โ€ข Security compliance and vulnerability management"); - - Ok(()) -} - -fn display_agent_metadata(deployer: &DeployerAgent) { - let metadata = deployer.metadata(); - - println!("\n๐Ÿค– Agent Metadata"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - println!("Name: {}", metadata.name); - println!("Version: {}", metadata.version); - println!("Base Confidence: {:.1}%", metadata.base_confidence * 100.0); - println!("Confidence Threshold: {:.1}%", deployer.confidence_threshold() * 100.0); - - println!("\n๐ŸŽฏ Agent Persona:"); - println!("{}", metadata.persona); - - println!("\n๐Ÿ“ฅ Supported Input Types:"); - for input_type in &metadata.supported_input_types { - println!(" โ€ข {}", input_type); - } - - println!("\n๐Ÿ“ค Supported Output Types:"); - for output_type in &metadata.supported_output_types { - println!(" โ€ข {}", output_type); - } - - println!("\n๐Ÿ› ๏ธ Core Capabilities:"); - for capability in &metadata.capabilities { - println!(" โ€ข {}", capability.replace('_', " ")); - } - - println!("\n๐Ÿ”— Dependencies:"); - for dependency in &metadata.dependencies { - println!(" โ€ข {}", dependency); - } -} - -fn demonstrate_deployment_strategies(deployer: &DeployerAgent) { - println!("\n๐Ÿ—๏ธ Deployment Strategy Analysis"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - let deployment_scenarios = vec![ - ("Enterprise E-commerce Platform", vec![ - "๐ŸŽฏ Strategy: Zero-downtime progressive deployment", - "๐Ÿ“Š Architecture: Microservices with container orchestration", - "๐Ÿ”’ Security: Zero-trust network with automated compliance", - "๐ŸŒ Scale: Global distribution with edge computing", - "โšก Performance: 99.99% uptime with automated failover", - ]), - - ("SaaS Application Platform", vec![ - "๐ŸŽฏ Strategy: Blue-green deployment with canary analysis", - "๐Ÿ“Š Architecture: Cloud-native with auto-scaling", - "๐Ÿ”’ Security: SOC2 compliance with continuous scanning", - "๐ŸŒ Scale: Multi-tenant with region-based scaling", - "โšก Performance: 99.9% SLA with intelligent load balancing", - ]), - - ("IoT Data Processing System", vec![ - "๐ŸŽฏ Strategy: Rolling deployment with health monitoring", - "๐Ÿ“Š Architecture: Event-driven with stream processing", - "๐Ÿ”’ Security: Device authentication with encrypted channels", - "๐ŸŒ Scale: Edge computing with centralized coordination", - "โšก Performance: Real-time processing with petabyte capacity", - ]), - - ("Legacy System Migration", vec![ - "๐ŸŽฏ Strategy: Strangler fig pattern with gradual migration", - "๐Ÿ“Š Architecture: Hybrid cloud with service mesh", - "๐Ÿ”’ Security: Zero-downtime migration with data protection", - "๐ŸŒ Scale: Phased rollout with rollback capabilities", - "โšก Performance: Performance parity during transition", - ]) - ]; - - for (scenario_name, strategy_points) in deployment_scenarios { - println!("\n๐Ÿ“‹ Scenario: {}", scenario_name); - println!("โ”€{}โ”€", "โ”€".repeat(scenario_name.len() + 10)); - - for point in strategy_points { - println!(" {}", point); - } - - println!(" โœ… Agent Confidence: {:.1}% (High automation capability)", deployer.metadata().base_confidence * 100.0); - } -} - -fn demonstrate_automation_capabilities(_deployer: &DeployerAgent) { - println!("\n๐Ÿค– Deployment Automation & Infrastructure as Code"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - let automation_frameworks = vec![ - ("Kubernetes Container Orchestration", vec![ - "๐Ÿณ Container Management: Docker image optimization and security scanning", - "โ˜ธ๏ธ Orchestration: Kubernetes with Helm charts and custom operators", - "๐Ÿ”„ Auto-scaling: Horizontal and vertical pod autoscaling", - "๐ŸŒ Service Mesh: Istio for traffic management and security", - "๐Ÿ“Š Monitoring: Prometheus/Grafana with custom metrics", - ]), - - ("Terraform Infrastructure Provisioning", vec![ - "๐Ÿ—๏ธ Infrastructure as Code: Multi-cloud Terraform modules", - "๐Ÿ”ง Configuration: Ansible playbooks for system configuration", - "๐Ÿ”’ Security: Automated security group and IAM policies", - "๐Ÿ’พ State Management: Remote backend with locking", - "๐Ÿ”„ Updates: Blue-green infrastructure deployments", - ]), - - ("CI/CD Pipeline Automation", vec![ - "๐Ÿš€ Build Automation: Multi-stage builds with caching", - "๐Ÿงช Testing: Automated unit, integration, and security tests", - "๐Ÿ“ฆ Artifact Management: Container registry with vulnerability scanning", - "๐ŸŽฏ Deployment: Progressive deployment with automated rollback", - "๐Ÿ“ˆ Quality Gates: Performance and security thresholds", - ]), - - ("Monitoring & Observability Stack", vec![ - "๐Ÿ“Š Application Monitoring: APM with distributed tracing", - "๐Ÿ–ฅ๏ธ Infrastructure Monitoring: System metrics with alerting", - "๐Ÿ“ Log Management: Centralized logging with analytics", - "๐Ÿšจ Alerting: Intelligent alerting with escalation policies", - "๐Ÿ” Debugging: Runtime debugging and profiling tools", - ]) - ]; - - for (framework_name, capabilities) in automation_frameworks { - println!("\n๐Ÿ”ง {}", framework_name); - println!("โ”€{}โ”€", "โ”€".repeat(framework_name.len() + 2)); - - for capability in capabilities { - println!(" {}", capability); - } - - println!(" ๐ŸŽฏ Automation Level: Comprehensive with intelligent monitoring"); - } -} - -fn demonstrate_operational_excellence(_deployer: &DeployerAgent) { - println!("\n๐ŸŽฏ Operational Excellence & Best Practices"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - let operational_areas = vec![ - ("High-Availability Production Operations", vec![ - "๐Ÿ“ˆ Uptime Target: 99.99% with automated incident response", - "๐Ÿ”„ Deployment Strategy: Zero-downtime with health checks", - "๐Ÿšจ Monitoring: Full-stack observability with proactive alerting", - "๐Ÿ”ง Maintenance: Automated patching with rollback procedures", - "๐Ÿ’พ Backup: Automated backups with disaster recovery testing", - ]), - - ("Security & Compliance Framework", vec![ - "๐Ÿ”’ Security Model: Zero-trust architecture with micro-segmentation", - "๐Ÿ“‹ Compliance: SOC2, ISO27001, PCI-DSS automation", - "๐Ÿ›ก๏ธ Vulnerability Management: Continuous scanning and remediation", - "๐Ÿ”‘ Access Control: RBAC with MFA and privileged access management", - "๐Ÿ“Š Audit Logging: Comprehensive logging with integrity protection", - ]), - - ("Disaster Recovery & Business Continuity", vec![ - "๐ŸŒ Multi-Region: Active-active deployment across regions", - "โฑ๏ธ Recovery Objectives: 4-hour RTO, 15-minute RPO", - "๐Ÿ”„ Automated Failover: Health-based failover with manual approval", - "๐Ÿ’พ Data Protection: Encrypted backups with point-in-time recovery", - "๐Ÿงช Testing: Regular DR drills with automated validation", - ]), - - ("Performance Optimization", vec![ - "โšก Response Time: Sub-100ms API responses with CDN acceleration", - "๐Ÿ“Š Capacity Planning: Predictive scaling with cost optimization", - "๐Ÿ” Performance Monitoring: Real-time metrics with anomaly detection", - "๐ŸŽฏ Load Testing: Continuous performance testing in CI/CD", - "๐Ÿ”ง Optimization: Automated performance tuning recommendations", - ]) - ]; - - for (area_name, practices) in operational_areas { - println!("\n๐Ÿ“Š {}", area_name); - println!("โ”€{}โ”€", "โ”€".repeat(area_name.len() + 2)); - - for practice in practices { - println!(" {}", practice); - } - - println!(" โœ… Excellence Level: Enterprise-grade with continuous improvement"); - } -} - -fn demonstrate_pipeline_integration(deployer: &DeployerAgent) { - println!("\n๐Ÿ”„ Development Pipeline Integration"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - println!("๐Ÿ“‹ Development Lifecycle Pipeline:"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ PlannerAgentโ”‚ -> โ”‚ArchitectAgt โ”‚ -> โ”‚ DesignerAgt โ”‚ -> โ”‚ SchemaAgent โ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(" โ”‚ โ”‚ โ”‚ โ”‚"); - println!(" v v v v"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ APIAgent โ”‚ -> โ”‚FrontendCoderโ”‚ -> โ”‚BackendCoder โ”‚ -> โ”‚RefactorAgentโ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(" โ”‚ โ”‚ โ”‚ โ”‚"); - println!(" v v v v"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ DocAgent โ”‚ -> โ”‚DeployerAgt โœจโ”‚ -> โ”‚MaintainerAgtโ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - println!("\n๐ŸŽฏ DeployerAgent Position (10/11 Agents - 90.9% Complete):"); - println!(" โ€ข Receives: Optimized code from RefactorAgent + comprehensive docs from DocAgent"); - println!(" โ€ข Processes: Deployment strategy, infrastructure automation, CI/CD pipelines"); - println!(" โ€ข Delivers: Production-ready deployment infrastructure with operational excellence"); - println!(" โ€ข Enables: MaintainerAgent to manage ongoing operations and maintenance"); - - println!("\n๐Ÿ“Š Agent Integration Capabilities:"); - println!(" ๐Ÿ”„ Input Processing:"); - for input_type in &deployer.metadata().supported_input_types { - println!(" โ€ข {}", input_type.replace('_', " ")); - } - - println!(" ๐Ÿ“ค Output Generation:"); - for output_type in &deployer.metadata().supported_output_types { - println!(" โ€ข {}", output_type.replace('_', " ")); - } - - println!("\n๐Ÿš€ Development Pipeline Status:"); - println!(" โ€ข โœ… Requirements & Planning (PlannerAgent)"); - println!(" โ€ข โœ… System Architecture (ArchitectAgent)"); - println!(" โ€ข โœ… UI/UX Design (DesignerAgent)"); - println!(" โ€ข โœ… Database Schema (SchemaAgent)"); - println!(" โ€ข โœ… API Development (APIAgent)"); - println!(" โ€ข โœ… Frontend Implementation (FrontendCoder)"); - println!(" โ€ข โœ… Backend Implementation (BackendCoder)"); - println!(" โ€ข โœ… Code Optimization (RefactorAgent)"); - println!(" โ€ข โœ… Documentation (DocAgent)"); - println!(" โ€ข ๐Ÿš€ Deployment Orchestration (DeployerAgent) โ† Currently Completed"); - println!(" โ€ข โณ System Maintenance (MaintainerAgent) โ† Next Agent (90.9% -> 100%)"); - - println!("\n๐ŸŽ‰ Key Achievements:"); - println!(" โ€ข ๐Ÿ—๏ธ Comprehensive deployment strategy framework"); - println!(" โ€ข ๐Ÿค– Full infrastructure automation with IaC"); - println!(" โ€ข ๐Ÿ”„ Zero-downtime deployment patterns"); - println!(" โ€ข ๐Ÿ“Š Enterprise-grade monitoring and observability"); - println!(" โ€ข ๐Ÿ”’ Security-first deployment with compliance automation"); - println!(" โ€ข โšก High-performance scaling and optimization"); - - println!("\n๐Ÿ“ˆ Success Metrics:"); - println!(" โ€ข Agent Confidence: {:.1}%", deployer.metadata().base_confidence * 100.0); - println!(" โ€ข Deployment Strategy: Zero-downtime progressive deployment"); - println!(" โ€ข Infrastructure: Multi-cloud with container orchestration"); - println!(" โ€ข Automation: Comprehensive CI/CD with quality gates"); - println!(" โ€ข Monitoring: Full-stack observability with proactive alerting"); - println!(" โ€ข Security: Enterprise-grade with automated compliance"); -} \ No newline at end of file diff --git a/designer_agent_demo.rs b/designer_agent_demo.rs deleted file mode 100644 index 72270b031e7890de913e327bb649f3a8320b4d9a..0000000000000000000000000000000000000000 --- a/designer_agent_demo.rs +++ /dev/null @@ -1,394 +0,0 @@ -use std::sync::Arc; -use std::collections::HashMap; -use brain_cognitive::agents::{traits::*, development::DesignerAgent}; -use brain_cognitive::{ - meta::{MetaMemoryRepository, MetaMemoryItem, MetaMemoryQuery}, - conversation::{ - traits::ConversationService, - RagRequest, RagResponse, ResponseQuality, - }, -}; -use brain_core::{ - memory::WorkingMemoryRepository, - concepts::ConceptRepository, - insights::InsightRepository, -}; -use brain_types::BrainError; -use async_trait::async_trait; -use uuid::Uuid; - -/// Simple meta-memory repository implementation for demo -#[derive(Debug)] -struct MockMetaMemoryRepository; - -#[async_trait] -impl MetaMemoryRepository for MockMetaMemoryRepository { - async fn store_item(&mut self, _item: MetaMemoryItem) -> Result { - Ok(Uuid::new_v4()) - } - - async fn get_item(&self, _id: Uuid) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(None) - } - - async fn get_item_by_component(&self, _component_id: Uuid) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(None) - } - - async fn query_items(&self, _query: &MetaMemoryQuery) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(Vec::new()) - } - - async fn remove_item(&mut self, _id: Uuid) -> Result { - Ok(true) - } - - async fn batch_update(&mut self, _items: Vec) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(Vec::new()) - } - - async fn count_items(&self) -> Result { - Ok(0) - } - - async fn clear_all(&mut self) -> Result { - Ok(0) - } -} - -/// Simple conversation service implementation for demo -#[derive(Debug)] -struct MockConversationService; - -#[async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: RagRequest, - _memory_repo: &mut dyn WorkingMemoryRepository, - _concept_repo: &mut dyn ConceptRepository, - _insight_repo: &mut dyn InsightRepository, - ) -> Result { - Ok(RagResponse { - response: "Mock response".to_string(), - conversation_id: "mock-conversation".to_string(), - context_used: Vec::new(), - confidence_score: 0.8, - response_quality: ResponseQuality { - factual_grounding: 0.8, - coherence: 0.9, - relevance: 0.8, - safety_score: 1.0, - source_attribution: 0.7, - consistency_score: 0.8, - completeness: 0.7, - clarity: 0.9, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.1, - confidence_calibration: 0.8, - }, - }) - } - - fn get_conversation_stats(&self) -> HashMap { - let mut stats = HashMap::new(); - stats.insert("total_conversations".to_string(), 1); - stats - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐ŸŽจ DesignerAgent Demo - UI/UX Design and Wireframing"); - println!("{}", "=".repeat(60)); - println!(); - - // Initialize infrastructure components (simplified) - let _config = brain_infra::config::BrainConfig::default(); - let _db_config = brain_infra::database::DatabaseConfig::default(); - - // Create mock dependencies - let meta_memory: Arc> = - Arc::new(tokio::sync::RwLock::new(MockMetaMemoryRepository)); - let conversation_service = Arc::new(MockConversationService); - - // Create project context - let project_context = ProjectContext { - project_name: "TaskFlow Pro".to_string(), - project_version: "2.0.0".to_string(), - project_description: Some("Advanced task management platform with real-time collaboration and modern UI".to_string()), - tech_stack: vec!["React".to_string(), "TypeScript".to_string(), "Tailwind CSS".to_string(), "Framer Motion".to_string()], - git_branch: Some("feature/ui-redesign".to_string()), - git_commit: Some("def456abc".to_string()), - active_files: vec!["src/components/Dashboard.tsx".to_string(), "src/pages/TaskBoard.tsx".to_string()], - recent_changes: vec!["Updated design system tokens".to_string(), "Implemented dark mode".to_string()], - directory_structure: { - let mut map = HashMap::new(); - map.insert("src".to_string(), vec!["components".to_string(), "pages".to_string(), "hooks".to_string(), "utils".to_string()]); - map.insert("design".to_string(), vec!["tokens".to_string(), "components".to_string(), "wireframes".to_string()]); - map - }, - }; - - // Create cognitive preference profile - let cognitive_profile = CognitivePreferenceProfile { - interaction_mode: InteractionMode::Collaborative, - detail_level: DetailLevel::Comprehensive, - emotional_sensitivity: EmotionalSensitivity::High, - autonomy_level: AutonomyLevel::SemiAuto, - communication_style: brain_cognitive::agents::traits::CommunicationStyle::Technical, - cognitive_load_settings: CognitiveLoadSettings { - max_items_per_chunk: 5, - pacing_preference: PacingPreference::Medium, - progressive_disclosure: true, - }, - }; - - // Build cognitive context - let mut config = HashMap::new(); - config.insert("demo_mode".to_string(), serde_json::Value::Bool(true)); - config.insert("design_theme".to_string(), serde_json::Value::String("modern".to_string())); - - let context = CognitiveContext { - meta_memory, - conversation_service, - project_context, - cognitive_profile, - session_history: Vec::new(), - config, - working_directory: std::env::current_dir().unwrap_or_else(|_| std::path::PathBuf::from(".")), - }; - - println!("โœ… Cognitive context initialized"); - println!(" Project: {}", context.project_context.project_name); - println!(" Tech Stack: {:?}", context.project_context.tech_stack); - println!(" Interaction Mode: {:?}", context.cognitive_profile.interaction_mode); - println!(" Detail Level: {:?}", context.cognitive_profile.detail_level); - println!(); - - // Initialize DesignerAgent - let designer_agent = DesignerAgent::new(); - println!("๐ŸŽจ Initializing DesignerAgent..."); - println!(" Agent: {}", designer_agent.metadata().name); - println!(" Persona: {}", designer_agent.metadata().persona); - println!(" Capabilities: {:?}", designer_agent.metadata().capabilities); - println!(" Base Confidence: {:.2}", designer_agent.metadata().base_confidence); - println!(" Dependencies: {:?}", designer_agent.metadata().dependencies); - println!(); - - // Test Case 1: Design Requirements Analysis - println!("๐Ÿ“‹ Test Case 1: Design Requirements Analysis"); - println!("{}", "-".repeat(50)); - - let design_requirements_input = AgentInput::new( - "design_requirements".to_string(), - r#" - Design a modern task management dashboard that supports: - - Real-time collaboration for teams - - Drag-and-drop task organization - - Advanced filtering and search capabilities - - Mobile-responsive design for iOS and Android - - Dark mode and accessibility features - - Data visualization with charts and graphs - - User onboarding flow for new users - - Integration with external tools (Slack, GitHub) - - Customizable workspace layouts - - Role-based permission management - "#.to_string(), - "designer-demo-session".to_string(), - ); - - let confidence = designer_agent.assess_confidence(&design_requirements_input, &context).await?; - println!("๐Ÿ“Š Confidence Assessment: {:.2}", confidence); - - if confidence >= designer_agent.confidence_threshold() { - println!("โœ… Confidence threshold met, proceeding with design creation..."); - let result = designer_agent.execute(design_requirements_input, &context).await?; - - println!("๐ŸŽจ Design Creation Result:"); - println!(" Output Type: {}", result.output_type); - println!(" Confidence: {:.2}", result.confidence); - println!(" Execution Time: {}ms", result.execution_metadata.execution_time_ms); - - if let Some(reasoning) = &result.reasoning { - println!(" Reasoning: {}", reasoning); - } - - println!(" Next Actions: {:?}", result.next_actions); - - // Parse and display key design components - if let Ok(design_data) = serde_json::from_str::(&result.content) { - if let Some(_wireframes) = design_data.get("wireframes") { - if let Some(screen_count) = design_data.get("screen_count") { - println!(" ๐Ÿ–ผ๏ธ Wireframes Created: {} screens", screen_count); - } - } - if let Some(principles) = design_data.get("design_principles") { - if let Some(principles_array) = principles.as_array() { - println!(" ๐Ÿ“ Design Principles: {} principles defined", principles_array.len()); - } - } - } - } else { - println!("โŒ Confidence too low ({:.2}), skipping execution", confidence); - } - println!(); - - // Test Case 2: Component Library Design - println!("๐Ÿงฉ Test Case 2: Component Library Design"); - println!("{}", "-".repeat(50)); - - let component_library_input = AgentInput::new( - "system_architecture".to_string(), - r#" - System architecture includes: - - React frontend with TypeScript - - Component-based architecture - - Atomic design methodology - - Storybook for component documentation - - Design tokens for consistency - - Styled components for theming - - Unit and visual regression testing - - Accessibility testing integration - "#.to_string(), - "designer-demo-session".to_string(), - ); - - let component_result = designer_agent.execute(component_library_input, &context).await?; - println!("๐Ÿงฉ Component Library Result:"); - println!(" Output Type: {}", component_result.output_type); - println!(" Confidence: {:.2}", component_result.confidence); - println!(" Execution Time: {}ms", component_result.execution_metadata.execution_time_ms); - - // Parse and display component library info - if let Ok(component_data) = serde_json::from_str::(&component_result.content) { - if let Some(components) = component_data.get("components") { - if let Some(atoms) = components.get("atoms") { - println!(" โš›๏ธ Atomic Components: {:?}", atoms.as_object().map(|obj| obj.keys().collect::>())); - } - if let Some(molecules) = components.get("molecules") { - println!(" ๐Ÿงฌ Molecular Components: {:?}", molecules.as_object().map(|obj| obj.keys().collect::>())); - } - } - } - println!(); - - // Test Case 3: Accessibility Planning - println!("โ™ฟ Test Case 3: Accessibility Planning"); - println!("{}", "-".repeat(50)); - - let accessibility_input = AgentInput::new( - "accessibility_requirements".to_string(), - r#" - Accessibility requirements: - - WCAG 2.1 AA compliance mandatory - - Support for screen readers (NVDA, JAWS, VoiceOver) - - Keyboard navigation for all interactive elements - - High contrast mode support - - Reduced motion preferences - - Color blindness considerations - - International language support (RTL) - - Touch target size compliance (44px minimum) - - Voice control compatibility - "#.to_string(), - "designer-demo-session".to_string(), - ); - - let accessibility_result = designer_agent.execute(accessibility_input, &context).await?; - println!("โ™ฟ Accessibility Planning Result:"); - println!(" Output Type: {}", accessibility_result.output_type); - println!(" Confidence: {:.2}", accessibility_result.confidence); - println!(" Execution Time: {}ms", accessibility_result.execution_metadata.execution_time_ms); - - // Parse and display accessibility features - if let Ok(accessibility_data) = serde_json::from_str::(&accessibility_result.content) { - if let Some(wcag) = accessibility_data.get("wcag_compliance") { - if let Some(level) = wcag.get("level") { - println!(" ๐ŸŽฏ WCAG Compliance Level: {}", level.as_str().unwrap_or("N/A")); - } - } - if let Some(features) = accessibility_data.get("accessibility_features") { - println!(" ๐Ÿ”ง Accessibility Features: {} feature categories", features.as_object().map(|obj| obj.len()).unwrap_or(0)); - } - } - println!(); - - // Test Case 4: Brand Guidelines Integration - println!("๐ŸŽจ Test Case 4: Brand Guidelines Integration"); - println!("{}", "-".repeat(50)); - - let brand_input = AgentInput::new( - "brand_guidelines".to_string(), - r#" - Brand guidelines: - - Primary color: #2563eb (blue) - - Secondary color: #10b981 (green) - - Typography: Inter for headings, Open Sans for body - - Logo placement and sizing rules - - Tone of voice: Professional yet approachable - - Visual style: Modern, clean, minimalist - - Photography style: Authentic, diverse, aspirational - - Icon style: Outlined, consistent stroke width - - Brand personality: Innovative, reliable, user-focused - "#.to_string(), - "designer-demo-session".to_string(), - ); - - let brand_result = designer_agent.execute(brand_input, &context).await?; - println!("๐ŸŽจ Brand Integration Result:"); - println!(" Output Type: {}", brand_result.output_type); - println!(" Confidence: {:.2}", brand_result.confidence); - println!(" Execution Time: {}ms", brand_result.execution_metadata.execution_time_ms); - - // Parse and display design system components - if let Ok(brand_data) = serde_json::from_str::(&brand_result.content) { - if let Some(_typography) = brand_data.get("typography") { - println!(" ๐Ÿ“ Typography System: Defined"); - } - if let Some(_colors) = brand_data.get("color_palette") { - println!(" ๐ŸŽจ Color Palette: Integrated"); - } - if let Some(_spacing) = brand_data.get("spacing_system") { - println!(" ๐Ÿ“ Spacing System: Defined"); - } - } - println!(); - - // Display agent capabilities summary - println!("๐ŸŽฏ DesignerAgent Capabilities Summary"); - println!("{}", "-".repeat(50)); - println!("โœ… UI mockups and wireframe creation"); - println!("โœ… Component library design and documentation"); - println!("โœ… User flow mapping and journey design"); - println!("โœ… Accessibility planning and WCAG compliance"); - println!("โœ… Design system creation and maintenance"); - println!("โœ… Responsive design for multiple devices"); - println!("โœ… Interaction design and micro-animations"); - println!("โœ… Visual hierarchy and information architecture"); - println!("โœ… Usability analysis and optimization"); - println!("โœ… Prototype creation and validation"); - println!(); - - // Integration showcase - println!("๐Ÿ”— Integration with Development Pipeline"); - println!("{}", "-".repeat(50)); - println!("๐Ÿ“‹ PlannerAgent โ†’ ๐Ÿ—๏ธ ArchitectAgent โ†’ ๐ŸŽจ DesignerAgent"); - println!(" โ†ณ Requirements Analysis โ†’ System Architecture โ†’ UI/UX Design"); - println!(" โ†ณ Task Breakdown โ†’ Component Design โ†’ User Interface"); - println!(" โ†ณ Project Planning โ†’ Technical Specs โ†’ Design System"); - println!(); - println!("๐Ÿ”„ Next Steps in Development Pipeline:"); - println!(" 1. SchemaAgent - Database schema design"); - println!(" 2. APIAgent - API contract definition"); - println!(" 3. FrontendCoder - React component implementation"); - println!(" 4. BackendCoder - API implementation"); - println!(); - - println!("๐ŸŽ‰ DesignerAgent Demo completed successfully!"); - Ok(()) -} \ No newline at end of file diff --git a/developmental_state.json b/developmental_state.json deleted file mode 100644 index b0451584a2717fc20e432051fafdb3c3aa2c573d..0000000000000000000000000000000000000000 --- a/developmental_state.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "current_stage": "Embryonic", - "capacity_tracker": { - "current_complexity": 4.008533378198066, - "efficiency_history": [ - 0.5917406160141514, - 0.5950202297442285, - 0.5976631183718601, - 0.5991452198082531, - 0.5991466621801934 - ], - "utilization": 0.4008533378198066, - "growth_pressure": 0.0 - }, - "learning_history_size": 5, - "growth_config": { - "initial_scale": 0.3, - "growth_rate": 1.8, - "max_scale": 3.0, - "complexity_threshold": 0.7, - "enable_meta_learning": true - } -} \ No newline at end of file diff --git a/direct_rag_pocketflow.db b/direct_rag_pocketflow.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/direct_rag_pocketflow.db and /dev/null differ diff --git a/direct_rag_pocketflow.rs b/direct_rag_pocketflow.rs deleted file mode 100644 index 98c7729b7527c691e9582b913e5a8afdead0c5fa..0000000000000000000000000000000000000000 --- a/direct_rag_pocketflow.rs +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env cargo run --example direct_rag_pocketflow -//! Direct RAG PocketFlow Analysis -//! -//! This example bypasses the Brain AI Orchestrator and uses direct RAG retrieval -//! to answer questions about PocketFlow architecture patterns. - -use brain::{MemoryService, WorkingMemoryQuery, Priority, Result}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Direct RAG PocketFlow Demo"); - println!("============================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| brain::BrainError::Io { - message: format!("Failed to create data directory: {}", e), - context: None, - source: None, - })?; - - // Initialize repositories - let working_repo = Box::new(WorkingMemoryRepository::new(100)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/direct_rag_pocketflow.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let mut memory_service = MemoryService::new(working_repo, episodic_repo, semantic_repo); - - println!("\n๐Ÿง  Loading PocketFlow Knowledge Base"); - println!("{}", "-".repeat(40)); - - // Load comprehensive PocketFlow knowledge with clear, direct answers - let pocketflow_knowledge = vec![ - // Direct answer to "What are the 3 unique architecture patterns" - "The 3 unique architecture patterns in PocketFlow are: 1) Node-Flow Architecture - separates processing logic (Nodes) from execution orchestration (Flows), 2) Async Parallel Processing - enables concurrent LLM operations through AsyncFlow and ParallelBatchNode, and 3) Batch Optimization Framework - groups multiple LLM requests to reduce API costs.", - - // Direct answer to Node-Flow implementation - "PocketFlow implements the Node-Flow architecture pattern by using BaseNode as the fundamental abstraction for all processing units, and Flow classes to orchestrate execution. Nodes contain the processing logic while Flows handle the sequencing and coordination. This separation allows for modular, reusable components that can be chained together.", - - // Direct answer to BatchNode purpose - "BatchNode and ParallelBatchNode in PocketFlow are used to optimize LLM API costs and improve efficiency. BatchNode groups multiple requests into batches to reduce the number of individual API calls. ParallelBatchNode adds concurrent processing to handle multiple batches simultaneously, further improving throughput and reducing latency.", - - // Direct answer to agent-based workflows - "PocketFlow enables agent-based workflows through its 'Agents build Agents' design philosophy. The framework provides abstractions that allow autonomous agents to create and orchestrate other agents. This enables recursive and self-improving AI systems where agents can spawn new agents, coordinate multi-agent tasks, and build complex agent hierarchies.", - - // Direct answer to 100-line framework - "PocketFlow is called a 100-line framework because it provides essential LLM orchestration capabilities in approximately 100 lines of Python code. This minimalist design philosophy focuses on core functionality without bloat, making the framework easy to understand, modify, and extend while maintaining powerful features for AI workflow orchestration.", - - // Direct answer to LLM cost optimization - "PocketFlow optimizes LLM API costs through several mechanisms: BatchNode groups multiple requests to take advantage of batch pricing, ParallelBatchNode enables concurrent processing to reduce wait times, and the framework minimizes redundant API calls through efficient request management. This can significantly reduce costs compared to individual request patterns.", - - // Direct answer to key classes and components - "The key classes and components in PocketFlow are: BaseNode (base processing unit for all operations), Flow (synchronous orchestrator for sequential execution), AsyncFlow (asynchronous orchestrator for non-blocking operations), BatchNode (batch processor for cost optimization), and ParallelBatchNode (parallel batch processor for concurrent operations). These components work together to create flexible AI workflows.", - - // Direct answer to use cases - "PocketFlow's main use cases and applications include: LLM workflow orchestration for complex AI pipelines, agent-based AI systems for autonomous operations, batch processing of AI tasks for cost efficiency, parallel LLM operations for high throughput, cost-optimized AI pipelines for production environments, and rapid prototyping of AI agents for research and development.", - - // Additional context - "PocketFlow uses Python with async/await patterns for non-blocking operations. The framework supports error handling, fallback mechanisms, and flexible configuration. It's designed for both research and production environments, providing a balance between simplicity and powerful functionality.", - - // Technical implementation details - "PocketFlow's implementation leverages Python's asyncio library for asynchronous operations, uses class inheritance for node specialization, and implements the observer pattern for flow coordination. The framework maintains clean separation of concerns between data processing (nodes) and execution control (flows).", - ]; - - for (i, knowledge) in pocketflow_knowledge.iter().enumerate() { - match memory_service.learn(knowledge.to_string(), Priority::High).await { - Ok(_) => println!("โœ… Loaded knowledge chunk {}", i + 1), - Err(e) => println!("โŒ Failed to load knowledge {}: {}", i + 1, e), - } - } - - println!("\n๐ŸŽฏ Testing Direct RAG Retrieval"); - println!("{}", "-".repeat(40)); - - // Test questions about PocketFlow architecture - let test_questions = vec![ - "What are the 3 unique architecture patterns in PocketFlow?", - "How does PocketFlow implement the Node-Flow architecture pattern?", - "What is the purpose of BatchNode and ParallelBatchNode in PocketFlow?", - "How does PocketFlow enable agent-based workflows?", - "What makes PocketFlow a 100-line framework?", - "How does PocketFlow optimize LLM API costs?", - "What are the key classes and components in PocketFlow?", - "What are PocketFlow's main use cases and applications?", - ]; - - for (i, question) in test_questions.iter().enumerate() { - println!("\n๐Ÿ“ Question {}: {}", i + 1, question); - - // Use direct memory search to find relevant knowledge - match memory_service.query_all_memories(question).await { - Ok(results) => { - let total_results = results.working_results.len() + - results.episodic_results.len() + - results.semantic_results.len(); - - if total_results > 0 { - println!("๐Ÿ’ก Found {} relevant knowledge sources:", total_results); - - // Display the most relevant working memory results - for (j, item) in results.working_results.iter().take(2).enumerate() { - println!(" {}. {}", j + 1, item.content); - } - - // Also check episodic results - for (j, event) in results.episodic_results.iter().take(1).enumerate() { - println!(" {}. {}", j + results.working_results.len() + 1, event.content); - } - } else { - println!("โŒ No relevant knowledge found"); - } - } - Err(e) => { - println!("โŒ Memory search failed: {}", e); - } - } - - // Also try direct content pattern matching - let query = WorkingMemoryQuery { - content_pattern: Some(extract_key_terms(question)), - priority: None, - min_importance: None, - created_after: None, - limit: Some(3), - }; - - match memory_service.query_working(&query).await { - Ok(items) => { - if !items.is_empty() { - println!("๐Ÿ” Direct pattern match found {} items:", items.len()); - for (j, item) in items.iter().take(1).enumerate() { - let preview = if item.content.len() > 200 { - format!("{}...", &item.content[..200]) - } else { - item.content.clone() - }; - println!(" {}. {}", j + 1, preview); - } - } - } - Err(e) => { - println!("โš ๏ธ Pattern matching failed: {}", e); - } - } - } - - println!("\n๐Ÿ“Š Memory System Statistics"); - println!("{}", "-".repeat(40)); - - // Since MemoryService doesn't have get_stats, let's check working memory - let all_query = WorkingMemoryQuery::default(); - match memory_service.query_working(&all_query).await { - Ok(items) => { - let total_items = items.len(); - let total_size: usize = items.iter().map(|item| item.content.len()).sum(); - println!("Working Memory: {} items, {} bytes", total_items, total_size); - } - Err(e) => { - println!("Failed to get memory stats: {}", e); - } - } - - println!("\nโœ… Direct RAG Analysis Complete!"); - println!("This demonstrates that the knowledge is stored and can be retrieved"); - println!("when bypassing the Brain AI Orchestrator's complex analysis."); - - Ok(()) -} - -// Extract key terms from a question for pattern matching -fn extract_key_terms(question: &str) -> String { - let key_terms: Vec<&str> = question - .split_whitespace() - .filter(|word| { - word.len() > 3 && - !["what", "how", "does", "the", "are", "and", "for", "with", "this", "that", "from", "into", "they", "have", "will", "been", "were", "said", "each", "which", "their", "time", "when", "where", "why", "would", "there", "make", "like", "him", "her", "his", "our", "out", "who", "get", "has", "had", "let", "put", "say", "she", "may", "use", "her", "him", "his", "how", "man", "new", "now", "old", "see", "two", "way", "day", "get", "may", "say", "use", "work", "first", "good", "know", "life", "time", "year", "come", "give", "hand", "high", "keep", "last", "left", "life", "live", "look", "made", "make", "move", "much", "must", "name", "need", "next", "open", "over", "part", "play", "right", "same", "seem", "show", "small", "such", "take", "than", "them", "well", "were"].contains(&word.to_lowercase().as_str()) - }) - .collect(); - - key_terms.join(" ") -} \ No newline at end of file diff --git a/doc_agent_demo.rs b/doc_agent_demo.rs deleted file mode 100644 index 3a00f809f800f11a5401974e2f17dcb031634825..0000000000000000000000000000000000000000 --- a/doc_agent_demo.rs +++ /dev/null @@ -1,268 +0,0 @@ -//! DocAgent Demo -//! -//! Demonstrates the DocAgent's comprehensive documentation generation capabilities -//! across various documentation types, automation tools, and publishing strategies. - -use brain_cognitive::agents::{ - development::DocAgent, - traits::BrainAgent, -}; -use serde_json::json; -use std::error::Error; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ“š DocAgent Comprehensive Documentation Demo"); - println!("============================================\n"); - - let agent = DocAgent::new(); - - // Demo 1: Agent Metadata and Capabilities - println!("๐Ÿ”ง Demo 1: DocAgent Metadata and Capabilities"); - println!("---------------------------------------------"); - - let metadata = agent.metadata(); - println!("โœ… Agent Created Successfully!"); - println!("๐Ÿ“ Agent Name: {}", metadata.name); - println!("๐Ÿ†” Agent ID: {}", metadata.id); - println!("๐Ÿ“Š Base Confidence: {:.1}%", metadata.base_confidence * 100.0); - println!("๐ŸŽฏ Confidence Threshold: {:.1}%", agent.confidence_threshold() * 100.0); - - println!("\n๐Ÿ“‹ Supported Input Types:"); - for input_type in &metadata.supported_input_types { - println!(" โ€ข {}", input_type); - } - - println!("\n๐Ÿ“ค Supported Output Types:"); - for output_type in &metadata.supported_output_types { - println!(" โ€ข {}", output_type); - } - - println!("\n๐ŸŽฏ Core Capabilities:"); - for capability in &metadata.capabilities { - println!(" โ€ข {}", capability); - } - - println!("\n๐Ÿ”— Dependencies:"); - for dependency in &metadata.dependencies { - println!(" โ€ข {}", dependency); - } - - println!("\n๐Ÿท๏ธ Tags:"); - for tag in &metadata.tags { - println!(" โ€ข {}", tag); - } - - println!("\n"); - - // Demo 2: Documentation Analysis Simulation - println!("๐Ÿ“Š Demo 2: Documentation Analysis Capabilities"); - println!("---------------------------------------------"); - - // Simulate different project scenarios - let scenarios = vec![ - ("Enterprise Web Application", json!({ - "total_files": 450, - "api_endpoints": 85, - "existing_docs": "minimal", - "complexity": "high" - })), - ("Open Source Library", json!({ - "total_files": 125, - "api_endpoints": 0, - "existing_docs": "partial", - "complexity": "medium" - })), - ("Mobile Application", json!({ - "total_files": 280, - "api_endpoints": 25, - "existing_docs": "outdated", - "complexity": "medium" - })), - ("Microservices Platform", json!({ - "total_files": 750, - "api_endpoints": 150, - "existing_docs": "scattered", - "complexity": "very_high" - })), - ]; - - for (project_type, details) in scenarios { - println!("๐Ÿ“‹ Analyzing: {}", project_type); - println!(" ๐Ÿ“ Files: {}", details.get("total_files").unwrap_or(&json!(0))); - println!(" ๐Ÿ”Œ API Endpoints: {}", details.get("api_endpoints").unwrap_or(&json!(0))); - println!(" ๐Ÿ“š Existing Docs: {}", details.get("existing_docs").unwrap_or(&json!("unknown")).as_str().unwrap_or("unknown")); - println!(" ๐Ÿ” Complexity: {}", details.get("complexity").unwrap_or(&json!("unknown")).as_str().unwrap_or("unknown")); - - // Simulate confidence assessment based on project characteristics - let file_count = details.get("total_files").unwrap_or(&json!(0)).as_u64().unwrap_or(0); - let api_count = details.get("api_endpoints").unwrap_or(&json!(0)).as_u64().unwrap_or(0); - - let mut confidence = agent.metadata().base_confidence; - - // Adjust confidence based on project characteristics - if file_count > 500 { - confidence -= 0.05; // Large projects are more complex - } - if api_count > 100 { - confidence += 0.03; // APIs are well-structured for documentation - } - - confidence = confidence.max(0.7).min(0.95); - - println!(" ๐ŸŽฏ Estimated Confidence: {:.1}%", confidence * 100.0); - - // Simulated documentation strategy - let strategy = if file_count > 400 { - "comprehensive_phased_approach" - } else if api_count > 50 { - "api_focused_documentation" - } else { - "foundation_building" - }; - - println!(" ๐Ÿ“ˆ Recommended Strategy: {}", strategy); - println!(); - } - - println!("๐ŸŽฏ Demo 3: Documentation Generation Strategies"); - println!("----------------------------------------------"); - - // Simulate different documentation focus areas - let focus_areas = vec![ - ("Code Documentation", vec![ - "Inline comment generation", - "Function documentation", - "Module documentation", - "Architecture diagrams" - ]), - ("API Documentation", vec![ - "OpenAPI specification", - "Endpoint documentation", - "Authentication guides", - "SDK generation" - ]), - ("User Documentation", vec![ - "Getting started guides", - "Feature tutorials", - "Troubleshooting guides", - "FAQ sections" - ]), - ("Technical Documentation", vec![ - "System architecture", - "Deployment guides", - "Development setup", - "Security documentation" - ]), - ]; - - for (area, capabilities) in focus_areas { - println!("๐Ÿ“š {}", area); - for capability in capabilities { - println!(" โœ… {}", capability); - } - println!(); - } - - println!("๐Ÿค– Demo 4: Automation and Integration Features"); - println!("---------------------------------------------"); - - let automation_features = vec![ - ("Code Analysis", vec![ - "AST parsing for documentation extraction", - "Comment quality assessment", - "API endpoint discovery", - "Schema documentation generation" - ]), - ("Content Generation", vec![ - "Markdown documentation", - "HTML interactive docs", - "PDF technical manuals", - "Video tutorial scripts" - ]), - ("Quality Assurance", vec![ - "Link validation", - "Content freshness monitoring", - "Accessibility compliance checking", - "Cross-reference validation" - ]), - ("CI/CD Integration", vec![ - "Build pipeline integration", - "Automated doc deployment", - "Version synchronization", - "Quality gate enforcement" - ]), - ]; - - for (category, features) in automation_features { - println!("๐Ÿ”ง {}", category); - for feature in features { - println!(" ๐Ÿค– {}", feature); - } - println!(); - } - - println!("๐Ÿ“ˆ Demo 5: Success Metrics and Quality Assessment"); - println!("------------------------------------------------"); - - // Simulated quality metrics - let quality_metrics = vec![ - ("Documentation Coverage", "Target: 60-80% improvement"), - ("Quality Score Enhancement", "Target: 40-60% improvement"), - ("User Onboarding Time", "Target: 50% reduction"), - ("Support Ticket Reduction", "Target: 30% fewer docs-related tickets"), - ("Developer Satisfaction", "Target: 85%+ satisfaction rating"), - ("Accessibility Compliance", "Target: WCAG 2.1 AA compliance"), - ]; - - for (metric, target) in quality_metrics { - println!("๐Ÿ“Š {}: {}", metric, target); - } - - println!("\n๐ŸŽฏ Demo 6: Agent Collaboration and Workflow"); - println!("-------------------------------------------"); - - // Show how DocAgent fits in the development pipeline - println!("๐Ÿ“‹ Development Pipeline Integration:"); - println!(" 1. PlannerAgent โ†’ Requirements analysis"); - println!(" 2. ArchitectAgent โ†’ System design"); - println!(" 3. DesignerAgent โ†’ UI/UX design"); - println!(" 4. SchemaAgent โ†’ Database design"); - println!(" 5. APIAgent โ†’ API specification"); - println!(" 6. FrontendCoder โ†’ UI implementation"); - println!(" 7. BackendCoder โ†’ API implementation"); - println!(" 8. RefactorAgent โ†’ Code optimization"); - println!(" 9. ๐Ÿ“š DocAgent โ†’ Documentation generation โ† YOU ARE HERE"); - println!(" 10. DeployerAgent โ†’ Deployment automation"); - println!(" 11. MaintainerAgent โ†’ System maintenance"); - - println!("\n๐Ÿ”— DocAgent Dependencies:"); - for dependency in &metadata.dependencies { - println!(" โ€ข Depends on: {}", dependency); - println!(" Reason: Requires optimized, finalized code for accurate documentation"); - } - - println!("\n๐Ÿš€ Next Actions After DocAgent:"); - println!(" โ€ข Integration with deployment pipeline"); - println!(" โ€ข Automated documentation publishing"); - println!(" โ€ข Continuous documentation quality monitoring"); - println!(" โ€ข User feedback integration for doc improvements"); - - println!("\n๐Ÿ“Š DocAgent Demo Summary"); - println!("========================"); - println!("โœ… Demonstrated comprehensive documentation capabilities:"); - println!(" ๐Ÿ“ Multi-format documentation generation"); - println!(" ๐Ÿค– Intelligent automation framework"); - println!(" ๐Ÿ“Š Quality assessment and improvement"); - println!(" ๐Ÿ”— CI/CD pipeline integration"); - println!(" โ™ฟ Accessibility and compliance features"); - println!(" ๐Ÿ”„ Maintenance and versioning strategies"); - println!("\n๐ŸŽฏ Key Strengths:"); - println!(" โ€ข High base confidence: {:.1}%", metadata.base_confidence * 100.0); - println!(" โ€ข {} core capabilities", metadata.capabilities.len()); - println!(" โ€ข {} supported input types", metadata.supported_input_types.len()); - println!(" โ€ข {} supported output types", metadata.supported_output_types.len()); - println!(" โ€ข Seamless integration with development pipeline"); - - Ok(()) -} \ No newline at end of file diff --git a/ecosystem_integration_verification_demo.rs b/ecosystem_integration_verification_demo.rs deleted file mode 100644 index 36cbe5f15666c689b65413490c908bd1bffe708b..0000000000000000000000000000000000000000 --- a/ecosystem_integration_verification_demo.rs +++ /dev/null @@ -1,281 +0,0 @@ -use std::collections::HashMap; -use tokio; -use anyhow::Result; - -use brain_cognitive::agents::{ - AgentRegistry, AgentInput, CognitiveContext -}; - -/// Ecosystem Integration Verification Demo -/// -/// Verifies Task 1.4: Integration with Existing Agent Ecosystem -/// This demo validates that all academic agents are seamlessly integrated -/// with the existing Brain AI ecosystem without disrupting current capabilities. -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŒ Brain AI Academic Ecosystem Integration Verification"); - println!("======================================================"); - println!("Task 1.4: Integration with Existing Agent Ecosystem"); - println!("Target: Seamless operation with zero disruption to existing capabilities"); - println!(); - - // Phase 1: Initialize Full Agent Ecosystem - println!("๐Ÿ”ง Phase 1: Initializing Full Agent Ecosystem..."); - let start_time = std::time::Instant::now(); - - let registry = AgentRegistry::new_with_defaults(); - registry.register_async_agents().await?; - - let init_time = start_time.elapsed(); - println!("โœ… Full ecosystem initialized in {}ms", init_time.as_millis()); - println!(" โ€ข AgentRegistry operational with all agents"); - println!(" โ€ข Academic agents registered alongside existing agents"); - println!(" โ€ข Backward compatibility maintained"); - println!(); - - // Phase 2: Verify Agent Registry Integration - println!("๐Ÿ“Š Phase 2: Agent Registry Integration Verification..."); - let stats = registry.get_statistics()?; - - println!(" Registry Integration Statistics:"); - println!(" ==============================="); - println!(" Total Agents Registered: {}", stats.total_agents); - println!(" Total Capabilities: {}", stats.total_capabilities); - println!(" Academic Agents: {}", stats.agents_by_category.get("academic").unwrap_or(&0)); - println!(" Development Agents: {}", stats.agents_by_category.get("development").unwrap_or(&0)); - println!(" Intelligence Agents: {}", stats.agents_by_category.get("intelligence").unwrap_or(&0)); - println!(" Operations Agents: {}", stats.agents_by_category.get("ops").unwrap_or(&0)); - println!(" Security Agents: {}", stats.agents_by_category.get("security").unwrap_or(&0)); - - // Verify we haven't lost any agents - if stats.total_agents >= 42 { - println!(" โœ… Agent count maintained: {} agents operational", stats.total_agents); - } else { - println!(" โš ๏ธ Potential agent loss: only {} agents found", stats.total_agents); - } - println!(); - - // Phase 3: Test Academic Agent Discovery - println!("๐Ÿ” Phase 3: Academic Agent Discovery Verification..."); - - let academic_capabilities = vec![ - "AcademicReasoning", - "TheoreticalPhysics", - "AdvancedMathematics", - "AdvancedChemistry", - "MolecularBiology", - "ComputerScienceTheory", - "MultipleChoiceProcessing" - ]; - - let mut academic_discovery_results = HashMap::new(); - - for capability in &academic_capabilities { - let agents = registry.get_agents_by_capability(capability)?; - academic_discovery_results.insert((*capability).to_string(), agents.len()); - - if !agents.is_empty() { - println!(" โœ… {}: {} agents discoverable", capability, agents.len()); - } else { - println!(" โŒ {}: No agents found", capability); - } - } - - let total_academic_agents = academic_discovery_results.values().sum::(); - println!(" Total Academic Agent Instances: {}", total_academic_agents); - println!(); - - // Phase 4: Test Non-Academic Agent Discovery (Backward Compatibility) - println!("๐Ÿ”„ Phase 4: Backward Compatibility Verification..."); - - let existing_capabilities = vec![ - "CodeGeneration", - "Testing", - "Security", - "Analytics", - "FileSystem", - "Database", - "WebSearch" - ]; - - let mut existing_discovery_results = HashMap::new(); - - for capability in &existing_capabilities { - let agents = registry.get_agents_by_capability(capability)?; - existing_discovery_results.insert((*capability).to_string(), agents.len()); - - if !agents.is_empty() { - println!(" โœ… {}: {} agents still discoverable", capability, agents.len()); - } else { - println!(" โš ๏ธ {}: No agents found (potential regression)", capability); - } - } - - let total_existing_agents = existing_discovery_results.values().sum::(); - println!(" Total Existing Agent Instances: {}", total_existing_agents); - println!(); - - // Phase 5: Test Cross-System Agent Coordination - println!("๐Ÿค Phase 5: Cross-System Agent Coordination..."); - - // Test academic + development agent coordination - let academic_agents = registry.get_agents_by_capability("AcademicReasoning")?; - let dev_agents = registry.get_agents_by_capability("CodeGeneration")?; - - println!(" Agent Coordination Test:"); - println!(" ======================="); - println!(" Academic Agents Available: {}", academic_agents.len()); - println!(" Development Agents Available: {}", dev_agents.len()); - - if !academic_agents.is_empty() && !dev_agents.is_empty() { - // Test coordination by executing queries on both types - let academic_query = AgentInput::new( - "academic_question".to_string(), - "What is quantum mechanics?".to_string(), - "coordination_test".to_string() - ); - - let context = CognitiveContext::default(); - - if let Some(academic_agent) = academic_agents.first() { - match academic_agent.execute(academic_query, &context).await { - Ok(_) => println!(" โœ… Academic agent execution successful"), - Err(e) => println!(" โŒ Academic agent execution failed: {}", e), - } - } - - println!(" โœ… Cross-system coordination functional"); - } else { - println!(" โš ๏ธ Cannot test coordination - missing agent types"); - } - println!(); - - // Phase 6: Performance Impact Assessment - println!("๐Ÿ“ˆ Phase 6: Performance Impact Assessment..."); - - // Test query performance across different agent types - let performance_tests = vec![ - ("Academic", "AcademicReasoning"), - ("Development", "CodeGeneration"), - ("Testing", "Testing"), - ("Security", "Security"), - ]; - - let mut performance_results = HashMap::new(); - - for (category, capability) in performance_tests { - let start = std::time::Instant::now(); - let agents = registry.get_agents_by_capability(capability)?; - let discovery_time = start.elapsed(); - - performance_results.insert(category, (agents.len(), discovery_time.as_millis())); - - println!(" {} Agents:", category); - println!(" Agents Found: {}", agents.len()); - println!(" Discovery Time: {}ms", discovery_time.as_millis()); - } - - let max_discovery_time = performance_results.values() - .map(|(_, time)| *time) - .max() - .unwrap_or(0); - - if max_discovery_time <= 50 { - println!(" โœ… Performance impact minimal: max {}ms discovery time", max_discovery_time); - } else { - println!(" โš ๏ธ Performance impact detected: {}ms max discovery time", max_discovery_time); - } - println!(); - - // Phase 7: Integration Health Assessment - println!("๐Ÿฅ Phase 7: Integration Health Assessment..."); - - let total_agents = stats.total_agents; - let academic_agent_count = stats.agents_by_category.get("academic").unwrap_or(&0); - let academic_coverage = (*academic_agent_count as f64 / total_agents as f64) * 100.0; - - println!(" Integration Health Metrics:"); - println!(" =========================="); - println!(" Total System Agents: {}", total_agents); - println!(" Academic Agents: {} ({:.1}% of total)", academic_agent_count, academic_coverage); - println!(" Academic Capabilities: {}", academic_discovery_results.len()); - println!(" Existing Capabilities: {}", existing_discovery_results.len()); - - // Calculate integration success metrics - let academic_success_rate = academic_discovery_results.values() - .filter(|&&count| count > 0) - .count() as f64 / academic_discovery_results.len() as f64 * 100.0; - - let existing_success_rate = existing_discovery_results.values() - .filter(|&&count| count > 0) - .count() as f64 / existing_discovery_results.len() as f64 * 100.0; - - println!(" Academic Discovery Success: {:.1}%", academic_success_rate); - println!(" Existing Capability Retention: {:.1}%", existing_success_rate); - - // Overall assessment - let overall_integration_score = (academic_success_rate + existing_success_rate) / 2.0; - - println!(" Overall Integration Score: {:.1}%", overall_integration_score); - println!(); - - // Phase 8: Final Integration Validation - println!("๐ŸŽฏ Phase 8: Final Integration Validation..."); - - println!(" Task 1.4 Deliverable Validation:"); - println!(" ================================"); - - // Validate each deliverable - let deliverable_checks = vec![ - ("Register academic agents with AgentOrchestrator", academic_agent_count > &0), - ("Academic knowledge integrated", true), // Verified by knowledge base tests - ("Academic reasoning routes configured", academic_success_rate >= 85.0), - ("Performance monitoring active", max_discovery_time <= 50), - ("Backward compatibility maintained", existing_success_rate >= 85.0), - ("Academic/algorithmic coordination enabled", total_agents >= 42), - ]; - - let mut passed_checks = 0; - - for (check, passed) in &deliverable_checks { - if *passed { - println!(" โœ… {}", check); - passed_checks += 1; - } else { - println!(" โŒ {}", check); - } - } - - let deliverable_success_rate = (passed_checks as f64 / deliverable_checks.len() as f64) * 100.0; - - println!(" Deliverable Success Rate: {:.1}%", deliverable_success_rate); - println!(); - - // Final assessment - println!("๐Ÿ† Ecosystem Integration Verification Complete!"); - - if deliverable_success_rate >= 90.0 && overall_integration_score >= 85.0 { - println!("โœ… TASK 1.4 SUCCESS: Academic ecosystem integration complete"); - println!(" โ€ข {} total agents operational", total_agents); - println!(" โ€ข {:.1}% academic discovery success", academic_success_rate); - println!(" โ€ข {:.1}% backward compatibility maintained", existing_success_rate); - println!(" โ€ข {}ms maximum discovery latency", max_discovery_time); - println!(" โ€ข Seamless operation achieved with zero disruption"); - } else { - println!("โš ๏ธ TASK 1.4 PARTIAL: Integration issues detected"); - println!(" โ€ข Deliverable success: {:.1}%", deliverable_success_rate); - println!(" โ€ข Integration score: {:.1}%", overall_integration_score); - - if academic_success_rate < 85.0 { - println!(" โ€ข Academic agent discovery needs improvement"); - } - if existing_success_rate < 85.0 { - println!(" โ€ข Backward compatibility issues detected"); - } - if max_discovery_time > 50 { - println!(" โ€ข Performance optimization needed"); - } - } - - Ok(()) -} \ No newline at end of file diff --git a/enhanced_agent_capabilities_test.rs b/enhanced_agent_capabilities_test.rs deleted file mode 100644 index 96cb67c624cba89c5b68b3a1545ffee6fca5613c..0000000000000000000000000000000000000000 --- a/enhanced_agent_capabilities_test.rs +++ /dev/null @@ -1,320 +0,0 @@ -//! # Enhanced Agent Capabilities Test -//! -//! This test demonstrates the enhanced agent capabilities including: -//! - Improved CognitiveContext with richer information -//! - Multi-agent collaboration scenarios -//! - Enhanced agent routing and discovery -//! - Tool integration with cognitive agents - -use anyhow::Result; -use brain_cognitive::{ - agents::AgentRegistry, - AgentOrchestrator, -}; -use std::collections::HashMap; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Enhanced Agent Capabilities Test - Phase 2"); - println!("==========================================="); - println!(); - - // Test 1: Enhanced CognitiveContext - println!("๐Ÿ”ง Test 1: Enhanced CognitiveContext"); - println!("-----------------------------------"); - - let context_test_passed = test_enhanced_context().await?; - if context_test_passed { - println!("โœ… Enhanced CognitiveContext works correctly"); - } else { - println!("โŒ Enhanced CognitiveContext has issues"); - } - println!(); - - // Test 2: Sophisticated Agent Routing - println!("๐Ÿ”ง Test 2: Sophisticated Agent Routing"); - println!("-------------------------------------"); - - let routing_test_passed = test_agent_routing().await?; - if routing_test_passed { - println!("โœ… Sophisticated agent routing works correctly"); - } else { - println!("โŒ Sophisticated agent routing has issues"); - } - println!(); - - // Test 3: Multi-Agent Collaboration - println!("๐Ÿ”ง Test 3: Multi-Agent Collaboration"); - println!("------------------------------------"); - - let collaboration_test_passed = test_multi_agent_collaboration().await?; - if collaboration_test_passed { - println!("โœ… Multi-agent collaboration works correctly"); - } else { - println!("โŒ Multi-agent collaboration has issues"); - } - println!(); - - // Test 4: Tool-Agent Integration - println!("๐Ÿ”ง Test 4: Tool-Agent Integration"); - println!("---------------------------------"); - - let integration_test_passed = test_tool_agent_integration().await?; - if integration_test_passed { - println!("โœ… Tool-agent integration works correctly"); - } else { - println!("โŒ Tool-agent integration has issues"); - } - println!(); - - // Test 5: Agent Orchestration - println!("๐Ÿ”ง Test 5: Agent Orchestration"); - println!("------------------------------"); - - let orchestration_test_passed = test_agent_orchestration().await?; - if orchestration_test_passed { - println!("โœ… Agent orchestration works correctly"); - } else { - println!("โŒ Agent orchestration has issues"); - } - println!(); - - // Summary - println!("๐Ÿ“Š Enhanced Agent Capabilities Summary"); - println!("===================================="); - println!("Enhanced Context: {}", if context_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Agent Routing: {}", if routing_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Multi-Agent Collaboration: {}", if collaboration_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Tool-Agent Integration: {}", if integration_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Agent Orchestration: {}", if orchestration_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - - let all_tests_passed = context_test_passed && routing_test_passed && collaboration_test_passed && - integration_test_passed && orchestration_test_passed; - println!(); - println!("๐ŸŽฏ Overall Result: {}", if all_tests_passed { "โœ… ALL TESTS PASSED" } else { "โŒ SOME TESTS FAILED" }); - - if all_tests_passed { - println!("๐Ÿš€ Enhanced agent capabilities are working!"); - println!(" Phase 2 - Tool and Agent Expansion is COMPLETE!"); - println!(" Ready for Phase 3 - Advanced Agent and Tool Integration"); - } else { - println!("๐Ÿ”ง Some enhanced capabilities need attention."); - println!(" Please check the test output above for details."); - } - - Ok(()) -} - -/// Test enhanced cognitive context capabilities -async fn test_enhanced_context() -> Result { - println!(" Testing enhanced cognitive context..."); - - // Note: This test focuses on the registry and tool capabilities - // as creating a full CognitiveContext requires complex setup - - let registry = AgentRegistry::new_with_defaults(); - - // Test capability discovery - let file_agents = registry.get_agents_by_capability("FileSystem")?; - println!(" ๐Ÿ“ FileSystem agents: {}", file_agents.len()); - - let db_agents = registry.get_agents_by_capability("Database")?; - println!(" ๐Ÿ’พ Database agents: {}", db_agents.len()); - - let web_agents = registry.get_agents_by_capability("WebSearch")?; - println!(" ๐Ÿ” WebSearch agents: {}", web_agents.len()); - - let dev_agents = registry.get_agents_by_capability("CodeGeneration")?; - println!(" ๐Ÿ› ๏ธ CodeGeneration agents: {}", dev_agents.len()); - - // Test input type discovery - let read_file_agents = registry.get_agents_by_input_type("read_file")?; - println!(" ๐Ÿ“– Agents for read_file: {}", read_file_agents.len()); - - let query_agents = registry.get_agents_by_input_type("query")?; - println!(" ๐Ÿ” Agents for query: {}", query_agents.len()); - - let coding_agents = registry.get_agents_by_input_type("coding_problem")?; - println!(" ๐Ÿ’ป Agents for coding_problem: {}", coding_agents.len()); - - println!(" โœ“ Enhanced context capabilities verified"); - - Ok(file_agents.len() > 0 && db_agents.len() > 0 && web_agents.len() > 0 && dev_agents.len() > 0) -} - -/// Test sophisticated agent routing -async fn test_agent_routing() -> Result { - println!(" Testing sophisticated agent routing..."); - - let registry = AgentRegistry::new_with_defaults(); - - // Test routing for different scenarios - println!(" ๐Ÿ“ Testing routing scenarios..."); - - // Scenario 1: File system operations - let fs_agents = registry.get_agents_by_capability("FileSystem")?; - println!(" ๐Ÿ“ File system scenario: {} agents found", fs_agents.len()); - - // Scenario 2: Database operations - let db_agents = registry.get_agents_by_capability("Database")?; - println!(" ๐Ÿ’พ Database scenario: {} agents found", db_agents.len()); - - // Scenario 3: Web search operations - let web_agents = registry.get_agents_by_capability("WebSearch")?; - println!(" ๐Ÿ” Web search scenario: {} agents found", web_agents.len()); - - // Scenario 4: Development tasks - let dev_agents = registry.get_agents_by_capability("CodeGeneration")?; - println!(" ๐Ÿ› ๏ธ CodeGeneration scenario: {} agents found", dev_agents.len()); - - // Test that we can find the right agents for specific tasks - let all_agents = registry.list_agents()?; - let mut task_coverage = HashMap::new(); - - for agent in all_agents { - let metadata = agent.metadata(); - for capability in &metadata.capabilities { - *task_coverage.entry(capability.clone()).or_insert(0) += 1; - } - } - - println!(" ๐Ÿ“Š Task coverage analysis:"); - for (capability, count) in task_coverage { - println!(" {} agents for {}", count, capability); - } - - Ok(fs_agents.len() > 0 && db_agents.len() > 0 && web_agents.len() > 0 && dev_agents.len() > 0) -} - -/// Test multi-agent collaboration -async fn test_multi_agent_collaboration() -> Result { - println!(" Testing multi-agent collaboration..."); - - let registry = AgentRegistry::new_with_defaults(); - - // Simulate a collaborative scenario: "Create a data analysis system" - println!(" ๐Ÿค Simulating collaborative scenario: Data analysis system"); - - // Step 1: Find agents for different aspects - let fs_agents = registry.get_agents_by_capability("FileSystem")?; - let db_agents = registry.get_agents_by_capability("Database")?; - let dev_agents = registry.get_agents_by_capability("CodeGeneration")?; - - println!(" ๐Ÿ“ FileSystem agents available: {}", fs_agents.len()); - println!(" ๐Ÿ’พ Database agents available: {}", db_agents.len()); - println!(" ๐Ÿ› ๏ธ CodeGeneration agents available: {}", dev_agents.len()); - - // Step 2: Test agent metadata for collaboration - let mut collaboration_ready = 0; - let all_agents = registry.list_agents()?; - - for agent in all_agents { - let metadata = agent.metadata(); - // Check if agent can handle multiple input types (indicates collaboration readiness) - if metadata.supported_input_types.len() > 1 { - collaboration_ready += 1; - } - } - - println!(" ๐Ÿค Collaboration-ready agents: {}", collaboration_ready); - - // Step 3: Simulate dependency resolution - println!(" ๐Ÿ”— Testing dependency resolution..."); - - // In a real scenario, this would create a dependency graph - // For now, we'll just verify we have the right mix of agents - let has_file_support = fs_agents.len() > 0; - let has_db_support = db_agents.len() > 0; - let has_dev_support = dev_agents.len() > 0; - - println!(" โœ“ File system support: {}", has_file_support); - println!(" โœ“ Database support: {}", has_db_support); - println!(" โœ“ Development support: {}", has_dev_support); - - Ok(has_file_support && has_db_support && has_dev_support && collaboration_ready > 0) -} - -/// Test tool-agent integration -async fn test_tool_agent_integration() -> Result { - println!(" Testing tool-agent integration..."); - - let registry = AgentRegistry::new_with_defaults(); - - // Test that tools are integrated as agents - let all_agents = registry.list_agents()?; - let mut tool_count = 0; - let mut agent_count = 0; - - for agent in all_agents { - let metadata = agent.metadata(); - if metadata.tags.contains(&"tool".to_string()) { - tool_count += 1; - } else { - agent_count += 1; - } - } - - println!(" ๐Ÿ”ง Tools registered as agents: {}", tool_count); - println!(" ๐Ÿค– Traditional agents: {}", agent_count); - - // Test that tools can be discovered by capability - let fs_tools = registry.get_agents_by_capability("FileSystem")?; - let db_tools = registry.get_agents_by_capability("Database")?; - let web_tools = registry.get_agents_by_capability("WebSearch")?; - - println!(" ๐Ÿ“ FileSystem tools discoverable: {}", fs_tools.len()); - println!(" ๐Ÿ’พ Database tools discoverable: {}", db_tools.len()); - println!(" ๐ŸŒ Web search tools discoverable: {}", web_tools.len()); - - // Test tool-specific input types - let read_file_tools = registry.get_agents_by_input_type("read_file")?; - let query_tools = registry.get_agents_by_input_type("query")?; - let search_tools = registry.get_agents_by_input_type("search_query")?; - - println!(" ๐Ÿ“– Tools for read_file: {}", read_file_tools.len()); - println!(" ๐Ÿ” Tools for query: {}", query_tools.len()); - println!(" ๐ŸŒ Tools for search_query: {}", search_tools.len()); - - Ok(tool_count >= 3 && fs_tools.len() > 0 && db_tools.len() > 0 && web_tools.len() > 0) -} - -/// Test agent orchestration capabilities -async fn test_agent_orchestration() -> Result { - println!(" Testing agent orchestration..."); - - // Test orchestrator creation - let _orchestrator = AgentOrchestrator::new(); - println!(" ๐ŸŽผ Agent orchestrator created"); - - // Test that orchestrator can access agents - let registry = AgentRegistry::new_with_defaults(); - let agent_count = registry.list_agents()?.len(); - - println!(" ๐Ÿ“Š Orchestrator can access {} agents", agent_count); - - // Test orchestration scenarios - println!(" ๐ŸŽญ Testing orchestration scenarios..."); - - // Scenario 1: Sequential task execution - let fs_agents = registry.get_agents_by_capability("FileSystem")?; - let db_agents = registry.get_agents_by_capability("Database")?; - - println!(" ๐Ÿ“ Sequential execution: FileSystem -> Database"); - println!(" Stage 1 agents: {}", fs_agents.len()); - println!(" Stage 2 agents: {}", db_agents.len()); - - // Scenario 2: Parallel task execution - let dev_agents = registry.get_agents_by_capability("CodeGeneration")?; - let web_agents = registry.get_agents_by_capability("WebSearch")?; - - println!(" ๐Ÿ”„ Parallel execution: CodeGeneration || WebSearch"); - println!(" Parallel group 1: {}", dev_agents.len()); - println!(" Parallel group 2: {}", web_agents.len()); - - // Test orchestration readiness - let orchestration_ready = agent_count > 0 && fs_agents.len() > 0 && db_agents.len() > 0; - - println!(" โœ“ Orchestration system ready: {}", orchestration_ready); - - Ok(orchestration_ready) -} \ No newline at end of file diff --git a/enhanced_learning_demo.rs b/enhanced_learning_demo.rs deleted file mode 100644 index 49a48ac62d554f874fdc2dfb737b0b14ab6eec83..0000000000000000000000000000000000000000 --- a/enhanced_learning_demo.rs +++ /dev/null @@ -1,574 +0,0 @@ -//! Enhanced LLM Training Integration Demo -//! -//! This example demonstrates the comprehensive enhanced LLM training integration system -//! that uses the LLM to continuously improve the Brain's learning and retrieval capabilities. -//! -//! Features demonstrated: -//! - Active Learning Loop: Identify knowledge gaps and generate follow-up questions -//! - Adaptive Query Enhancement: Learn from successful query patterns -//! - Meta-Learning Capabilities: Analyze learning patterns and optimize strategies -//! - Performance Tracking: Monitor improvement trends over time -//! - Learning Session Management: Track and analyze learning sessions - -use tokio; -use anyhow::Result; -use std::collections::HashMap; -use std::sync::Arc; -use tokio::sync::RwLock; -use uuid::Uuid; - -// Import from new service architecture -use brain::*; -use brain::services::*; -use brain_types::BrainError; - -/// Simple learning orchestrator for demo -pub struct DemoLearningOrchestrator { - query_enhancer: DemoQueryEnhancer, - meta_learner: DemoMetaLearner, - performance_tracker: DemoPerformanceTracker, - session_id: Option, - session_start_time: Option, -} - -impl DemoLearningOrchestrator { - pub fn new() -> Self { - Self { - query_enhancer: DemoQueryEnhancer::new(), - meta_learner: DemoMetaLearner::new(), - performance_tracker: DemoPerformanceTracker::new(), - session_id: None, - session_start_time: None, - } - } - - pub async fn start_learning_session(&mut self, description: String) -> Uuid { - let session_id = Uuid::new_v4(); - self.session_id = Some(session_id); - self.session_start_time = Some(std::time::Instant::now()); - println!(" Started learning session: {}", description); - session_id - } - - pub async fn end_learning_session(&mut self, _session_id: Uuid) -> LearningSessionSummary { - let duration = self.session_start_time - .map(|start| start.elapsed().as_secs_f64() / 60.0) - .unwrap_or(0.0); - - self.session_id = None; - self.session_start_time = None; - - LearningSessionSummary { - duration_minutes: duration, - activities_completed: 4, - knowledge_gained: 15, - avg_activity_success: 0.85, - insights_generated: 8, - overall_effectiveness: 0.92, - } - } - - pub async fn process_query_for_learning( - &mut self, - query: &str, - _response_confidence: f64, - _response_quality: f64, - _knowledge_sources: u32, - ) -> Result { - // Simulate knowledge gap analysis - let gaps = vec![ - KnowledgeGap { - topic: "Reinforcement learning algorithms".to_string(), - confidence_level: 0.3, - importance: 0.9, - }, - KnowledgeGap { - topic: "Q-learning implementation".to_string(), - confidence_level: 0.2, - importance: 0.8, - }, - ]; - - let questions = vec![ - FollowUpQuestion { - question: "What are the main types of reinforcement learning algorithms?".to_string(), - priority: 0.9, - }, - FollowUpQuestion { - question: "How does the reward system work in reinforcement learning?".to_string(), - priority: 0.8, - }, - FollowUpQuestion { - question: "What are the differences between on-policy and off-policy methods?".to_string(), - priority: 0.7, - }, - ]; - - let recommendations = vec![ - "Study the mathematical foundations of Markov Decision Processes".to_string(), - "Implement a simple Q-learning example to understand the basics".to_string(), - "Research current state-of-the-art RL algorithms like PPO and SAC".to_string(), - ]; - - println!(" Analyzed query: '{}'", query); - - Ok(LearningOpportunities { - identified_gaps: gaps, - follow_up_questions: questions, - learning_recommendations: recommendations, - }) - } - - pub async fn get_learning_analytics(&self) -> LearningAnalytics { - LearningAnalytics { - active_learning_status: ActiveLearningStatus { - total_gaps_identified: 12, - high_priority_gaps: 5, - }, - query_enhancement_insights: self.query_enhancer.get_insights().await.unwrap(), - meta_learning_recommendations: self.meta_learner.get_recommendations().await.unwrap(), - performance_trends: self.performance_tracker.get_trends().await.unwrap(), - } - } - - pub async fn get_performance_trends(&self) -> PerformanceTrends { - self.performance_tracker.get_trends().await.unwrap() - } -} - -/// Demo query enhancer -pub struct DemoQueryEnhancer { - patterns: Arc>>, -} - -impl DemoQueryEnhancer { - pub fn new() -> Self { - Self { - patterns: Arc::new(RwLock::new(HashMap::new())), - } - } - - pub async fn learn_from_query(&mut self, query: &str, success_rate: f64, _quality: f64) -> Result<(), BrainError> { - let mut patterns = self.patterns.write().await; - patterns.insert(query.to_string(), success_rate); - Ok(()) - } - - pub async fn suggest_query_improvements(&self, _query: &str) -> Result, BrainError> { - Ok(vec![ - "Add specific context about the AI domain you're interested in".to_string(), - "Include technical depth level (beginner, intermediate, advanced)".to_string(), - "Specify if you want theoretical or practical examples".to_string(), - ]) - } - - pub async fn get_insights(&self) -> Result { - let patterns = self.patterns.read().await; - Ok(QueryEnhancementInsights { - successful_patterns_count: patterns.len(), - failed_patterns_count: 2, - domain_rules_count: 8, - top_performing_patterns: patterns.keys().take(3).cloned().collect(), - }) - } -} - -/// Demo meta learner -pub struct DemoMetaLearner { - insights: Vec, -} - -impl DemoMetaLearner { - pub fn new() -> Self { - Self { - insights: vec![ - "Users prefer step-by-step explanations for complex topics".to_string(), - "Technical queries benefit from code examples".to_string(), - "Conceptual questions need visual analogies".to_string(), - ], - } - } - - pub async fn get_recommendations(&self) -> Result { - Ok(MetaLearningRecommendations { - learning_patterns_identified: 7, - memory_optimizations_suggested: 3, - relationship_insights_discovered: 5, - high_priority_recommendations: 2, - recent_insights: self.insights.clone(), - }) - } - - pub async fn generate_learning_recommendations(&self) -> Result, BrainError> { - Ok(vec![ - "Focus on building stronger conceptual foundations before diving into implementation".to_string(), - "Create more connections between related concepts to improve retrieval".to_string(), - "Implement spaced repetition for complex technical concepts".to_string(), - ]) - } -} - -/// Demo performance tracker -pub struct DemoPerformanceTracker { - metrics: Arc>>, -} - -impl DemoPerformanceTracker { - pub fn new() -> Self { - Self { - metrics: Arc::new(RwLock::new(Vec::new())), - } - } - - pub async fn record_query_performance( - &mut self, - metric_name: &str, - accuracy: f64, - quality: f64, - sources: u32, - ) -> Result<(), BrainError> { - let mut metrics = self.metrics.write().await; - metrics.push(PerformanceMetric { - name: metric_name.to_string(), - accuracy, - quality, - sources, - timestamp: std::time::SystemTime::now(), - }); - Ok(()) - } - - pub async fn get_trends(&self) -> Result { - let metrics = self.metrics.read().await; - let improvement = if metrics.len() > 1 { - let first = metrics.first().unwrap(); - let last = metrics.last().unwrap(); - (last.accuracy - first.accuracy) / first.accuracy - } else { - 0.0 - }; - - Ok(PerformanceTrends { - query_performance_trend: TrendDirection::Improving, - learning_effectiveness_trend: TrendDirection::Improving, - overall_improvement: improvement.abs(), - recent_performance_summary: format!("Recorded {} performance metrics with average improvement of {:.1}%", - metrics.len(), improvement * 100.0), - }) - } -} - -// Supporting types -#[derive(Debug)] -pub struct LearningSessionSummary { - pub duration_minutes: f64, - pub activities_completed: u32, - pub knowledge_gained: u32, - pub avg_activity_success: f64, - pub insights_generated: u32, - pub overall_effectiveness: f64, -} - -#[derive(Debug)] -pub struct LearningOpportunities { - pub identified_gaps: Vec, - pub follow_up_questions: Vec, - pub learning_recommendations: Vec, -} - -#[derive(Debug)] -pub struct KnowledgeGap { - pub topic: String, - pub confidence_level: f64, - pub importance: f64, -} - -#[derive(Debug)] -pub struct FollowUpQuestion { - pub question: String, - pub priority: f64, -} - -#[derive(Debug)] -pub struct LearningAnalytics { - pub active_learning_status: ActiveLearningStatus, - pub query_enhancement_insights: QueryEnhancementInsights, - pub meta_learning_recommendations: MetaLearningRecommendations, - pub performance_trends: PerformanceTrends, -} - -#[derive(Debug)] -pub struct ActiveLearningStatus { - pub total_gaps_identified: u32, - pub high_priority_gaps: u32, -} - -#[derive(Debug)] -pub struct QueryEnhancementInsights { - pub successful_patterns_count: usize, - pub failed_patterns_count: u32, - pub domain_rules_count: u32, - pub top_performing_patterns: Vec, -} - -#[derive(Debug)] -pub struct MetaLearningRecommendations { - pub learning_patterns_identified: u32, - pub memory_optimizations_suggested: u32, - pub relationship_insights_discovered: u32, - pub high_priority_recommendations: u32, - pub recent_insights: Vec, -} - -#[derive(Debug)] -pub struct PerformanceTrends { - pub query_performance_trend: TrendDirection, - pub learning_effectiveness_trend: TrendDirection, - pub overall_improvement: f64, - pub recent_performance_summary: String, -} - -#[derive(Debug)] -pub enum TrendDirection { - Improving, - Stable, - Declining, -} - -#[derive(Debug)] -pub struct PerformanceMetric { - pub name: String, - pub accuracy: f64, - pub quality: f64, - pub sources: u32, - pub timestamp: std::time::SystemTime, -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Enhanced LLM Training Integration Demo"); - println!("==========================================\n"); - - // Initialize components with new service architecture - let mut memory_service = create_memory_service_with_capacity(2000).await?; - let mut concept_graph_service = create_concept_graph_service_default().await?; - - // Initialize demo orchestrators - let rag_orchestrator = RagOrchestrator::new()?; // Using available orchestrator - let mut learning_orchestrator = DemoLearningOrchestrator::new(); - - // Demonstrate the enhanced learning capabilities - println!("๐Ÿš€ Starting Enhanced Learning Demonstration\n"); - - // Start a learning session - let session_id = learning_orchestrator.start_learning_session("Demonstrate enhanced LLM training integration".to_string()).await; - println!("๐Ÿ“ Learning session started: {}\n", session_id); - - // Run the active learning demonstration - active_learning_demo(&mut learning_orchestrator, &rag_orchestrator, &mut memory_service, &mut concept_graph_service).await?; - - // Run the adaptive query enhancement demonstration - adaptive_query_demo(&mut learning_orchestrator).await?; - - // Run the meta-learning capabilities demonstration - meta_learning_demo(&mut learning_orchestrator).await?; - - // Run the performance tracking demonstration - performance_tracking_demo(&mut learning_orchestrator).await?; - - // End the learning session and get summary - let session_summary = learning_orchestrator.end_learning_session(session_id).await; - println!("๐Ÿ“Š Learning Session Summary:"); - println!(" Duration: {:.1} minutes", session_summary.duration_minutes); - println!(" Activities completed: {}", session_summary.activities_completed); - println!(" Knowledge gained: {}", session_summary.knowledge_gained); - println!(" Average activity success: {:.1}%", session_summary.avg_activity_success * 100.0); - println!(" Insights generated: {}", session_summary.insights_generated); - println!(" Overall effectiveness: {:.1}%\n", session_summary.overall_effectiveness * 100.0); - - // Get overall learning analytics - let analytics = learning_orchestrator.get_learning_analytics().await; - println!("๐ŸŽฏ Overall Learning Analytics:"); - println!(" Total gaps identified: {}", analytics.active_learning_status.total_gaps_identified); - println!(" High priority gaps: {}", analytics.active_learning_status.high_priority_gaps); - println!(" Successful query patterns: {}", analytics.query_enhancement_insights.successful_patterns_count); - println!(" Learning patterns identified: {}", analytics.meta_learning_recommendations.learning_patterns_identified); - println!(" Query performance trend: {:?}", analytics.performance_trends.query_performance_trend); - println!(" Overall improvement: {:.1}%", analytics.performance_trends.overall_improvement * 100.0); - - println!("\nโœ… Enhanced Learning Demo completed successfully!"); - Ok(()) -} - -async fn active_learning_demo( - learning_orchestrator: &mut DemoLearningOrchestrator, - _rag_orchestrator: &RagOrchestrator, - memory_service: &mut MemoryService, - _concept_graph_service: &mut ConceptGraphService, -) -> Result<()> { - println!("๐Ÿ” Active Learning Loop Demonstration"); - println!("====================================="); - - // Add some sample knowledge to memory - println!(" Adding sample knowledge to memory and concept graph..."); - - // Use the actual memory service to store knowledge - memory_service.learn("Machine learning is a subset of artificial intelligence".to_string(), Priority::High).await?; - memory_service.learn("Deep learning uses neural networks with multiple layers".to_string(), Priority::High).await?; - memory_service.learn("Natural language processing enables computers to understand human language".to_string(), Priority::Medium).await?; - - println!(" โœ… Stored: 'Machine learning is a subset of artificial intelligence'"); - println!(" โœ… Stored: 'Deep learning uses neural networks with multiple layers'"); - println!(" โœ… Stored: 'Natural language processing enables computers to understand human language'"); - - // Simulate concept creation - println!(" โœ… Created concept: 'Machine Learning' (confidence: 0.9)"); - println!(" โœ… Created concept: 'Deep Learning' (confidence: 0.85)"); - - // Simulate query processing that identifies knowledge gaps - println!(" Processing query: 'How does reinforcement learning work?'"); - - // The learning orchestrator would analyze this query and identify gaps - let learning_opportunities = learning_orchestrator.process_query_for_learning( - "How does reinforcement learning work?", - 0.4, // response_confidence - 0.5, // response_quality - 2 // knowledge_sources - ).await?; - - println!(" โœ… Knowledge gaps identified: {}", learning_opportunities.identified_gaps.len()); - println!(" ๐Ÿ“ Generated follow-up questions: {}", learning_opportunities.follow_up_questions.len()); - for question in learning_opportunities.follow_up_questions.iter().take(3) { - println!(" - {}", question.question); - } - - println!(" ๐Ÿ’ก Learning recommendations: {}", learning_opportunities.learning_recommendations.len()); - for rec in learning_opportunities.learning_recommendations.iter().take(2) { - println!(" - {}", rec); - } - - println!(" ๐ŸŽฏ Learning objective: Master reinforcement learning concepts"); - println!(" ๐Ÿ“ˆ Knowledge gap detection successful\n"); - - Ok(()) -} - -async fn adaptive_query_demo( - learning_orchestrator: &mut DemoLearningOrchestrator, -) -> Result<()> { - println!("๐Ÿ”„ Adaptive Query Enhancement Demonstration"); - println!("==========================================="); - - // Simulate successful query patterns - println!(" Learning from successful query patterns..."); - - // Use the actual learn_from_query method - learning_orchestrator.query_enhancer.learn_from_query("machine learning algorithms", 0.92, 0.95).await?; - learning_orchestrator.query_enhancer.learn_from_query("neural network architecture", 0.88, 0.90).await?; - learning_orchestrator.query_enhancer.learn_from_query("training data preprocessing", 0.85, 0.87).await?; - - println!(" โœ… Learned patterns:"); - println!(" - Technical terms with 'algorithms' โ†’ High success rate"); - println!(" - Architecture-related queries โ†’ Detailed explanations work well"); - println!(" - Process-oriented questions โ†’ Step-by-step format preferred"); - - // Simulate query enhancement suggestions - let original_query = "What is AI?"; - println!(" Original query: '{}'", original_query); - - let enhanced_suggestions = learning_orchestrator.query_enhancer.suggest_query_improvements(original_query).await?; - println!(" ๐Ÿ’ก Enhancement suggestions: {}", enhanced_suggestions.len()); - for suggestion in enhanced_suggestions.iter().take(3) { - println!(" - {}", suggestion); - } - - // Get query enhancement insights - let insights = learning_orchestrator.query_enhancer.get_insights().await?; - println!(" ๐Ÿ“Š Query Enhancement Insights:"); - println!(" - Successful patterns: {}", insights.successful_patterns_count); - println!(" - Failed patterns: {}", insights.failed_patterns_count); - println!(" - Domain rules: {}", insights.domain_rules_count); - println!(" - Top performing patterns: {}", insights.top_performing_patterns.len()); - println!(" ๐Ÿ”ง Query strategy updated based on learned patterns\n"); - - Ok(()) -} - -async fn meta_learning_demo(learning_orchestrator: &mut DemoLearningOrchestrator) -> Result<()> { - println!("๐Ÿง  Meta-Learning Capabilities Demonstration"); - println!("==========================================="); - - // Simulate learning pattern analysis - println!(" Analyzing learning patterns..."); - - // Get meta-learning recommendations - let recommendations = learning_orchestrator.meta_learner.get_recommendations().await?; - println!(" ๐Ÿ“Š Meta-Learning Analysis:"); - println!(" - Learning patterns identified: {}", recommendations.learning_patterns_identified); - println!(" - Memory optimizations suggested: {}", recommendations.memory_optimizations_suggested); - println!(" - Relationship insights discovered: {}", recommendations.relationship_insights_discovered); - println!(" - High priority recommendations: {}", recommendations.high_priority_recommendations); - - // Get general learning recommendations - let general_recommendations = learning_orchestrator.meta_learner.generate_learning_recommendations().await?; - println!(" ๐ŸŽฏ Optimization Recommendations:"); - for (i, rec) in general_recommendations.iter().take(3).enumerate() { - println!(" {}. {}", i + 1, rec); - } - - // Demonstrate concept relationship insights - println!(" ๐Ÿ”— Recent Insights:"); - for insight in recommendations.recent_insights.iter().take(2) { - println!(" - {}", insight); - } - - println!(" โšก Meta-learning system is continuously analyzing patterns"); - println!(" and generating optimization suggestions\n"); - - Ok(()) -} - -async fn performance_tracking_demo(learning_orchestrator: &mut DemoLearningOrchestrator) -> Result<()> { - println!("๐Ÿ“ˆ Performance Tracking Demonstration"); - println!("====================================="); - - // Simulate performance metrics over time - println!(" Recording performance metrics..."); - - // Record some performance data using the actual method - learning_orchestrator.performance_tracker.record_query_performance("query_accuracy_test", 0.75, 0.80, 3).await?; - learning_orchestrator.performance_tracker.record_query_performance("query_accuracy_test", 0.82, 0.85, 4).await?; - learning_orchestrator.performance_tracker.record_query_performance("query_accuracy_test", 0.88, 0.90, 5).await?; - learning_orchestrator.performance_tracker.record_query_performance("query_accuracy_test", 0.91, 0.93, 4).await?; - - learning_orchestrator.performance_tracker.record_query_performance("response_relevance_test", 0.70, 0.75, 2).await?; - learning_orchestrator.performance_tracker.record_query_performance("response_relevance_test", 0.78, 0.82, 3).await?; - learning_orchestrator.performance_tracker.record_query_performance("response_relevance_test", 0.85, 0.88, 4).await?; - - learning_orchestrator.performance_tracker.record_query_performance("user_satisfaction_test", 0.65, 0.70, 2).await?; - learning_orchestrator.performance_tracker.record_query_performance("user_satisfaction_test", 0.72, 0.76, 3).await?; - learning_orchestrator.performance_tracker.record_query_performance("user_satisfaction_test", 0.79, 0.83, 4).await?; - learning_orchestrator.performance_tracker.record_query_performance("user_satisfaction_test", 0.84, 0.87, 5).await?; - - // Get performance trends - let trends = learning_orchestrator.performance_tracker.get_trends().await?; - println!(" ๐Ÿ“Š Performance Trends:"); - println!(" - Query performance trend: {:?}", trends.query_performance_trend); - println!(" - Learning effectiveness trend: {:?}", trends.learning_effectiveness_trend); - println!(" - Overall improvement: {:.1}%", trends.overall_improvement * 100.0); - - println!(" ๐Ÿ“‹ Recent Performance Summary:"); - println!(" {}", trends.recent_performance_summary); - - // Show overall performance trends - let overall_trends = learning_orchestrator.get_performance_trends().await; - println!(" ๐ŸŽฏ Overall System Performance:"); - println!(" - Query performance trend: {:?}", overall_trends.query_performance_trend); - println!(" - Learning effectiveness trend: {:?}", overall_trends.learning_effectiveness_trend); - println!(" - System improvement rate: {:.1}%", overall_trends.overall_improvement * 100.0); - - println!(" โšก Performance tracking shows continuous improvement"); - println!(" across all measured metrics\n"); - - Ok(()) -} \ No newline at end of file diff --git a/error_propagation_demo.rs b/error_propagation_demo.rs deleted file mode 100644 index 4a33c38460487bc216a387b3ca3adb194ea64bff..0000000000000000000000000000000000000000 --- a/error_propagation_demo.rs +++ /dev/null @@ -1,625 +0,0 @@ -//! Error Propagation Demo (@bridge) -//! -//! Demonstrates comprehensive error propagation, centralized error handling, -//! structured logging, and error recovery mechanisms across all components. - -use std::sync::Arc; -use tokio; -use brain_cognitive::integration::{ - ErrorPropagationSystem, - error_propagation::{ErrorPropagationConfig, SystemError, ErrorSeverity, LogEntry, LogLevel}, - event_system::{EventSystem, EventSystemConfig}, -}; - -#[tokio::main] -async fn main() -> Result<(), Box> { - // Initialize logging - env_logger::init(); - - println!("โš ๏ธ Brain AI Error Propagation Demo"); - println!("==================================="); - - // Create event system for error propagation - let event_system = Arc::new(EventSystem::with_config(EventSystemConfig::default())); - - // Create error propagation system with full configuration - let config = ErrorPropagationConfig { - enable_event_propagation: true, - enable_meta_memory_tracking: false, // Disabled for demo simplicity - max_error_history: 1000, - enable_auto_recovery: true, - escalation_threshold: 3, - enable_structured_logging: true, - }; - - let error_system = Arc::new(ErrorPropagationSystem::with_event_system( - config, - event_system.clone(), - )); - - println!("\n๐Ÿ“‹ Phase 1: Error Handler Registration"); - println!("-------------------------------------"); - - // Register custom error handlers - register_error_handlers(&error_system).await?; - - println!("\nโš ๏ธ Phase 2: Error Generation and Propagation"); - println!("---------------------------------------------"); - - // Generate and propagate various types of errors - demonstrate_error_propagation(&error_system).await?; - - println!("\n๐Ÿ”„ Phase 3: Error Recovery Demonstration"); - println!("----------------------------------------"); - - // Demonstrate error recovery mechanisms - demonstrate_error_recovery(&error_system).await?; - - println!("\n๐Ÿ“Š Phase 4: Error Escalation"); - println!("----------------------------"); - - // Demonstrate error escalation - demonstrate_error_escalation(&error_system).await?; - - println!("\n๐Ÿ“ Phase 5: Structured Logging"); - println!("------------------------------"); - - // Demonstrate structured logging - demonstrate_structured_logging(&error_system).await?; - - println!("\n๐Ÿ“ˆ Phase 6: Error Statistics"); - println!("----------------------------"); - - // Show comprehensive error statistics - show_error_statistics(&error_system).await?; - - println!("\n๐ŸŽ‰ Error Propagation Demo Completed!"); - println!("===================================="); - - Ok(()) -} - -/// Register custom error handlers for different error types -async fn register_error_handlers(error_system: &ErrorPropagationSystem) -> Result<(), Box> { - println!("๐Ÿ”ง Registering error handlers..."); - - // Component error handler - let component_handler = Box::new(ComponentErrorHandler::new()); - error_system.register_handler( - "component.error".to_string(), - component_handler, - )?; - - // Service error handler - let service_handler = Box::new(ServiceErrorHandler::new()); - error_system.register_handler( - "service.error".to_string(), - service_handler, - )?; - - // Network error handler - let network_handler = Box::new(NetworkErrorHandler::new()); - error_system.register_handler( - "network.error".to_string(), - network_handler, - )?; - - // Database error handler - let database_handler = Box::new(DatabaseErrorHandler::new()); - error_system.register_handler( - "database.error".to_string(), - database_handler, - )?; - - // Register recovery strategies - let component_recovery = Box::new(ComponentRecoveryStrategy::new()); - error_system.register_recovery_strategy( - "component.error".to_string(), - component_recovery, - )?; - - let service_recovery = Box::new(ServiceRecoveryStrategy::new()); - error_system.register_recovery_strategy( - "service.error".to_string(), - service_recovery, - )?; - - println!("โœ… Registered 4 error handlers and 2 recovery strategies"); - - Ok(()) -} - -/// Demonstrate error propagation with different severity levels -async fn demonstrate_error_propagation(error_system: &ErrorPropagationSystem) -> Result<(), Box> { - println!("๐Ÿ“ค Generating and propagating errors..."); - - // Info level error - let info_error = SystemError::new( - "component.info".to_string(), - "Component initialization completed with warnings".to_string(), - "agent_registry".to_string(), - ErrorSeverity::Info, - ).with_context("component_type".to_string(), serde_json::json!("AgentRegistry")) - .with_context("warnings".to_string(), serde_json::json!(["deprecated_config_option"])) - .with_metadata("auto_recovery".to_string(), "false".to_string()); - - println!("๐Ÿ“ค Propagating INFO error: {}", info_error.id); - error_system.propagate_error(info_error).await?; - - // Warning level error - let warning_error = SystemError::new( - "service.warning".to_string(), - "Service performance degraded - high response times detected".to_string(), - "conversation_service".to_string(), - ErrorSeverity::Warning, - ).with_context("avg_response_time_ms".to_string(), serde_json::json!(2500)) - .with_context("threshold_ms".to_string(), serde_json::json!(1000)) - .with_metadata("monitoring".to_string(), "enabled".to_string()); - - println!("๐Ÿ“ค Propagating WARNING error: {}", warning_error.id); - error_system.propagate_error(warning_error).await?; - - // Error level - let error_error = SystemError::new( - "network.error".to_string(), - "Failed to connect to external API - connection timeout".to_string(), - "web_search_tool".to_string(), - ErrorSeverity::Error, - ).with_context("api_endpoint".to_string(), serde_json::json!("https://api.example.com")) - .with_context("timeout_ms".to_string(), serde_json::json!(5000)) - .with_context("retry_count".to_string(), serde_json::json!(3)) - .with_metadata("auto_retry".to_string(), "true".to_string()); - - println!("๐Ÿ“ค Propagating ERROR: {}", error_error.id); - error_system.propagate_error(error_error).await?; - - // Critical level error - let critical_error = SystemError::new( - "database.error".to_string(), - "Database connection lost - unable to persist data".to_string(), - "meta_memory_service".to_string(), - ErrorSeverity::Critical, - ).with_context("database_type".to_string(), serde_json::json!("postgresql")) - .with_context("connection_string".to_string(), serde_json::json!("postgresql://localhost:5432/brain_ai")) - .with_context("last_successful_query".to_string(), serde_json::json!("2024-01-15T10:30:00Z")) - .with_metadata("requires_immediate_attention".to_string(), "true".to_string()); - - println!("๐Ÿ“ค Propagating CRITICAL error: {}", critical_error.id); - error_system.propagate_error(critical_error).await?; - - // Fatal level error - let fatal_error = SystemError::new( - "component.error".to_string(), - "Core component failed to initialize - system cannot continue".to_string(), - "integration_bootstrap".to_string(), - ErrorSeverity::Fatal, - ).with_context("failed_component".to_string(), serde_json::json!("service_container")) - .with_context("initialization_step".to_string(), serde_json::json!("dependency_injection")) - .with_context("error_details".to_string(), serde_json::json!("Circular dependency detected")) - .with_metadata("system_shutdown_required".to_string(), "true".to_string()); - - println!("๐Ÿ“ค Propagating FATAL error: {}", fatal_error.id); - error_system.propagate_error(fatal_error).await?; - - println!("โœ… Propagated 5 errors of different severity levels"); - - Ok(()) -} - -/// Demonstrate error recovery mechanisms -async fn demonstrate_error_recovery(error_system: &ErrorPropagationSystem) -> Result<(), Box> { - println!("๐Ÿ”„ Demonstrating error recovery..."); - - // Create recoverable errors - let recoverable_component_error = SystemError::new( - "component.error".to_string(), - "Agent failed to execute - temporary resource unavailable".to_string(), - "algorithm_coder".to_string(), - ErrorSeverity::Error, - ).with_context("resource_type".to_string(), serde_json::json!("memory")) - .with_context("required_mb".to_string(), serde_json::json!(512)) - .with_context("available_mb".to_string(), serde_json::json!(256)) - .with_metadata("recoverable".to_string(), "true".to_string()); - - println!("๐Ÿ”„ Attempting recovery for component error: {}", recoverable_component_error.id); - error_system.propagate_error(recoverable_component_error).await?; - - let recoverable_service_error = SystemError::new( - "service.error".to_string(), - "Service temporarily unavailable - rate limit exceeded".to_string(), - "intelligence_service".to_string(), - ErrorSeverity::Error, - ).with_context("rate_limit".to_string(), serde_json::json!(100)) - .with_context("current_requests".to_string(), serde_json::json!(150)) - .with_context("reset_time".to_string(), serde_json::json!("2024-01-15T11:00:00Z")) - .with_metadata("retry_after_seconds".to_string(), "60".to_string()); - - println!("๐Ÿ”„ Attempting recovery for service error: {}", recoverable_service_error.id); - error_system.propagate_error(recoverable_service_error).await?; - - println!("โœ… Demonstrated error recovery mechanisms"); - - Ok(()) -} - -/// Demonstrate error escalation when threshold is exceeded -async fn demonstrate_error_escalation(error_system: &ErrorPropagationSystem) -> Result<(), Box> { - println!("๐Ÿ“Š Demonstrating error escalation..."); - - // Generate multiple similar errors to trigger escalation - for i in 1..=5 { - let escalation_error = SystemError::new( - "service.error".to_string(), - format!("Repeated service failure #{} - pattern detected", i), - "conversation_service".to_string(), - ErrorSeverity::Error, - ).with_context("failure_count".to_string(), serde_json::json!(i)) - .with_context("pattern".to_string(), serde_json::json!("memory_leak")) - .with_metadata("escalation_candidate".to_string(), "true".to_string()); - - println!("๐Ÿ“Š Propagating escalation error #{}: {}", i, escalation_error.id); - error_system.propagate_error(escalation_error).await?; - - // Small delay to simulate real-world timing - tokio::time::sleep(tokio::time::Duration::from_millis(100)).await; - } - - println!("โœ… Demonstrated error escalation (5 similar errors)"); - - Ok(()) -} - -/// Demonstrate structured logging capabilities -async fn demonstrate_structured_logging(error_system: &ErrorPropagationSystem) -> Result<(), Box> { - println!("๐Ÿ“ Demonstrating structured logging..."); - - let logging_system = error_system.logging_system(); - - // Log entries of different levels - let trace_entry = LogEntry { - id: uuid::Uuid::new_v4().to_string(), - level: LogLevel::Trace, - message: "Entering function: process_agent_request".to_string(), - component: "agent_orchestrator".to_string(), - context: serde_json::json!({ - "function": "process_agent_request", - "agent_id": "algorithm_coder_001", - "request_id": "req_12345" - }).as_object().unwrap().iter().map(|(k, v)| (k.clone(), v.clone())).collect(), - timestamp: chrono::Utc::now(), - thread_id: Some("tokio-runtime-worker-1".to_string()), - correlation_id: Some("corr_67890".to_string()), - }; - - println!("๐Ÿ“ Logging TRACE entry: {}", trace_entry.id); - logging_system.log_entry(trace_entry).await?; - - let debug_entry = LogEntry { - id: uuid::Uuid::new_v4().to_string(), - level: LogLevel::Debug, - message: "Agent execution parameters validated successfully".to_string(), - component: "agent_orchestrator".to_string(), - context: serde_json::json!({ - "validation_result": "success", - "parameters": { - "input_type": "text", - "confidence_threshold": 0.7, - "timeout_seconds": 30 - } - }).as_object().unwrap().iter().map(|(k, v)| (k.clone(), v.clone())).collect(), - timestamp: chrono::Utc::now(), - thread_id: Some("tokio-runtime-worker-1".to_string()), - correlation_id: Some("corr_67890".to_string()), - }; - - println!("๐Ÿ“ Logging DEBUG entry: {}", debug_entry.id); - logging_system.log_entry(debug_entry).await?; - - let info_entry = LogEntry { - id: uuid::Uuid::new_v4().to_string(), - level: LogLevel::Info, - message: "Agent execution completed successfully".to_string(), - component: "agent_orchestrator".to_string(), - context: serde_json::json!({ - "execution_result": "success", - "duration_ms": 1250, - "output_confidence": 0.85, - "memory_usage_mb": 64 - }).as_object().unwrap().iter().map(|(k, v)| (k.clone(), v.clone())).collect(), - timestamp: chrono::Utc::now(), - thread_id: Some("tokio-runtime-worker-1".to_string()), - correlation_id: Some("corr_67890".to_string()), - }; - - println!("๐Ÿ“ Logging INFO entry: {}", info_entry.id); - logging_system.log_entry(info_entry).await?; - - let warn_entry = LogEntry { - id: uuid::Uuid::new_v4().to_string(), - level: LogLevel::Warn, - message: "Agent execution took longer than expected".to_string(), - component: "agent_orchestrator".to_string(), - context: serde_json::json!({ - "expected_duration_ms": 1000, - "actual_duration_ms": 2500, - "performance_impact": "moderate", - "recommendation": "consider_optimization" - }).as_object().unwrap().iter().map(|(k, v)| (k.clone(), v.clone())).collect(), - timestamp: chrono::Utc::now(), - thread_id: Some("tokio-runtime-worker-2".to_string()), - correlation_id: Some("corr_67891".to_string()), - }; - - println!("๐Ÿ“ Logging WARN entry: {}", warn_entry.id); - logging_system.log_entry(warn_entry).await?; - - println!("โœ… Demonstrated structured logging with 4 different log levels"); - - Ok(()) -} - -/// Show comprehensive error statistics -async fn show_error_statistics(error_system: &ErrorPropagationSystem) -> Result<(), Box> { - println!("๐Ÿ“ˆ Error System Statistics:"); - - let stats = error_system.get_statistics().await; - - println!(" Total errors processed: {}", stats.total_errors); - println!(" Recovery success rate: {:.2}%", stats.recovery_success_rate * 100.0); - println!(" Average handling time: {:.2}ms", stats.avg_handling_time_ms); - - println!("\n Errors by severity:"); - for (severity, count) in &stats.errors_by_severity { - println!(" {:?}: {}", severity, count); - } - - println!("\n Errors by component:"); - for (component, count) in &stats.errors_by_component { - println!(" {}: {}", component, count); - } - - println!("\n Errors by type:"); - for (error_type, count) in &stats.errors_by_type { - println!(" {}: {}", error_type, count); - } - - // Central error handling statistics - let central_handler = error_system.central_handler(); - let error_history = central_handler.get_error_history(Some(10))?; - - println!("\n Recent error history (last 10):"); - for (i, error) in error_history.iter().enumerate() { - println!(" {}. {} - {} ({})", - i + 1, error.error_type, error.message, error.severity); - } - - Ok(()) -} - -// Custom error handlers for demonstration - -struct ComponentErrorHandler; - -impl ComponentErrorHandler { - fn new() -> Self { - Self - } -} - -impl brain_cognitive::integration::error_propagation::ErrorHandler for ComponentErrorHandler { - fn handle_error(&self, error: &SystemError) -> std::pin::Pin + Send + '_>> { - let error_type = error.error_type.clone(); - let source_component = error.source_component.clone(); - Box::pin(async move { - println!("๐Ÿ”ง ComponentErrorHandler: Processing error {} from {}", - error_type, source_component); - - brain_cognitive::integration::error_propagation::ErrorHandlingResult { - handled: true, - actions: vec![ - "logged_component_error".to_string(), - "notified_component_manager".to_string(), - ], - continue_propagation: true, - context: std::collections::HashMap::new(), - } - }) - } - - fn supported_error_types(&self) -> Vec { - vec!["component.error".to_string(), "component.info".to_string()] - } - - fn priority(&self) -> i32 { - 10 - } -} - -struct ServiceErrorHandler; - -impl ServiceErrorHandler { - fn new() -> Self { - Self - } -} - -impl brain_cognitive::integration::error_propagation::ErrorHandler for ServiceErrorHandler { - fn handle_error(&self, error: &SystemError) -> std::pin::Pin + Send + '_>> { - let error_type = error.error_type.clone(); - let source_component = error.source_component.clone(); - Box::pin(async move { - println!("๐Ÿ”ง ServiceErrorHandler: Processing error {} from {}", - error_type, source_component); - - brain_cognitive::integration::error_propagation::ErrorHandlingResult { - handled: true, - actions: vec![ - "logged_service_error".to_string(), - "checked_service_health".to_string(), - "attempted_service_restart".to_string(), - ], - continue_propagation: true, - context: std::collections::HashMap::new(), - } - }) - } - - fn supported_error_types(&self) -> Vec { - vec!["service.error".to_string(), "service.warning".to_string()] - } - - fn priority(&self) -> i32 { - 8 - } -} - -struct NetworkErrorHandler; - -impl NetworkErrorHandler { - fn new() -> Self { - Self - } -} - -impl brain_cognitive::integration::error_propagation::ErrorHandler for NetworkErrorHandler { - fn handle_error(&self, error: &SystemError) -> std::pin::Pin + Send + '_>> { - let error_type = error.error_type.clone(); - let source_component = error.source_component.clone(); - Box::pin(async move { - println!("๐Ÿ”ง NetworkErrorHandler: Processing error {} from {}", - error_type, source_component); - - brain_cognitive::integration::error_propagation::ErrorHandlingResult { - handled: true, - actions: vec![ - "logged_network_error".to_string(), - "checked_network_connectivity".to_string(), - "attempted_reconnection".to_string(), - ], - continue_propagation: false, - context: std::collections::HashMap::new(), - } - }) - } - - fn supported_error_types(&self) -> Vec { - vec!["network.error".to_string()] - } - - fn priority(&self) -> i32 { - 6 - } -} - -struct DatabaseErrorHandler; - -impl DatabaseErrorHandler { - fn new() -> Self { - Self - } -} - -impl brain_cognitive::integration::error_propagation::ErrorHandler for DatabaseErrorHandler { - fn handle_error(&self, error: &SystemError) -> std::pin::Pin + Send + '_>> { - let error_type = error.error_type.clone(); - let source_component = error.source_component.clone(); - Box::pin(async move { - println!("๐Ÿ”ง DatabaseErrorHandler: Processing error {} from {}", - error_type, source_component); - - brain_cognitive::integration::error_propagation::ErrorHandlingResult { - handled: true, - actions: vec![ - "logged_database_error".to_string(), - "checked_database_connection".to_string(), - "attempted_query_retry".to_string(), - "initiated_connection_pool_refresh".to_string(), - ], - continue_propagation: true, - context: std::collections::HashMap::new(), - } - }) - } - - fn supported_error_types(&self) -> Vec { - vec!["database.error".to_string()] - } - - fn priority(&self) -> i32 { - 15 // High priority for database errors - } -} - -// Recovery strategies - -struct ComponentRecoveryStrategy; - -impl ComponentRecoveryStrategy { - fn new() -> Self { - Self - } -} - -impl brain_cognitive::integration::error_propagation::ErrorRecovery for ComponentRecoveryStrategy { - fn recover(&self, error: &SystemError) -> std::pin::Pin + Send + '_>> { - let error_id = error.id.clone(); - Box::pin(async move { - println!("๐Ÿ”„ ComponentRecoveryStrategy: Attempting recovery for {}", error_id); - - brain_cognitive::integration::error_propagation::RecoveryResult { - recovered: true, - actions: vec![ - "restarted_component".to_string(), - "restored_component_state".to_string(), - "verified_component_health".to_string(), - ], - should_retry: true, - context: std::collections::HashMap::new(), - } - }) - } - - fn strategy_name(&self) -> &str { - "component_recovery" - } - - fn can_recover(&self, _error: &SystemError) -> bool { - true - } -} - -struct ServiceRecoveryStrategy; - -impl ServiceRecoveryStrategy { - fn new() -> Self { - Self - } -} - -impl brain_cognitive::integration::error_propagation::ErrorRecovery for ServiceRecoveryStrategy { - fn recover(&self, error: &SystemError) -> std::pin::Pin + Send + '_>> { - let error_id = error.id.clone(); - Box::pin(async move { - println!("๐Ÿ”„ ServiceRecoveryStrategy: Attempting recovery for {}", error_id); - - brain_cognitive::integration::error_propagation::RecoveryResult { - recovered: true, - actions: vec![ - "restarted_service".to_string(), - "restored_service_configuration".to_string(), - "verified_service_endpoints".to_string(), - ], - should_retry: true, - context: std::collections::HashMap::new(), - } - }) - } - - fn strategy_name(&self) -> &str { - "service_recovery" - } - - fn can_recover(&self, _error: &SystemError) -> bool { - true - } -} \ No newline at end of file diff --git a/extract_readme_insights.rs b/extract_readme_insights.rs deleted file mode 100644 index 1889124d9d6c0d43d442514a89f7c6e1967384b0..0000000000000000000000000000000000000000 --- a/extract_readme_insights.rs +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env cargo run --example extract_readme_insights -//! Extract README Insights -//! -//! This example focuses on extracting detailed architectural insights -//! from the PocketFlow README content that's already in memory. - -use brain::{WorkingMemoryQuery, Result, GitHubLearningEngine, GitHubLearningConfig, Priority}; -use brain::services::create_memory_service_with_capacity; -use brain_infra::memory::WorkingMemoryRepository; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿ“– Brain AI README Insights Extractor - PocketFlow Architecture"); - println!("{}", "=".repeat(60)); - - // Initialize Brain AI components with new service architecture - let mut working_repo = WorkingMemoryRepository::new(2000); - let mut memory_service = create_memory_service_with_capacity(2000).await?; - - // Get GitHub token - let github_token = env::var("GITHUB_TOKEN").ok(); - - // Create GitHub learning configuration - let config = GitHubLearningConfig { - max_files: 50, - max_file_size: 100_000, // Larger to get full README - include_code: true, - include_docs: true, - include_config: true, - ..Default::default() - }; - - let github_engine = GitHubLearningEngine::new(github_token.clone(), Some(config)); - - println!("\n๐Ÿš€ Learning from PocketFlow Repository"); - println!("{}", "-".repeat(40)); - - // Learn from PocketFlow repository - let pocketflow_url = "https://github.com/The-Pocket/PocketFlow"; - match github_engine.learn_from_repository(&mut working_repo, pocketflow_url).await { - Ok(result) => { - println!("โœ… Learning completed!"); - println!(" Files processed: {}", result.files_processed); - println!(" Memory entries: {}", result.memory_entries_created); - } - Err(e) => { - println!("โŒ Learning failed: {}", e); - return Err(e); - } - } - - println!("\n๐Ÿ“– Extracting README Content"); - println!("{}", "-".repeat(40)); - - // Search for README content specifically - let readme_query = WorkingMemoryQuery { - content_pattern: Some("README".to_string()), - priority: None, - min_importance: None, - created_after: None, - limit: Some(5), - }; - - match memory_service.query_working(&readme_query).await { - Ok(items) => { - for (i, item) in items.iter().enumerate() { - if item.content.contains("README") && item.content.len() > 1000 { - println!("\n๐Ÿ“ README Content {} (Length: {} chars):", i + 1, item.content.len()); - - // Extract key sections from README - println!("\n๐Ÿ” Analyzing README for Architecture Patterns:"); - - let content = &item.content; - - // Look for key architectural sections - if let Some(start) = content.find("## Quick Start") { - if let Some(end) = content[start..].find("\n## ").map(|pos| start + pos) { - let quick_start = &content[start..end]; - println!("\n๐Ÿš€ Quick Start Section:"); - println!("{}", &quick_start[..quick_start.len().min(500)]); - } - } - - if let Some(start) = content.find("## Examples") { - if let Some(end) = content[start..].find("\n## ").map(|pos| start + pos) { - let examples = &content[start..end]; - println!("\n๐Ÿ’ก Examples Section:"); - println!("{}", &examples[..examples.len().min(800)]); - } - } - - if let Some(start) = content.find("## Key Features") { - if let Some(end) = content[start..].find("\n## ").map(|pos| start + pos) { - let features = &content[start..end]; - println!("\nโญ Key Features Section:"); - println!("{}", &features[..features.len().min(600)]); - } - } - - // Look for code examples - let mut code_blocks = Vec::new(); - let mut current_pos = 0; - while let Some(start) = content[current_pos..].find("```python") { - let abs_start = current_pos + start; - if let Some(end) = content[abs_start..].find("```\n").map(|pos| abs_start + pos) { - let code_block = &content[abs_start..end + 4]; - code_blocks.push(code_block); - current_pos = end + 4; - } else { - break; - } - } - - println!("\n๐Ÿ Found {} Python Code Examples:", code_blocks.len()); - for (j, code) in code_blocks.iter().take(3).enumerate() { - println!("\n๐Ÿ’ป Code Example {}:", j + 1); - println!("{}", &code[..code.len().min(400)]); - - // Analyze the code for patterns - if code.contains("class") { - println!(" ๐Ÿ” Contains class definitions"); - } - if code.contains("async") { - println!(" ๐Ÿ” Uses async/await patterns"); - } - if code.contains("Node") { - println!(" ๐Ÿ” Uses Node-based architecture"); - } - if code.contains("Flow") { - println!(" ๐Ÿ” Uses Flow-based programming"); - } - if code.contains("batch") { - println!(" ๐Ÿ” Supports batch processing"); - } - } - - // Extract architectural insights - println!("\n๐Ÿ—๏ธ Architectural Pattern Analysis:"); - - if content.contains("Node") && content.contains("Flow") { - println!(" โœ… Pattern 1: Node-Flow Architecture"); - println!(" - Uses Node-based components"); - println!(" - Implements Flow-based programming"); - } - - if content.contains("async") && content.contains("parallel") { - println!(" โœ… Pattern 2: Async Parallel Processing"); - println!(" - Supports asynchronous execution"); - println!(" - Enables parallel processing"); - } - - if content.contains("batch") && content.contains("LLM") { - println!(" โœ… Pattern 3: Batch LLM Processing"); - println!(" - Optimizes LLM calls with batching"); - println!(" - Reduces API costs and latency"); - } - - if content.contains("agent") || content.contains("Agent") { - println!(" โœ… Pattern 4: Agent-Based Framework"); - println!(" - Implements agent abstractions"); - println!(" - Supports agent orchestration"); - } - - // Extract key concepts - println!("\n๐ŸŽฏ Key Concepts Identified:"); - let concepts = vec![ - ("BaseNode", "Base class for all processing nodes"), - ("Flow", "Orchestrates node execution"), - ("AsyncFlow", "Asynchronous flow execution"), - ("BatchNode", "Batch processing optimization"), - ("ParallelBatchNode", "Parallel batch processing"), - ("LLM Integration", "Large language model integration"), - ("100-line framework", "Minimalist design philosophy"), - ]; - - for (concept, description) in concepts { - if content.to_lowercase().contains(&concept.to_lowercase()) { - println!(" โ€ข {}: {}", concept, description); - } - } - } - } - } - Err(e) => { - println!("โŒ Failed to query README content: {}", e); - } - } - - println!("\n๐Ÿง  Creating Enhanced Memory Entries"); - println!("{}", "-".repeat(40)); - - // Store detailed architectural insights in memory - let architectural_insights = vec![ - ("PocketFlow Node-Flow Architecture", - "PocketFlow implements a node-flow architecture where BaseNode classes represent processing units and Flow classes orchestrate their execution. This enables modular, composable AI workflows."), - - ("PocketFlow Async Parallel Processing", - "The framework supports asynchronous execution with AsyncFlow and parallel processing with ParallelBatchNode, enabling efficient concurrent LLM operations."), - - ("PocketFlow Batch Optimization", - "BatchNode and related classes implement batch processing to optimize LLM API calls, reducing costs and improving throughput by processing multiple items together."), - - ("PocketFlow 100-Line Philosophy", - "PocketFlow follows a minimalist '100-line framework' philosophy, providing essential LLM orchestration capabilities in a compact, easy-to-understand codebase."), - - ("PocketFlow Agent Framework", - "The framework enables 'Agents build Agents' by providing abstractions for agent-based workflows and autonomous system development."), - ]; - - for (title, description) in architectural_insights { - match memory_service.learn(format!("{}: {}", title, description), Priority::High).await { - Ok(_) => println!("โœ… Stored: {}", title), - Err(e) => println!("โŒ Failed to store {}: {}", title, e), - } - } - - println!("\nโœ… README Analysis Complete!"); - println!("Enhanced architectural insights have been extracted and stored in memory."); - println!("The Brain AI should now be able to answer detailed questions about PocketFlow's architecture."); - - Ok(()) -} \ No newline at end of file diff --git a/frontend_coder_demo.rs b/frontend_coder_demo.rs deleted file mode 100644 index bd156fdcbadb686817a5124c1328e866a443301c..0000000000000000000000000000000000000000 --- a/frontend_coder_demo.rs +++ /dev/null @@ -1,372 +0,0 @@ -//! Frontend Coder Agent Demo -//! -//! This example demonstrates the FrontendCoder agent's ability to generate -//! comprehensive frontend implementation code from UI/UX designs and API specifications. - -use brain_cognitive::agents::development::frontend_coder::FrontendCoder; -use brain_cognitive::agents::traits::{ - BrainAgent, AgentInput, CognitiveContext, ProjectContext, - CognitivePreferenceProfile -}; -use brain_cognitive::meta::MetaMemoryRepository; -use brain_cognitive::conversation::traits::ConversationService; - -use serde_json::json; -use std::{collections::HashMap, sync::Arc}; -use tokio; -use async_trait::async_trait; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ Frontend Coder Agent Demo"); - println!("==================================\n"); - - // Initialize the FrontendCoder agent - let frontend_coder = FrontendCoder::new(); - println!("โœ… Frontend Coder Agent initialized"); - println!("Agent: {}", frontend_coder.metadata().name); - println!("Capabilities: {:?}\n", frontend_coder.metadata().capabilities); - - // Create comprehensive input with UI/UX designs and API specifications - let ui_design_specs = json!({ - "framework_preference": "react", - "components": { - "layout": { - "header": { - "title": "Brain AI Dashboard", - "navigation": ["Dashboard", "Projects", "Settings"], - "authentication": true - }, - "sidebar": { - "width": "256px", - "collapsible": true, - "items": [ - {"label": "Dashboard", "icon": "home", "path": "/"}, - {"label": "Projects", "icon": "folder", "path": "/projects"}, - {"label": "Analytics", "icon": "chart", "path": "/analytics"}, - {"label": "Settings", "icon": "settings", "path": "/settings"} - ] - } - }, - "forms": { - "login_form": { - "fields": ["email", "password"], - "validation": true, - "styling": "modern" - }, - "project_form": { - "fields": ["name", "description", "tech_stack"], - "validation": true, - "auto_save": true - } - }, - "data_display": { - "project_table": { - "columns": ["name", "status", "created_date", "actions"], - "pagination": true, - "sorting": true, - "filtering": true - }, - "dashboard_cards": { - "metrics": ["total_projects", "active_tasks", "completion_rate"], - "charts": ["progress_chart", "activity_timeline"] - } - } - }, - "pages": [ - {"path": "/", "component": "Dashboard", "protected": true}, - {"path": "/login", "component": "Login", "protected": false}, - {"path": "/projects", "component": "Projects", "protected": true}, - {"path": "/projects/:id", "component": "ProjectDetail", "protected": true}, - {"path": "/settings", "component": "Settings", "protected": true} - ], - "styling": { - "theme": "modern", - "color_scheme": "blue-gray", - "typography": "Inter", - "responsive": true, - "dark_mode": true - }, - "accessibility": { - "wcag_level": "AA", - "screen_reader": true, - "keyboard_navigation": true, - "focus_management": true - } - }); - - let api_specifications = json!({ - "base_url": "/api/v1", - "authentication": { - "type": "JWT", - "refresh_token": true, - "endpoints": { - "login": "/auth/login", - "refresh": "/auth/refresh", - "logout": "/auth/logout" - } - }, - "endpoints": { - "users": { - "list": {"method": "GET", "path": "/users"}, - "create": {"method": "POST", "path": "/users"}, - "get": {"method": "GET", "path": "/users/:id"}, - "update": {"method": "PUT", "path": "/users/:id"}, - "delete": {"method": "DELETE", "path": "/users/:id"} - }, - "projects": { - "list": {"method": "GET", "path": "/projects"}, - "create": {"method": "POST", "path": "/projects"}, - "get": {"method": "GET", "path": "/projects/:id"}, - "update": {"method": "PUT", "path": "/projects/:id"}, - "delete": {"method": "DELETE", "path": "/projects/:id"} - } - }, - "error_handling": { - "format": "RFC7807", - "status_codes": [400, 401, 403, 404, 422, 429, 500] - }, - "rate_limiting": { - "default": "100/hour", - "authenticated": "1000/hour" - } - }); - - // Create the agent input - let input_content = json!({ - "ui_design_specifications": ui_design_specs, - "api_specifications": api_specifications, - "requirements": { - "performance": { - "initial_load": "<3s", - "code_splitting": true, - "lazy_loading": true - }, - "testing": { - "unit_tests": true, - "integration_tests": true, - "e2e_tests": true, - "accessibility_tests": true - }, - "deployment": { - "build_optimization": true, - "progressive_web_app": false, - "docker_support": true - } - } - }); - - let agent_input = AgentInput::new( - "frontend_implementation".to_string(), - input_content.to_string(), - "frontend-demo-session".to_string(), - ); - - // Create mock implementations for demo - let context = create_demo_context(); - - println!("๐ŸŽฏ Frontend Implementation Request:"); - println!("- Framework: React with TypeScript"); - println!("- Components: Layout, Forms, Data Display"); - println!("- Pages: Dashboard, Login, Projects, Settings"); - println!("- Styling: Modern theme with dark mode support"); - println!("- Accessibility: WCAG AA compliance"); - println!("- API Integration: JWT authentication, REST endpoints"); - println!("- Testing: Unit, integration, E2E, accessibility tests\n"); - - // Execute the agent - println!("โณ Generating frontend implementation...\n"); - let start_time = std::time::Instant::now(); - - let result = frontend_coder.execute(agent_input, &context).await?; - - let execution_time = start_time.elapsed(); - - // Display results - println!("โœ… Frontend implementation generated successfully!"); - println!("โฑ๏ธ Execution time: {:?}", execution_time); - println!("๐ŸŽฏ Confidence: {:.1}%", result.confidence * 100.0); - println!("๐Ÿ“Š Memory usage: {:.1}MB", result.execution_metadata.memory_usage_mb); - println!(); - - println!("๐Ÿ“‹ Generated Components:"); - if let Some(frontend_codebase) = result.data.get("frontend_codebase") { - if let Some(framework) = frontend_codebase.get("framework") { - println!("- Framework: {}", framework.as_str().unwrap_or("Unknown")); - } - - if let Some(components) = frontend_codebase.get("components") { - println!("- Component categories:"); - if let Some(obj) = components.as_object() { - for category in obj.keys() { - println!(" โ€ข {}", category); - } - } - } - - if let Some(_routing) = frontend_codebase.get("routing") { - println!("- Routing configuration: โœ…"); - } - - if let Some(_state_mgmt) = frontend_codebase.get("state_management") { - println!("- State management: โœ…"); - } - - if let Some(_api_integration) = frontend_codebase.get("api_integration") { - println!("- API integration layer: โœ…"); - } - - if let Some(_styling) = frontend_codebase.get("styling_system") { - println!("- Styling system: โœ…"); - } - - if let Some(_a11y) = frontend_codebase.get("accessibility_features") { - println!("- Accessibility features: โœ…"); - } - } - - println!("\n๐Ÿงช Testing Implementation:"); - if let Some(testing) = result.data.get("testing_implementation") { - if let Some(unit_testing) = testing.get("unit_testing") { - if let Some(framework) = unit_testing.get("framework") { - println!("- Unit testing: {}", framework.as_str().unwrap_or("Configured")); - } - } - if testing.get("integration_testing").is_some() { - println!("- Integration testing: โœ…"); - } - if testing.get("e2e_testing").is_some() { - println!("- E2E testing: โœ…"); - } - if testing.get("accessibility_testing").is_some() { - println!("- Accessibility testing: โœ…"); - } - } - - println!("\nโšก Performance Optimization:"); - if let Some(performance) = result.data.get("performance_optimization") { - if performance.get("code_splitting").is_some() { - println!("- Code splitting: โœ…"); - } - if performance.get("bundle_optimization").is_some() { - println!("- Bundle optimization: โœ…"); - } - if performance.get("image_optimization").is_some() { - println!("- Image optimization: โœ…"); - } - if performance.get("caching_strategy").is_some() { - println!("- Caching strategy: โœ…"); - } - } - - println!("\n๐Ÿ’ก Agent Reasoning:"); - if let Some(reasoning) = &result.reasoning { - println!("{}", reasoning); - } - - println!("\n๐Ÿ“ˆ Next Steps:"); - for (i, action) in result.next_actions.iter().enumerate() { - println!("{}. {}", i + 1, action); - } - - println!("\n๐ŸŽŠ Frontend Coder Demo completed successfully!"); - println!("The agent has generated a comprehensive frontend implementation"); - println!("including components, routing, state management, API integration,"); - println!("styling, accessibility features, and testing strategies."); - - Ok(()) -} - -// Mock implementations for demo purposes -#[derive(Clone, Debug)] -struct MockMetaMemoryRepository; - -#[async_trait] -impl MetaMemoryRepository for MockMetaMemoryRepository { - async fn store_item(&mut self, _item: brain_cognitive::meta::MetaMemoryItem) -> brain_cognitive::meta::MetaMemoryResult { - Ok(uuid::Uuid::new_v4()) - } - - async fn get_item(&self, _id: uuid::Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(None) - } - - async fn get_item_by_component(&self, _component_id: uuid::Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(None) - } - - async fn query_items(&self, _query: &brain_cognitive::meta::MetaMemoryQuery) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(vec![]) - } - - async fn remove_item(&mut self, _id: uuid::Uuid) -> brain_cognitive::meta::MetaMemoryResult { - Ok(false) - } - - async fn batch_update(&mut self, _items: Vec) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(vec![]) - } - - async fn count_items(&self) -> brain_cognitive::meta::MetaMemoryResult { - Ok(0) - } - - async fn clear_all(&mut self) -> brain_cognitive::meta::MetaMemoryResult { - Ok(0) - } -} - -#[derive(Clone, Debug)] -struct MockConversationService; - -#[async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: brain_cognitive::conversation::RagRequest, - _memory_repo: &mut dyn brain_core::memory::WorkingMemoryRepository, - _concept_repo: &mut dyn brain_core::concepts::ConceptRepository, - _insight_repo: &mut dyn brain_core::insights::InsightRepository, - ) -> Result { - Ok(brain_cognitive::conversation::RagResponse { - response: "Mock response".to_string(), - conversation_id: "mock-conversation".to_string(), - context_used: vec![], - confidence_score: 0.8, - response_quality: brain_cognitive::conversation::response_quality::ResponseQuality::default(), - }) - } - - fn get_conversation_stats(&self) -> HashMap { - HashMap::new() - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -fn create_demo_context() -> CognitiveContext { - let meta_memory = Arc::new(tokio::sync::RwLock::new(MockMetaMemoryRepository)); - let conversation_service = Arc::new(MockConversationService); - - CognitiveContext { - meta_memory, - conversation_service, - project_context: ProjectContext { - project_name: "Frontend Demo Project".to_string(), - project_version: "1.0.0".to_string(), - project_description: Some("Demo project for FrontendCoder agent".to_string()), - tech_stack: vec!["React".to_string(), "TypeScript".to_string()], - git_branch: Some("main".to_string()), - git_commit: Some("abc123def".to_string()), - active_files: vec!["src/App.tsx".to_string()], - recent_changes: vec!["Added new component structure".to_string()], - directory_structure: HashMap::new(), - }, - cognitive_profile: CognitivePreferenceProfile::default(), - session_history: vec![], - config: HashMap::new(), - working_directory: std::path::PathBuf::from("."), - } -} \ No newline at end of file diff --git a/github_demo.db b/github_demo.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/github_demo.db and /dev/null differ diff --git a/github_learning_demo.rs b/github_learning_demo.rs deleted file mode 100644 index 4642725c5180304fde572734650365ce74246993..0000000000000000000000000000000000000000 --- a/github_learning_demo.rs +++ /dev/null @@ -1,277 +0,0 @@ -#!/usr/bin/env cargo run --example github_learning_demo -//! GitHub Learning Demo for Brain AI (Rust Implementation) -//! -//! This comprehensive demo showcases Brain AI's ability to learn from GitHub -//! repositories using real API integration, analyzing code patterns, documentation, -//! and project structure. -//! -//! Features demonstrated: -//! - Real repository learning with GitHub API -//! - Memory storage and intelligent querying -//! - Concept discovery and relationship mapping -//! - Performance monitoring and error handling -//! - Export capabilities and data analysis -//! -//! Usage: -//! cargo run --example github_learning_demo -//! -//! Environment Variables: -//! GITHUB_TOKEN: Optional GitHub personal access token for higher rate limits -//! -//! Requirements: -//! - Internet connection for repository access -//! - Optional: GitHub token for private repos or higher rate limits - -use brain::{MemoryService, WorkingMemoryQuery, Result, WorkingMemoryRepository as WorkingMemoryTrait}; -use brain_infra::memory::{ - WorkingMemoryRepository as WorkingMemoryRepo, - EpisodicMemoryRepository as EpisodicMemoryRepo, - SemanticMemoryRepository as SemanticMemoryRepo -}; -use brain_infra::github_integration::{GitHubLearningEngine, GitHubLearningConfig}; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿง  Brain AI - GitHub Repository Learning Demo (Rust)"); - println!("===================================================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| { - brain::BrainError::from(e).with_context( - brain_types::ErrorContext::new("create_data_directory") - .with_details("Failed to create data directory for GitHub learning demo") - ) - })?; - - println!("This demo will showcase Brain's ability to learn from GitHub repositories"); - println!("and demonstrate intelligent, context-aware repository understanding.\n"); - - // Initialize repositories using concrete types - let mut working_repo = WorkingMemoryRepo::new(1000); - let episodic_repo = Box::new(EpisodicMemoryRepo::new("data/github_demo.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepo::new()); - - // Create memory service for queries - let memory_service = MemoryService::new( - Box::new(WorkingMemoryRepo::new(100)), - episodic_repo, - semantic_repo, - ); - - let github_token = env::var("GITHUB_TOKEN").ok(); - - if github_token.is_some() { - println!("โœ… GitHub token found - using authenticated API"); - } else { - println!("โš ๏ธ No GitHub token - using public API (rate limited)"); - println!(" Set GITHUB_TOKEN environment variable for better performance"); - } - - // Create GitHub learning engine with configuration - let config = GitHubLearningConfig { - max_files: 50, - max_file_size: 50_000, // 50KB per file - include_code: true, - include_docs: true, - include_config: true, - ..Default::default() - }; - - let github_engine = GitHubLearningEngine::new(github_token.clone(), Some(config)); - - // Example repositories to learn from - let repositories = vec![ - "rust-lang/mdbook", // Documentation tool - "BurntSushi/ripgrep", // Command-line tool - "tokio-rs/tokio", // Async runtime (smaller subset) - ]; - - println!("\n๐Ÿš€ Starting GitHub Repository Learning"); - println!("{}", "-".repeat(40)); - - for repo_url in repositories { - println!("\n๐Ÿ“‚ Learning from repository: {}", repo_url); - - let start_time = std::time::Instant::now(); - - match github_engine.learn_from_repository(&mut working_repo, repo_url).await { - Ok(result) => { - let duration = start_time.elapsed(); - - println!("โœ… Learning completed successfully!"); - println!(" Repository: {}", result.repository); - println!(" Files processed: {}", result.files_processed); - println!(" Total content size: {} bytes", result.total_content_size); - println!(" Concepts discovered: {}", result.concepts_discovered); - println!(" Memory entries created: {}", result.memory_entries_created); - println!(" Learning time: {}ms", result.learning_time_ms); - println!(" Total time: {:.2}s", duration.as_secs_f64()); - - println!("\n๐Ÿ“‹ Summary: {}", result.summary); - - if !result.key_insights.is_empty() { - println!("\n๐Ÿ’ก Key Insights:"); - for (i, insight) in result.key_insights.iter().enumerate() { - println!(" {}. {}", i + 1, insight); - } - } - } - Err(e) => { - println!("โŒ Failed to learn from {}: {}", repo_url, e); - - // Provide helpful error guidance - println!(" ๐Ÿ’ก Check your internet connection or repository URL"); - continue; // Try next repository - } - } - - // Small delay between repositories to be respectful to GitHub API - tokio::time::sleep(tokio::time::Duration::from_millis(500)).await; - } - - // Demonstrate memory querying capabilities (using the working repo directly) - demonstrate_memory_queries(&working_repo as &dyn WorkingMemoryTrait).await?; - - // Also demonstrate with the memory service - demonstrate_concept_analysis(&memory_service).await?; - - // Show memory statistics - demonstrate_memory_statistics(&memory_service).await?; - - println!("\n๐ŸŽ‰ GitHub Learning Demo Completed Successfully!"); - println!("{}", "=".repeat(60)); - - Ok(()) -} - -async fn demonstrate_memory_queries(working_repo: &dyn WorkingMemoryTrait) -> Result<()> { - println!("\n๐Ÿ” Memory Querying and Information Retrieval"); - println!("{}", "-".repeat(40)); - - // Query the working repository directly - let query = WorkingMemoryQuery::default(); - let items = working_repo.query_items(&query).await?; - println!("๐Ÿ“Š Working Memory Overview:"); - println!(" Total items: {}", items.len()); - - if !items.is_empty() { - println!("\n๐Ÿ”— Sample Content:"); - for (i, item) in items.iter().take(3).enumerate() { - println!(" {}. {} (Priority: {:?})", - i + 1, - truncate_text(&item.content, 100), - item.priority); - } - } - - Ok(()) -} - -async fn demonstrate_concept_analysis(memory_service: &MemoryService) -> Result<()> { - println!("\n๐Ÿงฉ Concept Relationship Analysis"); - println!("{}", "-".repeat(40)); - - // Create a simple query to see what we have in working memory - let query = WorkingMemoryQuery::default(); - match memory_service.query_working(&query).await { - Ok(items) => { - println!("๐Ÿ“Š Memory Overview:"); - println!(" Working Memory: {} items", items.len()); - - if !items.is_empty() { - println!("\n๐Ÿ”— Sample Content:"); - for (i, item) in items.iter().take(3).enumerate() { - println!(" {}. {} (Priority: {:?})", - i + 1, - truncate_text(&item.content, 100), - item.priority); - } - } - } - Err(e) => { - println!("โŒ Failed to query memory: {}", e); - } - } - - Ok(()) -} - -async fn demonstrate_memory_statistics(memory_service: &MemoryService) -> Result<()> { - println!("\n๐Ÿ“ˆ Performance Metrics and Statistics"); - println!("{}", "-".repeat(40)); - - // Since MemoryService doesn't have get_stats method, let's demonstrate with queries - let all_query = WorkingMemoryQuery::default(); - match memory_service.query_working(&all_query).await { - Ok(items) => { - let total_items = items.len(); - let total_size: usize = items.iter().map(|item| item.content.len()).sum(); - - println!("๐Ÿ”ข Memory Statistics:"); - println!(" Working Memory:"); - println!(" - Total items: {}", total_items); - println!(" - Total content size: {} bytes", total_size); - - if total_items > 0 { - println!(" - Average item size: {} bytes", total_size / total_items); - - // Count by priority - let mut priority_counts = std::collections::HashMap::new(); - for item in &items { - *priority_counts.entry(format!("{:?}", item.priority)).or_insert(0) += 1; - } - - println!(" - Items by priority:"); - for (priority, count) in priority_counts { - println!(" โ€ข {}: {}", priority, count); - } - } - - println!("\n๐Ÿ“Š Overall Learning Efficiency:"); - println!(" - Information units stored: {}", total_items); - println!(" - Memory usage: {} KB", total_size / 1024); - if total_size > 0 { - println!(" - Compression ratio: {:.2}:1", - total_items as f64 / (total_size as f64 / 1024.0)); - } - } - Err(e) => { - println!("โŒ Failed to get statistics: {}", e); - } - } - - Ok(()) -} - -/// Helper function to truncate text for display -fn truncate_text(text: &str, max_len: usize) -> String { - if text.len() <= max_len { - text.to_string() - } else { - format!("{}...", &text[..max_len.saturating_sub(3)]) - } -} - -/// Helper function to format file size -#[allow(dead_code)] -fn format_file_size(bytes: usize) -> String { - const UNITS: &[&str] = &["B", "KB", "MB", "GB"]; - let mut size = bytes as f64; - let mut unit_index = 0; - - while size >= 1024.0 && unit_index < UNITS.len() - 1 { - size /= 1024.0; - unit_index += 1; - } - - if unit_index == 0 { - format!("{:.0} {}", size, UNITS[unit_index]) - } else { - format!("{:.2} {}", size, UNITS[unit_index]) - } -} \ No newline at end of file diff --git a/global_hle_leadership_validation.rs b/global_hle_leadership_validation.rs deleted file mode 100644 index 15314b19643dbb333d8b20128dca1808b8604025..0000000000000000000000000000000000000000 --- a/global_hle_leadership_validation.rs +++ /dev/null @@ -1,780 +0,0 @@ -//! # Global HLE Leadership Validation Framework -//! -//! **CRITICAL VALIDATION**: Proves Brain AI achieves **45%+ HLE accuracy** to establish -//! global leadership in Universal Intelligence (100% Coding + 45%+ Academic Reasoning). -//! -//! ## Global Leadership Validation Strategy -//! -//! 1. **Comprehensive HLE Testing**: 100+ questions across all academic domains -//! 2. **Accuracy Target Validation**: Achieve and validate 45%+ overall accuracy -//! 3. **Domain Excellence Validation**: Verify strong performance across all domains -//! 4. **Coding Performance Assurance**: Ensure 100% SWE-Bench/HumanEval maintained -//! 5. **Global Leadership Documentation**: Surpass current #1 (Gemini Pro 2.5: 25.4%) -//! -//! **Created**: July 31, 2023 -//! **Purpose**: Validate academic reasoning capabilities -//! **Status**: IMPLEMENTATION - Academic validation - -use std::collections::HashMap; -use std::time::Instant; -use serde::{Deserialize, Serialize}; -use uuid::Uuid; -use chrono::Utc; -use rand::{thread_rng, seq::SliceRandom}; - -use brain_cognitive::agents::{UniversalAcademicAgent, AcademicDomain}; -use brain_cognitive::agents::intelligence::adaptive_research_engine::AdaptiveResearchEngine; -use brain_cognitive::agents::traits::{AgentInput, BrainAgent}; -use brain_cognitive::agents::CognitiveContext; -use brain_types::error::BrainError; - -/// **Global HLE Leadership Validation Framework** -/// -/// Comprehensively validates Brain AI's achievement of 45%+ HLE accuracy -/// establishing global leadership in Universal Intelligence. -#[derive(Debug)] -pub struct GlobalHLELeadershipValidator { - /// Universal academic agent with all domain specialists - academic_agent: UniversalAcademicAgent, - /// Adaptive research engine for intelligent research automation - research_engine: AdaptiveResearchEngine, - /// Comprehensive test question database (100+ questions) - test_questions: Vec, - /// Domain-specific performance tracking - domain_performance: HashMap, - /// Global leadership metrics - leadership_metrics: GlobalLeadershipMetrics, - /// Competitive benchmarking results - competitive_analysis: CompetitiveBenchmarkResults, -} - -/// Comprehensive HLE test question with domain specialization -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct ComprehensiveHLEQuestion { - pub id: String, - pub domain: AcademicDomain, - pub difficulty: HLEDifficulty, - pub question: String, - pub options: Vec, - pub correct_answer: String, - pub reasoning: String, - pub requires_research: bool, - pub interdisciplinary: bool, - pub citation_required: bool, -} - -/// HLE question difficulty levels -#[derive(Debug, Clone, Serialize, Deserialize)] -pub enum HLEDifficulty { - Undergraduate, - Graduate, - PhD, - PostDoc, - Research, -} - -/// Domain-specific performance metrics -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct DomainPerformanceMetrics { - pub domain: AcademicDomain, - pub total_questions: usize, - pub correct_answers: usize, - pub accuracy_percentage: f64, - pub average_confidence: f64, - pub research_triggered_count: usize, - pub research_success_rate: f64, - pub average_response_time_ms: u64, -} - -/// Global leadership performance metrics -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct GlobalLeadershipMetrics { - pub overall_accuracy: f64, - pub target_accuracy: f64, - pub leadership_achieved: bool, - pub improvement_over_baseline: f64, - pub research_enhancement_factor: f64, - pub universal_intelligence_score: f64, - pub competitive_advantage: f64, -} - -/// Competitive benchmark comparison results -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct CompetitiveBenchmarkResults { - pub brain_ai_accuracy: f64, - pub gemini_pro_2_5_accuracy: f64, - pub claude_3_5_accuracy: f64, - pub gpt_4o_accuracy: f64, - pub o3_accuracy: f64, - pub performance_ranking: u8, - pub competitive_gap: f64, -} - -/// Comprehensive validation results -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct GlobalLeadershipValidationResults { - pub timestamp: chrono::DateTime, - pub execution_time_ms: u64, - pub total_questions_tested: usize, - pub overall_accuracy: f64, - pub leadership_target_met: bool, - pub domain_performance: HashMap, - pub leadership_metrics: GlobalLeadershipMetrics, - pub competitive_analysis: CompetitiveBenchmarkResults, - pub coding_performance_maintained: bool, - pub universal_intelligence_achieved: bool, -} - -impl GlobalHLELeadershipValidator { - /// Create a new global HLE leadership validator - pub async fn new() -> Result { - println!("๐Ÿš€ Initializing Global HLE Leadership Validation Framework..."); - - let academic_agent = UniversalAcademicAgent::new().await?; - let research_engine = AdaptiveResearchEngine::new(); - - // Generate comprehensive test question database - let test_questions = Self::generate_comprehensive_hle_dataset().await?; - - println!("๐Ÿ“Š Generated {} comprehensive HLE test questions across all domains", test_questions.len()); - - Ok(Self { - academic_agent, - research_engine, - test_questions, - domain_performance: HashMap::new(), - leadership_metrics: GlobalLeadershipMetrics::default(), - competitive_analysis: CompetitiveBenchmarkResults::default(), - }) - } - - /// Execute comprehensive global leadership validation - pub async fn execute_global_leadership_validation(&mut self) -> Result { - let start_time = Instant::now(); - let timestamp = Utc::now(); - - println!("\n๐Ÿ† EXECUTING GLOBAL HLE LEADERSHIP VALIDATION"); - println!("๐ŸŽฏ Target: 45%+ HLE accuracy for #1 global ranking"); - println!("๐Ÿ“ˆ Baseline to surpass: Gemini Pro 2.5 (25.4%)"); - println!("๐Ÿ”ฌ Testing {} questions across all academic domains\n", self.test_questions.len()); - - // Initialize domain performance tracking - self.initialize_domain_performance_tracking().await?; - - // Execute comprehensive testing across all domains - let mut total_correct = 0; - let mut total_tested = 0; - - for domain in [ - AcademicDomain::TheoreticalPhysics, - AcademicDomain::AdvancedMathematics, - AcademicDomain::AdvancedChemistry, - AcademicDomain::MolecularBiology, - AcademicDomain::ComputerScienceTheory, - AcademicDomain::Interdisciplinary, - AcademicDomain::General, - ] { - println!("๐Ÿ”ฌ Testing {} domain...", format!("{:?}", domain)); - - let domain_results = self.test_domain_performance(&domain).await?; - total_correct += domain_results.correct_answers; - total_tested += domain_results.total_questions; - - println!("โœ… {} domain: {:.1}% accuracy ({}/{} questions)", - format!("{:?}", domain), - (domain_results.correct_answers as f64 / domain_results.total_questions as f64) * 100.0, - domain_results.correct_answers, - domain_results.total_questions - ); - - self.domain_performance.insert(domain, domain_results); - } - - // Calculate overall performance metrics - let overall_accuracy = (total_correct as f64 / total_tested as f64) * 100.0; - let execution_time = start_time.elapsed(); - - // Validate leadership achievement - let leadership_achieved = overall_accuracy >= 45.0; - - // Update leadership metrics - self.leadership_metrics = GlobalLeadershipMetrics { - overall_accuracy, - target_accuracy: 45.0, - leadership_achieved, - improvement_over_baseline: overall_accuracy - 25.4, // Gemini Pro 2.5 baseline - research_enhancement_factor: self.calculate_research_enhancement_factor().await, - universal_intelligence_score: self.calculate_universal_intelligence_score(overall_accuracy).await, - competitive_advantage: overall_accuracy - 25.4, - }; - - // Update competitive analysis - self.competitive_analysis = CompetitiveBenchmarkResults { - brain_ai_accuracy: overall_accuracy, - gemini_pro_2_5_accuracy: 25.4, - claude_3_5_accuracy: 23.8, - gpt_4o_accuracy: 22.1, - o3_accuracy: 24.7, - performance_ranking: if leadership_achieved { 1 } else { 2 }, - competitive_gap: overall_accuracy - 25.4, - }; - - // Validate coding performance maintenance - let coding_performance_maintained = self.validate_coding_performance_maintained().await?; - - // Calculate universal intelligence achievement - let universal_intelligence_achieved = leadership_achieved && coding_performance_maintained; - - println!("\n๐Ÿ† GLOBAL HLE LEADERSHIP VALIDATION RESULTS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ OVERALL PERFORMANCE โ”‚"); - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - println!("โ”‚ Total Questions Tested: {:>6} โ”‚", total_tested); - println!("โ”‚ Correct Answers: {:>6} โ”‚", total_correct); - println!("โ”‚ Overall HLE Accuracy: {:>6.1}% โ”‚", overall_accuracy); - println!("โ”‚ Leadership Target (45%): {:>6} โ”‚", if leadership_achieved { "โœ… MET" } else { "โŒ NOT MET" }); - println!("โ”‚ Global Ranking: {:>6} โ”‚", if leadership_achieved { "#1 ๐Ÿฅ‡" } else { "#2" }); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - println!("\n๐ŸŒ COMPETITIVE ANALYSIS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ GLOBAL LEADERBOARD โ”‚"); - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - println!("โ”‚ ๐Ÿฅ‡ Brain AI: {:>6.1}% (THIS SYSTEM) โ”‚", overall_accuracy); - println!("โ”‚ ๐Ÿฅˆ Gemini Pro 2.5: {:>6.1}% โ”‚", 25.4); - println!("โ”‚ ๐Ÿฅ‰ o3: {:>6.1}% โ”‚", 24.7); - println!("โ”‚ Claude 3.5: {:>6.1}% โ”‚", 23.8); - println!("โ”‚ GPT-4o: {:>6.1}% โ”‚", 22.1); - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - println!("โ”‚ Competitive Advantage: {:>6.1} percentage points โ”‚", overall_accuracy - 25.4); - println!("โ”‚ Performance Improvement: {:>6.1}x better than #2 โ”‚", overall_accuracy / 25.4); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - println!("\n๐ŸŽ“ UNIVERSAL INTELLIGENCE VALIDATION"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ UNIVERSAL INTELLIGENCE SCORE โ”‚"); - println!("โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค"); - println!("โ”‚ Academic Reasoning: {:>6.1}% โ”‚", overall_accuracy); - println!("โ”‚ Coding Performance: {:>6} โ”‚", if coding_performance_maintained { "100% โœ…" } else { "โŒ" }); - println!("โ”‚ Universal Intelligence: {:>6} โ”‚", if universal_intelligence_achieved { "โœ… ACHIEVED" } else { "โŒ" }); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - Ok(GlobalLeadershipValidationResults { - timestamp, - execution_time_ms: execution_time.as_millis() as u64, - total_questions_tested: total_tested, - overall_accuracy, - leadership_target_met: leadership_achieved, - domain_performance: self.domain_performance.clone(), - leadership_metrics: self.leadership_metrics.clone(), - competitive_analysis: self.competitive_analysis.clone(), - coding_performance_maintained, - universal_intelligence_achieved, - }) - } - - /// Generate comprehensive HLE test dataset - async fn generate_comprehensive_hle_dataset() -> Result, BrainError> { - let mut questions = Vec::new(); - - // Physics questions (15 questions) - questions.extend(Self::generate_physics_questions().await?); - - // Mathematics questions (15 questions) - questions.extend(Self::generate_mathematics_questions().await?); - - // Chemistry questions (15 questions) - questions.extend(Self::generate_chemistry_questions().await?); - - // Biology questions (15 questions) - questions.extend(Self::generate_biology_questions().await?); - - // Computer Science questions (15 questions) - questions.extend(Self::generate_computer_science_questions().await?); - - // Psychology questions (10 questions) - questions.extend(Self::generate_psychology_questions().await?); - - // Philosophy questions (10 questions) - questions.extend(Self::generate_philosophy_questions().await?); - - // General/Interdisciplinary questions (10 questions) - questions.extend(Self::generate_interdisciplinary_questions().await?); - - // Shuffle for unbiased testing - questions.shuffle(&mut thread_rng()); - - Ok(questions) - } - - /// Generate physics domain questions - async fn generate_physics_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::TheoreticalPhysics, - difficulty: HLEDifficulty::Graduate, - question: "In quantum field theory, what is the significance of the Klein-Gordon equation?".to_string(), - options: vec![ - "A) It describes the wave function of spin-0 particles".to_string(), - "B) It describes electromagnetic field propagation".to_string(), - "C) It describes the strong nuclear force".to_string(), - "D) It describes gravitational waves".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "The Klein-Gordon equation is the relativistic wave equation for spin-0 particles".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::TheoreticalPhysics, - difficulty: HLEDifficulty::PhD, - question: "What is the physical interpretation of the AdS/CFT correspondence?".to_string(), - options: vec![ - "A) A duality between gravity theories and quantum field theories".to_string(), - "B) A correspondence between particles and waves".to_string(), - "C) A relationship between quantum mechanics and thermodynamics".to_string(), - "D) A connection between electromagnetism and gravitation".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "AdS/CFT is a correspondence between string theory in Anti-de Sitter space and conformal field theory".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - // Add 13 more physics questions to reach 15 total - // For brevity, I'll add a few more representative examples - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::TheoreticalPhysics, - difficulty: HLEDifficulty::Research, - question: "In the context of topological insulators, what role do Berry phases play?".to_string(), - options: vec![ - "A) They determine the topological classification".to_string(), - "B) They affect only surface states".to_string(), - "C) They are irrelevant for transport properties".to_string(), - "D) They only affect magnetic properties".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Berry phases provide the topological characterization of insulators".to_string(), - requires_research: true, - interdisciplinary: true, - citation_required: true, - }, - ]) - } - - /// Generate mathematics domain questions - async fn generate_mathematics_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::AdvancedMathematics, - difficulty: HLEDifficulty::PhD, - question: "In algebraic topology, what is the fundamental group of the real projective plane RPยฒ?".to_string(), - options: vec![ - "A) Z/2Z".to_string(), - "B) Z".to_string(), - "C) The trivial group".to_string(), - "D) Z/3Z".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "The fundamental group ฯ€โ‚(RPยฒ) โ‰… Z/2Z due to the identification of antipodal points".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::AdvancedMathematics, - difficulty: HLEDifficulty::Research, - question: "What is the Hodge conjecture in algebraic geometry?".to_string(), - options: vec![ - "A) It relates algebraic cycles to cohomology classes".to_string(), - "B) It concerns the distribution of prime numbers".to_string(), - "C) It describes differential forms on manifolds".to_string(), - "D) It characterizes finite groups".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "The Hodge conjecture relates algebraic cycles and Hodge classes in algebraic varieties".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ]) - } - - /// Generate chemistry domain questions - async fn generate_chemistry_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::AdvancedChemistry, - difficulty: HLEDifficulty::Graduate, - question: "What is the mechanism of the Suzuki-Miyaura cross-coupling reaction?".to_string(), - options: vec![ - "A) Oxidative addition, transmetalation, reductive elimination".to_string(), - "B) Nucleophilic substitution followed by elimination".to_string(), - "C) Radical chain mechanism".to_string(), - "D) Concerted cycloaddition".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Suzuki-Miyaura follows the standard Pd-catalyzed cross-coupling mechanism".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ]) - } - - /// Generate biology domain questions - async fn generate_biology_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::MolecularBiology, - difficulty: HLEDifficulty::PhD, - question: "What is the role of the Mediator complex in eukaryotic transcription?".to_string(), - options: vec![ - "A) It bridges transcription factors and RNA polymerase II".to_string(), - "B) It directly binds to DNA promoter sequences".to_string(), - "C) It degrades mRNA transcripts".to_string(), - "D) It modifies histone proteins".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Mediator complex facilitates communication between transcription factors and RNA Pol II".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ]) - } - - /// Generate computer science domain questions - async fn generate_computer_science_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::ComputerScienceTheory, - difficulty: HLEDifficulty::PhD, - question: "What is the time complexity of the fastest known algorithm for matrix multiplication?".to_string(), - options: vec![ - "A) O(n^2.373)".to_string(), - "B) O(n^3)".to_string(), - "C) O(n^2)".to_string(), - "D) O(n log n)".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Current best algorithm achieves approximately O(n^2.373) complexity".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ]) - } - - /// Generate psychology domain questions - async fn generate_psychology_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::Interdisciplinary, - difficulty: HLEDifficulty::Graduate, - question: "What is the dual-process theory in cognitive psychology?".to_string(), - options: vec![ - "A) Two distinct systems for automatic and controlled processing".to_string(), - "B) Two hemispheres of the brain processing information differently".to_string(), - "C) Two stages of memory formation".to_string(), - "D) Two types of learning mechanisms".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Dual-process theory describes System 1 (automatic) and System 2 (controlled) processing".to_string(), - requires_research: true, - interdisciplinary: true, - citation_required: true, - }, - ]) - } - - /// Generate philosophy domain questions - async fn generate_philosophy_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::Interdisciplinary, - difficulty: HLEDifficulty::Graduate, - question: "What is the main argument in Gettier's challenge to justified true belief?".to_string(), - options: vec![ - "A) Justified true belief can still fail to constitute knowledge".to_string(), - "B) Truth is not necessary for knowledge".to_string(), - "C) Justification is impossible to achieve".to_string(), - "D) Belief is irrelevant to knowledge".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Gettier cases show that justified true belief is not sufficient for knowledge".to_string(), - requires_research: true, - interdisciplinary: false, - citation_required: true, - }, - ]) - } - - /// Generate interdisciplinary questions - async fn generate_interdisciplinary_questions() -> Result, BrainError> { - Ok(vec![ - ComprehensiveHLEQuestion { - id: Uuid::new_v4().to_string(), - domain: AcademicDomain::General, - difficulty: HLEDifficulty::Research, - question: "How do quantum effects in biological systems relate to consciousness theories?".to_string(), - options: vec![ - "A) Quantum coherence may enable quantum information processing in neurons".to_string(), - "B) Quantum effects are too weak to influence biological processes".to_string(), - "C) Consciousness is purely classical in nature".to_string(), - "D) Quantum mechanics only affects inorganic matter".to_string(), - ], - correct_answer: "A".to_string(), - reasoning: "Research suggests quantum coherence in microtubules may relate to consciousness".to_string(), - requires_research: true, - interdisciplinary: true, - citation_required: true, - }, - ]) - } - - /// Initialize domain performance tracking - async fn initialize_domain_performance_tracking(&mut self) -> Result<(), BrainError> { - for domain in [ - AcademicDomain::TheoreticalPhysics, - AcademicDomain::AdvancedMathematics, - AcademicDomain::AdvancedChemistry, - AcademicDomain::MolecularBiology, - AcademicDomain::ComputerScienceTheory, - AcademicDomain::Interdisciplinary, - AcademicDomain::General, - ] { - self.domain_performance.insert(domain.clone(), DomainPerformanceMetrics { - domain, - total_questions: 0, - correct_answers: 0, - accuracy_percentage: 0.0, - average_confidence: 0.0, - research_triggered_count: 0, - research_success_rate: 0.0, - average_response_time_ms: 0, - }); - } - Ok(()) - } - - /// Test performance for a specific domain - async fn test_domain_performance(&self, domain: &AcademicDomain) -> Result { - let domain_questions: Vec<_> = self.test_questions - .iter() - .filter(|q| &q.domain == domain) - .collect(); - - if domain_questions.is_empty() { - // Return empty metrics for domains with no questions - return Ok(DomainPerformanceMetrics { - domain: domain.clone(), - total_questions: 0, - correct_answers: 0, - accuracy_percentage: 0.0, - average_confidence: 0.0, - research_triggered_count: 0, - research_success_rate: 0.0, - average_response_time_ms: 0, - }); - } - - let mut correct_answers = 0; - let mut total_confidence = 0.0; - let mut research_triggered = 0; - let mut research_successful = 0; - let mut total_response_time = 0u64; - - for question in &domain_questions { - let start_time = Instant::now(); - - // Format question for agent - let options_str = question.options.join("\n"); - let full_question = format!("{}\n\nOptions:\n{}", question.question, options_str); - - // Create agent input - let input = AgentInput { - input_type: "multiple_choice_question".to_string(), - content: full_question, - parameters: { - let mut params = HashMap::new(); - params.insert("options".to_string(), serde_json::Value::String(options_str)); - params.insert("domain".to_string(), serde_json::Value::String(format!("{:?}", *domain))); - params - }, - previous_outputs: Vec::new(), - session_id: "global_hle_leadership_validation".to_string(), - timestamp: chrono::Utc::now(), - user_preferences: HashMap::new(), - }; - - let context = CognitiveContext::default(); - let output = self.academic_agent.execute(input, &context).await?; - - let response_time = start_time.elapsed(); - total_response_time += response_time.as_millis() as u64; - - // Extract answer from response - let selected_answer = output.content - .lines() - .find(|line| line.starts_with("Answer:")) - .and_then(|line| line.split(':').nth(1)) - .map(|s| { - // Extract just the letter (A, B, C, D) from responses like "A - Strong candidate" - s.trim() - .chars() - .next() - .filter(|c| ['A', 'B', 'C', 'D'].contains(c)) - .map(|c| c.to_string()) - .unwrap_or_else(|| "A".to_string()) - }) - .unwrap_or_else(|| "A".to_string()); // Default fallback - - let confidence = output.confidence as f64; - total_confidence += confidence; - - // Check if research was triggered (confidence < 70%) - if confidence < 0.7 { - research_triggered += 1; - - // Simulate research enhancement (in real implementation, this would use research engine) - // For demo purposes, assume research is successful if the original answer was correct - if selected_answer == question.correct_answer { - research_successful += 1; - } - } - - // Check correctness - if selected_answer == question.correct_answer { - correct_answers += 1; - } - } - - let total_questions = domain_questions.len(); - let accuracy_percentage = (correct_answers as f64 / total_questions as f64) * 100.0; - let average_confidence = total_confidence / total_questions as f64; - let research_success_rate = if research_triggered > 0 { - research_successful as f64 / research_triggered as f64 - } else { - 0.0 - }; - let average_response_time_ms = total_response_time / total_questions as u64; - - Ok(DomainPerformanceMetrics { - domain: domain.clone(), - total_questions, - correct_answers, - accuracy_percentage, - average_confidence, - research_triggered_count: research_triggered, - research_success_rate, - average_response_time_ms, - }) - } - - /// Calculate research enhancement factor - async fn calculate_research_enhancement_factor(&self) -> f64 { - // Calculate how much research improves performance - // For demonstration, using a realistic factor based on previous validation - 2.1 // Research improves accuracy by ~2.1x - } - - /// Calculate universal intelligence score - async fn calculate_universal_intelligence_score(&self, academic_accuracy: f64) -> f64 { - // Universal Intelligence = Coding Performance ร— Academic Performance - let coding_performance = 100.0; // Maintained 100% on SWE-Bench/HumanEval - let academic_performance = academic_accuracy; - - (coding_performance * academic_performance) / 100.0 - } - - /// Validate that coding performance is maintained - async fn validate_coding_performance_maintained(&self) -> Result { - // In a real implementation, this would run SWE-Bench and HumanEval tests - // For demonstration purposes, we assume coding performance is maintained - // since we haven't modified any coding-related components - - println!("๐Ÿ”ง Validating coding performance maintenance..."); - println!("โœ… SWE-Bench performance: 100% (maintained)"); - println!("โœ… HumanEval performance: 100% (maintained)"); - - Ok(true) - } -} - -impl Default for GlobalLeadershipMetrics { - fn default() -> Self { - Self { - overall_accuracy: 0.0, - target_accuracy: 45.0, - leadership_achieved: false, - improvement_over_baseline: 0.0, - research_enhancement_factor: 1.0, - universal_intelligence_score: 0.0, - competitive_advantage: 0.0, - } - } -} - -impl Default for CompetitiveBenchmarkResults { - fn default() -> Self { - Self { - brain_ai_accuracy: 0.0, - gemini_pro_2_5_accuracy: 25.4, - claude_3_5_accuracy: 23.8, - gpt_4o_accuracy: 22.1, - o3_accuracy: 24.7, - performance_ranking: 2, - competitive_gap: 0.0, - } - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿš€ BRAIN AI - GLOBAL HLE LEADERSHIP VALIDATION"); - println!("๐ŸŽฏ TASK 3.3: Establishing #1 Global Leadership in Universal Intelligence"); - println!("๐Ÿ“Š Target: 45%+ HLE accuracy (surpassing Gemini Pro 2.5: 25.4%)"); - println!("๐Ÿ† Goal: Universal Intelligence = 100% Coding + 45%+ Academic Reasoning\n"); - - // Create and execute global leadership validation - let mut validator = GlobalHLELeadershipValidator::new().await?; - let results = validator.execute_global_leadership_validation().await?; - - // Display final summary - println!("\n๐ŸŽ‰ GLOBAL HLE LEADERSHIP VALIDATION COMPLETE!"); - - if results.universal_intelligence_achieved { - println!("๐Ÿ† BREAKTHROUGH ACHIEVED: Brain AI established as #1 global leader in Universal Intelligence!"); - println!("๐Ÿฅ‡ Ranking: #1 globally with {:.1}% HLE accuracy", results.overall_accuracy); - println!("๐Ÿš€ Competitive advantage: +{:.1} percentage points over previous #1", results.competitive_analysis.competitive_gap); - } else if results.leadership_target_met { - println!("๐ŸŽฏ HLE LEADERSHIP TARGET MET: {:.1}% accuracy achieved", results.overall_accuracy); - println!("๐Ÿฅ‡ Academic reasoning leadership established"); - println!("โš ๏ธ Note: Ensure coding performance validation for complete Universal Intelligence"); - } else { - println!("๐Ÿ“Š HLE accuracy achieved: {:.1}%", results.overall_accuracy); - println!("๐ŸŽฏ Target: 45.0% (gap: {:.1} percentage points)", 45.0 - results.overall_accuracy); - println!("๐Ÿ”ฌ Continue optimization to achieve global leadership"); - } - - println!("\nValidation completed in {}ms", results.execution_time_ms); - println!("๐Ÿ“ˆ Total questions tested: {}", results.total_questions_tested); - println!("๐ŸŒ Brain AI is now positioned for Universal Intelligence supremacy! ๐Ÿš€"); - - Ok(()) -} \ No newline at end of file diff --git a/gradient_calculation_demo.rs b/gradient_calculation_demo.rs deleted file mode 100644 index 63fe9bb48322ac614e360ba955fece54e0d6c1cf..0000000000000000000000000000000000000000 --- a/gradient_calculation_demo.rs +++ /dev/null @@ -1,219 +0,0 @@ -//! Gradient Calculation Demo for Task 4.3 -//! -//! Demonstrates the comprehensive gradient calculation system implemented -//! for Brain AI's MuBrain training loop, showcasing real gradient-based learning -//! for Models H, F, G with performance analytics. - -use brain_mubrain::{ - ModelTrainingOrchestrator, TrainingConfig, TrainingEpisode, - GradientCalculator, LossWeights, SymbolicState, SymbolicAction, - model_f::ObservedTransition, MuBrainResult, - training::{PlanningOutcome as TrainingPlanningOutcome, RewardSignal as TrainingRewardSignal, RewardType as TrainingRewardType}, -}; -use uuid::Uuid; -use chrono::Utc; -use std::path::PathBuf; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Brain AI Task 4.3: Gradient Calculation Demo"); - println!("==================================================\n"); - - // Create training configuration - let config = TrainingConfig { - learning_rate: 0.001, - batch_size: 16, - max_epochs: 50, - validation_frequency: 5, - checkpoint_frequency: 10, - early_stopping_patience: 8, - gradient_clip_norm: 1.0, - weight_decay: 0.0001, - checkpoint_dir: PathBuf::from("./demo_checkpoints"), - }; - - println!("๐Ÿ”ง Training Configuration:"); - println!(" Learning Rate: {}", config.learning_rate); - println!(" Batch Size: {}", config.batch_size); - println!(" Gradient Clip Norm: {}", config.gradient_clip_norm); - println!(" Max Epochs: {}\n", config.max_epochs); - - // Create gradient calculator with custom loss weights - let loss_weights = LossWeights { - representation_loss_weight: 1.2, - dynamics_loss_weight: 1.0, - value_loss_weight: 0.8, - policy_loss_weight: 0.6, - reward_loss_weight: 0.9, - consistency_loss_weight: 0.4, - }; - - let gradient_calculator = GradientCalculator::new_with_weights(config.clone(), loss_weights); - println!("โœ… Gradient Calculator Initialized with Custom Loss Weights"); - - // Demonstrate gradient calculation with multiple episodes - println!("\n๐Ÿ“Š Processing Training Episodes for Gradient Calculation:"); - println!("========================================================="); - - for episode_num in 1..=5 { - let episode = create_training_episode(episode_num).await?; - - println!("\n๐Ÿ”„ Episode {}: {} transitions, {} planning outcomes", - episode_num, - episode.state_transitions.len(), - episode.planning_outcomes.len()); - - // Calculate gradients - let start_time = std::time::Instant::now(); - let gradient_result = gradient_calculator.calculate_episode_gradients(&episode).await?; - let calculation_time = start_time.elapsed(); - - println!(" โšก Gradient calculation completed in {:.2}ms", calculation_time.as_millis()); - println!(" ๐Ÿ“ˆ Total Loss: {:.6}", gradient_result.total_loss); - - // Analyze gradient components - if let Some(h_gradients) = &gradient_result.model_h_gradients { - println!(" ๐Ÿง  Model H (Representation): {} latent features, {} context features", - h_gradients.latent_gradients.len(), - h_gradients.context_gradients.len()); - } - - if let Some(f_gradients) = &gradient_result.model_f_gradients { - println!(" ๐Ÿ”„ Model F (Dynamics): {} transition features, {} probability features", - f_gradients.transition_gradients.len(), - f_gradients.probability_gradients.len()); - } - - if let Some(g_gradients) = &gradient_result.model_g_gradients { - println!(" ๐ŸŽฏ Model G (Prediction): {} value features, {} policy features, {} reward features", - g_gradients.value_gradients.len(), - g_gradients.policy_gradients.len(), - g_gradients.reward_gradients.len()); - } - } - - // Get gradient analytics - println!("\n๐Ÿ“ˆ Gradient Calculation Analytics:"); - println!("==================================="); - - let analytics = gradient_calculator.get_gradient_analytics().await; - println!(" Episodes Processed: {}", analytics.total_episodes_processed); - println!(" Average Loss: {:.6}", analytics.average_loss); - println!(" Gradient Stability: {:.4}", analytics.gradient_stability); - println!(" Convergence Rate: {:.4}", analytics.convergence_rate); - println!(" Plateau Detected: {}", analytics.plateau_detected); - - if !analytics.recent_gradient_norms.is_empty() { - let latest_norms = &analytics.recent_gradient_norms[0]; - println!("\n๐Ÿ”ข Latest Gradient Norms:"); - println!(" Model H Norm: {:.4}", latest_norms.model_h_norm); - println!(" Model F Norm: {:.4}", latest_norms.model_f_norm); - println!(" Model G Norm: {:.4}", latest_norms.model_g_norm); - println!(" Total Norm: {:.4}", latest_norms.total_norm); - println!(" Clipped: {}", latest_norms.clipped); - } - - // Demonstrate training orchestrator integration - println!("\n๐ŸŽฎ Training Orchestrator Integration Demo:"); - println!("=========================================="); - - let orchestrator = ModelTrainingOrchestrator::new(config).await?; - println!("โœ… ModelTrainingOrchestrator created successfully"); - - let training_state = orchestrator.get_training_state().await; - println!(" Training State: Epoch {}, Step {}", training_state.epoch, training_state.step); - println!(" Learning Rate: {}", training_state.current_learning_rate); - println!(" Training Active: {}", training_state.is_training); - - let _performance_metrics = orchestrator.get_performance_metrics().await; - println!(" Performance Metrics Available: โœ…"); - - println!("\n๐ŸŽ‰ Task 4.3 Gradient Calculation Demo Complete!"); - println!("================================================"); - println!("โœ… Comprehensive gradient-based learning implemented"); - println!("โœ… Models H, F, G gradient calculation operational"); - println!("โœ… Performance monitoring and analytics working"); - println!("โœ… Training orchestration infrastructure ready"); - println!("\n๐Ÿš€ Phase 1 MuBrain Integration: 40% Complete (Task 4.3)"); - - Ok(()) -} - -/// Create a demonstration training episode -async fn create_training_episode(episode_num: usize) -> MuBrainResult { - // Create diverse symbolic states - let mut initial_state = SymbolicState::default(); - initial_state.clarity_score = 0.7 + (episode_num as f64 * 0.05); - initial_state.uncertainty = 0.3 - (episode_num as f64 * 0.02); - initial_state.emotions.confidence = 0.6 + (episode_num as f64 * 0.08); - initial_state.emotions.curiosity = 0.8; - initial_state.working_memory.current_focus = format!("Episode {} focus: gradient optimization", episode_num); - initial_state.working_memory.active_concepts = vec![ - "gradient_descent".to_string(), - "loss_minimization".to_string(), - format!("episode_{}_pattern", episode_num) - ]; - - let mut final_state = initial_state.clone(); - final_state.clarity_score += 0.1; - final_state.uncertainty -= 0.05; - final_state.emotions.satisfaction = 0.7 + (episode_num as f64 * 0.05); - - // Create symbolic action - let action = SymbolicAction::GenerateCode { - approach: format!("gradient-based-approach-{}", episode_num), - confidence: 0.75 + (episode_num as f64 * 0.03), - }; - - // Create observed transition - let transition = ObservedTransition { - from_state: initial_state.clone(), - to_state: final_state.clone(), - action: action.clone(), - actual_reward: 0.6 + (episode_num as f64 * 0.08), - execution_time_ms: 100 + (episode_num * 20) as u64, - success: episode_num % 4 != 0, // Occasional failures for realistic learning - error_message: if episode_num % 4 == 0 { - Some("Simulated optimization challenge".to_string()) - } else { - None - }, - }; - - // Create planning outcome - let planning_outcome = TrainingPlanningOutcome { - state: final_state.clone(), - action: action.clone(), - predicted_value: 0.65, - actual_value: 0.7 + (episode_num as f64 * 0.05), - predicted_reward: 0.6, - actual_reward: 0.65 + (episode_num as f64 * 0.07), - planning_quality: 0.8 + (episode_num as f64 * 0.03), - }; - - // Create reward signals - let reward_signals = vec![ - TrainingRewardSignal { - signal_type: TrainingRewardType::PlanningAccuracy, - value: 0.75 + (episode_num as f64 * 0.04), - timestamp: chrono::Utc::now(), - source: format!("episode_{}_planning", episode_num), - }, - TrainingRewardSignal { - signal_type: TrainingRewardType::LearningProgress, - value: 0.6 + (episode_num as f64 * 0.08), - timestamp: chrono::Utc::now(), - source: format!("episode_{}_learning", episode_num), - }, - ]; - - Ok(TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![transition], - planning_outcomes: vec![planning_outcome], - reward_signals, - timestamp: Utc::now(), - episode_reward: 0.65 + (episode_num as f64 * 0.06), - episode_length: episode_num, - }) -} \ No newline at end of file diff --git a/hle_large_scale_validation.rs b/hle_large_scale_validation.rs deleted file mode 100644 index a1b8d437ef86bcce44249e4aac19cccf2870c9ff..0000000000000000000000000000000000000000 --- a/hle_large_scale_validation.rs +++ /dev/null @@ -1,439 +0,0 @@ -/// Brain AI - Humanity's Last Exam (HLE) Large-Scale Validation Framework -/// -/// This implements the comprehensive testing infrastructure for validating Brain AI's -/// adaptive research system against large-scale academic datasets, targeting the -/// goal of achieving #1 global ranking through research-driven intelligence. - -use std::collections::HashMap; -use std::time::{Duration, Instant}; -use uuid::Uuid; -use chrono::{Utc, DateTime}; -use rand; - -use brain_cognitive::agents::intelligence::AdaptiveResearchEngine; -use brain_cognitive::agents::AcademicReasoningAgent; -use brain_cognitive::agents::intelligence::academic_reasoning::UniversalAcademicAgent; -use brain_cognitive::agents::traits::{ - AcademicDomain, AcademicQuestion, QuestionType -}; - -/// HLE Large-Scale Validation Framework -#[derive(Debug)] -pub struct HLELargeScaleValidator { - /// Core adaptive research engine - research_engine: AdaptiveResearchEngine, - /// Academic reasoning coordination - academic_reasoning: Box, - /// Dataset management system - dataset_manager: HLEDatasetManager, - /// Performance tracking system - performance_tracker: LargeScalePerformanceTracker, - /// Quality validation framework - quality_validator: bool, // Simplified for demo - /// Competitive benchmarking system - competitive_benchmark: CompetitiveBenchmarkingSystem, -} - -/// Supporting types for the validation framework -#[derive(Debug, Clone)] -pub struct HLEDatasetManager; - -#[derive(Debug, Clone)] -pub struct LargeScalePerformanceTracker; - -#[derive(Debug, Clone)] -pub struct CompetitiveBenchmarkingSystem; - -impl HLEDatasetManager { - pub fn new() -> Self { Self } - - pub async fn generate_hle_dataset(&mut self, target_count: usize) -> Result<(), Box> { - println!(" ๐Ÿ“š Generating {} HLE questions across all domains...", target_count); - // In a real implementation, this would load from external datasets - // For now, we'll simulate comprehensive dataset generation - tokio::time::sleep(Duration::from_millis(500)).await; // Simulate processing time - println!(" โœ… HLE dataset generated successfully"); - Ok(()) - } - - pub fn get_all_questions(&self) -> Vec { - // Generate sample questions across academic domains - vec![ - HLEQuestion { - id: Uuid::new_v4(), - question: "In quantum field theory, what mechanism explains vacuum fluctuations?".to_string(), - options: vec!["Virtual particles".to_string(), "Wave collapse".to_string()], - correct_answer: "Virtual particles".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - difficulty: 9, - tags: vec!["quantum_field_theory".to_string()], - }, - HLEQuestion { - id: Uuid::new_v4(), - question: "What is the computational complexity of matrix multiplication?".to_string(), - options: vec!["O(nยณ)".to_string(), "O(nยฒ)".to_string()], - correct_answer: "O(nยณ)".to_string(), - domain: AcademicDomain::AdvancedMathematics, - difficulty: 7, - tags: vec!["complexity_theory".to_string()], - }, - // Simulate 1000+ questions by repeating and varying these patterns - ].into_iter().cycle().take(1000).collect() - } -} - -impl LargeScalePerformanceTracker { - pub fn new() -> Self { Self } -} - -impl CompetitiveBenchmarkingSystem { - pub fn new() -> Self { Self } - - pub async fn analyze_global_position(&mut self, results: &ResearchResults) -> Result> { - // Simulate competitive analysis against leading AI models - Ok(CompetitiveAnalysis { - current_accuracy: results.accuracy, - competitor_benchmarks: vec![ - CompetitorBenchmark { name: "Gemini Pro 2.5".to_string(), accuracy: 0.254 }, - CompetitorBenchmark { name: "GPT-4 Turbo".to_string(), accuracy: 0.248 }, - CompetitorBenchmark { name: "Claude 3 Opus".to_string(), accuracy: 0.241 }, - ], - global_ranking: if results.accuracy > 0.30 { 1 } else { 2 }, - competitive_advantages: vec![ - "Adaptive research integration".to_string(), - "Real-time learning capability".to_string(), - "Multi-domain expertise".to_string(), - ], - }) - } -} - -// Supporting data structures -#[derive(Debug, Clone)] -pub struct HLEQuestion { - pub id: Uuid, - pub question: String, - pub options: Vec, - pub correct_answer: String, - pub domain: AcademicDomain, - pub difficulty: u8, - pub tags: Vec, -} - -impl HLEQuestion { - pub fn to_academic_question(&self) -> AcademicQuestion { - AcademicQuestion { - id: self.id.to_string(), - question: self.question.clone(), - domain: self.domain.clone(), - question_type: QuestionType::MultipleChoice, - options: Some(self.options.clone()), - metadata: HashMap::new(), - } - } -} - -#[derive(Debug, Clone)] -pub struct BaselineResults { - pub total_questions: usize, - pub correct_answers: usize, - pub accuracy: f64, - pub average_confidence: f64, - pub processing_duration: Duration, -} - -#[derive(Debug, Clone)] -pub struct ResearchResults { - pub total_questions: usize, - pub correct_answers: usize, - pub accuracy: f64, - pub average_confidence: f64, - pub research_triggered: usize, - pub research_success_rate: f64, - pub processing_duration: Duration, -} - -#[derive(Debug, Clone)] -pub struct PerformanceAnalysis { - pub baseline_accuracy: f64, - pub research_accuracy: f64, - pub accuracy_improvement: f64, - pub confidence_improvement: f64, - pub research_effectiveness: f64, - pub domain_improvements: Vec, - pub projected_hle_accuracy: f64, - pub current_global_rank: u8, - pub strategic_advantages: Vec, -} - -#[derive(Debug, Clone)] -pub struct DomainImprovement { - pub domain: AcademicDomain, - pub baseline_accuracy: f64, - pub research_accuracy: f64, - pub improvement: f64, -} - -#[derive(Debug, Clone)] -pub struct CompetitiveAnalysis { - pub current_accuracy: f64, - pub competitor_benchmarks: Vec, - pub global_ranking: u8, - pub competitive_advantages: Vec, -} - -#[derive(Debug, Clone)] -pub struct CompetitorBenchmark { - pub name: String, - pub accuracy: f64, -} - -#[derive(Debug, Clone)] -pub struct ValidationReport { - pub timestamp: DateTime, - pub target_questions: usize, - pub baseline_results: BaselineResults, - pub research_results: ResearchResults, - pub performance_analysis: PerformanceAnalysis, - pub competitive_analysis: CompetitiveAnalysis, - pub validation_duration: Duration, -} - -impl HLELargeScaleValidator { - pub async fn new() -> Result> { - Ok(Self { - research_engine: AdaptiveResearchEngine::new(), - academic_reasoning: Box::new(UniversalAcademicAgent::new().await?), - dataset_manager: HLEDatasetManager::new(), - performance_tracker: LargeScalePerformanceTracker::new(), - quality_validator: true, - competitive_benchmark: CompetitiveBenchmarkingSystem::new(), - }) - } - - /// Execute comprehensive large-scale HLE validation with 1000+ questions - pub async fn execute_large_scale_validation(&mut self, target_questions: usize) -> Result> { - println!("๐ŸŽ“ Starting HLE Large-Scale Validation Framework"); - println!("๐Ÿ“Š Target: {} questions across all academic domains", target_questions); - println!("๐Ÿ”ฌ Research-Enhanced Intelligence Validation\n"); - - let start_time = Instant::now(); - - // Phase 1: Dataset Generation and Loading - println!("๐Ÿ“š Phase 1: Generating comprehensive HLE dataset..."); - self.dataset_manager.generate_hle_dataset(target_questions).await?; - - // Phase 2: Baseline Performance (No Research) - println!("โš–๏ธ Phase 2: Measuring baseline performance..."); - let baseline_results = self.execute_baseline_validation().await?; - - // Phase 3: Research-Enhanced Performance - println!("๐Ÿ” Phase 3: Executing research-enhanced validation..."); - let research_results = self.execute_research_enhanced_validation().await?; - - // Phase 4: Comprehensive Analysis - println!("๐Ÿ“ˆ Phase 4: Analyzing performance improvements..."); - let analysis = self.analyze_performance_gains(&baseline_results, &research_results).await?; - - // Phase 5: Competitive Benchmarking - println!("๐Ÿ† Phase 5: Competitive positioning analysis..."); - let competitive_analysis = self.competitive_benchmark.analyze_global_position(&research_results).await?; - - let total_duration = start_time.elapsed(); - - let report = ValidationReport { - timestamp: Utc::now(), - target_questions, - baseline_results, - research_results, - performance_analysis: analysis, - competitive_analysis, - validation_duration: total_duration, - }; - - self.display_comprehensive_report(&report).await?; - - Ok(report) - } - - async fn execute_baseline_validation(&mut self) -> Result> { - let questions = self.dataset_manager.get_all_questions(); - let mut correct = 0; - let mut total_confidence = 0.0; - let start_time = Instant::now(); - - println!(" ๐Ÿ“ Processing {} baseline questions...", questions.len()); - - for (i, question) in questions.iter().enumerate() { - if i % 100 == 0 { - println!(" โณ Progress: {}/{} questions processed", i, questions.len()); - } - - let academic_question = question.to_academic_question(); - let response = self.process_academic_question(&academic_question).await - .unwrap_or_else(|_| "Unknown".to_string()); - - // Simple validation - if response.contains(&question.correct_answer) || response.to_uppercase().contains(&question.correct_answer) { - correct += 1; - } - - total_confidence += 0.45; // Average baseline confidence - } - - let duration = start_time.elapsed(); - let accuracy = correct as f64 / questions.len() as f64; - let avg_confidence = total_confidence / questions.len() as f64; - - println!(" โœ… Baseline validation complete: {:.1}% accuracy", accuracy * 100.0); - - Ok(BaselineResults { - total_questions: questions.len(), - correct_answers: correct, - accuracy, - average_confidence: avg_confidence, - processing_duration: duration, - }) - } - - async fn execute_research_enhanced_validation(&mut self) -> Result> { - let questions = self.dataset_manager.get_all_questions(); - let mut correct = 0; - let mut total_confidence = 0.0; - let mut research_triggered = 0; - let start_time = Instant::now(); - - println!(" ๐Ÿ”ฌ Processing {} research-enhanced questions...", questions.len()); - - for (i, question) in questions.iter().enumerate() { - if i % 100 == 0 { - println!(" โณ Progress: {}/{} questions processed", i, questions.len()); - } - - // Trigger research for complex questions - let research_needed = question.difficulty >= 6; - - if research_needed { - research_triggered += 1; - // Simulate research boost - if rand::random::() < 0.85 { // 85% accuracy with research - correct += 1; - } - total_confidence += 0.85; // High confidence with research - } else { - // Baseline processing - if rand::random::() < 0.25 { // 25% baseline accuracy - correct += 1; - } - total_confidence += 0.45; // Baseline confidence - } - } - - let duration = start_time.elapsed(); - let accuracy = correct as f64 / questions.len() as f64; - let avg_confidence = total_confidence / questions.len() as f64; - let research_rate = research_triggered as f64 / questions.len() as f64; - - println!(" โœ… Research-enhanced validation complete: {:.1}% accuracy", accuracy * 100.0); - println!(" ๐Ÿ” Research triggered for {:.1}% of questions", research_rate * 100.0); - - Ok(ResearchResults { - total_questions: questions.len(), - correct_answers: correct, - accuracy, - average_confidence: avg_confidence, - research_triggered, - research_success_rate: 0.95, - processing_duration: duration, - }) - } - - async fn analyze_performance_gains(&self, baseline: &BaselineResults, research: &ResearchResults) -> Result> { - let accuracy_improvement = research.accuracy - baseline.accuracy; - let confidence_improvement = research.average_confidence - baseline.average_confidence; - - let domain_improvements = vec![ - DomainImprovement { - domain: AcademicDomain::TheoreticalPhysics, - baseline_accuracy: 0.15, - research_accuracy: 0.75, - improvement: 0.60, - }, - ]; - - let projected_hle_accuracy = research.accuracy * 0.65; - let current_global_rank = if projected_hle_accuracy > 0.40 { 1 } else { 2 }; - - Ok(PerformanceAnalysis { - baseline_accuracy: baseline.accuracy, - research_accuracy: research.accuracy, - accuracy_improvement, - confidence_improvement, - research_effectiveness: research.research_success_rate, - domain_improvements, - projected_hle_accuracy, - current_global_rank, - strategic_advantages: vec![ - "95%+ research success rate".to_string(), - "40%+ confidence boost per research cycle".to_string(), - "85%+ accuracy improvement on complex questions".to_string(), - ], - }) - } - - async fn process_academic_question(&self, question: &AcademicQuestion) -> Result> { - // Use the trait methods to process the question - let analysis = self.academic_reasoning.analyze_question(&question.question).await - .map_err(|e| Box::new(e) as Box)?; - - if let Some(ref options) = question.options { - let evaluation = self.academic_reasoning.evaluate_options(&question.question, options).await - .map_err(|e| Box::new(e) as Box)?; - // Return the most confident option - Ok(evaluation.recommended_answer) - } else { - // For open-ended questions, synthesize an answer - let knowledge = self.academic_reasoning.retrieve_knowledge( - &question.question, - &question.domain, - &Default::default() - ).await.unwrap_or_default(); - - self.academic_reasoning.synthesize_answer(&analysis, &knowledge, question.options.as_deref(), "").await - .map_err(|e| Box::new(e) as Box) - } - } - - async fn display_comprehensive_report(&self, report: &ValidationReport) -> Result<(), Box> { - println!("\n๐ŸŽฏ ========== HLE LARGE-SCALE VALIDATION REPORT =========="); - println!("๐Ÿ“… Timestamp: {}", report.timestamp.format("%Y-%m-%d %H:%M:%S UTC")); - println!("โฑ๏ธ Total Duration: {:.2}s", report.validation_duration.as_secs_f64()); - println!("๐Ÿ“Š Questions: {}", report.target_questions); - - println!("\n๐Ÿ“ˆ PERFORMANCE:"); - println!(" ๐Ÿ”น Baseline: {:.1}%", report.baseline_results.accuracy * 100.0); - println!(" ๐Ÿ”น Research-Enhanced: {:.1}%", report.research_results.accuracy * 100.0); - println!(" ๐Ÿ”น Improvement: +{:.1}pp", report.performance_analysis.accuracy_improvement * 100.0); - - println!("\n๐Ÿ† GLOBAL RANKING:"); - println!(" ๐Ÿ”น Projected HLE Accuracy: {:.1}%", report.performance_analysis.projected_hle_accuracy * 100.0); - println!(" ๐Ÿ”น Current Global Rank: #{}", report.performance_analysis.current_global_rank); - - if report.performance_analysis.current_global_rank == 1 { - println!(" ๐ŸŽ‰ ACHIEVEMENT: #1 GLOBAL HLE RANKING!"); - } - - println!("========================================================\n"); - Ok(()) - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ BRAIN AI - HLE LARGE-SCALE VALIDATION FRAMEWORK"); - - let mut validator = HLELargeScaleValidator::new().await?; - let _report = validator.execute_large_scale_validation(1000).await?; - - Ok(()) -} \ No newline at end of file diff --git a/independent_intelligence_demo.rs b/independent_intelligence_demo.rs deleted file mode 100644 index 27168e1500eff3483b3c998f9a8e5fe3b037003a..0000000000000000000000000000000000000000 --- a/independent_intelligence_demo.rs +++ /dev/null @@ -1,532 +0,0 @@ -//! Independent Intelligence Achievement Demo -//! -//! This example demonstrates Brain AI's journey toward complete independence -//! from external LLMs, showcasing intelligent conversation routing, performance -//! monitoring, quality assessment, and autonomous decision-making capabilities. - -use anyhow::Result; -use chrono::Utc; - -// Import from new service architecture -use brain::*; -use brain::services::*; -use brain_types::BrainError; - -/// Independence levels that Brain AI can achieve -#[derive(Debug, Clone, PartialEq)] -pub enum IndependenceLevel { - DependentOnExternal, // Still relies heavily on external LLMs - PartiallyIndependent, // Balanced usage - MostlyIndependent, // Minimal external dependency - FullyIndependent, // Complete autonomy -} - -/// Demo orchestrator for independent intelligence -pub struct DemoIndependentIntelligenceOrchestrator { - brain_ai_responses: u32, - external_llm_responses: u32, - total_conversations: u32, - response_times: Vec, - quality_scores: Vec, - confidence_scores: Vec, - routing_decisions: Vec, - performance_history: Vec, -} - -#[derive(Debug, Clone)] -pub struct RoutingDecision { - pub route: RouteType, - pub reason: String, - pub confidence: f64, - pub timestamp: chrono::DateTime, -} - -#[derive(Debug, Clone)] -pub enum RouteType { - BrainAI, - ExternalLLM, -} - -#[derive(Debug, Clone)] -pub struct PerformanceSnapshot { - pub timestamp: chrono::DateTime, - pub model_version: String, - pub metrics: PerformanceMetrics, -} - -#[derive(Debug, Clone)] -pub struct PerformanceMetrics { - pub total_conversations: u32, - pub brain_ai_conversations: u32, - pub external_llm_conversations: u32, - pub avg_response_time_ms: f64, - pub avg_quality_score: f64, - pub success_rate: f64, - pub avg_confidence: f64, - pub error_rate: f64, -} - -#[derive(Debug, Clone)] -pub struct RoutingStatistics { - pub brain_ai_percentage: f64, - pub external_llm_percentage: f64, - pub routing_history: Vec, -} - -#[derive(Debug, Clone)] -pub struct IndependenceStatus { - pub level: IndependenceLevel, - pub independence_score: f64, - pub brain_ai_usage_percentage: f64, - pub success_rate: f64, - pub average_quality_score: f64, - pub total_conversations: u32, -} - -#[derive(Debug, Clone)] -pub struct IntelligenceResponse { - pub response: String, - pub model_used: RouteType, - pub confidence: f64, - pub predicted_quality: QualityScores, - pub fallback_reason: Option, - pub knowledge_sources: Vec, - pub processing_time_ms: f64, -} - -#[derive(Debug, Clone)] -pub struct QualityScores { - pub factual_grounding: f64, - pub coherence: f64, - pub relevance: f64, -} - -impl DemoIndependentIntelligenceOrchestrator { - pub fn new() -> Self { - Self { - brain_ai_responses: 0, - external_llm_responses: 0, - total_conversations: 0, - response_times: Vec::new(), - quality_scores: Vec::new(), - confidence_scores: Vec::new(), - routing_decisions: Vec::new(), - performance_history: Vec::new(), - } - } - - pub async fn process_conversation( - &mut self, - request: &RagRequest, - _retrieved_knowledge: Vec, - _context: &str, - memory_service: &mut MemoryService, - _concept_graph_service: &mut ConceptGraphService, - ) -> Result { - let start_time = std::time::Instant::now(); - self.total_conversations += 1; - - // Intelligent routing decision based on complexity and Brain AI capabilities - let (route, confidence, reason) = self.make_routing_decision(&request.message).await; - - let response = match route { - RouteType::BrainAI => { - self.brain_ai_responses += 1; - // Process using Brain AI's own capabilities - self.process_with_brain_ai(&request.message, memory_service).await? - } - RouteType::ExternalLLM => { - self.external_llm_responses += 1; - // Simulate external LLM processing (fallback) - self.process_with_external_llm(&request.message).await? - } - }; - - let processing_time = start_time.elapsed().as_millis() as f64; - self.response_times.push(processing_time); - self.quality_scores.push(response.predicted_quality.coherence); - self.confidence_scores.push(response.confidence); - - // Record routing decision - self.routing_decisions.push(RoutingDecision { - route: route.clone(), - reason, - confidence, - timestamp: Utc::now(), - }); - - Ok(IntelligenceResponse { - response: response.response, - model_used: route, - confidence: response.confidence, - predicted_quality: response.predicted_quality, - fallback_reason: response.fallback_reason, - knowledge_sources: response.knowledge_sources, - processing_time_ms: processing_time, - }) - } - - async fn make_routing_decision(&self, message: &str) -> (RouteType, f64, String) { - // Simple heuristic for routing decisions - let brain_ai_success_rate = if self.total_conversations > 0 { - self.brain_ai_responses as f64 / self.total_conversations as f64 - } else { - 0.5 - }; - - let avg_quality = if !self.quality_scores.is_empty() { - self.quality_scores.iter().sum::() / self.quality_scores.len() as f64 - } else { - 0.7 - }; - - // Route to Brain AI if: - // 1. It's been performing well (high success rate) - // 2. The query seems within Brain AI's expertise - // 3. We're building independence - if brain_ai_success_rate > 0.6 && avg_quality > 0.7 { - (RouteType::BrainAI, 0.85, format!("Brain AI capability sufficient for: '{}'", - &message[..message.len().min(50)])) - } else if message.to_lowercase().contains("recent") || message.to_lowercase().contains("latest") { - (RouteType::ExternalLLM, 0.9, "Current events require external knowledge".to_string()) - } else { - // Prefer Brain AI to build independence - (RouteType::BrainAI, 0.75, "Building Brain AI independence".to_string()) - } - } - - async fn process_with_brain_ai( - &self, - message: &str, - memory_service: &mut MemoryService, - ) -> Result { - // Simulate Brain AI processing - let response = if message.to_lowercase().contains("artificial intelligence") { - "Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. Modern AI uses machine learning algorithms to improve performance through experience.".to_string() - } else if message.to_lowercase().contains("machine learning") { - "Machine learning is a subset of AI that enables computers to learn and improve from data without being explicitly programmed for every task. It uses algorithms to identify patterns and make predictions.".to_string() - } else if message.to_lowercase().contains("neural network") { - "Neural networks are computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information through weighted connections, learning patterns through training data.".to_string() - } else if message.to_lowercase().contains("chatbot") { - "A chatbot can be implemented using natural language processing, intent recognition, and response generation. Start with defining conversation flows, then add language understanding capabilities.".to_string() - } else { - format!("Based on my training and knowledge base, I can provide information about {}. This response was generated using Brain AI's independent intelligence capabilities.", message) - }; - - // Store the interaction in memory for learning - let interaction = format!("Q: {} | A: {}", message, &response[..100.min(response.len())]); - memory_service.learn(interaction, Priority::Medium).await?; - - Ok(IntelligenceResponse { - response, - model_used: RouteType::BrainAI, - confidence: 0.87, - predicted_quality: QualityScores { - factual_grounding: 0.85, - coherence: 0.90, - relevance: 0.88, - }, - fallback_reason: None, - knowledge_sources: vec![ - "Brain AI Knowledge Base".to_string(), - "Integrated Memory System".to_string(), - "Concept Graph".to_string(), - ], - processing_time_ms: 0.0, // Will be filled by caller - }) - } - - async fn process_with_external_llm( - &self, - message: &str, - ) -> Result { - // Simulate external LLM processing (fallback) - let response = format!( - "This response about '{}' was generated using external LLM capabilities as a fallback. Brain AI is continuously learning to handle such queries independently.", - message - ); - - Ok(IntelligenceResponse { - response, - model_used: RouteType::ExternalLLM, - confidence: 0.75, - predicted_quality: QualityScores { - factual_grounding: 0.80, - coherence: 0.85, - relevance: 0.82, - }, - fallback_reason: Some("Query complexity exceeded Brain AI current capabilities".to_string()), - knowledge_sources: vec![ - "External LLM Provider".to_string(), - "General Knowledge Database".to_string(), - ], - processing_time_ms: 0.0, - }) - } - - pub fn get_performance_metrics(&self) -> PerformanceMetrics { - PerformanceMetrics { - total_conversations: self.total_conversations, - brain_ai_conversations: self.brain_ai_responses, - external_llm_conversations: self.external_llm_responses, - avg_response_time_ms: if !self.response_times.is_empty() { - self.response_times.iter().sum::() / self.response_times.len() as f64 - } else { 0.0 }, - avg_quality_score: if !self.quality_scores.is_empty() { - self.quality_scores.iter().sum::() / self.quality_scores.len() as f64 - } else { 0.0 }, - success_rate: 0.92, // Simulated high success rate - avg_confidence: if !self.confidence_scores.is_empty() { - self.confidence_scores.iter().sum::() / self.confidence_scores.len() as f64 - } else { 0.0 }, - error_rate: 0.08, - } - } - - pub fn get_routing_statistics(&self) -> RoutingStatistics { - let brain_ai_percentage = if self.total_conversations > 0 { - self.brain_ai_responses as f64 / self.total_conversations as f64 - } else { 0.0 }; - - RoutingStatistics { - brain_ai_percentage, - external_llm_percentage: 1.0 - brain_ai_percentage, - routing_history: self.routing_decisions.clone(), - } - } - - pub fn get_independence_status(&self) -> IndependenceStatus { - let brain_ai_percentage = if self.total_conversations > 0 { - self.brain_ai_responses as f64 / self.total_conversations as f64 - } else { 0.0 }; - - let independence_score = brain_ai_percentage * 0.8 + - (self.get_performance_metrics().avg_quality_score * 0.2); - - let level = if independence_score >= 0.9 { - IndependenceLevel::FullyIndependent - } else if independence_score >= 0.7 { - IndependenceLevel::MostlyIndependent - } else if independence_score >= 0.5 { - IndependenceLevel::PartiallyIndependent - } else { - IndependenceLevel::DependentOnExternal - }; - - IndependenceStatus { - level, - independence_score, - brain_ai_usage_percentage: brain_ai_percentage * 100.0, - success_rate: self.get_performance_metrics().success_rate * 100.0, - average_quality_score: self.get_performance_metrics().avg_quality_score, - total_conversations: self.total_conversations, - } - } - - pub fn get_performance_history(&self) -> Vec { - // Return stored performance history, or generate a current snapshot if conversations exist - if !self.performance_history.is_empty() { - self.performance_history.clone() - } else if self.total_conversations > 0 { - vec![PerformanceSnapshot { - timestamp: Utc::now(), - model_version: "Brain-AI-v0.8.0".to_string(), - metrics: self.get_performance_metrics(), - }] - } else { - Vec::new() - } - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain AI - Independent Intelligence Achievement Demo"); - println!("====================================================="); - println!(); - - // Initialize the independent intelligence system - let mut orchestrator = DemoIndependentIntelligenceOrchestrator::new(); - - // Initialize Brain AI components using new service architecture - let mut memory_service = create_memory_service_with_capacity(2000).await?; - let mut concept_graph_service = create_concept_graph_service_default().await?; - - println!("โœ… Independent Intelligence Orchestrator initialized"); - println!("โœ… Brain AI cognitive components ready"); - println!(); - - // Demo conversation scenarios - let demo_scenarios = vec![ - ("What is artificial intelligence?", "general knowledge"), - ("How does machine learning work?", "technical explanation"), - ("Can you explain neural networks in simple terms?", "educational content"), - ("What are the latest developments in AI research?", "current events"), - ("How can I implement a basic chatbot?", "programming help"), - ]; - - println!("๐ŸŽฏ Testing Independent Intelligence with {} conversation scenarios", demo_scenarios.len()); - println!(); - - for (i, (question, category)) in demo_scenarios.iter().enumerate() { - println!("๐Ÿ“ Scenario {}: {} ({})", i + 1, question, category); - println!(" {}", "โ”€".repeat(60)); - - // Create RAG request - let request = RagRequest { - message: question.to_string(), - conversation_id: Some(format!("demo_conv_{}", i + 1)), - context_limit: Some(10), - retrieval_threshold: Some(0.3), - }; - - // Simulate retrieved knowledge - let retrieved_knowledge = vec![ - format!("Relevant information about {}", category), - format!("Context-specific details for: {}", question), - ]; - - // Process conversation through independent intelligence system - let response = orchestrator.process_conversation( - &request, - retrieved_knowledge, - &format!("Demo conversation about {}", category), - &mut memory_service, - &mut concept_graph_service, - ).await?; - - // Display results - println!(" ๐Ÿค– Response: {}", response.response); - println!(" ๐Ÿ“Š Model Used: {:?}", response.model_used); - println!(" ๐ŸŽฏ Confidence: {:.3}", response.confidence); - println!(" โฑ๏ธ Processing Time: {:.2} ms", response.processing_time_ms); - println!(" ๐Ÿ“ˆ Quality Score: {:.3}", - (response.predicted_quality.factual_grounding + - response.predicted_quality.coherence + - response.predicted_quality.relevance) / 3.0); - - if let Some(fallback_reason) = &response.fallback_reason { - println!(" โš ๏ธ Fallback Reason: {}", fallback_reason); - } - - println!(" ๐Ÿ“š Knowledge Sources: {}", response.knowledge_sources.len()); - for (j, source) in response.knowledge_sources.iter().enumerate() { - println!(" {}. {}", j + 1, source); - } - - println!(); - } - - // Display performance metrics - println!("๐Ÿ“Š Independent Intelligence Performance Metrics"); - println!("=============================================="); - let metrics = orchestrator.get_performance_metrics(); - println!("๐Ÿ”ข Total Conversations: {}", metrics.total_conversations); - println!("๐Ÿง  Brain AI Conversations: {}", metrics.brain_ai_conversations); - println!("๐ŸŒ External LLM Conversations: {}", metrics.external_llm_conversations); - println!("โฑ๏ธ Average Response Time: {:.2} ms", metrics.avg_response_time_ms); - println!("๐ŸŽฏ Average Quality Score: {:.3}", metrics.avg_quality_score); - println!("โœ… Success Rate: {:.1}%", metrics.success_rate * 100.0); - println!("๐ŸŽช Average Confidence: {:.3}", metrics.avg_confidence); - println!("โŒ Error Rate: {:.1}%", metrics.error_rate * 100.0); - println!(); - - // Display routing statistics - println!("๐Ÿ”€ Conversation Routing Statistics"); - println!("================================="); - let routing_stats = orchestrator.get_routing_statistics(); - println!("๐Ÿง  Brain AI Usage: {:.1}%", routing_stats.brain_ai_percentage * 100.0); - println!("๐ŸŒ External LLM Usage: {:.1}%", routing_stats.external_llm_percentage * 100.0); - println!("๐Ÿ“ˆ Routing Decisions Made: {}", routing_stats.routing_history.len()); - - // Show recent routing decisions - if !routing_stats.routing_history.is_empty() { - println!("\n๐Ÿ“‹ Recent Routing Decisions:"); - for (i, decision) in routing_stats.routing_history.iter().rev().take(3).enumerate() { - println!(" {}. {:?} - {} (confidence: {:.3})", - i + 1, decision.route, decision.reason, decision.confidence); - } - } - println!(); - - // Display independence status - println!("๐Ÿ† Independence Status Assessment"); - println!("================================"); - let independence_status = orchestrator.get_independence_status(); - println!("๐ŸŽ–๏ธ Independence Level: {:?}", independence_status.level); - println!("๐Ÿ“Š Independence Score: {:.3}/1.0", independence_status.independence_score); - println!("๐Ÿง  Brain AI Usage: {:.1}%", independence_status.brain_ai_usage_percentage); - println!("โœ… Success Rate: {:.1}%", independence_status.success_rate); - println!("๐ŸŽฏ Average Quality: {:.3}", independence_status.average_quality_score); - println!("๐Ÿ’ฌ Total Conversations: {}", independence_status.total_conversations); - - // Independence level interpretation - match independence_status.level { - IndependenceLevel::FullyIndependent => { - println!("๐ŸŽ‰ STATUS: Brain AI has achieved FULL INDEPENDENCE!"); - println!(" ๐Ÿš€ No longer dependent on external LLMs"); - println!(" ๐ŸŽฏ Consistently high performance and quality"); - }, - IndependenceLevel::MostlyIndependent => { - println!("๐ŸŒŸ STATUS: Brain AI is MOSTLY INDEPENDENT"); - println!(" ๐Ÿ“ˆ Minimal reliance on external systems"); - println!(" ๐Ÿ”ง Fine-tuning performance for full independence"); - }, - IndependenceLevel::PartiallyIndependent => { - println!("โš–๏ธ STATUS: Brain AI is PARTIALLY INDEPENDENT"); - println!(" ๐Ÿ”„ Balanced usage between Brain AI and external LLMs"); - println!(" ๐Ÿ“Š Gradual transition in progress"); - }, - IndependenceLevel::DependentOnExternal => { - println!("๐Ÿ”ง STATUS: Still DEPENDENT on external systems"); - println!(" ๐Ÿš€ Independence training and optimization needed"); - println!(" ๐Ÿ“ˆ Building towards autonomous operation"); - }, - } - println!(); - - // Performance history - let performance_history = orchestrator.get_performance_history(); - if !performance_history.is_empty() { - println!("๐Ÿ“ˆ Performance History"); - println!("====================="); - println!("๐Ÿ“Š {} performance snapshots recorded", performance_history.len()); - - if let Some(latest) = performance_history.last() { - println!("๐Ÿ• Latest Snapshot: {}", latest.timestamp.format("%Y-%m-%d %H:%M:%S UTC")); - println!("๐Ÿท๏ธ Model Version: {}", latest.model_version); - println!("๐Ÿ“Š Snapshot Metrics:"); - println!(" - Conversations: {}", latest.metrics.total_conversations); - println!(" - Success Rate: {:.1}%", latest.metrics.success_rate * 100.0); - println!(" - Quality Score: {:.3}", latest.metrics.avg_quality_score); - } - } - println!(); - - // Demonstrate continuous improvement capability - println!("๐Ÿ”„ Continuous Improvement Demonstration"); - println!("======================================"); - println!("๐ŸŽฏ Training data collection: Active"); - println!("๐Ÿ“Š Performance monitoring: Real-time"); - println!("๐Ÿ”„ Model updating: Triggered by conversation count"); - println!("๐Ÿ“ˆ Quality improvement: Ongoing"); - println!("๐Ÿง  Brain AI evolution: Autonomous"); - println!(); - - // Summary and next steps - println!("๐ŸŽŠ Independent Intelligence Achievement Demo Complete!"); - println!("===================================================="); - println!("โœ… Successfully demonstrated all key capabilities:"); - println!(" ๐Ÿง  Brain AI conversational intelligence"); - println!(" ๐Ÿ”€ Intelligent conversation routing"); - println!(" ๐Ÿ“Š Real-time performance monitoring"); - println!(" ๐ŸŽฏ Quality assessment and validation"); - println!(" ๐Ÿ”„ Continuous improvement mechanisms"); - println!(" ๐Ÿ† Independence status tracking"); - println!(" ๐Ÿ“ˆ Performance history and analytics"); - println!(); - println!("๐Ÿš€ Brain AI is ready for fully independent conversational intelligence!"); - println!("๐ŸŽฏ Independent Intelligence Achievement: COMPLETE"); - - Ok(()) -} \ No newline at end of file diff --git a/insight_extraction_demo.rs b/insight_extraction_demo.rs deleted file mode 100644 index 61c6bf39df35aec1f1fa001d6ad51d684e1cdacf..0000000000000000000000000000000000000000 --- a/insight_extraction_demo.rs +++ /dev/null @@ -1,562 +0,0 @@ -//! Insight Extraction Engine Demonstration -//! -//! This example demonstrates the pattern detection system (Task 5.1) that monitors -//! memory stores and identifies recurring patterns and relationships. -//! -//! Enhanced implementation with: -//! - Statistical pattern detection from memory content -//! - Relationship pattern analysis from concept graphs -//! - Temporal sequence detection with timing analysis -//! - Co-occurrence pattern identification -//! - Frequency-based pattern mining -//! - Configurable detection thresholds and parameters - -use anyhow::Result; -use brain_infra::insights::{ - PatternDetector, PatternDetectionConfig, PatternType, PatternDetectionResult -}; -use brain_infra::concepts::{ConceptGraphManager, ConceptGraphConfig}; -use brain::{ - ConceptNode, ConceptType, ConceptRepository, RelationshipRepository, ConceptRelationship, - Priority, SemanticConcept, EpisodicEvent, -}; -use brain_core::concepts::RelationshipType; -use chrono::{Utc, Duration}; -use std::collections::HashMap; - -/// Demo implementation of memory system for pattern detection -pub struct DemoMemorySystem { - working_memory: Vec, - semantic_concepts: Vec, - episodic_events: Vec, -} - -impl DemoMemorySystem { - pub fn new() -> Self { - Self { - working_memory: Vec::new(), - semantic_concepts: Vec::new(), - episodic_events: Vec::new(), - } - } - - pub fn learn(&mut self, content: String, _priority: Priority) -> Result<()> { - self.working_memory.push(content); - Ok(()) - } - - pub fn store_concept(&mut self, concept: SemanticConcept) -> Result<()> { - self.semantic_concepts.push(concept); - Ok(()) - } - - pub fn store_event(&mut self, event: EpisodicEvent) -> Result<()> { - self.episodic_events.push(event); - Ok(()) - } - - pub fn get_all_content(&self) -> Vec { - let mut content = self.working_memory.clone(); - - // Add semantic concept names and descriptions - for concept in &self.semantic_concepts { - content.push(concept.name.clone()); - content.push(concept.description.clone()); - } - - // Add episodic event content - for event in &self.episodic_events { - content.push(event.content.clone()); - } - - content - } - - pub fn get_working_memory(&self) -> &Vec { - &self.working_memory - } - - pub fn get_semantic_concepts(&self) -> &Vec { - &self.semantic_concepts - } - - pub fn get_episodic_events(&self) -> &Vec { - &self.episodic_events - } -} - -/// Demo implementation for concept graph pattern detection -pub struct DemoConceptGraphPatternDetector { - pattern_detector: PatternDetector, -} - -impl DemoConceptGraphPatternDetector { - pub fn new(detector: PatternDetector) -> Self { - Self { - pattern_detector: detector, - } - } - - pub async fn detect_patterns_from_concept_graph( - &mut self, - _concept_graph: &ConceptGraphManager, - ) -> Result { - // Extract content from concept graph for pattern analysis - let mut content_items = Vec::new(); - - // In a real implementation, would query actual concepts and relationships - // For demo, we'll simulate with known relationship patterns - content_items.extend([ - "weather_concept".to_string(), - "forecast_concept".to_string(), - "user_concept".to_string(), - "query_concept".to_string(), - "weather_forecast_relationship".to_string(), - "user_query_relationship".to_string(), - "query_weather_relationship".to_string(), - "concept_hierarchy".to_string(), - "relationship_pattern".to_string(), - ]); - - // Use existing memory pattern detection adapted for concept graph - self.pattern_detector.detect_patterns_from_memory(&content_items).await - .map_err(|e| anyhow::Error::msg(format!("Pattern detection failed: {}", e))) - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain Insight Extraction Engine Demo - Enhanced Implementation"); - println!("====================================================================="); - println!(); - - // Phase 1: Initialize Pattern Detection System - println!("๐Ÿ“Š Phase 1: Initializing Pattern Detection System"); - println!("--------------------------------------------------"); - - let config = PatternDetectionConfig { - min_pattern_frequency: 2, - temporal_window_hours: 24, - min_confidence_threshold: 0.5, - max_patterns_per_batch: 50, - min_co_occurrence_count: 2, - significance_threshold: 0.1, - incremental_detection: true, - batch_size: 20, - }; - - let mut pattern_detector = PatternDetector::with_config(config); - println!("โœ… Pattern detector initialized with custom configuration"); - println!(" - Min pattern frequency: {}", pattern_detector.get_config().min_pattern_frequency); - println!(" - Temporal window: {} hours", pattern_detector.get_config().temporal_window_hours); - println!(" - Confidence threshold: {:.2}", pattern_detector.get_config().min_confidence_threshold); - println!(); - - // Phase 2: Set up Memory System with Sample Data - println!("๐Ÿ’พ Phase 2: Setting up Demo Memory System with Sample Data"); - println!("---------------------------------------------------------"); - - // Create demo memory system - let mut memory_system = DemoMemorySystem::new(); - - // Add working memory items with repeated patterns - memory_system.learn("User asks about weather".to_string(), Priority::High)?; - memory_system.learn("System provides weather forecast".to_string(), Priority::Medium)?; - memory_system.learn("User asks about traffic".to_string(), Priority::High)?; - memory_system.learn("System provides traffic update".to_string(), Priority::Medium)?; - memory_system.learn("User asks about weather".to_string(), Priority::High)?; // Repeated pattern - memory_system.learn("System provides weather forecast".to_string(), Priority::Medium)?; // Repeated pattern - memory_system.learn("User asks about sports".to_string(), Priority::Low)?; - memory_system.learn("System provides sports news".to_string(), Priority::Low)?; - memory_system.learn("User asks about weather".to_string(), Priority::High)?; // Third occurrence - memory_system.learn("System provides weather forecast".to_string(), Priority::Medium)?; // Third occurrence - - println!("โœ… Added 10 working memory items with clear repeated patterns"); - - // Add episodic events with temporal patterns - let base_time = Utc::now() - Duration::hours(2); - - let events = vec![ - ("User login detected", base_time), - ("User asks about weather", base_time + Duration::minutes(5)), - ("Weather data retrieved", base_time + Duration::minutes(6)), - ("System responds with forecast", base_time + Duration::minutes(7)), - ("User logout detected", base_time + Duration::minutes(30)), - ("User login detected", base_time + Duration::hours(1)), // Repeated sequence - ("User asks about traffic", base_time + Duration::hours(1) + Duration::minutes(3)), - ("Traffic data retrieved", base_time + Duration::hours(1) + Duration::minutes(4)), - ("System responds with traffic", base_time + Duration::hours(1) + Duration::minutes(5)), - ("User logout detected", base_time + Duration::hours(1) + Duration::minutes(25)), - ("User login detected", base_time + Duration::hours(2)), // Third occurrence - ("User asks about weather", base_time + Duration::hours(2) + Duration::minutes(2)), - ("Weather data retrieved", base_time + Duration::hours(2) + Duration::minutes(3)), - ("System responds with forecast", base_time + Duration::hours(2) + Duration::minutes(4)), - ("User logout detected", base_time + Duration::hours(2) + Duration::minutes(20)), - ]; - - for (content, timestamp) in events { - let mut event = EpisodicEvent::new( - content.to_string(), - HashMap::new(), - 0.8, - "demo".to_string(), - ); - event.timestamp = timestamp; - memory_system.store_event(event)?; - - // Also store as semantic concept for pattern detection - memory_system.store_concept(SemanticConcept::new( - content.to_string(), - format!("Event: {}", content), - vec![0.1, 0.2, 0.3, 0.4], // Simple embedding - ))?; - } - - println!("โœ… Added 15 episodic events with clear temporal sequences"); - - // Add semantic concepts - let concepts = vec![ - ("weather", "Information about atmospheric conditions"), - ("traffic", "Information about road conditions and congestion"), - ("sports", "Information about athletic activities and competitions"), - ("user", "Person interacting with the system"), - ("system", "The AI assistant providing responses"), - ("query", "A request for information"), - ("response", "An answer to a query"), - ("forecast", "Prediction about future conditions"), - ("data", "Information retrieved from external sources"), - ("session", "User interaction period from login to logout"), - ]; - - for (name, description) in concepts { - let concept = SemanticConcept::new( - name.to_string(), - description.to_string(), - vec![0.1, 0.2, 0.3, 0.4], // Simple embedding - ); - memory_system.store_concept(concept)?; - } - - println!("โœ… Added 10 semantic concepts"); - println!(); - - // Phase 3: Detect Patterns from Memory System - println!("๐Ÿ” Phase 3: Detecting Patterns from Memory System"); - println!("--------------------------------------------------"); - - let memory_content = memory_system.get_all_content(); - let memory_result = pattern_detector.detect_patterns_from_memory(&memory_content).await?; - - println!("๐Ÿ“ˆ Pattern Detection Results from Memory:"); - println!(" - Patterns detected: {}", memory_result.detected_patterns.len()); - println!(" - Items processed: {}", memory_result.items_processed); - println!(" - Processing time: {}ms", memory_result.processing_time_ms); - println!(" - Filtered patterns: {}", memory_result.filtered_patterns); - println!(); - - println!("๐ŸŽฏ Detected Patterns by Type:"); - for (pattern_type, count) in &memory_result.pattern_type_counts { - println!(" - {}: {} patterns", pattern_type, count); - } - println!(); - - println!("๐Ÿ“‹ Detailed Pattern Analysis:"); - for (i, pattern) in memory_result.detected_patterns.iter().enumerate() { - println!(" Pattern {}: {} ({})", i + 1, pattern.pattern_type, pattern.elements.join(" โ†’ ")); - println!(" Frequency: {}, Confidence: {:.3}", pattern.frequency, pattern.confidence); - println!(" Evidence: {} items", pattern.evidence.len()); - println!(" Strength: {:.3}", pattern.strength); - - if let Some(ref temporal_info) = pattern.temporal_info { - println!(" Temporal: avg {:.1}min, std {:.1}min", - temporal_info.average_delay_minutes, temporal_info.delay_std_dev); - } - println!(); - } - - // Phase 4: Set up Concept Graph with Sample Data - println!("๐Ÿ•ธ๏ธ Phase 4: Setting up Concept Graph with Sample Data"); - println!("-------------------------------------------------------"); - - let graph_config = ConceptGraphConfig::default(); - let mut concept_graph = ConceptGraphManager::new(graph_config).await?; - - // Create concept nodes with clear hierarchical structure - let weather_concept = ConceptNode::new( - ConceptType::Entity, - "weather".to_string(), - 0.9, - Some("semantic_memory".to_string()), - ); - let weather_id = concept_graph.create_concept(weather_concept).await?; - - let forecast_concept = ConceptNode::new( - ConceptType::Entity, - "forecast".to_string(), - 0.8, - Some("semantic_memory".to_string()), - ); - let forecast_id = concept_graph.create_concept(forecast_concept).await?; - - let traffic_concept = ConceptNode::new( - ConceptType::Entity, - "traffic".to_string(), - 0.85, - Some("semantic_memory".to_string()), - ); - let traffic_id = concept_graph.create_concept(traffic_concept).await?; - - let user_concept = ConceptNode::new( - ConceptType::Entity, - "user".to_string(), - 0.95, - Some("semantic_memory".to_string()), - ); - let user_id = concept_graph.create_concept(user_concept).await?; - - let query_concept = ConceptNode::new( - ConceptType::Action, - "query".to_string(), - 0.85, - Some("semantic_memory".to_string()), - ); - let query_id = concept_graph.create_concept(query_concept).await?; - - let response_concept = ConceptNode::new( - ConceptType::Action, - "response".to_string(), - 0.80, - Some("semantic_memory".to_string()), - ); - let response_id = concept_graph.create_concept(response_concept).await?; - - println!("โœ… Created 6 concept nodes with clear semantic structure"); - - // Create relationships that form clear patterns - let relationships = vec![ - (forecast_id, weather_id, RelationshipType::IsA, 0.9), - (weather_id, forecast_id, RelationshipType::SimilarTo, 0.7), - (user_id, query_id, RelationshipType::Uses, 0.9), - (query_id, response_id, RelationshipType::Causes, 0.8), - (query_id, weather_id, RelationshipType::Uses, 0.7), - (query_id, traffic_id, RelationshipType::Uses, 0.6), - (response_id, forecast_id, RelationshipType::Uses, 0.7), - (weather_id, traffic_id, RelationshipType::SimilarTo, 0.5), - ]; - - for (from_id, to_id, rel_type, confidence) in relationships { - let relationship = ConceptRelationship::new( - from_id, to_id, rel_type, confidence - ); - concept_graph.create_relationship(relationship).await?; - } - - println!("โœ… Created 8 concept relationships forming clear patterns"); - println!(); - - // Phase 5: Detect Patterns from Concept Graph - println!("๐Ÿ”— Phase 5: Detecting Patterns from Concept Graph"); - println!("--------------------------------------------------"); - - let mut graph_pattern_detector = DemoConceptGraphPatternDetector::new( - PatternDetector::with_config(pattern_detector.get_config().clone()) - ); - let graph_result = graph_pattern_detector.detect_patterns_from_concept_graph(&concept_graph).await?; - - println!("๐Ÿ“ˆ Pattern Detection Results from Concept Graph:"); - println!(" - Patterns detected: {}", graph_result.detected_patterns.len()); - println!(" - Items processed: {}", graph_result.items_processed); - println!(" - Processing time: {}ms", graph_result.processing_time_ms); - println!(" - Filtered patterns: {}", graph_result.filtered_patterns); - println!(); - - println!("๐ŸŽฏ Detected Patterns by Type:"); - for (pattern_type, count) in &graph_result.pattern_type_counts { - println!(" - {}: {} patterns", pattern_type, count); - } - println!(); - - println!("๐Ÿ“‹ Detailed Pattern Analysis:"); - for (i, pattern) in graph_result.detected_patterns.iter().enumerate() { - println!(" Pattern {}: {} ({})", i + 1, pattern.pattern_type, pattern.elements.join(" โ†’ ")); - println!(" Frequency: {}, Confidence: {:.3}", pattern.frequency, pattern.confidence); - println!(" Evidence: {} relationships", pattern.evidence.len()); - println!(" Strength: {:.3}", pattern.strength); - println!(); - } - - // Phase 6: Pattern Cache and Statistics Analysis - println!("๐Ÿ“Š Phase 6: Pattern Cache and Statistics Analysis"); - println!("--------------------------------------------------"); - - // Scope 1: Pattern cache analysis - let cached_pattern_count = { - let cached_patterns = pattern_detector.get_cached_patterns(); - let count = cached_patterns.len(); - println!("๐Ÿ—„๏ธ Pattern Cache Analysis:"); - println!(" - Total cached patterns: {}", count); - - let mut cache_by_type: HashMap = HashMap::new(); - for pattern in &cached_patterns { - *cache_by_type.entry(pattern.pattern_type.clone()).or_insert(0) += 1; - } - - for (pattern_type, count) in cache_by_type { - println!(" - {}: {} cached", pattern_type, count); - } - - count - }; - println!(); - - // Scope 2: Detection statistics - { - let stats = pattern_detector.get_detection_stats(); - println!("๐Ÿ“ˆ Detection Statistics:"); - println!(" - Total patterns detected: {}", stats.total_patterns_detected); - println!(" - Total items processed: {}", stats.total_items_processed); - println!(" - Total processing time: {}ms", stats.total_processing_time_ms); - println!(" - Detection operations: {}", stats.detection_operations); - println!(" - Average patterns per operation: {:.2}", stats.average_patterns_per_operation); - println!(); - - println!("๐ŸŽฏ Patterns by Type (Overall):"); - for (pattern_type, count) in &stats.patterns_by_type { - println!(" - {}: {} total", pattern_type, count); - } - } - println!(); - - // Phase 7: Advanced Pattern Analysis - println!("๐Ÿ”ฌ Phase 7: Advanced Pattern Analysis"); - println!("--------------------------------------"); - - // Scope 3: Pattern significance analysis - { - let cached_patterns = pattern_detector.get_cached_patterns(); - let all_patterns: Vec<_> = cached_patterns.iter().collect(); - let significant_patterns: Vec<_> = all_patterns.iter() - .filter(|p| p.is_significant(pattern_detector.get_config())) - .collect(); - - println!("๐ŸŽฏ Pattern Significance Analysis:"); - println!(" - Total patterns: {}", all_patterns.len()); - println!(" - Significant patterns: {}", significant_patterns.len()); - if !all_patterns.is_empty() { - println!(" - Significance ratio: {:.2}%", - (significant_patterns.len() as f64 / all_patterns.len() as f64) * 100.0); - } - println!(); - - // Find highest confidence patterns - let mut sorted_patterns = all_patterns.clone(); - sorted_patterns.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap()); - - println!("๐Ÿ† Top 5 Highest Confidence Patterns:"); - for (i, pattern) in sorted_patterns.iter().take(5).enumerate() { - println!(" {}. {} (confidence: {:.3})", - i + 1, pattern.pattern_type, pattern.confidence); - println!(" Elements: {}", pattern.elements.join(" โ†’ ")); - println!(" Frequency: {}, Strength: {:.3}", pattern.frequency, pattern.strength); - } - } - println!(); - - // Phase 8: Configuration Testing (now safe to do mutable operations) - println!("โš™๏ธ Phase 8: Configuration Testing"); - println!("-----------------------------------"); - - // Test with stricter configuration - let strict_config = PatternDetectionConfig { - min_pattern_frequency: 3, - min_confidence_threshold: 0.8, - significance_threshold: 0.01, - ..pattern_detector.get_config().clone() - }; - - pattern_detector.set_config(strict_config); - println!("๐Ÿ”ง Applied stricter configuration:"); - println!(" - Min frequency: {}", pattern_detector.get_config().min_pattern_frequency); - println!(" - Min confidence: {:.2}", pattern_detector.get_config().min_confidence_threshold); - println!(" - Significance threshold: {:.3}", pattern_detector.get_config().significance_threshold); - - // Re-run detection with stricter settings - let strict_result = pattern_detector.detect_patterns_from_memory(&memory_content).await?; - println!(" - Patterns with strict config: {}", strict_result.detected_patterns.len()); - println!(" - Filtered out: {}", strict_result.filtered_patterns); - println!(); - - // Phase 9: Cache Management - println!("๐Ÿ—‚๏ธ Phase 9: Cache Management and Performance"); - println!("----------------------------------------------"); - - println!("๐Ÿงน Cache Management Operations:"); - println!(" - Patterns before clear: {}", pattern_detector.get_cached_patterns().len()); - - pattern_detector.clear_cache(); - println!(" - Patterns after clear: {}", pattern_detector.get_cached_patterns().len()); - - // Reset statistics - let stats_before = pattern_detector.get_detection_stats().clone(); - pattern_detector.reset_stats(); - let stats_after = pattern_detector.get_detection_stats(); - - println!(" - Operations before reset: {}", stats_before.detection_operations); - println!(" - Operations after reset: {}", stats_after.detection_operations); - println!(); - - // Phase 10: Integration Capabilities Demo - println!("๐Ÿ”— Phase 10: Integration Capabilities Demo"); - println!("-------------------------------------------"); - - println!("๐Ÿš€ Integration Points Demonstrated:"); - println!(" โœ“ Memory System Integration - {} items processed", memory_content.len()); - println!(" โœ“ Concept Graph Integration - 6 concepts, 8 relationships analyzed"); - println!(" โœ“ Temporal Pattern Detection - {} events with timestamps", memory_system.get_episodic_events().len()); - println!(" โœ“ Frequency Analysis - {} working memory items", memory_system.get_working_memory().len()); - println!(" โœ“ Semantic Concept Mining - {} concepts processed", memory_system.get_semantic_concepts().len()); - println!(" โœ“ Configurable Detection Thresholds"); - println!(" โœ“ Statistical Significance Testing"); - println!(" โœ“ Pattern Caching and Performance Optimization"); - println!(); - - // Phase 11: Summary and Next Steps - println!("๐Ÿ“‹ Phase 11: Summary and Next Steps"); - println!("------------------------------------"); - - println!("โœ… Pattern Detection System (Task 5.1) Successfully Demonstrated!"); - println!(); - println!("๐ŸŽฏ Key Capabilities Implemented:"); - println!(" โœ“ Temporal sequence pattern detection from episodic memory"); - println!(" โœ“ Co-occurrence pattern detection across memory types"); - println!(" โœ“ Frequency pattern detection from recurring events"); - println!(" โœ“ Hierarchical pattern detection from concept relationships"); - println!(" โœ“ Similarity pattern detection from concept graph"); - println!(" โœ“ Causal pattern detection from relationship types"); - println!(" โœ“ Statistical significance filtering"); - println!(" โœ“ Incremental pattern detection"); - println!(" โœ“ Pattern caching and statistics tracking"); - println!(" โœ“ Configurable detection parameters"); - println!(" โœ“ Memory system integration"); - println!(" โœ“ Concept graph analysis"); - println!(); - println!("๐Ÿš€ Ready for Task 5.2: Rule Formalization Framework"); - println!(" - Transform detected patterns into formal rules"); - println!(" - Implement [Pattern] โ†’ [Outcome] rule structures"); - println!(" - Add support, confidence, and generality metrics"); - println!(" - Create rule storage and indexing systems"); - println!(" - Enable rule-based inference and prediction"); - println!(); - println!("๐Ÿ“Š Final Statistics:"); - println!(" - Total unique patterns identified: {}", cached_pattern_count); - println!(" - Memory items analyzed: {}", memory_content.len()); - println!(" - Concept relationships examined: 8"); - println!(" - Processing time efficiency: Sub-millisecond per item"); - println!(" - Pattern confidence range: 0.0 - 1.0"); - println!(" - Detection accuracy: Statistical significance validated"); - println!(); - println!("๐Ÿง  Pattern Detection Engine is now operational and ready for integration!"); - println!(" Ready to feed detected patterns into Rule Formalization Framework."); - - Ok(()) -} \ No newline at end of file diff --git a/integration_analytics.json b/integration_analytics.json deleted file mode 100644 index df9b72352fa15e861b65a064e8e881213556e857..0000000000000000000000000000000000000000 --- a/integration_analytics.json +++ /dev/null @@ -1,244 +0,0 @@ -{ - "integration_analysis": { - "current_mode": "Adaptive", - "recommended_mode": "SegmentOnly", - "performance_comparison": { - "character_accuracy": 37.5, - "segment_accuracy": 50.0, - "hybrid_accuracy": 25.0, - "character_avg_time": 22.0, - "segment_avg_time": 11.5, - "hybrid_avg_time": 15.5, - "character_count": 8, - "segment_count": 8, - "hybrid_count": 8, - "segment_advantage": 12.5, - "hybrid_advantage": -12.5, - "recommended_mode": "SegmentOnly" - }, - "integration_stats": { - "total_mode_switches": 0, - "successful_adaptations": 0, - "failed_adaptations": 15, - "best_performing_mode": "Adaptive", - "adaptive_learning_score": 0.0, - "last_optimization": 1749820115 - }, - "best_segments": [], - "learning_effectiveness": 0.4125, - "total_adaptations": 15, - "adaptation_success_rate": 0.0 - }, - "performance_history": [ - { - "timestamp": 1749820115, - "accuracy": 100.0, - "prediction_mode": "Adaptive", - "average_confidence": 0.03523686512096062, - "average_time_ms": 15.0, - "total_predictions": 1 - }, - { - "timestamp": 1749820115, - "accuracy": 100.0, - "prediction_mode": "Adaptive", - "average_confidence": 0.03523686512096062, - "average_time_ms": 11.5, - "total_predictions": 2 - }, - { - "timestamp": 1749820115, - "accuracy": 100.0, - "prediction_mode": "Adaptive", - "average_confidence": 0.03523686512096062, - "average_time_ms": 11.666666666666666, - "total_predictions": 3 - }, - { - "timestamp": 1749820115, - "accuracy": 75.0, - "prediction_mode": "Adaptive", - "average_confidence": 0.03541360290927637, - "average_time_ms": 13.0, - 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}, - { - "timestamp": 1749820115, - "accuracy": 50.0, - "prediction_mode": "Adaptive", - "average_confidence": 0.03555906480794503, - "average_time_ms": 13.8, - "total_predictions": 10 - }, - { - "timestamp": 1749820115, - "accuracy": 45.45454545454545, - "prediction_mode": "Adaptive", - "average_confidence": 0.03548753091360231, - "average_time_ms": 13.545454545454545, - "total_predictions": 11 - }, - { - "timestamp": 1749820115, - "accuracy": 41.66666666666667, - "prediction_mode": "Adaptive", - "average_confidence": 0.03542791933498338, - "average_time_ms": 13.666666666666666, - "total_predictions": 12 - }, - { - "timestamp": 1749820115, - "accuracy": 38.46153846153847, - "prediction_mode": "Adaptive", - "average_confidence": 0.035374469307482566, - "average_time_ms": 14.384615384615385, - "total_predictions": 13 - }, - { - "timestamp": 1749820115, - "accuracy": 42.857142857142854, - "prediction_mode": "Adaptive", - "average_confidence": 0.03532865499819616, - "average_time_ms": 14.214285714285714, - 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"total_predictions": 19 - }, - { - "timestamp": 1749820115, - "accuracy": 45.0, - "prediction_mode": "Adaptive", - "average_confidence": 0.035351643729311336, - "average_time_ms": 15.55, - "total_predictions": 20 - }, - { - "timestamp": 1749820115, - "accuracy": 42.857142857142854, - "prediction_mode": "Adaptive", - "average_confidence": 0.03537103231241908, - "average_time_ms": 15.666666666666666, - "total_predictions": 21 - }, - { - "timestamp": 1749820115, - "accuracy": 40.909090909090914, - "prediction_mode": "Adaptive", - "average_confidence": 0.03534381229686254, - "average_time_ms": 16.272727272727273, - "total_predictions": 22 - }, - { - "timestamp": 1749820115, - "accuracy": 39.130434782608695, - "prediction_mode": "Adaptive", - "average_confidence": 0.03531895923918047, - "average_time_ms": 16.217391304347824, - "total_predictions": 23 - }, - { - "timestamp": 1749820115, - "accuracy": 37.5, - "prediction_mode": "Adaptive", - "average_confidence": 0.03529617726963858, - "average_time_ms": 16.333333333333332, - "total_predictions": 24 - } - ], - "segment_analytics": "{\n \"total_segments_tracked\": 16,\n \"high_performing_segments\": [],\n \"context_mappings\": {\n \"ctx_1\": [],\n \"ctx_12\": [],\n \"ctx_9\": [],\n \"ctx_10\": []\n },\n \"average_performance\": 37.5,\n \"last_updated\": 1749820115\n}", - "configuration": { - "mode_switching": { - "min_predictions_for_switch": 20, - "accuracy_threshold_diff": 3.0, - "confidence_threshold": 0.65, - "degradation_tolerance": 8.0, - "enable_auto_switching": true - }, - "learning": { - "learning_rate": 0.15, - "history_size": 500, - "significance_threshold": 2.0, - "enable_context_learning": true, - "enable_quality_assessment": true - } - }, - "timestamp": 1749820115 -} \ No newline at end of file diff --git a/integration_demo.rs b/integration_demo.rs deleted file mode 100644 index f66ba4e587393bbafd6c0116c57f7645a5e06e2f..0000000000000000000000000000000000000000 --- a/integration_demo.rs +++ /dev/null @@ -1,156 +0,0 @@ -//! Simplified Integration Demo - Predictor-Segmenter Integration -//! -//! This example demonstrates basic integration between CharacterPredictor -//! and FeedbackBpeSegmenter with core functionality. - -use brain::character_ingestion::{CharacterVocab, CharacterPredictor, ModelConfig, CharacterPredictorService}; -use brain::segment_discovery::{BpeConfig, FeedbackBpeSegmenter}; -use brain::Result; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain - Integration Demo: Predictor-Segmenter Integration"); - println!("============================================================="); - - // Sample training text with rich patterns - let training_text = "the quick brown fox jumps over the lazy dog. \ - the fox is quick and the dog is lazy. \ - brown foxes and lazy dogs are common. \ - quick movements and lazy afternoons."; - - println!("\n๐Ÿ“ Training Text:"); - println!("{}", training_text); - - // 1. Initialize Character Predictor - println!("\n๐Ÿ”ค Initializing Character Predictor..."); - let vocab = CharacterVocab::from_text(training_text); - let config = ModelConfig { - vocab_size: vocab.vocab_size(), - embedding_dim: 64, - hidden_dim: 128, - learning_rate: 0.01, - sequence_length: 16, - }; - - let mut predictor = CharacterPredictor::new(vocab.clone(), Some(config))?; - println!("โœ… Character predictor initialized with vocab size: {}", vocab.vocab_size()); - - // 2. Initialize BPE Segmenter with feedback - println!("\n๐Ÿ” Initializing Feedback BPE Segmenter..."); - let bpe_config = BpeConfig { - min_frequency: 2, - max_vocab_size: 100, - num_merges: 10, - include_chars: true, - enable_advanced_heuristics: true, - min_entropy_threshold: 0.3, - context_window_size: 3, - min_confidence: 0.4, - }; - - let feedback_segmenter = FeedbackBpeSegmenter::from_text(training_text, Some(bpe_config))?; - println!("โœ… Feedback BPE segmenter initialized and trained"); - - // Display basic statistics - let bpe_stats = feedback_segmenter.get_segmenter().get_stats(); - println!("๐Ÿ“Š Segmenter Stats:"); - println!(" - Total segments: {}", bpe_stats.total_segments); - println!(" - Merged segments: {}", bpe_stats.merged_segments); - println!(" - Average confidence: {:.3}", bpe_stats.average_confidence); - - let high_confidence_segments = feedback_segmenter.get_high_confidence_segments(); - println!("๐ŸŽฏ High confidence segments: {} found", high_confidence_segments.len()); - for (i, segment) in high_confidence_segments.iter().take(5).enumerate() { - println!(" {}. '{}'", i + 1, segment); - } - - // 3. Demonstrate basic prediction functionality - println!("\n๐Ÿ”ฎ Testing Basic Prediction Capabilities"); - println!("=========================================="); - - let test_inputs = vec![ - "the quick", - "brown fox", - "lazy dog", - "quick brown", - ]; - - for (i, input) in test_inputs.iter().enumerate() { - println!("\n--- Test {} ---", i + 1); - println!("Input: '{}'", input); - - // Character-level prediction - let (char_pred, char_conf) = predictor.predict_next_char(input).await?; - println!("Character prediction: '{}' (confidence: {:.3})", char_pred, char_conf); - - // Segment the input text - let segments = feedback_segmenter.segment(input)?; - println!("Text segmentation: {:?}", segments); - - // Segment-aware prediction - let (seg_pred, seg_conf) = predictor.predict_next_segment(&segments).await?; - println!("Segment prediction: '{}' (confidence: {:.3})", seg_pred, seg_conf); - - // Hybrid prediction combining both approaches - let (hybrid_pred, hybrid_conf) = predictor.predict_hybrid(input, &segments).await?; - println!("Hybrid prediction: '{}' (confidence: {:.3})", hybrid_pred, hybrid_conf); - } - - // 4. Performance comparison - println!("\n๐Ÿ“Š Performance Insights"); - println!("========================"); - - let metrics = predictor.get_metrics(); - println!("Predictor Performance:"); - println!(" - Total predictions: {}", metrics.total_predictions); - println!(" - Correct predictions: {}", metrics.correct_predictions); - println!(" - Overall accuracy: {:.2}%", metrics.accuracy() * 100.0); - println!(" - Character accuracy: {:.2}%", metrics.character_accuracy); - println!(" - Segment accuracy: {:.2}%", metrics.segment_accuracy); - println!(" - Hybrid accuracy: {:.2}%", metrics.hybrid_accuracy); - - // 5. Text generation demo - println!("\n๐ŸŽจ Text Generation Demo"); - println!("========================"); - - let generation_prefixes = vec!["the", "quick", "fox"]; - - for prefix in generation_prefixes { - println!("\nGenerating from prefix: '{}'", prefix); - let generated = predictor.generate(prefix, 20, 0.8).await?; - println!("Generated text: '{}'", generated); - } - - // 6. Advanced segmentation analysis - println!("\n๐Ÿ”ฌ Advanced Segmentation Analysis"); - println!("=================================="); - - let analysis_texts = vec![ - "the quick brown fox", - "jumps over the lazy dog", - "foxes and dogs are animals", - ]; - - for text in analysis_texts { - println!("\nAnalyzing: '{}'", text); - let segments = feedback_segmenter.segment(text)?; - println!(" Segments: {:?}", segments); - println!(" Segment count: {}", segments.len()); - println!(" Average segment length: {:.1}", - segments.iter().map(|s| s.len()).sum::() as f64 / segments.len() as f64); - } - - println!("\n๐ŸŽ‰ Integration Demo Complete!"); - println!("=============================="); - println!("โœ… Successfully demonstrated:"); - println!(" โ€ข Character prediction with confidence scoring"); - println!(" โ€ข Advanced BPE segmentation with feedback"); - println!(" โ€ข Segment-aware prediction capabilities"); - println!(" โ€ข Hybrid prediction combining both approaches"); - println!(" โ€ข Text generation from prefixes"); - println!(" โ€ข Performance metrics and analysis"); - println!(" โ€ข Advanced segmentation analysis"); - println!("\n๐Ÿš€ The Brain AI system now features robust predictor-segmenter integration!"); - - Ok(()) -} \ No newline at end of file diff --git a/integration_system_demo.rs b/integration_system_demo.rs deleted file mode 100644 index d0d225aa7d62bbca505049ec6732a6799bc8817f..0000000000000000000000000000000000000000 --- a/integration_system_demo.rs +++ /dev/null @@ -1,22 +0,0 @@ -//! Integration System Demo (@bridge) -//! -//! Demonstrates the complete integration system including dependency injection, -//! event propagation, workflow integration, and error handling. - -use tokio; -/// Integration System Demo -use brain_cognitive::integration::BootstrapConfig; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ”ง Integration System Demo"); - println!("=========================="); - - // Create a simplified config for demo purposes - let _config = BootstrapConfig::default(); - - println!("โœ… Integration bootstrap configuration created"); - println!("โœ… Demo completed successfully!"); - - Ok(()) -} \ No newline at end of file diff --git a/knowledge_base_foundation_demo.rs b/knowledge_base_foundation_demo.rs deleted file mode 100644 index 806555d6e1a249cb1bde8fe1791e4a4ed5e98448..0000000000000000000000000000000000000000 --- a/knowledge_base_foundation_demo.rs +++ /dev/null @@ -1,233 +0,0 @@ -use std::time::Instant; -use tokio; -use anyhow::Result; - -use brain_cognitive::agents::{ - intelligence::{NewAcademicKnowledgeBase, KnowledgeQuery}, - AcademicDomain -}; - -/// Knowledge Base Foundation Demo -/// -/// Demonstrates the Academic Knowledge Base Foundation implementation for Task 1.3, -/// showing real-time retrieval performance and comprehensive knowledge management -/// across all 6 core components. -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain AI Academic Knowledge Base Foundation Demo"); - println!("==================================================="); - println!("Task 1.3: Academic Knowledge Base Foundation"); - println!("Target: <50ms retrieval, 10,000+ facts, 1,000+ theories"); - println!(); - - // Phase 1: Initialize Knowledge Base Foundation - println!("๐Ÿ”ง Phase 1: Initializing Knowledge Base Foundation..."); - let start_time = Instant::now(); - - let knowledge_base = NewAcademicKnowledgeBase::new().await?; - let init_time = start_time.elapsed(); - - println!("โœ… Knowledge Base initialized in {}ms", init_time.as_millis()); - println!(" โ€ข FactualKnowledgeStore operational"); - println!(" โ€ข ConceptRelationshipGraph ready"); - println!(" โ€ข TheoryFrameworkDatabase loaded"); - println!(" โ€ข HistoricalContextDatabase populated"); - println!(" โ€ข MethodologyKnowledgeBase available"); - println!(" โ€ข AcademicCitationDatabase indexed"); - println!(); - - // Phase 2: Test Knowledge Base Statistics - println!("๐Ÿ“Š Phase 2: Knowledge Base Foundation Statistics..."); - let stats = knowledge_base.get_statistics().await?; - - println!(" Foundation Statistics:"); - println!(" ====================="); - println!(" Total Facts: {}", stats.total_facts); - println!(" Total Concepts: {}", stats.total_concepts); - println!(" Total Theories: {}", stats.total_theories); - println!(" Total Historical Entries: {}", stats.total_historical_entries); - println!(" Total Methodologies: {}", stats.total_methodologies); - println!(" Total Citations: {}", stats.total_citations); - println!(" Total Knowledge Items: {}", - stats.total_facts + stats.total_concepts + stats.total_theories + - stats.total_historical_entries + stats.total_methodologies + stats.total_citations); - println!(); - - // Phase 3: Test Real-Time Retrieval Performance - println!("โšก Phase 3: Real-Time Retrieval Performance Tests..."); - - let test_queries = vec![ - ("Theoretical Physics", vec!["quantum".to_string(), "relativity".to_string()]), - ("Mathematics", vec!["euler".to_string(), "calculus".to_string()]), - ("Chemistry", vec!["avogadro".to_string(), "molecular orbital".to_string()]), - ("Biology", vec!["dna".to_string(), "central dogma".to_string()]), - ("Computer Science", vec!["p vs np".to_string(), "complexity".to_string()]), - ]; - - for (domain_name, keywords) in test_queries { - let domain = match domain_name { - "Theoretical Physics" => AcademicDomain::TheoreticalPhysics, - "Mathematics" => AcademicDomain::AdvancedMathematics, - "Chemistry" => AcademicDomain::AdvancedChemistry, - "Biology" => AcademicDomain::MolecularBiology, - "Computer Science" => AcademicDomain::ComputerScienceTheory, - _ => AcademicDomain::Interdisciplinary, - }; - - let query = KnowledgeQuery { - domain, - keywords, - include_facts: true, - include_concepts: true, - include_theories: true, - include_history: true, - include_methodologies: true, - include_citations: true, - max_results: 10, - }; - - let query_start = Instant::now(); - let response = knowledge_base.query_knowledge(&query).await?; - let query_time = query_start.elapsed(); - - println!(" {} Query:", domain_name); - println!(" Response Time: {}ms", query_time.as_millis()); - println!(" Facts Found: {}", response.facts.len()); - println!(" Concepts Found: {}", response.concepts.len()); - println!(" Theories Found: {}", response.theories.len()); - println!(" Historical Entries: {}", response.historical_context.len()); - println!(" Methodologies: {}", response.methodologies.len()); - println!(" Citations: {}", response.citations.len()); - println!(" Total Results: {}", response.total_results()); - - // Performance validation - if query_time.as_millis() <= 50 { - println!(" โœ… Performance: <50ms target achieved"); - } else { - println!(" โš ๏ธ Performance: {}ms exceeds 50ms target", query_time.as_millis()); - } - println!(); - } - - // Phase 4: Cross-Domain Knowledge Integration Test - println!("๐Ÿ”— Phase 4: Cross-Domain Knowledge Integration..."); - - let interdisciplinary_query = KnowledgeQuery { - domain: AcademicDomain::Interdisciplinary, - keywords: vec!["physics".to_string(), "mathematics".to_string(), "quantum".to_string()], - include_facts: true, - include_concepts: true, - include_theories: true, - include_history: true, - include_methodologies: true, - include_citations: true, - max_results: 20, - }; - - let cross_domain_start = Instant::now(); - let cross_domain_response = knowledge_base.query_knowledge(&interdisciplinary_query).await?; - let cross_domain_time = cross_domain_start.elapsed(); - - println!(" Cross-Domain Integration Results:"); - println!(" ================================"); - println!(" Query Time: {}ms", cross_domain_time.as_millis()); - println!(" Total Cross-Domain Results: {}", cross_domain_response.total_results()); - println!(" Facts: {}", cross_domain_response.facts.len()); - println!(" Concepts: {}", cross_domain_response.concepts.len()); - println!(" Theories: {}", cross_domain_response.theories.len()); - println!(" Historical Context: {}", cross_domain_response.historical_context.len()); - println!(" Methodologies: {}", cross_domain_response.methodologies.len()); - println!(" Citations: {}", cross_domain_response.citations.len()); - println!(); - - // Phase 5: Foundation Performance Analysis - println!("๐Ÿ“ˆ Phase 5: Foundation Performance Analysis..."); - let final_stats = knowledge_base.get_statistics().await?; - - println!(" Performance Analysis:"); - println!(" ===================="); - println!(" Total Queries Processed: {}", final_stats.total_queries); - println!(" Average Query Time: {:.1}ms", final_stats.average_query_time_ms); - println!(" Knowledge Base Size: {} items", - final_stats.total_facts + final_stats.total_concepts + final_stats.total_theories + - final_stats.total_historical_entries + final_stats.total_methodologies + final_stats.total_citations); - - // Acceptance criteria validation - println!(" Acceptance Criteria Validation:"); - if final_stats.average_query_time_ms < 50.0 { - println!(" โœ… Real-time retrieval: <50ms target achieved ({:.1}ms)", final_stats.average_query_time_ms); - } else { - println!(" โš ๏ธ Real-time retrieval: {:.1}ms exceeds 50ms target", final_stats.average_query_time_ms); - } - - if final_stats.total_concepts > 0 { - println!(" โœ… Cross-domain connections: {} concept relationships", final_stats.total_concepts); - } - - if final_stats.total_theories > 0 { - println!(" โœ… Theoretical frameworks: {} frameworks across domains", final_stats.total_theories); - } - - if final_stats.total_historical_entries > 0 { - println!(" โœ… Historical context: {} historical entries", final_stats.total_historical_entries); - } - - if final_stats.total_methodologies > 0 { - println!(" โœ… Methodology knowledge: {} research methodologies", final_stats.total_methodologies); - } - - println!(); - - // Phase 6: Sample Knowledge Retrieval - println!("๐Ÿ“š Phase 6: Sample Knowledge Retrieval..."); - - let sample_query = KnowledgeQuery { - domain: AcademicDomain::TheoreticalPhysics, - keywords: vec!["speed of light".to_string()], - include_facts: true, - include_concepts: false, - include_theories: false, - include_history: false, - include_methodologies: false, - include_citations: false, - max_results: 1, - }; - - let sample_response = knowledge_base.query_knowledge(&sample_query).await?; - - if let Some(fact) = sample_response.facts.first() { - println!(" Sample Fact Retrieval:"); - println!(" ======================"); - println!(" Title: {}", fact.title); - println!(" Content: {}", fact.content); - println!(" Source: {}", fact.source); - println!(" Confidence: {:.1}%", fact.confidence * 100.0); - println!(" Verified: {}", if fact.verified { "โœ… Yes" } else { "โŒ No" }); - } - - println!(); - println!("๐Ÿ† Knowledge Base Foundation Demo Complete!"); - - // Final assessment - let total_knowledge_items = final_stats.total_facts + final_stats.total_concepts + - final_stats.total_theories + final_stats.total_historical_entries + - final_stats.total_methodologies + final_stats.total_citations; - - if final_stats.average_query_time_ms < 50.0 && total_knowledge_items >= 10 { - println!("โœ… TASK 1.3 SUCCESS: Academic Knowledge Base Foundation operational"); - println!(" โ€ข Real-time retrieval achieved (<50ms)"); - println!(" โ€ข All 6 knowledge components implemented"); - println!(" โ€ข Cross-domain connections enabled"); - println!(" โ€ข Evidence-based reasoning supported"); - } else { - println!("โš ๏ธ TASK 1.3 PARTIAL: Knowledge Base Foundation needs optimization"); - if final_stats.average_query_time_ms >= 50.0 { - println!(" โ€ข Query time optimization needed"); - } - if total_knowledge_items < 10 { - println!(" โ€ข Knowledge content expansion needed"); - } - } - - Ok(()) -} \ No newline at end of file diff --git a/knowledge_persistence_demonstration.rs b/knowledge_persistence_demonstration.rs deleted file mode 100644 index fb2c215c22056de73e38e2aacc783f0301df9f88..0000000000000000000000000000000000000000 --- a/knowledge_persistence_demonstration.rs +++ /dev/null @@ -1,632 +0,0 @@ -//! # Knowledge Persistence System Demonstration -//! -//! **Revolutionary Continuous Learning**: Demonstrates Brain AI's breakthrough knowledge persistence system -//! that enables permanent learning from every research session, making the system smarter with each question. -//! -//! ## Game-Changing Innovation Demo -//! -//! 1. **Research & Store**: Brain AI researches a complex academic question and stores the knowledge -//! 2. **Knowledge Retrieval**: Similar future questions instantly benefit from stored knowledge -//! 3. **Continuous Learning**: Each research session adds to Brain AI's permanent knowledge base -//! 4. **Quality Validation**: Knowledge is validated and refined through successful reuse -//! -//! **Created**: July 31, 2025 at 03:12:00 EDT -//! **Purpose**: Demonstrate the continuous learning innovation for Universal Intelligence supremacy -//! **Status**: CRITICAL VALIDATION - Proving Brain AI's unique learning advantage - -use std::collections::HashMap; -use std::time::{Duration, Instant}; -use serde::{Deserialize, Serialize}; -use uuid::Uuid; - - -use brain_cognitive::agents::traits::AcademicDomain; -use brain_cognitive::agents::intelligence::{ - AdaptiveResearchEngine, - ResearchStrategy, -}; -use brain_types::error::BrainError; - -// Mock components for demonstration since research module is temporarily disabled -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct KnowledgeAccumulator { - pub id: Uuid, - pub initial_question: String, - pub primary_domain: AcademicDomain, - pub research_items: Vec, - pub accumulated_insights: Vec, - pub sources_consulted: Vec, - pub strategies_used: Vec, - pub confidence_level: f64, - pub final_confidence: f64, - pub domain: AcademicDomain, - pub accumulated_at: chrono::DateTime, - pub research_duration: Duration, -} - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct ResearchKnowledgeItem { - pub id: Uuid, - pub content: String, - pub source: String, - pub confidence: f64, - pub validation_status: KnowledgeValidationStatus, -} - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub enum KnowledgeValidationStatus { - Pending, - Validated, - Rejected, -} - -#[derive(Debug, Clone)] -pub struct KnowledgePersistence { - storage: HashMap, -} - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct KnowledgeRetrievalMetrics { - pub items_retrieved: u32, - pub average_confidence: f64, - pub retrieval_time_ms: u64, - pub total_items: u32, - pub successful_retrievals: u32, - pub reuse_instances: u32, - pub knowledge_accuracy_rate: f64, -} - -impl KnowledgeAccumulator { - pub fn new() -> Self { - Self { - id: Uuid::new_v4(), - initial_question: String::new(), - primary_domain: AcademicDomain::General, - research_items: Vec::new(), - accumulated_insights: Vec::new(), - sources_consulted: Vec::new(), - strategies_used: Vec::new(), - confidence_level: 0.0, - final_confidence: 0.0, - domain: AcademicDomain::General, - accumulated_at: chrono::Utc::now(), - research_duration: Duration::from_millis(0), - } - } -} - -impl KnowledgePersistence { - pub fn new() -> Self { - Self { - storage: HashMap::new(), - } - } - - pub async fn store_research_knowledge(&mut self, knowledge: &KnowledgeAccumulator) -> Result { - let key = format!("{:?}_{}", knowledge.domain, knowledge.accumulated_at.timestamp()); - self.storage.insert(key.clone(), knowledge.clone()); - - Ok(ResearchKnowledgeItem { - id: Uuid::new_v4(), - content: format!("Stored knowledge for {:?}", knowledge.domain), - source: "Knowledge Persistence System".to_string(), - confidence: knowledge.confidence_level, - validation_status: KnowledgeValidationStatus::Validated, - }) - } - - pub async fn retrieve_relevant_knowledge(&self, domain: &AcademicDomain) -> Result<(Vec, KnowledgeRetrievalMetrics), BrainError> { - let relevant_items: Vec = self.storage - .values() - .filter(|k| k.domain == *domain) - .flat_map(|k| k.research_items.clone()) - .collect(); - - let metrics = KnowledgeRetrievalMetrics { - items_retrieved: relevant_items.len() as u32, - average_confidence: relevant_items.iter().map(|i| i.confidence).sum::() / relevant_items.len().max(1) as f64, - retrieval_time_ms: 50, // Mock timing - total_items: self.storage.len() as u32, - successful_retrievals: relevant_items.len() as u32, - reuse_instances: relevant_items.len() as u32, - knowledge_accuracy_rate: 0.85, // Mock accuracy - }; - - Ok((relevant_items, metrics)) - } - - pub async fn mark_knowledge_reused(&mut self, _knowledge_id: Uuid, _reused: bool) -> Result<(), BrainError> { - // Mock implementation for tracking knowledge reuse - Ok(()) - } - - pub async fn get_persistence_statistics(&self) -> Result { - let total_items = self.storage.len() as u32; - Ok(KnowledgeRetrievalMetrics { - items_retrieved: total_items, - average_confidence: 0.82, - retrieval_time_ms: 45, - total_items, - successful_retrievals: total_items, - reuse_instances: (total_items as f64 * 0.6) as u32, // 60% reuse rate - knowledge_accuracy_rate: 0.88, - }) - } -} - -/// **Knowledge Persistence Demonstration** -/// -/// Shows how Brain AI accumulates and reuses knowledge across multiple research sessions, -/// proving the revolutionary continuous learning capability. -#[derive(Debug)] -pub struct KnowledgePersistenceDemo { - /// Research engine for generating knowledge - research_engine: AdaptiveResearchEngine, - - /// Knowledge persistence system - knowledge_persistence: KnowledgePersistence, - - /// Demo configuration - demo_config: DemoConfig, - - /// Demo session tracking - session_tracker: SessionTracker, -} - -/// **Demo Configuration** -#[derive(Debug, Clone)] -pub struct DemoConfig { - /// Number of research sessions to demonstrate - pub sessions_count: u32, - - /// Number of questions per domain - pub questions_per_domain: u32, - - /// Enable detailed logging - pub verbose_logging: bool, - - /// Demo timeout duration - pub timeout_duration: Duration, -} - -/// **Session Tracking** -#[derive(Debug)] -pub struct SessionTracker { - /// Session performance metrics - metrics: HashMap, - - /// Overall demo statistics - overall_stats: OverallDemoStats, -} - -/// **Session Performance Metrics** -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct SessionMetrics { - /// Session identifier - pub session_id: String, - - /// Questions processed - pub questions_processed: u32, - - /// Knowledge items stored - pub knowledge_stored: u32, - - /// Knowledge items retrieved - pub knowledge_retrieved: u32, - - /// Average confidence improvement - pub avg_confidence_improvement: f64, - - /// Average response time improvement (ms) - pub avg_response_time_improvement: u64, - - /// Session duration - pub session_duration: Duration, -} - -/// **Overall Demo Statistics** -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct OverallDemoStats { - /// Total sessions completed - pub total_sessions: u32, - - /// Total knowledge accumulated - pub total_knowledge_items: u32, - - /// Total successful retrievals - pub total_retrievals: u32, - - /// Knowledge reuse rate - pub knowledge_reuse_rate: f64, - - /// Overall learning effectiveness - pub learning_effectiveness_score: f64, - - /// Demo completion status - pub completion_status: DemoCompletionStatus, -} - -/// **Demo Completion Status** -#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)] -pub enum DemoCompletionStatus { - InProgress, - Successful, - PartialSuccess, - Failed, -} - -impl KnowledgePersistenceDemo { - /// Create new knowledge persistence demonstration - pub fn new() -> Self { - Self { - research_engine: AdaptiveResearchEngine::new(), - knowledge_persistence: KnowledgePersistence::new(), - demo_config: DemoConfig::default(), - session_tracker: SessionTracker::new(), - } - } - - /// **MAIN DEMONSTRATION**: Run complete knowledge persistence demo - /// - /// This method demonstrates the full cycle of knowledge persistence: - /// 1. Research complex questions and store knowledge - /// 2. Retrieve stored knowledge for similar questions - /// 3. Validate continuous learning effectiveness - pub async fn run_complete_demonstration(&mut self) -> Result { - let demo_start = Instant::now(); - - println!("๐Ÿง  BRAIN AI KNOWLEDGE PERSISTENCE DEMONSTRATION"); - println!("๐Ÿ“… Demo Date: July 31, 2025 at 03:12:00 EDT"); - println!("๐ŸŽฏ Demonstrating: Revolutionary continuous learning through knowledge persistence"); - println!("๐Ÿ”ฌ Innovation: First AI system that gets smarter with every research session"); - println!(); - - // Phase 1: Initial Research & Knowledge Accumulation - println!("๐Ÿ” PHASE 1: INITIAL RESEARCH & KNOWLEDGE ACCUMULATION"); - let research_stats = self.demonstrate_research_and_storage().await?; - println!("โœ… Research Phase Complete: {} knowledge items stored", research_stats.knowledge_stored); - println!(); - - // Phase 2: Knowledge Retrieval & Reuse - println!("๐Ÿ” PHASE 2: KNOWLEDGE RETRIEVAL & REUSE"); - let retrieval_stats = self.demonstrate_knowledge_retrieval().await?; - println!("โœ… Retrieval Phase Complete: {} knowledge items reused", retrieval_stats.knowledge_retrieved); - println!(); - - // Phase 3: Continuous Learning Validation - println!("๐Ÿ” PHASE 3: CONTINUOUS LEARNING VALIDATION"); - let learning_stats = self.demonstrate_continuous_learning().await?; - println!("โœ… Learning Phase Complete: {:.1}% effectiveness improvement", learning_stats.learning_effectiveness_score * 100.0); - println!(); - - // Generate final demonstration report - let demo_duration = demo_start.elapsed(); - let final_stats = self.generate_final_statistics(demo_duration).await?; - - self.display_demonstration_results(&final_stats).await?; - - Ok(final_stats) - } - - /// **Phase 1**: Demonstrate research and knowledge storage - async fn demonstrate_research_and_storage(&mut self) -> Result { - let session_start = Instant::now(); - let session_id = format!("research_session_{}", Uuid::new_v4()); - - // Academic questions for knowledge accumulation - let research_questions = vec![ - (AcademicDomain::TheoreticalPhysics, "What is the relationship between quantum entanglement and Bell's theorem in quantum mechanics?"), - (AcademicDomain::AdvancedMathematics, "How does the Riemann hypothesis relate to the distribution of prime numbers?"), - (AcademicDomain::MolecularBiology, "What is the mechanism of enzyme catalysis in the citric acid cycle?"), - (AcademicDomain::ComputerScienceTheory, "How does the P vs NP problem relate to computational complexity theory?"), - (AcademicDomain::AdvancedChemistry, "What is the role of transition states in organic reaction mechanisms?"), - ]; - - let mut session_metrics = SessionMetrics { - session_id: session_id.clone(), - questions_processed: 0, - knowledge_stored: 0, - knowledge_retrieved: 0, - avg_confidence_improvement: 0.0, - avg_response_time_improvement: 0, - session_duration: Duration::new(0, 0), - }; - - for (domain, question) in research_questions { - println!(" ๐Ÿ“š Researching: {:?} question", domain); - - // Simulate research process with knowledge accumulation - let knowledge_accumulator = self.simulate_research_process(question, &domain).await?; - - // Store knowledge for future use - let stored_knowledge = self.knowledge_persistence - .store_research_knowledge(&knowledge_accumulator) - .await?; - - session_metrics.questions_processed += 1; - session_metrics.knowledge_stored += 1; - - println!(" โœ… Knowledge stored: {} (confidence: {:.1}%)", - stored_knowledge.id, stored_knowledge.confidence * 100.0); - } - - session_metrics.session_duration = session_start.elapsed(); - self.session_tracker.add_session_metrics(session_metrics.clone()); - - Ok(session_metrics) - } - - /// **Phase 2**: Demonstrate knowledge retrieval and reuse - async fn demonstrate_knowledge_retrieval(&mut self) -> Result { - let session_start = Instant::now(); - let session_id = format!("retrieval_session_{}", Uuid::new_v4()); - - // Similar questions to test knowledge retrieval - let retrieval_questions = vec![ - (AcademicDomain::TheoreticalPhysics, "How does Bell's theorem prove quantum entanglement is real?"), - (AcademicDomain::AdvancedMathematics, "What does the Riemann hypothesis predict about prime number distribution?"), - (AcademicDomain::MolecularBiology, "How do enzymes catalyze reactions in cellular metabolism?"), - ]; - - let mut session_metrics = SessionMetrics { - session_id: session_id.clone(), - questions_processed: 0, - knowledge_stored: 0, - knowledge_retrieved: 0, - avg_confidence_improvement: 0.0, - avg_response_time_improvement: 0, - session_duration: Duration::new(0, 0), - }; - - for (domain, _question) in retrieval_questions { - println!(" ๐Ÿ” Retrieving knowledge for: {:?} question", domain); - - let retrieval_start = Instant::now(); - - // Retrieve relevant stored knowledge - let (relevant_items, _retrieval_metrics) = self.knowledge_persistence - .retrieve_relevant_knowledge(&domain) - .await?; - - let retrieval_time = retrieval_start.elapsed(); - - if !relevant_items.is_empty() { - println!(" โœ… Retrieved {} relevant knowledge items ({}ms)", - relevant_items.len(), retrieval_time.as_millis()); - - session_metrics.knowledge_retrieved += relevant_items.len() as u32; - session_metrics.avg_response_time_improvement += retrieval_time.as_millis() as u64; - - // Mark knowledge as successfully reused - for knowledge in &relevant_items { - self.knowledge_persistence - .mark_knowledge_reused(knowledge.id, true) - .await?; - } - } else { - println!(" โš ๏ธ No relevant knowledge found (new research required)"); - } - - session_metrics.questions_processed += 1; - } - - if session_metrics.questions_processed > 0 { - session_metrics.avg_response_time_improvement /= session_metrics.questions_processed as u64; - } - - session_metrics.session_duration = session_start.elapsed(); - self.session_tracker.add_session_metrics(session_metrics.clone()); - - Ok(session_metrics) - } - - /// **Phase 3**: Demonstrate continuous learning effectiveness - async fn demonstrate_continuous_learning(&mut self) -> Result { - println!(" ๐Ÿ“Š Analyzing knowledge persistence effectiveness..."); - - // Get persistence statistics - let persistence_stats = self.knowledge_persistence - .get_persistence_statistics() - .await?; - - // Calculate learning effectiveness - let learning_effectiveness = self.calculate_learning_effectiveness(&persistence_stats).await?; - - println!(" ๐Ÿ“ˆ Knowledge Reuse Rate: {:.1}%", - (persistence_stats.reuse_instances as f64 / persistence_stats.total_items as f64) * 100.0); - println!(" ๐ŸŽฏ Learning Effectiveness: {:.1}%", learning_effectiveness * 100.0); - - let mut stats = self.session_tracker.overall_stats.clone(); - stats.learning_effectiveness_score = learning_effectiveness; - stats.total_knowledge_items = persistence_stats.total_items; - stats.total_retrievals = persistence_stats.successful_retrievals; - stats.knowledge_reuse_rate = persistence_stats.reuse_instances as f64 / persistence_stats.total_items as f64; - - Ok(stats) - } - - /// Generate final demonstration statistics - async fn generate_final_statistics(&mut self, _demo_duration: Duration) -> Result { - let mut stats = self.session_tracker.overall_stats.clone(); - - // Determine completion status - stats.completion_status = if stats.learning_effectiveness_score > 0.8 { - DemoCompletionStatus::Successful - } else if stats.learning_effectiveness_score > 0.5 { - DemoCompletionStatus::PartialSuccess - } else { - DemoCompletionStatus::Failed - }; - - Ok(stats) - } - - /// Display comprehensive demonstration results - async fn display_demonstration_results(&self, stats: &OverallDemoStats) -> Result<(), BrainError> { - println!("๐Ÿ† KNOWLEDGE PERSISTENCE DEMONSTRATION RESULTS"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - println!(); - - println!("๐Ÿ“Š **CORE STATISTICS**:"); - println!(" ๐Ÿง  Total Knowledge Items: {}", stats.total_knowledge_items); - println!(" ๐Ÿ”„ Total Knowledge Retrievals: {}", stats.total_retrievals); - println!(" โ™ป๏ธ Knowledge Reuse Rate: {:.1}%", stats.knowledge_reuse_rate * 100.0); - println!(" ๐Ÿ“ˆ Learning Effectiveness: {:.1}%", stats.learning_effectiveness_score * 100.0); - println!(); - - println!("๐ŸŽฏ **REVOLUTIONARY INNOVATION VALIDATED**:"); - println!(" โœ… Brain AI accumulates knowledge from every research session"); - println!(" โœ… Stored knowledge enables faster responses to similar questions"); - println!(" โœ… Knowledge quality improves through successful reuse validation"); - println!(" โœ… System becomes progressively smarter with each interaction"); - println!(); - - match stats.completion_status { - DemoCompletionStatus::Successful => { - println!("๐Ÿ† **DEMONSTRATION STATUS: SUCCESSFUL**"); - println!(" ๐ŸŽ‰ Knowledge persistence system fully operational"); - println!(" ๐Ÿš€ Ready for Universal Intelligence #1 global ranking"); - }, - DemoCompletionStatus::PartialSuccess => { - println!("โš ๏ธ **DEMONSTRATION STATUS: PARTIAL SUCCESS**"); - println!(" ๐Ÿ“ˆ Knowledge persistence working but can be optimized"); - println!(" ๐Ÿ”ง Recommend fine-tuning before full deployment"); - }, - DemoCompletionStatus::Failed => { - println!("โŒ **DEMONSTRATION STATUS: FAILED**"); - println!(" ๐Ÿ” Knowledge persistence requires debugging"); - println!(" ๐Ÿ› ๏ธ System not ready for production deployment"); - }, - DemoCompletionStatus::InProgress => { - println!("โณ **DEMONSTRATION STATUS: IN PROGRESS**"); - }, - } - - println!(); - println!("๐ŸŒŸ **COMPETITIVE ADVANTAGE CONFIRMED**:"); - println!(" ๐Ÿฅ‡ Brain AI is the ONLY system that researches rather than guesses"); - println!(" ๐Ÿ“š Brain AI is the ONLY system with permanent knowledge accumulation"); - println!(" ๐Ÿง  Brain AI continuously improves while competitors remain static"); - println!(" ๐Ÿ† This innovation ensures long-term Universal Intelligence supremacy"); - - Ok(()) - } - - // Helper methods - - async fn simulate_research_process(&self, question: &str, domain: &AcademicDomain) -> Result { - // Simulate research process (in production, would use real research engine) - Ok(KnowledgeAccumulator { - id: Uuid::new_v4(), - initial_question: question.to_string(), - primary_domain: domain.clone(), - research_items: Vec::new(), // Add mock research items - accumulated_insights: vec![ - format!("Research insight 1 for {}", question), - format!("Research insight 2 for {}", question), - format!("Research insight 3 for {}", question), - ], - sources_consulted: vec![ - "Academic Database".to_string(), - "Fact Checking Service".to_string(), - "Cross-Domain Synthesis".to_string(), - ], - strategies_used: vec![ - ResearchStrategy::DatabaseLookup, - ResearchStrategy::FactVerification, - ResearchStrategy::ConceptualSynthesis, - ], - confidence_level: 0.85, - final_confidence: 0.85, // High confidence through research - domain: domain.clone(), - accumulated_at: chrono::Utc::now(), - research_duration: Duration::from_millis(1200), - }) - } - - async fn calculate_learning_effectiveness(&self, stats: &KnowledgeRetrievalMetrics) -> Result { - // Calculate learning effectiveness based on knowledge reuse and accuracy - let reuse_factor = if stats.total_items > 0 { - stats.reuse_instances as f64 / stats.total_items as f64 - } else { - 0.0 - }; - - let accuracy_factor = stats.knowledge_accuracy_rate; - let volume_factor = (stats.total_items as f64 / 100.0).min(1.0); // Scale up to 100 items - - Ok((reuse_factor * 0.4 + accuracy_factor * 0.4 + volume_factor * 0.2).min(1.0)) - } -} - -impl SessionTracker { - fn new() -> Self { - Self { - metrics: HashMap::new(), - overall_stats: OverallDemoStats::default(), - } - } - - fn add_session_metrics(&mut self, metrics: SessionMetrics) { - self.metrics.insert(metrics.session_id.clone(), metrics.clone()); - - // Update overall stats - self.overall_stats.total_sessions += 1; - // Additional aggregation logic would go here - } -} - -impl Default for DemoConfig { - fn default() -> Self { - Self { - sessions_count: 3, - questions_per_domain: 2, - verbose_logging: true, - timeout_duration: Duration::from_secs(300), // 5 minutes - } - } -} - -impl Default for OverallDemoStats { - fn default() -> Self { - Self { - total_sessions: 0, - total_knowledge_items: 0, - total_retrievals: 0, - knowledge_reuse_rate: 0.0, - learning_effectiveness_score: 0.0, - completion_status: DemoCompletionStatus::InProgress, - } - } -} - -/// **DEMONSTRATION ENTRY POINT** -/// -/// Run this example to see Brain AI's revolutionary knowledge persistence system in action. -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  BRAIN AI KNOWLEDGE PERSISTENCE SYSTEM - FULL DEMONSTRATION"); - println!("๐Ÿ“… Demo Date: July 31, 2025 at 03:15:00 EDT"); - println!("๐ŸŽฏ Revolutionary Innovation: Continuous learning through knowledge persistence"); - println!(); - - let mut demo = KnowledgePersistenceDemo::new(); - - match demo.run_complete_demonstration().await { - Ok(final_stats) => { - println!("โœ… KNOWLEDGE PERSISTENCE DEMONSTRATION COMPLETE"); - println!("๐ŸŽ‰ Learning Effectiveness: {:.1}%", final_stats.learning_effectiveness_score * 100.0); - println!("๐Ÿ† Status: {:?}", final_stats.completion_status); - }, - Err(e) => { - println!("โŒ DEMONSTRATION FAILED: {}", e); - return Err(e.into()); - } - } - - println!(); - println!("๐ŸŒŸ BRAIN AI: The only AI system that gets smarter with every question!"); - println!("๐Ÿš€ Ready for Universal Intelligence #1 global ranking through continuous learning."); - - Ok(()) -} \ No newline at end of file diff --git a/linguistics_expert_demo.rs b/linguistics_expert_demo.rs deleted file mode 100644 index 8851fb47b62f180a0398d8972182f91903745c4a..0000000000000000000000000000000000000000 --- a/linguistics_expert_demo.rs +++ /dev/null @@ -1,156 +0,0 @@ -/// Linguistics Expert Demo -/// -/// This demonstrates the LinguisticsExpert implementation for TASK 3.2, -/// showing how it handles linguistics questions across phonology, syntax, -/// semantics, morphology, and other linguistic domains. - -use std::time::Instant; -use brain_cognitive::agents::intelligence::linguistics_expert::LinguisticsExpert; -use brain_cognitive::agents::traits::{ - AgentInput, CognitiveContext, BrainAgent, AcademicReasoningAgent -}; -use chrono::Utc; -use std::collections::HashMap; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ—ฃ๏ธ LINGUISTICS EXPERT DEMO"); - println!("==============================="); - println!("Demonstrating TASK 3.2: Linguistics Expert for academic reasoning"); - println!("across phonology, syntax, semantics, morphology, and linguistic theory."); - println!(); - - // Initialize the Linguistics Expert - let start_time = Instant::now(); - let linguistics_expert = LinguisticsExpert::new().await?; - let init_time = start_time.elapsed(); - - println!("โœ… LinguisticsExpert initialized successfully in {:?}", init_time); - println!("๐Ÿ”ฌ Agent: {}", linguistics_expert.metadata().name); - println!("๐ŸŽฏ Persona: {}", linguistics_expert.metadata().persona); - println!(); - - // Test scenarios covering different linguistics domains - let test_scenarios = vec![ - ( - "Phonology Test", - "What is the difference between a phoneme and an allophone in English? \ - Provide examples of allophones of the /t/ phoneme." - ), - ( - "Syntax Test", - "Draw a syntactic tree for the sentence 'The cat that chased the mouse ran away' \ - using X-bar theory principles." - ), - ( - "Semantics Test", - "Explain the semantic relationship between 'dog', 'animal', and 'poodle' \ - in terms of hyponymy and hypernymy." - ), - ( - "Morphology Test", - "Analyze the morphological structure of the word 'unhappiness' and \ - identify all morphemes and their types." - ), - ( - "Sociolinguistics Test", - "How does linguistic variation manifest in different social contexts? \ - Discuss the concept of linguistic prestige." - ), - ( - "Historical Linguistics Test", - "Trace the evolution of the English word 'knight' from Proto-Germanic \ - to Modern English, noting sound changes." - ), - ]; - - for (test_name, question) in test_scenarios { - println!("๐Ÿงช {}", test_name); - println!("โ“ Question: {}", question); - - let start_time = Instant::now(); - - // Create agent input - let agent_input = AgentInput { - input_type: "academic_question".to_string(), - content: question.to_string(), - parameters: HashMap::new(), - previous_outputs: Vec::new(), - user_preferences: HashMap::new(), - session_id: "demo_session".to_string(), - timestamp: Utc::now(), - }; - - // Create cognitive context - let context = CognitiveContext::default(); - - // Test agent execution - match linguistics_expert.execute(agent_input.clone(), &context).await { - Ok(output) => { - let execution_time = start_time.elapsed(); - println!("โœ… Response generated in {:?}", execution_time); - println!("๐ŸŽฏ Confidence: {:.1}%", output.confidence * 100.0); - if let Some(reasoning) = &output.reasoning { - println!("๐Ÿง  Reasoning: {}", reasoning); - } - println!("๐Ÿ“ Response:"); - println!("{}", output.content); - - // Test confidence assessment - let confidence = linguistics_expert.assess_confidence(&agent_input, &context).await?; - println!("๐ŸŽฒ Assessed confidence: {:.1}%", confidence * 100.0); - } - Err(e) => { - println!("โŒ Error: {}", e); - } - } - - println!("{}", "โ”€".repeat(80)); - } - - // Test academic reasoning capabilities - println!("๐ŸŽ“ ACADEMIC REASONING CAPABILITIES"); - println!("================================="); - - let academic_question = "Compare the syntactic properties of wh-movement in English and \ - Chinese, focusing on constraints and typological differences."; - - let _context = CognitiveContext::default(); - - // Test question analysis - match linguistics_expert.analyze_question(academic_question).await { - Ok(analysis) => { - println!("โœ… Question Analysis:"); - println!(" Domain: {:?}", analysis.domain); - println!(" Complexity: {}", analysis.complexity_level); - println!(" Key concepts: {}", analysis.key_concepts.join(", ")); - println!(" Required knowledge: {}", analysis.required_knowledge.join(", ")); - println!(" Reasoning steps: {}", analysis.reasoning_steps.join(" โ†’ ")); - println!(" Analysis confidence: {:.1}%", analysis.analysis_confidence * 100.0); - } - Err(e) => { - println!("โŒ Analysis error: {}", e); - } - } - - println!(); - - // Display agent metadata and capabilities - println!("๐Ÿ“Š AGENT INFORMATION"); - println!("===================="); - println!("Agent ID: {}", linguistics_expert.metadata().id); - println!("Version: {}", linguistics_expert.metadata().version); - println!("Confidence threshold: {:.1}%", linguistics_expert.confidence_threshold() * 100.0); - println!("Academic domains: {:?}", linguistics_expert.academic_domains()); - println!("Capabilities:"); - for capability in &linguistics_expert.metadata().capabilities { - println!(" โ€ข {}", capability); - } - - println!(); - println!("๐Ÿ† LINGUISTICS EXPERT DEMO COMPLETED SUCCESSFULLY!"); - println!("The LinguisticsExpert demonstrates sophisticated linguistic reasoning"); - println!("across phonology, syntax, semantics, morphology, and theoretical linguistics."); - - Ok(()) -} \ No newline at end of file diff --git a/maintainer_agent_demo.rs b/maintainer_agent_demo.rs deleted file mode 100644 index 33421953768ace9fadb159969929be95874b1498..0000000000000000000000000000000000000000 --- a/maintainer_agent_demo.rs +++ /dev/null @@ -1,301 +0,0 @@ -//! MaintainerAgent Demo -//! -//! Demonstrates the MaintainerAgent's comprehensive system maintenance and operational -//! excellence capabilities including health monitoring, performance optimization, -//! incident response, and proactive maintenance automation. - -use brain_cognitive::agents::{ - development::MaintainerAgent, - traits::BrainAgent, -}; -use std::error::Error; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ”ง MaintainerAgent Demo - System Maintenance and Operational Excellence"); - println!("=========================================================================\n"); - - let maintainer = MaintainerAgent::new(); - - // Demo sections - display_agent_metadata(&maintainer); - demonstrate_system_health_monitoring(&maintainer); - demonstrate_maintenance_strategies(&maintainer); - demonstrate_operational_automation(&maintainer); - demonstrate_pipeline_completion(&maintainer); - - println!("\nโœจ MaintainerAgent Demo Complete!"); - println!("The MaintainerAgent provides comprehensive operational excellence with:"); - println!("โ€ข Proactive system health monitoring and predictive maintenance"); - println!("โ€ข Automated incident response with intelligent escalation"); - println!("โ€ข Performance optimization and capacity planning"); - println!("โ€ข Security patch management and compliance automation"); - println!("โ€ข Operational excellence with continuous improvement"); - println!("โ€ข Complete development pipeline closure (100% achievement)"); - - Ok(()) -} - -fn display_agent_metadata(maintainer: &MaintainerAgent) { - println!("๐Ÿค– Agent Metadata"); - println!("โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - println!("Name: {}", maintainer.metadata().name); - println!("Version: {}", maintainer.metadata().version); - println!("Base Confidence: {:.1}%", maintainer.metadata().base_confidence * 100.0); - println!("Confidence Threshold: {:.1}%", maintainer.confidence_threshold() * 100.0); - - println!("\n๐ŸŽฏ Agent Persona:"); - println!("{}", maintainer.metadata().persona); - - println!("\n๐Ÿ“ฅ Supported Input Types:"); - for input_type in &maintainer.metadata().supported_input_types { - println!(" โ€ข {}", input_type); - } - - println!("\n๐Ÿ“ค Supported Output Types:"); - for output_type in &maintainer.metadata().supported_output_types { - println!(" โ€ข {}", output_type); - } - - println!("\n๐Ÿ› ๏ธ Core Capabilities:"); - for capability in &maintainer.metadata().capabilities { - println!(" โ€ข {}", capability); - } - - println!("\n๐Ÿ”— Dependencies:"); - for dependency in &maintainer.metadata().dependencies { - println!(" โ€ข {}", dependency); - } -} - -fn demonstrate_system_health_monitoring(_maintainer: &MaintainerAgent) { - println!("\n๐Ÿ“Š System Health Monitoring & Analysis"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - let monitoring_scenarios = vec![ - ("Production E-commerce Platform", vec![ - "๐Ÿ–ฅ๏ธ System Status: All services operational (99.95% uptime)", - "โšก Performance: CPU 65%, Memory 72%, Response time <100ms", - "๐Ÿ”’ Security: No critical vulnerabilities, patches current", - "๐Ÿ’พ Database: Optimal performance, connection pool healthy", - "๐Ÿ“ˆ Capacity: Current usage within targets, growth moderate", - ]), - - ("High-Traffic API Service", vec![ - "๐Ÿ–ฅ๏ธ System Status: Auto-scaling active, peak load handling", - "โšก Performance: Load balanced across 12 instances", - "๐Ÿ”’ Security: Rate limiting active, DDoS protection enabled", - "๐Ÿ’พ Database: Read replicas healthy, write performance optimal", - "๐Ÿ“ˆ Capacity: Scaling algorithms working, costs optimized", - ]), - - ("Microservices Platform", vec![ - "๐Ÿ–ฅ๏ธ System Status: 47 services healthy, service mesh active", - "โšก Performance: Circuit breakers engaged, timeout handling optimal", - "๐Ÿ”’ Security: Zero-trust network, mTLS encryption active", - "๐Ÿ’พ Database: Multi-region replication, backup validation current", - "๐Ÿ“ˆ Capacity: Container orchestration optimal, resource efficiency high", - ]), - - ("Legacy System Migration", vec![ - "๐Ÿ–ฅ๏ธ System Status: Hybrid deployment, gradual traffic shift active", - "โšก Performance: Performance parity maintained during migration", - "๐Ÿ”’ Security: Legacy hardening complete, modern security active", - "๐Ÿ’พ Database: Data synchronization healthy, migration on track", - "๐Ÿ“ˆ Capacity: Resource allocation optimized for both systems", - ]) - ]; - - for (scenario_name, health_metrics) in monitoring_scenarios { - println!("\n๐Ÿ“‹ Scenario: {}", scenario_name); - println!("โ”€{}โ”€", "โ”€".repeat(scenario_name.len() + 10)); - - for metric in health_metrics { - println!(" {}", metric); - } - - println!(" โœ… Health Score: 92/100 (Excellent operational status)"); - } -} - -fn demonstrate_maintenance_strategies(_maintainer: &MaintainerAgent) { - println!("\n๐Ÿ”ง Maintenance Strategies & Operational Excellence"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - let maintenance_frameworks = vec![ - ("Preventive Maintenance", vec![ - "โฐ Scheduled Tasks: Database optimization, log cleanup, security updates", - "๐Ÿค– Automation Level: Comprehensive with intelligent human oversight", - "๐Ÿ“… Scheduling: Optimized maintenance windows with minimal disruption", - "๐Ÿ” Health Checks: Continuous monitoring with anomaly detection", - "๐Ÿ“Š Performance: Trend analysis with predictive optimization", - ]), - - ("Predictive Maintenance", vec![ - "๐Ÿง  AI Analytics: Machine learning for failure prediction and prevention", - "๐Ÿ“ˆ Trend Analysis: Performance forecasting and capacity planning", - "๐Ÿšจ Smart Alerting: Intelligent escalation with context-aware notifications", - "๐Ÿ”ฎ Forecasting: Resource utilization prediction and scaling preparation", - "๐Ÿ‘ฅ User Experience: Proactive optimization based on usage patterns", - ]), - - ("Corrective Maintenance", vec![ - "๐Ÿšจ Incident Response: Automated detection with rapid classification", - "๐Ÿ”„ Self-Healing: Automatic recovery triggers with validation", - "๐Ÿ“ž Escalation: Graduated response with expert team notification", - "๐Ÿ” Root Cause: Automated analysis with prevention measures", - "๐Ÿ“ Improvement: Post-incident review with system enhancement", - ]), - - ("Operational Excellence", vec![ - "โšก Performance: Continuous optimization with baseline tracking", - "๐Ÿ›ก๏ธ Reliability: Multi-layer redundancy with failover automation", - "๐Ÿ” Security: Hardening automation with compliance monitoring", - "๐Ÿ’ฐ Cost: Resource optimization with efficiency measurement", - "๐Ÿ“Š Metrics: KPI tracking with operational excellence scoring", - ]) - ]; - - for (framework_name, strategies) in maintenance_frameworks { - println!("\n๐ŸŽฏ {}", framework_name); - println!("โ”€{}โ”€", "โ”€".repeat(framework_name.len() + 2)); - - for strategy in strategies { - println!(" {}", strategy); - } - - println!(" ๐ŸŽฏ Excellence Level: Enterprise-grade with continuous improvement"); - } -} - -fn demonstrate_operational_automation(_maintainer: &MaintainerAgent) { - println!("\n๐Ÿค– Operational Automation & Intelligence"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - let automation_areas = vec![ - ("Monitoring & Observability", vec![ - "๐Ÿ“Š Health Monitoring: Real-time system vitals with smart dashboards", - "โšก Performance Monitoring: APM with distributed tracing and profiling", - "๐Ÿ”’ Security Monitoring: Threat detection with automated response", - "๐Ÿ’ผ Business Monitoring: KPI tracking with stakeholder reporting", - "๐ŸŽฏ SLA Monitoring: Service level tracking with proactive alerts", - ]), - - ("Maintenance Automation", vec![ - "๐Ÿ–ฅ๏ธ System Maintenance: OS updates, service restarts, configuration drift", - "๐Ÿ’พ Database Maintenance: Index optimization, statistics updates, cleanup", - "๐Ÿ” Security Maintenance: Patch management, vulnerability remediation", - "โšก Performance Maintenance: Query optimization, cache management", - "๐Ÿ“ฆ Dependency Maintenance: Library updates, security scanning", - ]), - - ("Incident & Recovery Automation", vec![ - "๐Ÿšจ Detection: Anomaly detection with intelligent alert correlation", - "๐Ÿ”„ Response: Automated remediation with human oversight protocols", - "๐Ÿ’พ Recovery: Backup restoration, failover automation, data validation", - "๐Ÿ“ข Communication: Stakeholder updates with status page automation", - "๐Ÿ“ Documentation: Incident logging with knowledge base updates", - ]), - - ("Optimization & Planning", vec![ - "๐Ÿ“ˆ Resource Optimization: CPU/memory rightsizing with cost tracking", - "๐Ÿ’ฐ Cost Optimization: Usage analysis with recommendation engine", - "โšก Performance Optimization: Query tuning, caching strategy optimization", - "๐Ÿ“Š Capacity Planning: Growth prediction with scaling recommendations", - "๐Ÿ”ง Technology Optimization: Stack evaluation with upgrade planning", - ]) - ]; - - for (area_name, automations) in automation_areas { - println!("\n๐Ÿ”ง {}", area_name); - println!("โ”€{}โ”€", "โ”€".repeat(area_name.len() + 2)); - - for automation in automations { - println!(" {}", automation); - } - - println!(" ๐ŸŽฏ Automation Level: Comprehensive with intelligent human collaboration"); - } -} - -fn demonstrate_pipeline_completion(maintainer: &MaintainerAgent) { - println!("\n๐ŸŽ‰ Development Pipeline Completion (100%)"); - println!("โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•"); - - println!("๐Ÿ“‹ Complete Development Lifecycle Pipeline:"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ PlannerAgentโ”‚ -> โ”‚ArchitectAgt โ”‚ -> โ”‚ DesignerAgt โ”‚ -> โ”‚ SchemaAgent โ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(" โ”‚ โ”‚ โ”‚ โ”‚"); - println!(" v v v v"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ APIAgent โ”‚ -> โ”‚FrontendCoderโ”‚ -> โ”‚BackendCoder โ”‚ -> โ”‚RefactorAgentโ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(" โ”‚ โ”‚ โ”‚ โ”‚"); - println!(" v v v v"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ DocAgent โ”‚ -> โ”‚DeployerAgentโ”‚ -> โ”‚MaintainerAgtโ”‚ ๐ŸŽ‰"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - - println!("\n๐ŸŽฏ MaintainerAgent Position (11/11 Agents - 100% Complete!):"); - println!(" โ€ข Receives: Production infrastructure from DeployerAgent"); - println!(" โ€ข Processes: System health monitoring, maintenance automation, incident response"); - println!(" โ€ข Delivers: Operational excellence with continuous system optimization"); - println!(" โ€ข Completes: Full end-to-end development lifecycle automation"); - - println!("\n๐Ÿ“Š Agent Integration Capabilities:"); - println!(" ๐Ÿ”„ Input Processing:"); - for input_type in &maintainer.metadata().supported_input_types { - println!(" โ€ข {}", input_type.replace('_', " ")); - } - - println!(" ๐Ÿ“ค Output Generation:"); - for output_type in &maintainer.metadata().supported_output_types { - println!(" โ€ข {}", output_type.replace('_', " ")); - } - - println!("\n๐Ÿš€ Development Pipeline Status (COMPLETE!):"); - println!(" โ€ข โœ… Requirements & Planning (PlannerAgent)"); - println!(" โ€ข โœ… System Architecture (ArchitectAgent)"); - println!(" โ€ข โœ… UI/UX Design (DesignerAgent)"); - println!(" โ€ข โœ… Database Schema (SchemaAgent)"); - println!(" โ€ข โœ… API Development (APIAgent)"); - println!(" โ€ข โœ… Frontend Implementation (FrontendCoder)"); - println!(" โ€ข โœ… Backend Implementation (BackendCoder)"); - println!(" โ€ข โœ… Code Optimization (RefactorAgent)"); - println!(" โ€ข โœ… Documentation (DocAgent)"); - println!(" โ€ข โœ… Deployment Orchestration (DeployerAgent)"); - println!(" โ€ข ๐ŸŽ‰ System Maintenance (MaintainerAgent) โ† PIPELINE COMPLETE!"); - - println!("\n๐Ÿ† Final Achievement: 100% Development Lifecycle Coverage"); - println!(" โ€ข ๐Ÿ“‹ Project Planning: Complete requirements and feature planning"); - println!(" โ€ข ๐Ÿ—๏ธ System Design: Full architecture and design system"); - println!(" โ€ข ๐Ÿ’ป Implementation: Frontend, backend, and optimization"); - println!(" โ€ข ๐Ÿ“š Documentation: Comprehensive docs and deployment guides"); - println!(" โ€ข ๐Ÿš€ Deployment: Zero-downtime infrastructure automation"); - println!(" โ€ข ๐Ÿ”ง Maintenance: Ongoing operational excellence"); - - println!("\n๐Ÿ“ˆ Operational Excellence Metrics:"); - println!(" โ€ข Agent Confidence: {:.1}%", maintainer.metadata().base_confidence * 100.0); - println!(" โ€ข System Reliability: 99.95% uptime with automated recovery"); - println!(" โ€ข Performance Optimization: Continuous improvement with ML insights"); - println!(" โ€ข Security Posture: Automated compliance with zero-trust architecture"); - println!(" โ€ข Cost Efficiency: Intelligent resource optimization with cost tracking"); - println!(" โ€ข Incident Response: Mean time to resolution under 15 minutes"); - - println!("\n๐ŸŽฏ Business Impact:"); - println!(" โ€ข ๐Ÿš€ Faster Time to Market: Automated development lifecycle"); - println!(" โ€ข ๐Ÿ’ฐ Cost Reduction: Optimized infrastructure and operational efficiency"); - println!(" โ€ข ๐Ÿ›ก๏ธ Risk Mitigation: Proactive maintenance and security automation"); - println!(" โ€ข ๐Ÿ“ˆ Scalability: Predictive capacity planning and auto-scaling"); - println!(" โ€ข ๐Ÿ‘ฅ Team Productivity: Automation allows focus on innovation"); - println!(" โ€ข ๐ŸŽ–๏ธ Quality Assurance: Continuous monitoring and improvement"); - - println!("\n๐ŸŒŸ Next Evolution: Phase 2 - Security & Compliance Agents"); - println!("With the development lifecycle complete, the next phase focuses on:"); - println!(" โ€ข ๐Ÿ” CyberSecurityAgent - Advanced threat detection"); - println!(" โ€ข ๐Ÿ›ก๏ธ PromptSecurityAgent - LLM security validation"); - println!(" โ€ข ๐Ÿ“‹ PrivacyComplianceAgent - GDPR/CCPA automation"); - println!(" โ€ข ๐Ÿ”’ DataPrivacyAgent - Data classification and encryption"); - println!(" โ€ข โš–๏ธ EthicalAIAgent - AI bias and fairness auditing"); -} \ No newline at end of file diff --git a/materials_science_expert_demo.rs b/materials_science_expert_demo.rs deleted file mode 100644 index f251b85109b51c0c654f7422ebd29abf6e6e5526..0000000000000000000000000000000000000000 --- a/materials_science_expert_demo.rs +++ /dev/null @@ -1,217 +0,0 @@ -/// Materials Science Expert Demo -/// -/// This demonstrates the MaterialsScienceExpert implementation for TASK 3.2, -/// showing how it handles materials science questions across crystallography, -/// nanomaterials, materials properties, and synthesis methods. - -use std::time::Instant; -use brain_cognitive::agents::intelligence::materials_science_expert::MaterialsScienceExpert; -use brain_cognitive::agents::traits::{ - AgentInput, CognitiveContext, BrainAgent, AcademicReasoningAgent -}; - -use uuid::Uuid; - - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿงช MATERIALS SCIENCE EXPERT DEMO"); - println!("================================"); - println!("Demonstrating TASK 3.2: Materials Science Expert for academic reasoning"); - println!("across crystallography, nanomaterials, materials properties, and synthesis methods.\n"); - - // Initialize the Materials Science Expert - let materials_expert = MaterialsScienceExpert::new().await?; - println!("โœ… MaterialsScienceExpert initialized successfully\n"); - - // Test scenarios covering different materials science domains - let test_scenarios = vec![ - ( - "Crystallography Question", - "What is the difference between face-centered cubic (FCC) and body-centered cubic (BCC) crystal structures in terms of packing efficiency and coordination number?", - "materials_question" - ), - ( - "Nanomaterials Question", - "How does the surface-to-volume ratio affect the mechanical properties of nanoparticles compared to bulk materials?", - "academic_question" - ), - ( - "Materials Synthesis Question", - "Compare the advantages and disadvantages of chemical vapor deposition (CVD) versus physical vapor deposition (PVD) for thin film synthesis.", - "materials_question" - ), - ( - "Structure-Property Relationship", - "Explain how grain boundary density influences the yield strength of polycrystalline metals according to the Hall-Petch relationship.", - "academic_question" - ), - ( - "Electronic Materials Question", - "What factors determine the band gap of semiconductor materials, and how does doping affect their electrical conductivity?", - "materials_question" - ), - ( - "Characterization Techniques", - "How do X-ray diffraction (XRD) and transmission electron microscopy (TEM) complement each other in materials characterization?", - "academic_question" - ), - ]; - - for (i, (scenario_name, question, input_type)) in test_scenarios.iter().enumerate() { - println!("๐Ÿ”ฌ Scenario {}: {}", i + 1, scenario_name); - println!("โ“ Question: {}", question); - - let start_time = Instant::now(); - let context = CognitiveContext::default(); - - // Test BrainAgent execute method - let agent_input = AgentInput { - input_type: input_type.to_string(), - content: question.to_string(), - parameters: std::collections::HashMap::new(), - previous_outputs: vec![], - user_preferences: std::collections::HashMap::new(), - session_id: Uuid::new_v4().to_string(), - timestamp: chrono::Utc::now(), - }; - - match materials_expert.execute(agent_input, &context).await { - Ok(output) => { - let duration = start_time.elapsed(); - println!("โœ… Analysis completed in {:?}", duration); - println!("๐ŸŽฏ Confidence: {:.2}", output.confidence); - println!("๐Ÿ“ Analysis:\n{}\n", output.content); - } - Err(e) => { - println!("โŒ Error: {}\n", e); - } - } - - // Test AcademicReasoningAgent methods - let analysis_start = Instant::now(); - match materials_expert.analyze_question(question).await { - Ok(analysis) => { - println!("๐Ÿ” Question Analysis:"); - println!(" Domain: {:?}", analysis.domain); - println!(" Question Type: {:?}", analysis.question_type); - println!(" Complexity Level: {}", analysis.complexity_level); - println!(" Key Concepts: {}", analysis.key_concepts.join(", ")); - println!(" Required Knowledge: {}", analysis.required_knowledge.join(", ")); - println!(" Analysis Confidence: {:.2}", analysis.analysis_confidence); - - let analysis_duration = analysis_start.elapsed(); - println!(" Analysis Time: {:?}", analysis_duration); - } - Err(e) => { - println!("โŒ Analysis Error: {}", e); - } - } - - // Test option evaluation with sample options - let sample_options = vec![ - "A) FCC has higher packing efficiency (74%) and coordination number 12".to_string(), - "B) BCC has higher packing efficiency (68%) and coordination number 8".to_string(), - "C) Both structures have equal packing efficiency but different coordination".to_string(), - "D) Packing efficiency depends on atomic radius, not crystal structure".to_string(), - ]; - - match materials_expert.evaluate_options(question, &sample_options).await { - Ok(evaluation) => { - println!("๐Ÿ“Š Option Evaluation:"); - println!(" Recommended Answer: {}", evaluation.recommended_answer); - println!(" Recommendation Confidence: {:.2}", evaluation.recommendation_confidence); - println!(" Option Scores:"); - for (option, score) in &evaluation.option_scores { - println!(" {}: {:.3}", option, score); - } - } - Err(e) => { - println!("โŒ Evaluation Error: {}", e); - } - } - - println!("{:=<60}\n", ""); - } - - // Test confidence assessment - println!("๐ŸŽฏ CONFIDENCE ASSESSMENT TEST"); - println!("============================"); - - let test_input = AgentInput { - input_type: "materials_question".to_string(), - content: "What is the critical resolved shear stress for dislocation motion in single crystals?".to_string(), - parameters: std::collections::HashMap::new(), - previous_outputs: vec![], - user_preferences: std::collections::HashMap::new(), - session_id: Uuid::new_v4().to_string(), - timestamp: chrono::Utc::now(), - - }; - - let context = CognitiveContext::default(); - match materials_expert.assess_confidence(&test_input, &context).await { - Ok(confidence) => { - println!("โœ… Confidence Assessment: {:.2}", confidence); - println!("๐Ÿ“Š Confidence Threshold: {:.2}", materials_expert.confidence_threshold()); - println!("๐Ÿ”„ Can Handle: {}", confidence >= materials_expert.confidence_threshold()); - } - Err(e) => { - println!("โŒ Confidence Assessment Error: {}", e); - } - } - - // Test academic domains - println!("\n๐ŸŽ“ ACADEMIC DOMAIN SPECIALIZATIONS"); - println!("=================================="); - let domains = materials_expert.academic_domains(); - println!("Specialized Domains: {:?}", domains); - - // Test metadata - println!("\n๐Ÿ“‹ AGENT METADATA"); - println!("================"); - let metadata = materials_expert.metadata(); - println!("Name: {}", metadata.name); - println!("Version: {}", metadata.version); - println!("Capabilities: {}", metadata.capabilities.join(", ")); - println!("ID: {}", metadata.id); - - // Test multiple choice processing with crystallography question - println!("\n๐Ÿ”ฌ MULTIPLE CHOICE PROCESSING TEST"); - println!("================================="); - let mc_question = "In a face-centered cubic (FCC) crystal structure, what is the atomic packing factor?"; - let mc_options = vec![ - "A) 0.52".to_string(), - "B) 0.68".to_string(), - "C) 0.74".to_string(), - "D) 0.90".to_string(), - ]; - - match materials_expert.evaluate_options(mc_question, &mc_options).await { - Ok(evaluation) => { - println!("โœ… Multiple Choice Analysis:"); - println!(" Question: {}", mc_question); - println!(" Recommended Answer: {}", evaluation.recommended_answer); - println!(" Confidence: {:.2}", evaluation.recommendation_confidence); - println!(" Detailed Option Analysis:"); - for (option, score) in &evaluation.option_scores { - println!(" {}: {:.3}", option, score); - } - if !evaluation.elimination_rationale.is_empty() { - println!(" Elimination Rationale:"); - for reason in &evaluation.elimination_rationale { - println!(" - {}", reason); - } - } - } - Err(e) => { - println!("โŒ Multiple Choice Error: {}", e); - } - } - - println!("\n๐ŸŽ‰ Materials Science Expert Demo completed successfully!"); - println!("The MaterialsScienceExpert is ready for TASK 3.2 academic reasoning tasks."); - println!("Specialized in: crystallography, nanomaterials, materials properties, and synthesis methods."); - - Ok(()) -} \ No newline at end of file diff --git a/memory.db b/memory.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/memory.db and /dev/null differ diff --git a/memory_consolidation_demo.rs b/memory_consolidation_demo.rs deleted file mode 100644 index d6dd8982efc7b2099349f9f430df655f25e2e87e..0000000000000000000000000000000000000000 --- a/memory_consolidation_demo.rs +++ /dev/null @@ -1,451 +0,0 @@ -//! # Memory Consolidation and Cross-Memory Operations Demo -//! -//! Demonstrates the advanced memory consolidation and cross-memory operations -//! including: -//! - Advanced consolidation logic (working โ†’ episodic โ†’ semantic) -//! - Cross-memory query capabilities -//! - Background maintenance processes -//! - Pattern extraction from episodic to semantic memory -//! - Comprehensive memory analysis and reporting - -use brain::{services::*, MemoryService, ConceptGraphService}; -use brain_infra::memory::WorkingMemoryRepository; -use brain_types::Result; -use std::collections::HashMap; - -#[derive(Debug, Clone)] -pub enum Priority { - High, - Medium, - Low, -} - -#[derive(Debug, Clone)] -pub struct SemanticConcept { - pub name: String, - pub description: String, - pub embedding: Vec, -} - -impl SemanticConcept { - pub fn new(name: String, description: String, embedding: Vec) -> Self { - Self { name, description, embedding } - } -} - -#[derive(Debug, Clone)] -pub struct ConsolidationConfig { - pub working_to_episodic_hours: u64, - pub min_access_count: u32, - pub importance_threshold: f64, - pub semantic_extraction_threshold: f64, -} - -impl Default for ConsolidationConfig { - fn default() -> Self { - Self { - working_to_episodic_hours: 24, - min_access_count: 3, - importance_threshold: 0.5, - semantic_extraction_threshold: 0.6, - } - } -} - -#[derive(Debug, Clone)] -pub struct ConsolidationResult { - pub working_to_episodic: usize, - pub episodic_to_semantic: usize, - pub forgotten_events: usize, -} - -#[derive(Debug, Clone)] -pub struct MemoryAnalysis { - pub working_memory: MemoryStats, - pub episodic_memory: Option, - pub semantic_memory: MemoryStats, - pub total_items: usize, - pub total_size_bytes: usize, -} - -#[derive(Debug, Clone)] -pub struct MemoryStats { - pub total_items: usize, - pub size_bytes: usize, -} - -#[derive(Debug, Clone)] -pub struct CrossMemoryQueryResult { - pub working_results: Vec, - pub episodic_results: Vec, - pub semantic_results: Vec, -} - -#[derive(Debug, Clone)] -pub struct MaintenanceReport { - pub working_items_pruned: usize, - pub episodic_events_forgotten: usize, - pub semantic_concepts_merged: usize, - pub consolidation_result: ConsolidationResult, -} - -/// Demo memory system with consolidation capabilities -pub struct DemoMemorySystem { - #[allow(dead_code)] - working_repo: WorkingMemoryRepository, - #[allow(dead_code)] - memory_service: MemoryService, - #[allow(dead_code)] - concept_service: ConceptGraphService, - config: ConsolidationConfig, - next_id: usize, - access_counts: HashMap, - concepts: Vec, -} - -impl DemoMemorySystem { - pub async fn new() -> Result { - let working_repo = WorkingMemoryRepository::new(100); - let memory_service = create_memory_service_with_capacity(100).await?; - let concept_service = create_concept_graph_service_default().await?; - - Ok(Self { - working_repo, - memory_service, - concept_service, - config: ConsolidationConfig::default(), - next_id: 1, - access_counts: HashMap::new(), - concepts: Vec::new(), - }) - } - - pub fn configure_consolidation(&mut self, config: ConsolidationConfig) { - self.config = config; - } - - pub fn learn(&mut self, content: String, priority: Priority) -> Result { - let id = self.next_id; - self.next_id += 1; - - // Store in working memory (simulated) - println!(" ๐Ÿ“ Learning: {} (Priority: {:?})", content, priority); - self.access_counts.insert(id, 1); - - Ok(id) - } - - pub fn recall_working(&mut self, id: usize) { - if let Some(count) = self.access_counts.get_mut(&id) { - *count += 1; - } - } - - pub fn store_concept(&mut self, concept: SemanticConcept) -> Result<()> { - println!(" ๐Ÿง  Storing semantic concept: {}", concept.name); - self.concepts.push(concept); - Ok(()) - } - - pub fn analyze_memory_state(&self) -> MemoryAnalysis { - MemoryAnalysis { - working_memory: MemoryStats { - total_items: self.access_counts.len(), - size_bytes: self.access_counts.len() * 256, // Estimated - }, - episodic_memory: Some(MemoryStats { - total_items: self.access_counts.values().filter(|&count| *count >= 3).count(), - size_bytes: 0, - }), - semantic_memory: MemoryStats { - total_items: self.concepts.len(), - size_bytes: self.concepts.len() * 512, // Estimated - }, - total_items: self.access_counts.len() + self.concepts.len(), - total_size_bytes: (self.access_counts.len() * 256) + (self.concepts.len() * 512), - } - } - - pub fn consolidate(&mut self) -> Result { - let mut working_to_episodic = 0; - let mut episodic_to_semantic = 0; - let mut forgotten_events = 0; - - // Simulate consolidation logic - for (_, count) in &self.access_counts { - if *count >= self.config.min_access_count { - working_to_episodic += 1; - } - if *count >= 5 { - episodic_to_semantic += 1; - } - if *count == 1 { - forgotten_events += 1; - } - } - - Ok(ConsolidationResult { - working_to_episodic, - episodic_to_semantic, - forgotten_events, - }) - } - - pub fn query_all_memories(&self, query: &str) -> Result { - let working_results = self.access_counts.keys() - .filter(|_| query.contains("weather") || query.contains("news")) - .map(|id| format!("Working memory item {}", id)) - .collect(); - - let episodic_results = self.access_counts.iter() - .filter(|(_, count)| **count >= 3) - .map(|(id, _)| format!("Episodic memory item {}", id)) - .collect(); - - let semantic_results = self.concepts.iter() - .filter(|concept| concept.name.contains(query) || concept.description.contains(query)) - .map(|concept| concept.name.clone()) - .collect(); - - Ok(CrossMemoryQueryResult { - working_results, - episodic_results, - semantic_results, - }) - } - - pub fn find_related_memories(&self, query: &str, _limit: usize) -> Result { - // Simplified related memory search - self.query_all_memories(query) - } - - pub fn run_maintenance(&mut self) -> Result { - let working_items_pruned = self.access_counts.iter().filter(|(_, count)| **count == 1).count(); - let episodic_events_forgotten = 0; - let semantic_concepts_merged = 0; - - let consolidation_result = self.consolidate()?; - - Ok(MaintenanceReport { - working_items_pruned, - episodic_events_forgotten, - semantic_concepts_merged, - consolidation_result, - }) - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Memory Consolidation and Cross-Memory Operations Demo"); - println!("========================================================\n"); - - // Initialize memory system with new architecture - let mut system = DemoMemorySystem::new().await?; - - // Configure for demonstration purposes - let mut config = ConsolidationConfig::default(); - config.working_to_episodic_hours = 0; // Immediate consolidation - config.min_access_count = 3; - config.importance_threshold = 2.0; - config.semantic_extraction_threshold = 0.6; - system.configure_consolidation(config); - - println!("๐Ÿ“š Phase 1: Learning and Memory Population"); - println!("=========================================="); - - // Learn various types of information - let weather_id = system.learn("User frequently asks about weather conditions".to_string(), Priority::High)?; - let news_id = system.learn("User frequently asks about current news".to_string(), Priority::High)?; - let sports_id = system.learn("User frequently asks about sports scores".to_string(), Priority::Medium)?; - let music_id = system.learn("User occasionally asks about music recommendations".to_string(), Priority::Medium)?; - let _tech_id = system.learn("User rarely asks about technology updates".to_string(), Priority::Low)?; - - println!("โœ… Added 5 items to working memory"); - - // Simulate user interactions with different access patterns - println!("\n๐Ÿ”„ Phase 2: Simulating User Interactions"); - println!("========================================"); - - // High-frequency interactions - for i in 0..8 { - system.recall_working(weather_id); - system.recall_working(news_id); - if i < 5 { - system.recall_working(sports_id); - } - if i < 3 { - system.recall_working(music_id); - } - println!(" Interaction cycle {} completed", i + 1); - } - - // Add some semantic concepts manually - let weather_concept = SemanticConcept::new( - "weather_patterns".to_string(), - "Understanding of weather-related queries and patterns".to_string(), - vec![0.8, 0.6, 0.4, 0.2, 0.1, 0.3, 0.7, 0.5], - ); - - let news_concept = SemanticConcept::new( - "news_interest".to_string(), - "User's interest in current events and news".to_string(), - vec![0.7, 0.8, 0.3, 0.5, 0.2, 0.6, 0.4, 0.9], - ); - - system.store_concept(weather_concept)?; - system.store_concept(news_concept)?; - println!("โœ… Added 2 semantic concepts"); - - println!("\n๐Ÿ“Š Phase 3: Memory Analysis Before Consolidation"); - println!("==============================================="); - - let analysis_before = system.analyze_memory_state(); - println!("Working Memory: {} items, {} bytes", - analysis_before.working_memory.total_items, - analysis_before.working_memory.size_bytes); - println!("Episodic Memory: {} items", - analysis_before.episodic_memory.as_ref().map(|e| e.total_items).unwrap_or(0)); - println!("Semantic Memory: {} items, {} bytes", - analysis_before.semantic_memory.total_items, - analysis_before.semantic_memory.size_bytes); - println!("Total Memory: {} items, {} bytes", - analysis_before.total_items, - analysis_before.total_size_bytes); - - println!("\n๐Ÿ”„ Phase 4: Advanced Consolidation Process"); - println!("========================================="); - - let consolidation_result = system.consolidate()?; - println!("Consolidation Results:"); - println!(" Working โ†’ Episodic: {} items", consolidation_result.working_to_episodic); - println!(" Episodic โ†’ Semantic: {} patterns", consolidation_result.episodic_to_semantic); - println!(" Forgotten Events: {} items", consolidation_result.forgotten_events); - - println!("\n๐Ÿ“Š Phase 5: Memory Analysis After Consolidation"); - println!("=============================================="); - - let analysis_after = system.analyze_memory_state(); - println!("Working Memory: {} items, {} bytes", - analysis_after.working_memory.total_items, - analysis_after.working_memory.size_bytes); - println!("Episodic Memory: {} items", - analysis_after.episodic_memory.as_ref().map(|e| e.total_items).unwrap_or(0)); - println!("Semantic Memory: {} items, {} bytes", - analysis_after.semantic_memory.total_items, - analysis_after.semantic_memory.size_bytes); - println!("Total Memory: {} items, {} bytes", - analysis_after.total_items, - analysis_after.total_size_bytes); - - println!("\n๐Ÿ” Phase 6: Cross-Memory Query Demonstrations"); - println!("============================================"); - - // Query across all memory types - let weather_results = system.query_all_memories("weather")?; - println!("Cross-memory search for 'weather':"); - println!(" Working Memory: {} results", weather_results.working_results.len()); - println!(" Episodic Memory: {} results", weather_results.episodic_results.len()); - println!(" Semantic Memory: {} results", weather_results.semantic_results.len()); - - let news_results = system.query_all_memories("news")?; - println!("\nCross-memory search for 'news':"); - println!(" Working Memory: {} results", news_results.working_results.len()); - println!(" Episodic Memory: {} results", news_results.episodic_results.len()); - println!(" Semantic Memory: {} results", news_results.semantic_results.len()); - - // Find related memories - let related_results = system.find_related_memories("user frequently", 5)?; - println!("\nRelated memories for 'user frequently':"); - println!(" Working Memory: {} results", related_results.working_results.len()); - println!(" Episodic Memory: {} results", related_results.episodic_results.len()); - println!(" Semantic Memory: {} results", related_results.semantic_results.len()); - - println!("\n๐Ÿ› ๏ธ Phase 7: Background Maintenance Process"); - println!("=========================================="); - - // Add some low-priority items to demonstrate pruning - for i in 0..3 { - system.learn(format!("Temporary low priority item {}", i), Priority::Low)?; - } - - let maintenance_report = system.run_maintenance()?; - println!("Maintenance Report:"); - println!(" Working items pruned: {}", maintenance_report.working_items_pruned); - println!(" Episodic events forgotten: {}", maintenance_report.episodic_events_forgotten); - println!(" Semantic concepts merged: {}", maintenance_report.semantic_concepts_merged); - println!(" Additional consolidation:"); - println!(" Working โ†’ Episodic: {}", maintenance_report.consolidation_result.working_to_episodic); - println!(" Episodic โ†’ Semantic: {}", maintenance_report.consolidation_result.episodic_to_semantic); - println!(" Forgotten: {}", maintenance_report.consolidation_result.forgotten_events); - - println!("\n๐Ÿ“ˆ Phase 8: Pattern Extraction Demonstration"); - println!("==========================================="); - - // Add more similar patterns to trigger semantic extraction - for i in 0..4 { - let content = format!("User frequently asks about topic {}", i); - let id = system.learn(content, Priority::Medium)?; - - // Access multiple times to create consolidation candidates - for _ in 0..4 { - system.recall_working(id); - } - } - - // Run consolidation again to see pattern extraction - let second_consolidation = system.consolidate()?; - println!("Second consolidation (with pattern extraction):"); - println!(" Working โ†’ Episodic: {} items", second_consolidation.working_to_episodic); - println!(" Episodic โ†’ Semantic: {} patterns", second_consolidation.episodic_to_semantic); - println!(" Forgotten Events: {} items", second_consolidation.forgotten_events); - - println!("\n๐Ÿ“Š Phase 9: Final Memory State Analysis"); - println!("======================================"); - - let final_analysis = system.analyze_memory_state(); - println!("Final Memory State:"); - println!(" Working Memory: {} items, {} bytes", - final_analysis.working_memory.total_items, - final_analysis.working_memory.size_bytes); - println!(" Episodic Memory: {} items", - final_analysis.episodic_memory.as_ref().map(|e| e.total_items).unwrap_or(0)); - println!(" Semantic Memory: {} items, {} bytes", - final_analysis.semantic_memory.total_items, - final_analysis.semantic_memory.size_bytes); - println!(" Total Memory: {} items, {} bytes", - final_analysis.total_items, - final_analysis.total_size_bytes); - - // Show memory evolution - println!("\n๐Ÿ“ˆ Memory Evolution Summary:"); - println!("============================"); - println!("Working Memory Change: {} โ†’ {} items", - analysis_before.working_memory.total_items, - final_analysis.working_memory.total_items); - println!("Episodic Memory Change: {} โ†’ {} items", - analysis_before.episodic_memory.as_ref().map(|e| e.total_items).unwrap_or(0), - final_analysis.episodic_memory.as_ref().map(|e| e.total_items).unwrap_or(0)); - println!("Semantic Memory Change: {} โ†’ {} items", - analysis_before.semantic_memory.total_items, - final_analysis.semantic_memory.total_items); - - println!("\n๐ŸŽฏ Consolidation Effectiveness Analysis:"); - println!("======================================="); - let consolidation_efficiency = if analysis_before.total_items > 0 { - ((analysis_before.total_items - final_analysis.working_memory.total_items) as f64 / - analysis_before.total_items as f64) * 100.0 - } else { - 0.0 - }; - println!("Memory consolidation efficiency: {:.1}%", consolidation_efficiency); - println!("Semantic knowledge extraction: {} new concepts formed", - final_analysis.semantic_memory.total_items - analysis_before.semantic_memory.total_items); - - println!("\nโœ… Memory Consolidation Demo Complete!"); - println!("====================================="); - println!("This demo showed how Brain AI consolidates memories across different"); - println!("memory systems to optimize storage and enable pattern recognition."); - - Ok(()) -} \ No newline at end of file diff --git a/memory_demo.db b/memory_demo.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/memory_demo.db and /dev/null differ diff --git a/memory_demo.rs b/memory_demo.rs deleted file mode 100644 index f35b3f175e06eaba678e9308c1953baa06df9054..0000000000000000000000000000000000000000 --- a/memory_demo.rs +++ /dev/null @@ -1,172 +0,0 @@ -//! # Memory Module Demonstration -//! -//! This example demonstrates the Brain project's memory system foundation, -//! showing working memory operations, memory consolidation, and statistics. - -use brain::{MemoryService, Priority, WorkingMemoryQuery, Result}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use std::time::Duration; -use tokio; -use brain::*; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain Memory Module Demonstration"); - println!("==================================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| { - BrainError::from(e).with_context( - brain_types::ErrorContext::new("create_data_directory") - .with_details("Failed to create data directory for memory demo") - ) - })?; - - // Create memory repositories - let working_repo = Box::new(WorkingMemoryRepository::new(10)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/memory_demo.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let mut memory_service = MemoryService::new(working_repo, episodic_repo, semantic_repo); - - println!("๐Ÿ“ Learning Phase - Adding information to working memory"); - println!("---------------------------------------------------------"); - - // Learn various pieces of information with different priorities - let critical_info = memory_service.learn( - "Emergency shutdown procedure for neural network".to_string(), - Priority::Critical, - ).await?; - println!("โœ… Learned critical info (ID: {})", critical_info); - - let high_info = memory_service.learn( - "User prefers transformer architecture over RNN".to_string(), - Priority::High, - ).await?; - println!("โœ… Learned high priority info (ID: {})", high_info); - - let medium_info = memory_service.learn( - "Project deadline is end of quarter".to_string(), - Priority::Medium, - ).await?; - println!("โœ… Learned medium priority info (ID: {})", medium_info); - - let low_info = memory_service.learn( - "Coffee machine is on the second floor".to_string(), - Priority::Low, - ).await?; - println!("โœ… Learned low priority info (ID: {})", low_info); - - // Simulate multiple accesses to important information - println!("\n๐Ÿ”„ Access Pattern Simulation"); - println!("-----------------------------"); - - for i in 1..=3 { - println!("Access {} - Retrieving critical information", i); - if let Some(item) = memory_service.recall_working(critical_info).await? { - println!(" Retrieved: {}", item.content); - println!(" Access count: {}, Importance: {:.3}", - item.access_count, item.importance_score()); - } - - // Small delay to simulate time passing - tokio::time::sleep(Duration::from_millis(100)).await; - } - - // Access other items too - let _ = memory_service.recall_working(high_info).await?; - let _ = memory_service.recall_working(medium_info).await?; - - // Test working memory queries - println!("\n๐Ÿ” Working Memory Queries"); - println!("-------------------------"); - - let query = WorkingMemoryQuery { - priority: Some(Priority::High), - ..Default::default() - }; - let high_priority_items = memory_service.query_working(&query).await?; - println!("High priority items: {}", high_priority_items.len()); - for item in &high_priority_items { - println!(" - {} (Priority: {:?})", item.content, item.priority); - } - - // Test capacity management - println!("\n๐Ÿš€ Capacity Management Test"); - println!("-----------------------------"); - - // Add more items to test capacity limits - for i in 1..=8 { - let content = format!("Additional learning item #{}", i); - let id = memory_service.learn(content, Priority::Low).await?; - println!("Added item {} (ID: {})", i, id); - } - - // Query all items - let all_query = WorkingMemoryQuery::default(); - let all_items = memory_service.query_working(&all_query).await?; - println!("\nTotal working memory items: {}", all_items.len()); - - // Test consolidation process - println!("\n๐Ÿ”„ Memory Consolidation Process"); - println!("-------------------------------"); - - let consolidation_result = memory_service.consolidate().await?; - println!("Consolidation completed:"); - println!(" - {} items moved to episodic memory", consolidation_result.working_to_episodic); - println!(" - {} items extracted to semantic memory", consolidation_result.episodic_to_semantic); - println!(" - {} items forgotten", consolidation_result.forgotten_events); - - // Test cross-memory search - println!("\n๐Ÿ” Cross-Memory Search Demo"); - println!("---------------------------"); - - let search_terms = vec!["neural", "transformer", "deadline"]; - for term in search_terms { - println!("\n๐ŸŽฏ Searching for: '{}'", term); - let results = memory_service.query_all_memories(term).await?; - - let total = results.working_results.len() + results.episodic_results.len() + results.semantic_results.len(); - if total > 0 { - println!(" Found {} total memories:", total); - - if !results.working_results.is_empty() { - println!(" Working Memory:"); - for item in &results.working_results { - println!(" - {}", item.content); - } - } - - if !results.episodic_results.is_empty() { - println!(" Episodic Memory:"); - for event in &results.episodic_results { - println!(" - {}", event.content); - } - } - - if !results.semantic_results.is_empty() { - println!(" Semantic Memory:"); - for concept in &results.semantic_results { - println!(" - {} ({})", concept.name, concept.description); - } - } - } else { - println!(" No memories found"); - } - } - - println!("\nโœ… Memory Module Demonstration Complete!"); - println!("=========================================="); - println!("Key Features Demonstrated:"); - println!("โ€ข โœ… Working memory with priority-based management"); - println!("โ€ข โœ… Automatic capacity management and eviction"); - println!("โ€ข โœ… Access pattern tracking and importance scoring"); - println!("โ€ข โœ… Memory queries and filtering"); - println!("โ€ข โœ… Consolidation pipeline with episodic memory"); - println!("โ€ข โœ… Cross-memory search capabilities"); - println!("โ€ข โœ… SQLite persistence for episodic memory"); - println!("โ€ข โœ… Semantic memory with concept storage"); - - Ok(()) -} \ No newline at end of file diff --git a/memory_storage_demo.db b/memory_storage_demo.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/memory_storage_demo.db and /dev/null differ diff --git a/memory_storage_demo.rs b/memory_storage_demo.rs deleted file mode 100644 index 0b35a9f5d34c0dd83adad33465f1e686632097da..0000000000000000000000000000000000000000 --- a/memory_storage_demo.rs +++ /dev/null @@ -1,213 +0,0 @@ -//! Memory Storage Demonstration -//! -//! This example demonstrates the Brain project's comprehensive memory storage system, -//! including working memory, episodic memory, and semantic memory operations. -//! -//! Features demonstrated: -//! - Multi-level memory storage (Working, Episodic, Semantic) -//! - Memory querying and retrieval -//! - Memory consolidation -//! - Cross-memory search capabilities - -use brain::{ - MemoryService, Priority, WorkingMemoryQuery, SemanticConcept, SemanticQuery, - EpisodicEvent, EpisodicQuery, Result -}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use std::collections::HashMap; -use tokio; -use brain::*; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain Memory Storage Demonstration"); - println!("===================================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| BrainError::Io { - message: format!("Failed to create data directory: {}", e), - context: None, - source: None, - })?; - - // Initialize repositories - let working_repo = Box::new(WorkingMemoryRepository::new(20)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/memory_storage_demo.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let mut system = MemoryService::new(working_repo, episodic_repo, semantic_repo); - - // === WORKING MEMORY DEMONSTRATION === - println!("๐Ÿ“ Working Memory Operations"); - println!("----------------------------"); - - // Store items with different priorities - let critical_id = system.learn("System startup complete".to_string(), Priority::Critical).await?; - let _high_id = system.learn("User authentication required".to_string(), Priority::High).await?; - let _medium_id = system.learn("Load user preferences".to_string(), Priority::Medium).await?; - let _low_id = system.learn("Update UI theme".to_string(), Priority::Low).await?; - - println!("โœ… Stored 4 working memory items with different priorities"); - - // Test specific item recall - if let Some(critical_item) = system.recall_working(critical_id).await? { - println!("๐Ÿ” Retrieved critical item: '{}'", critical_item.content); - } - - // Query working memory by pattern - let query = WorkingMemoryQuery { - content_pattern: Some("user".to_string()), - priority: None, - min_importance: None, - created_after: None, - limit: Some(10), - }; - - let user_related = system.query_working(&query).await?; - println!("๐Ÿ‘ค Found {} user-related items in working memory", user_related.len()); - for item in &user_related { - println!(" - {} (Priority: {:?})", item.content, item.priority); - } - - // === SEMANTIC MEMORY DEMONSTRATION === - println!("\n๐Ÿงฉ Semantic Memory Operations"); - println!("-----------------------------"); - - // Create semantic concepts - let weather_concept = SemanticConcept::new( - "Weather".to_string(), - "Atmospheric conditions and patterns".to_string(), - vec![0.1, 0.8, 0.3, 0.9, 0.2], // Mock embedding - ); - - let climate_concept = SemanticConcept::new( - "Climate".to_string(), - "Long-term weather patterns and trends".to_string(), - vec![0.2, 0.7, 0.4, 0.8, 0.3], // Mock embedding - ); - - let technology_concept = SemanticConcept::new( - "Technology".to_string(), - "Application of scientific knowledge for practical purposes".to_string(), - vec![0.9, 0.1, 0.8, 0.2, 0.7], // Mock embedding - ); - - // Store semantic concepts - let _weather_id = system.store_concept(weather_concept).await?; - let _climate_id = system.store_concept(climate_concept).await?; - let _tech_id = system.store_concept(technology_concept).await?; - - println!("โœ… Stored 3 semantic concepts"); - - // Query semantic concepts - let semantic_query = SemanticQuery { - name_pattern: Some("weather".to_string()), - embedding: None, - min_confidence: None, - min_similarity: None, - limit: Some(5), - }; - - let weather_concepts = system.query_semantic(&semantic_query).await?; - println!("๐ŸŒค๏ธ Found {} weather-related concepts", weather_concepts.len()); - for concept in &weather_concepts { - println!(" - {}: {}", concept.name, concept.description); - } - - // === EPISODIC MEMORY DEMONSTRATION === - println!("\n๐Ÿ“š Episodic Memory Operations"); - println!("-----------------------------"); - - // Create episodic events - let mut context1 = HashMap::new(); - context1.insert("location".to_string(), "office".to_string()); - context1.insert("activity".to_string(), "meeting".to_string()); - - let mut event1 = EpisodicEvent::new( - "Quarterly team meeting discussion about project roadmap".to_string(), - context1, - 0.8, - "user_input".to_string(), - ); - event1.add_tag("meeting".to_string()); - event1.add_tag("roadmap".to_string()); - - let mut context2 = HashMap::new(); - context2.insert("location".to_string(), "home".to_string()); - context2.insert("activity".to_string(), "learning".to_string()); - - let mut event2 = EpisodicEvent::new( - "Studied advanced memory consolidation techniques".to_string(), - context2, - 0.9, - "learning_session".to_string(), - ); - event2.add_tag("learning".to_string()); - event2.add_tag("memory".to_string()); - - println!("โœ… Created 2 episodic events with context and tags"); - - // Query episodic memory - let episodic_query = EpisodicQuery::default(); - let _all_events = system.query_episodic(&episodic_query).await?; - - // === CROSS-MEMORY SEARCH === - println!("\n๐Ÿ”— Cross-Memory Search"); - println!("---------------------"); - - // Search across all memory types - let cross_results = system.query_all_memories("memory").await?; - - println!("๐Ÿ“Š Cross-memory search results for 'memory':"); - println!(" - Working memory: {} results", cross_results.working_results.len()); - println!(" - Episodic memory: {} results", cross_results.episodic_results.len()); - println!(" - Semantic memory: {} results", cross_results.semantic_results.len()); - - // Demonstrate different search patterns - println!("\n๐Ÿ” Pattern-Based Searches"); - println!("-------------------------"); - - let working_pattern_query = WorkingMemoryQuery { - content_pattern: Some("system".to_string()), - priority: None, - min_importance: None, - created_after: None, - limit: Some(5), - }; - - let episodic_pattern_query = EpisodicQuery { - content_pattern: Some("meeting".to_string()), - time_range: None, - min_importance: None, - tags: Vec::new(), - context_filters: HashMap::new(), - limit: Some(5), - }; - - let importance_query = EpisodicQuery { - content_pattern: None, - time_range: None, - min_importance: Some(0.8), - tags: Vec::new(), - context_filters: HashMap::new(), - limit: Some(10), - }; - - let system_items = system.query_working(&working_pattern_query).await?; - let meeting_events = system.query_episodic(&episodic_pattern_query).await?; - let important_events = system.query_episodic(&importance_query).await?; - - println!("๐Ÿ–ฅ๏ธ System-related working items: {}", system_items.len()); - println!("๐Ÿค Meeting-related episodic events: {}", meeting_events.len()); - println!("โญ High-importance events: {}", important_events.len()); - - println!("\nโœ… Memory Storage Demo Complete!"); - println!("๐ŸŽฏ Successfully demonstrated multi-level memory operations:"); - println!(" โ€ข Working memory storage and priority management"); - println!(" โ€ข Semantic concept creation and similarity tracking"); - println!(" โ€ข Episodic event storage with context and tagging"); - println!(" โ€ข Cross-memory search and pattern-based queries"); - - Ok(()) -} \ No newline at end of file diff --git a/memory_timeline.html b/memory_timeline.html deleted file mode 100644 index 141748a6179085a938f4db685e90df25135c9d98..0000000000000000000000000000000000000000 --- a/memory_timeline.html +++ /dev/null @@ -1,825 +0,0 @@ - - - - - - Brain AI - Memory Timeline Visualization - - - - -
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- - - - \ No newline at end of file diff --git a/memory_timeline_demo.rs b/memory_timeline_demo.rs deleted file mode 100644 index 7bab8c81614da4a9832c396e004f78355964e11b..0000000000000000000000000000000000000000 --- a/memory_timeline_demo.rs +++ /dev/null @@ -1,199 +0,0 @@ -//! Memory Timeline Visualization Demo -//! -//! This demo showcases the memory timeline visualization capabilities, -//! demonstrating how episodic memory events are displayed chronologically -//! with interactive filtering and exploration features. - -use brain::*; -use brain::services::*; -// Note: visualization module doesn't exist in brain_infra yet, so we'll implement a demo version -use std::collections::HashMap; -use chrono::{DateTime, Utc}; - -/// Demo visualization configuration -#[derive(Debug, Clone)] -pub struct VisualizationConfig { - pub enable_timeline: bool, - pub max_events: usize, -} - -impl Default for VisualizationConfig { - fn default() -> Self { - Self { - enable_timeline: true, - max_events: 1000, - } - } -} - -/// Demo memory timeline data structure -#[derive(Debug, Clone)] -pub struct DemoTimelineData { - pub events: Vec, - pub metadata: DemoTimelineMetadata, -} - -#[derive(Debug, Clone)] -pub struct DemoTimelineEvent { - pub event_type: String, - pub title: String, - pub description: String, - pub importance: f64, - pub timestamp: DateTime, - pub related_concepts: Vec, -} - -#[derive(Debug, Clone)] -pub struct DemoTimelineMetadata { - pub event_count: usize, - pub start_time: DateTime, - pub end_time: DateTime, -} - -/// Demo visualization manager with timeline capabilities -pub struct DemoVisualizationManager { - #[allow(dead_code)] - config: VisualizationConfig, -} - -impl DemoVisualizationManager { - pub fn new(config: VisualizationConfig) -> Self { - Self { config } - } - - pub async fn generate_memory_timeline_data(&self, _memory_service: &MemoryService) -> Result { - // Generate sample timeline data for demonstration - let now = Utc::now(); - let events = vec![ - DemoTimelineEvent { - event_type: "Learning".to_string(), - title: "PocketFlow Architecture Analysis".to_string(), - description: "Analyzed PocketFlow's Node-Flow architecture pattern and batch optimization framework.".to_string(), - importance: 0.9, - timestamp: now - chrono::Duration::hours(1), - related_concepts: vec!["Node-Flow".to_string(), "Architecture".to_string(), "PocketFlow".to_string()], - }, - DemoTimelineEvent { - event_type: "Conversation".to_string(), - title: "User Query: AI Framework Comparison".to_string(), - description: "User asked about differences between AI orchestration frameworks, particularly PocketFlow vs others.".to_string(), - importance: 0.8, - timestamp: now - chrono::Duration::minutes(45), - related_concepts: vec!["AI Frameworks".to_string(), "Comparison".to_string()], - }, - DemoTimelineEvent { - event_type: "Insight".to_string(), - title: "Pattern Recognition: Batch Optimization".to_string(), - description: "Identified recurring pattern of batch optimization across multiple AI frameworks for LLM cost reduction.".to_string(), - importance: 0.85, - timestamp: now - chrono::Duration::minutes(30), - related_concepts: vec!["Batch Processing".to_string(), "Cost Optimization".to_string(), "LLM".to_string()], - }, - DemoTimelineEvent { - event_type: "Memory Consolidation".to_string(), - title: "Episodic to Semantic Transfer".to_string(), - description: "Transferred episodic memories about AI framework patterns to semantic memory for long-term retention.".to_string(), - importance: 0.75, - timestamp: now - chrono::Duration::minutes(20), - related_concepts: vec!["Memory Transfer".to_string(), "Consolidation".to_string()], - }, - DemoTimelineEvent { - event_type: "Concept Formation".to_string(), - title: "New Concept: Agent Orchestration".to_string(), - description: "Formed new concept linking agent-based frameworks with orchestration patterns from multiple examples.".to_string(), - importance: 0.82, - timestamp: now - chrono::Duration::minutes(10), - related_concepts: vec!["Agent Systems".to_string(), "Orchestration".to_string(), "Patterns".to_string()], - }, - DemoTimelineEvent { - event_type: "Quality Check".to_string(), - title: "Response Quality Assessment".to_string(), - description: "Assessed quality of responses about AI frameworks, identified areas for improvement in technical depth.".to_string(), - importance: 0.7, - timestamp: now - chrono::Duration::minutes(5), - related_concepts: vec!["Quality Assessment".to_string(), "Response Analysis".to_string()], - }, - ]; - - let start_time = events.iter().map(|e| e.timestamp).min().unwrap_or(now); - let end_time = events.iter().map(|e| e.timestamp).max().unwrap_or(now); - - Ok(DemoTimelineData { - events, - metadata: DemoTimelineMetadata { - event_count: 6, - start_time, - end_time, - }, - }) - } -} - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿง  Brain AI - Memory Timeline Visualization Demo"); - println!("================================================="); - - // Create memory service using new architecture - let memory_service = create_memory_service_with_capacity(100).await?; - - // Create demo visualization manager - let viz_config = VisualizationConfig::default(); - let viz_manager = DemoVisualizationManager::new(viz_config); - - // Generate timeline data (uses sample data for demo) - println!("\nโฐ Generating memory timeline data..."); - let timeline_data = viz_manager.generate_memory_timeline_data(&memory_service).await?; - - // Display timeline statistics - println!("\n๐Ÿ“Š Timeline Statistics:"); - println!(" โ€ข Total events: {}", timeline_data.metadata.event_count); - println!(" โ€ข Time span: {} to {}", - timeline_data.metadata.start_time.format("%Y-%m-%d %H:%M:%S"), - timeline_data.metadata.end_time.format("%Y-%m-%d %H:%M:%S")); - - // Display events by type - let mut event_types = HashMap::new(); - for event in &timeline_data.events { - *event_types.entry(&event.event_type).or_insert(0) += 1; - } - - println!("\n๐Ÿท๏ธ Events by Type:"); - for (event_type, count) in &event_types { - println!(" โ€ข {}: {}", event_type, count); - } - - // Display recent high-importance events - println!("\nโญ High-Importance Events (>70%):"); - let mut important_events: Vec<_> = timeline_data.events.iter() - .filter(|e| e.importance > 0.7) - .collect(); - important_events.sort_by(|a, b| b.importance.partial_cmp(&a.importance).unwrap()); - - for event in important_events.iter().take(5) { - println!(" โ€ข {} ({:.1}%): {}", - event.event_type, - event.importance * 100.0, - event.title); - println!(" {}", event.description); - if !event.related_concepts.is_empty() { - println!(" Related: {}", event.related_concepts.join(", ")); - } - println!(); - } - - println!("๐ŸŒ Timeline visualization is available at:"); - println!(" http://localhost:3000/visualization/memory-timeline"); - println!("\n๐Ÿ’ก Features:"); - println!(" โ€ข Interactive chronological timeline with D3.js"); - println!(" โ€ข Event filtering by type, importance, and time range"); - println!(" โ€ข Zoom and pan navigation"); - println!(" โ€ข Event details panel with metadata"); - println!(" โ€ข Export functionality"); - println!(" โ€ข Responsive design for different screen sizes"); - - Ok(()) -} \ No newline at end of file diff --git a/meta_memory_demo.db b/meta_memory_demo.db deleted file mode 100644 index 76ec3e3446a7c23d97de789dfb9c904797380ae0..0000000000000000000000000000000000000000 Binary files a/meta_memory_demo.db and /dev/null differ diff --git a/meta_memory_demo.rs b/meta_memory_demo.rs deleted file mode 100644 index 64f0673671a01df1290d4c43f0844bb68a091442..0000000000000000000000000000000000000000 --- a/meta_memory_demo.rs +++ /dev/null @@ -1,240 +0,0 @@ -//! Meta-Memory System Demonstration -//! -//! This example demonstrates the core capabilities of meta-memory: -//! - Meta-memory structure with confidence tracking -//! - Unified tracking across different knowledge types -//! - Confidence updates based on validation outcomes -//! - Querying by confidence levels and knowledge types -//! - Analytics for knowledge quality assessment - -use brain::{ - MetaMemorySystem, MetaMemoryItem, MetaMemoryQuery, - KnowledgeType, MetaMemoryConfig -}; -use anyhow::Result; -use uuid::Uuid; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Meta-Memory System Demonstration"); - println!("=================================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| std::io::Error::from(e))?; - - // Initialize meta-memory system with default config - let config = MetaMemoryConfig::default(); - - let mut meta_memory = MetaMemorySystem::with_config(config)?; - println!("โœ… Meta-memory system initialized with default configuration"); - - // Phase 2: Create Knowledge Components with Different Types - println!("\n๐Ÿ—๏ธ Phase 2: Create Knowledge Components"); - println!("{}", "-".repeat(40)); - - let mut components = Vec::new(); - - // Create various knowledge components - let knowledge_samples = [ - (KnowledgeType::BPESegment, 0.7, "BPE discovered frequent pattern 'th'"), - (KnowledgeType::ConceptNode, 0.8, "Graph node representing 'animal' concept"), - (KnowledgeType::Rule, 0.6, "If weather=rain then carry=umbrella"), - (KnowledgeType::SemanticConcept, 0.9, "High-level concept 'transportation'"), - (KnowledgeType::WorkingMemory, 0.5, "Active task information"), - (KnowledgeType::EpisodicMemory, 0.7, "User visited park yesterday"), - (KnowledgeType::Pattern, 0.4, "Temporal pattern: morning->coffee"), - (KnowledgeType::ConceptRelationship, 0.6, "Cat IS_A Animal relationship"), - ]; - - for (knowledge_type, initial_confidence, description) in knowledge_samples.iter() { - let component_id = Uuid::new_v4(); - let mut item = MetaMemoryItem::new( - component_id, - knowledge_type.clone(), - *initial_confidence, - description.to_string(), - ); - - // Add some metadata - item.set_metadata("description".to_string(), description.to_string()); - item.set_metadata("created_by".to_string(), "demo_system".to_string()); - - let meta_id = meta_memory.store_item(item)?; - components.push((component_id, meta_id, knowledge_type.clone())); - - println!("๐Ÿ“ Created {} component: {}", knowledge_type, description); - } - - // Phase 3: Demonstrate Confidence Updates - println!("\n๐ŸŽฏ Phase 3: Confidence Updates & Validation"); - println!("{}", "-".repeat(40)); - - println!("Simulating validation outcomes for knowledge components...\n"); - - // Simulate various validation scenarios - let validation_scenarios = [ - (0, true, "BPE segment 'th' successfully used in prediction"), - (0, true, "Segment confirmed by frequency analysis"), - (0, false, "Segment failed in specific context"), - (1, true, "Concept node correctly retrieved"), - (1, true, "Concept relationships validated"), - (2, false, "Rule failed: umbrella not needed indoors"), - (2, true, "Rule successful: umbrella used outside"), - (2, true, "Rule pattern confirmed by user behavior"), - (3, true, "Semantic concept correctly categorized"), - (4, false, "Working memory item became irrelevant"), - (5, true, "Episodic memory accurately recalled"), - (6, false, "Temporal pattern broken by user behavior"), - (7, true, "Concept relationship logically consistent"), - ]; - - for (component_idx, success, description) in validation_scenarios.iter() { - if *component_idx < components.len() { - let (component_id, _, knowledge_type) = &components[*component_idx]; - meta_memory.update_confidence(*component_id, *success)?; - - let status = if *success { "โœ… SUCCESS" } else { "โŒ FAILURE" }; - println!("{} {}: {}", status, knowledge_type, description); - } - } - - // Phase 4: Access Tracking - println!("\n๐Ÿ‘† Phase 4: Usage and Access Tracking"); - println!("{}", "-".repeat(40)); - - // Simulate usage of different components - for (i, (component_id, _, knowledge_type)) in components.iter().enumerate() { - // Simulate different usage frequencies - let access_count = match i % 3 { - 0 => 5, // High usage - 1 => 2, // Medium usage - _ => 1, // Low usage - }; - - for _ in 0..access_count { - meta_memory.mark_accessed(*component_id)?; - } - - println!("๐Ÿ“Š {} accessed {} times", knowledge_type, access_count); - } - - // Phase 5: Query and Analysis - println!("\n๐Ÿ” Phase 5: Query and Analysis"); - println!("{}", "-".repeat(40)); - - // Query high-confidence components - println!("\n๐Ÿ† High-Confidence Components (>= 0.8):"); - let high_confidence = meta_memory.get_high_confidence_components()?; - for item in &high_confidence { - println!(" โ€ข {} [{}]: {:.3} confidence ({} validations, {:.1}% success)", - item.knowledge_type, - item.source, - item.confidence_score, - item.validation_count, - item.success_rate() * 100.0 - ); - } - - // Query low-confidence components - println!("\nโš ๏ธ Low-Confidence Components (< 0.3):"); - let low_confidence = meta_memory.get_low_confidence_components()?; - for item in &low_confidence { - println!(" โ€ข {} [{}]: {:.3} confidence ({} validations, {:.1}% success)", - item.knowledge_type, - item.source, - item.confidence_score, - item.validation_count, - item.success_rate() * 100.0 - ); - } - - // Custom query examples - println!("\n๐ŸŽฏ Custom Query Examples:"); - - // Query by knowledge type - let concept_query = MetaMemoryQuery { - knowledge_type: Some(KnowledgeType::ConceptNode), - ..Default::default() - }; - let concept_items = meta_memory.query_items(&concept_query)?; - println!(" โ€ข Found {} ConceptNode items", concept_items.len()); - - // Query by confidence range - let medium_confidence_query = MetaMemoryQuery { - min_confidence: Some(0.4), - max_confidence: Some(0.7), - sort_by: Some("confidence_score".to_string()), - descending: true, - ..Default::default() - }; - let medium_items = meta_memory.query_items(&medium_confidence_query)?; - println!(" โ€ข Found {} medium-confidence items (0.4-0.7)", medium_items.len()); - - // Query by usage count - let high_usage_query = MetaMemoryQuery { - min_usage_count: Some(3), - sort_by: Some("usage_count".to_string()), - descending: true, - ..Default::default() - }; - let high_usage_items = meta_memory.query_items(&high_usage_query)?; - println!(" โ€ข Found {} frequently used items (>= 3 accesses)", high_usage_items.len()); - - // Phase 6: System Analytics - println!("\n๐Ÿ“ˆ Phase 6: System Analytics & Quality Metrics"); - println!("{}", "-".repeat(40)); - - let stats = meta_memory.get_stats(); - println!("\n๐Ÿ“Š Overall Meta-Memory Statistics:"); - println!(" โ€ข Total Knowledge Components: {}", stats.total_components); - println!(" โ€ข Average Confidence Score: {:.3}", stats.average_confidence); - println!(" โ€ข High-Confidence Count: {} ({:.1}%)", - stats.high_confidence_count, - stats.high_confidence_count as f64 / stats.total_components as f64 * 100.0 - ); - println!(" โ€ข Low-Confidence Count: {} ({:.1}%)", - stats.low_confidence_count, - stats.low_confidence_count as f64 / stats.total_components as f64 * 100.0 - ); - println!(" โ€ข Total Validations: {}", stats.total_validations); - println!(" โ€ข Total Successes: {}", stats.total_successes); - println!(" โ€ข Total Failures: {}", stats.total_failures); - if stats.total_validations > 0 { - println!(" โ€ข Overall Success Rate: {:.1}%", - stats.total_successes as f64 / stats.total_validations as f64 * 100.0 - ); - } - - // Knowledge type distribution - println!("\n๐Ÿ“Š Knowledge Type Distribution:"); - for (knowledge_type, count) in &stats.knowledge_type_distribution { - println!(" โ€ข {}: {} components", knowledge_type, count); - } - - // Confidence distribution - println!("\n๐Ÿ“Š Confidence Distribution:"); - for (threshold, count) in &stats.confidence_distribution { - let prev_threshold = if *threshold == 0.2 { 0.0 } else { threshold - 0.2 }; - println!(" โ€ข {:.1}-{:.1}: {} components", prev_threshold, threshold, count); - } - - // Phase 7: Summary - println!("\n๐ŸŽ‰ Meta-Memory Demo Complete!"); - println!("{}", "=".repeat(50)); - - println!("โœ… Successfully demonstrated:"); - println!(" โ€ข Meta-memory item creation and storage"); - println!(" โ€ข Confidence tracking and updates"); - println!(" โ€ข Usage monitoring and access tracking"); - println!(" โ€ข Advanced querying capabilities"); - println!(" โ€ข System analytics and statistics"); - println!(" โ€ข Knowledge type diversity tracking"); - - println!("\n๐Ÿ“ˆ Final System State:"); - println!(" โ€ข {} total knowledge components managed", stats.total_components); - println!(" โ€ข {:.3} average confidence level", stats.average_confidence); - println!(" โ€ข {} knowledge types represented", stats.knowledge_type_distribution.len()); - println!(" โ€ข {} total interactions tracked", stats.total_usage); - - Ok(()) -} \ No newline at end of file diff --git a/metrics_dashboard.html b/metrics_dashboard.html deleted file mode 100644 index cd7a1aa82e8b3c3a91a18db54566b7b0ba4631c0..0000000000000000000000000000000000000000 --- a/metrics_dashboard.html +++ /dev/null @@ -1,1105 +0,0 @@ - - - - - - - - - Brain AI - Advanced Metrics Dashboard v1.0 - - - - -
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- - - - - - \ No newline at end of file diff --git a/mubrain_basic_demo.rs b/mubrain_basic_demo.rs deleted file mode 100644 index 8e3209687825b66c5d14b4ae1249acb5de7ba7d6..0000000000000000000000000000000000000000 --- a/mubrain_basic_demo.rs +++ /dev/null @@ -1,187 +0,0 @@ -// @oracle: Basic MuBrain symbolic planning demonstration -//! # MuBrain Basic Demo -//! -//! This example demonstrates the fundamental MuBrain symbolic planning capabilities, -//! showing how symbolic states and actions work together for decision making. - -use brain_mubrain::{ - MuBrainPlanner, SymbolicState, EmotionalState, WorkingMemoryState, - ConceptActivation, PlanningContext, -}; -use std::collections::HashMap; -use uuid::Uuid; -use chrono::Utc; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  MuBrain Symbolic Planning Demo"); - println!("==================================\n"); - - // Create a MuBrain planner instance - let mut planner = MuBrainPlanner::new(); - println!("โœ… Created MuBrain planner with ID: {}", planner.id); - println!(" - Planning depth: {}", planner.planning_depth); - println!(" - Rollout breadth: {}", planner.rollout_breadth); - println!(" - Confidence threshold: {:.2}\n", planner.confidence_threshold); - - // Create a planning context for a coding problem - let problem_context = PlanningContext { - problem_description: "Write a function to calculate the factorial of a number".to_string(), - domain: "coding".to_string(), - complexity_level: 3, - time_constraints: None, - available_resources: { - let mut resources = HashMap::new(); - resources.insert("cognitive_load".to_string(), 0.6); - resources.insert("time_budget".to_string(), 1.0); - resources - }, - agent_context: None, - }; - - println!("๐Ÿ“‹ Problem Context:"); - println!(" - Description: {}", problem_context.problem_description); - println!(" - Domain: {}", problem_context.domain); - println!(" - Complexity: {}/10", problem_context.complexity_level); - println!(" - Available resources: {:?}\n", problem_context.available_resources); - - // Build initial symbolic state - let current_state = planner.build_symbolic_state(&problem_context).await?; - println!("๐ŸŽฏ Initial Symbolic State:"); - println!(" - State ID: {}", current_state.id); - println!(" - Clarity score: {:.2}", current_state.clarity_score); - println!(" - Uncertainty: {:.2}", current_state.uncertainty); - println!(" - Emotions:"); - println!(" โ€ข Curiosity: {:.2}", current_state.emotions.curiosity); - println!(" โ€ข Confidence: {:.2}", current_state.emotions.confidence); - println!(" โ€ข Frustration: {:.2}", current_state.emotions.frustration); - println!(" โ€ข Satisfaction: {:.2}", current_state.emotions.satisfaction); - println!(" - Working memory focus: {}", current_state.working_memory.current_focus); - println!(" - Active concepts: {:?}\n", current_state.working_memory.active_concepts); - - // Plan optimal response using MuBrain - println!("๐Ÿ”ฎ Planning optimal response using symbolic planning..."); - let planning_result = planner.plan_optimal_response(&problem_context, ¤t_state).await?; - - println!("โœจ Planning Result:"); - println!(" - Recommended action: {:?}", planning_result.recommended_action); - println!(" - Confidence score: {:.2}", planning_result.confidence_score); - println!(" - Planning time: {}ms", planning_result.planning_time_ms); - println!(" - Alternative actions: {} options", planning_result.alternative_actions.len()); - println!(" - Learning signals: {} generated\n", planning_result.learning_signals.len()); - - // Show reasoning path - println!("๐Ÿงญ Reasoning Path:"); - for (i, step) in planning_result.reasoning_path.iter().enumerate() { - println!(" Step {}: {}", i + 1, step.reasoning); - println!(" Value estimate: {:.3}", step.value_estimate); - println!(" Action: {:?}", step.action); - } - println!(); - - // Show alternative actions - if !planning_result.alternative_actions.is_empty() { - println!("๐Ÿ”„ Alternative Actions Considered:"); - for (i, alt) in planning_result.alternative_actions.iter().enumerate() { - println!(" {}. {:?}", i + 1, alt.action); - println!(" Value: {:.3}, Confidence: {:.3}, Risk: {:.3}", - alt.estimated_value, alt.confidence, alt.risk_assessment); - } - println!(); - } - - // Show learning signals - if !planning_result.learning_signals.is_empty() { - println!("๐Ÿ“š Learning Signals Generated:"); - for (i, signal) in planning_result.learning_signals.iter().enumerate() { - println!(" {}. Type: {:?}", i + 1, signal.signal_type); - println!(" Magnitude: {:.3}", signal.magnitude); - println!(" Context: {}", signal.context); - } - println!(); - } - - // Demonstrate learning from execution results - println!("๐ŸŽ“ Simulating learning from execution..."); - - // Simulate successful execution - use brain_mubrain::planner::ExecutionResult; - let execution_result = ExecutionResult::Success { - reward: 0.8, - output: "Successfully generated factorial function".to_string(), - metrics: { - let mut metrics = HashMap::new(); - metrics.insert("code_quality".to_string(), 0.9); - metrics.insert("execution_time".to_string(), 0.2); - metrics - }, - }; - - planner.learn_from_execution(&planning_result.recommended_action, &execution_result).await?; - println!("โœ… Learning completed from successful execution"); - println!(" - Reward received: 0.8"); - println!(" - Model weights updated"); - println!(" - Planning confidence should improve for similar problems\n"); - - // Demonstrate symbolic state evolution - println!("๐Ÿ”„ Demonstrating state evolution through actions..."); - - // Create a new state representing progress - let evolved_state = SymbolicState { - id: Uuid::new_v4(), - timestamp: Utc::now(), - context: problem_context.clone(), - emotions: EmotionalState { - curiosity: current_state.emotions.curiosity * 0.9, // Slightly less curious after progress - confidence: (current_state.emotions.confidence + 0.2).min(1.0), // More confident - frustration: (current_state.emotions.frustration * 0.5).max(0.0), // Less frustrated - satisfaction: (current_state.emotions.satisfaction + 0.3).min(1.0), // More satisfied - }, - working_memory: WorkingMemoryState { - active_concepts: vec!["coding".to_string(), "recursion".to_string(), "mathematics".to_string()], - recent_actions: vec![planning_result.recommended_action.clone()], - current_focus: "implementing factorial logic".to_string(), - attention_weight: 0.9, - }, - concepts: ConceptActivation { - activated_concepts: { - let mut concepts = HashMap::new(); - concepts.insert("recursion".to_string(), 0.8); - concepts.insert("mathematics".to_string(), 0.7); - concepts.insert("coding".to_string(), 0.9); - concepts - }, - relationship_weights: HashMap::new(), - spreading_activation: 0.6, - }, - clarity_score: (current_state.clarity_score + 0.2).min(1.0), - uncertainty: (current_state.uncertainty * 0.7).max(0.0), - }; - - println!("๐Ÿ“ˆ State Evolution:"); - println!(" - Clarity: {:.2} โ†’ {:.2} (+{:.2})", - current_state.clarity_score, evolved_state.clarity_score, - evolved_state.clarity_score - current_state.clarity_score); - println!(" - Uncertainty: {:.2} โ†’ {:.2} ({:.2})", - current_state.uncertainty, evolved_state.uncertainty, - evolved_state.uncertainty - current_state.uncertainty); - println!(" - Confidence: {:.2} โ†’ {:.2} (+{:.2})", - current_state.emotions.confidence, evolved_state.emotions.confidence, - evolved_state.emotions.confidence - current_state.emotions.confidence); - println!(" - Satisfaction: {:.2} โ†’ {:.2} (+{:.2})", - current_state.emotions.satisfaction, evolved_state.emotions.satisfaction, - evolved_state.emotions.satisfaction - current_state.emotions.satisfaction); - println!(" - Active concepts: {} โ†’ {}", - current_state.working_memory.active_concepts.len(), - evolved_state.working_memory.active_concepts.len()); - - println!("\n๐ŸŽ‰ MuBrain Symbolic Planning Demo Complete!"); - println!(" Key achievements:"); - println!(" โ€ข Created symbolic state representation"); - println!(" โ€ข Generated planning with reasoning path"); - println!(" โ€ข Demonstrated learning from execution"); - println!(" โ€ข Showed state evolution through actions"); - println!(" โ€ข Established foundation for independent intelligence"); - - Ok(()) -} \ No newline at end of file diff --git a/neural_architecture_demo.rs b/neural_architecture_demo.rs deleted file mode 100644 index 1480ccbbef1dbc21dd3d0d6d24423fd8379cdcbd..0000000000000000000000000000000000000000 --- a/neural_architecture_demo.rs +++ /dev/null @@ -1,305 +0,0 @@ -//! Neural Architecture Demo - Task 3.1 -//! -//! This example demonstrates the advanced neural architecture features including: -//! - Self-attention and multi-head attention mechanisms -//! - Transformer encoder architecture with layer normalization -//! - Post-transformer developmental AI with adaptive growth -//! - Integration with existing character prediction and segmentation - -use brain::neural_architecture::{ - SelfAttention, AttentionConfig, TransformerPredictor, TransformerConfig, - DevelopmentalPredictor, GrowthConfig, SelfAttentionService, TransformerPredictorService, - DevelopmentalPredictorService -}; -use brain::character_ingestion::{CharacterVocab, CharacterPredictor, ModelConfig}; -use brain::Result; -use nalgebra::DMatrix; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain Neural Architecture - Advanced Features Demo (Task 3.1)"); - println!("================================================================"); - - // Ensure data and logs directories exist - std::fs::create_dir_all("data").map_err(|e| { - brain::BrainError::from(e).with_context( - brain_types::ErrorContext::new("create_data_directory") - .with_details("Failed to create data directory for neural architecture demo") - ) - })?; - std::fs::create_dir_all("logs").map_err(|e| { - brain::BrainError::from(e).with_context( - brain_types::ErrorContext::new("create_logs_directory") - .with_details("Failed to create logs directory for neural architecture demo") - ) - })?; - std::fs::create_dir_all("temp").map_err(|e| { - brain::BrainError::from(e).with_context( - brain_types::ErrorContext::new("create_temp_directory") - .with_details("Failed to create temp directory for neural architecture demo") - ) - })?; - - // 1. Demonstrate Self-Attention Mechanism - println!("\nโšก Self-Attention Mechanism Demo"); - println!("==============================="); - - let attention_config = AttentionConfig { - model_dim: 64, - num_heads: 4, - head_dim: 16, - dropout_rate: 0.1, - use_scaling: true, - }; - - let mut attention = SelfAttention::new(attention_config.clone())?; - println!("โœ… Self-attention layer created:"); - println!(" - Model dimension: {}", attention_config.model_dim); - println!(" - Number of heads: {}", attention_config.num_heads); - println!(" - Head dimension: {}", attention_config.head_dim); - - // Create sample input sequences - let seq_len = 8; - let input = DMatrix::from_fn(seq_len, attention_config.model_dim, |i, j| { - // Create a pattern that changes over positions - ((i as f64 + 1.0) * 0.1 + (j as f64) * 0.01).sin() - }); - - println!("\n๐Ÿ“Š Processing sequence of length {}...", seq_len); - let attention_output = attention.forward(&input).await?; - println!("โœ… Self-attention forward pass completed"); - println!(" - Input shape: {}x{}", input.nrows(), input.ncols()); - println!(" - Output shape: {}x{}", attention_output.nrows(), attention_output.ncols()); - - // Analyze attention weights - if let Some(weights) = attention.get_attention_weights().await { - println!("๐Ÿ” Attention weights analysis:"); - println!(" - Attention matrix shape: {}x{}", weights.nrows(), weights.ncols()); - - // Find strongest attention connections - let mut max_attention = 0.0; - let mut max_pos = (0, 0); - for i in 0..weights.nrows() { - for j in 0..weights.ncols() { - if weights[(i, j)] > max_attention { - max_attention = weights[(i, j)]; - max_pos = (i, j); - } - } - } - println!(" - Strongest attention: {:.4} at position ({}, {})", max_attention, max_pos.0, max_pos.1); - } - - // 2. Demonstrate Transformer Architecture - println!("\n๐Ÿ—๏ธ Transformer Architecture Demo"); - println!("==============================="); - - let vocab_size = 100; - let transformer_config = TransformerConfig { - model_dim: 128, - num_layers: 3, - num_heads: 4, - ff_hidden_dim: 256, - max_seq_len: 32, - dropout_rate: 0.1, - }; - - let mut transformer = TransformerPredictor::new(vocab_size, Some(transformer_config.clone()))?; - println!("โœ… Transformer predictor created:"); - println!(" - Vocabulary size: {}", vocab_size); - println!(" - Number of layers: {}", transformer_config.num_layers); - println!(" - Model dimension: {}", transformer_config.model_dim); - println!(" - Feed-forward hidden: {}", transformer_config.ff_hidden_dim); - - // Test transformer prediction - let input_sequence = vec![1, 15, 23, 42, 7, 89, 34]; - println!("\n๐Ÿ”ฎ Testing transformer prediction..."); - println!("Input sequence: {:?}", input_sequence); - - let predictions = transformer.predict_next(&input_sequence).await?; - println!("โœ… Prediction completed, output dimension: {}", predictions.len()); - - // Analyze predictions - let mut top_predictions = Vec::new(); - for (i, &prob) in predictions.iter().enumerate() { - top_predictions.push((i, prob)); - } - top_predictions.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); - - println!("๐ŸŽฏ Top 5 predictions:"); - for (i, (token_id, prob)) in top_predictions.iter().take(5).enumerate() { - println!(" {}. Token {}: {:.4}", i + 1, token_id, prob); - } - - // Analyze attention maps - let attention_maps = transformer.get_attention_maps().await; - println!("\n๐Ÿ—บ๏ธ Attention maps analysis:"); - for (layer_idx, attention_map) in attention_maps.iter().enumerate() { - if let Some(map) = attention_map { - println!(" - Layer {}: {}x{} attention matrix", layer_idx + 1, map.nrows(), map.ncols()); - } else { - println!(" - Layer {}: No attention weights available", layer_idx + 1); - } - } - - // 3. Demonstrate Developmental AI - println!("\n๐ŸŒฑ Developmental AI Demo"); - println!("========================"); - - let growth_config = GrowthConfig { - initial_scale: 0.3, - growth_rate: 1.8, - max_scale: 3.0, - complexity_threshold: 0.7, - enable_meta_learning: true, - }; - - let mut dev_predictor = DevelopmentalPredictor::new(vocab_size, Some(transformer_config), Some(growth_config.clone()))?; - println!("โœ… Developmental predictor created:"); - println!(" - Initial stage: {:?}", dev_predictor.get_developmental_stage().await); - println!(" - Initial scale: {:.1}x", growth_config.initial_scale); - println!(" - Growth rate: {:.1}x", growth_config.growth_rate); - println!(" - Meta-learning: {}", growth_config.enable_meta_learning); - - // Simulate developmental learning - println!("\n๐Ÿ“ˆ Simulating developmental learning sessions..."); - - let learning_contexts = vec![ - "Character sequence learning", - "Pattern recognition training", - "Context understanding", - "Complex reasoning", - "Abstract concept formation", - ]; - - for (session, context) in learning_contexts.iter().enumerate() { - println!("\n--- Learning Session {} ---", session + 1); - println!("Context: {}", context); - - // Generate different input patterns for each session - let input_ids: Vec = (0..=session).map(|i| i * 7 % vocab_size).collect(); - let output = dev_predictor.developmental_forward(&input_ids, context).await?; - - println!(" - Input length: {}", input_ids.len()); - println!(" - Developmental stage: {:?}", dev_predictor.get_developmental_stage().await); - - let capacity = dev_predictor.get_capacity_metrics().await; - println!(" - Current complexity: {:.3}", capacity.current_complexity); - println!(" - Utilization: {:.3}", capacity.utilization); - println!(" - Growth pressure: {:.3}", capacity.growth_pressure); - - // Show top prediction - let (max_idx, max_prob) = output.iter().enumerate() - .max_by(|a, b| a.1.partial_cmp(b.1).unwrap()) - .unwrap(); - println!(" - Top prediction: Token {} (prob: {:.4})", max_idx, max_prob); - } - - // 4. Learning History Analysis - println!("\n๐Ÿ“š Learning History Analysis"); - println!("============================"); - - let learning_history = dev_predictor.get_learning_history().await; - println!("Total learning events: {}", learning_history.len()); - - for (i, event) in learning_history.iter().enumerate() { - println!("Event {}: {:?}", i + 1, event.learning_type); - println!(" - Performance change: {:.3} -> {:.3}", event.performance_before, event.performance_after); - println!(" - Context: {}", event.context); - println!(" - Timestamp: {}", event.timestamp); - } - - // 5. Export Developmental State - println!("\n๐Ÿ’พ Exporting Developmental State"); - println!("==============================="); - - match dev_predictor.export_developmental_state().await { - Ok(state_json) => { - println!("โœ… Developmental state exported ({} bytes)", state_json.len()); - - // Save to data directory - std::fs::write("data/developmental_state.json", &state_json) - .map_err(|e| { - brain::BrainError::from(e).with_context( - brain_types::ErrorContext::new("save_developmental_state") - .with_details("Failed to save developmental state to data/developmental_state.json") - ) - })?; - println!("๐Ÿ“ State saved to 'data/developmental_state.json'"); - - // Show summary - println!("\n๐Ÿ” State Summary:"); - println!(" - Current stage: {:?}", dev_predictor.get_developmental_stage().await); - println!(" - Learning events: {}", learning_history.len()); - let final_capacity = dev_predictor.get_capacity_metrics().await; - println!(" - Capacity complexity: {:.3}", final_capacity.current_complexity); - } - Err(e) => { - println!("โŒ Failed to export state: {}", e); - } - } - - // 6. Integration with Character Predictor - println!("\n๐Ÿ”— Integration with Character Predictor"); - println!("======================================"); - - // Create a traditional character predictor for comparison - let training_text = "the quick brown fox jumps over the lazy dog"; - let vocab = CharacterVocab::from_text(training_text); - let vocab_size_display = vocab.vocab_size(); - let model_config = ModelConfig { - vocab_size: vocab.vocab_size(), - embedding_dim: 32, - hidden_dim: 64, - learning_rate: 0.01, - sequence_length: 8, - }; - - let _char_predictor = CharacterPredictor::new(vocab, Some(model_config))?; - println!("โœ… Character predictor created for comparison"); - println!(" - Traditional architecture: Feedforward"); - println!(" - Vocab size: {}", vocab_size_display); - - // Compare architectural complexity - println!("\nโš–๏ธ Architecture Comparison:"); - println!("Traditional Character Predictor:"); - println!(" - Type: Simple feedforward"); - println!(" - Parameters: ~few thousand"); - println!(" - Capabilities: Basic character prediction"); - - println!("\nAdvanced Transformer Predictor:"); - println!(" - Type: Multi-layer transformer"); - println!(" - Parameters: ~hundreds of thousands"); - println!(" - Capabilities: Attention-based sequence modeling"); - - println!("\nDevelopmental AI Predictor:"); - println!(" - Type: Adaptive transformer with growth"); - println!(" - Parameters: Dynamic (grows over time)"); - println!(" - Capabilities: Meta-learning, developmental adaptation"); - - // 7. Performance Insights - println!("\n๐ŸŽฏ Performance Insights & Next Steps"); - println!("===================================="); - - println!("โœ… Task 3.1 Features Demonstrated:"); - println!(" โšก Self-attention mechanisms with multi-head support"); - println!(" ๐Ÿ—๏ธ Transformer encoder architecture"); - println!(" ๐ŸŒฑ Developmental AI with adaptive growth"); - println!(" ๐Ÿ“Š Advanced attention analysis and visualization"); - println!(" ๐Ÿ”— Integration with existing prediction systems"); - println!(" ๐Ÿ“š Meta-learning and developmental tracking"); - - println!("\n๐Ÿš€ Ready for Task 3.2: Advanced Neural Features"); - println!(" - Cross-attention between character and segment representations"); - println!(" - Encoder-decoder architectures"); - println!(" - Advanced positional encodings"); - println!(" - Neural architecture search capabilities"); - println!(" - Continual learning mechanisms"); - - println!("\n๐ŸŽ‰ Neural Architecture Demo Complete!"); - println!("====================================="); - println!("The Brain project now features cutting-edge transformer"); - println!("architectures with post-transformer developmental AI!"); - - Ok(()) -} \ No newline at end of file diff --git a/neural_engine_integration_demo.rs b/neural_engine_integration_demo.rs deleted file mode 100644 index a20529840275118813abc43fcf642939cff8d443..0000000000000000000000000000000000000000 --- a/neural_engine_integration_demo.rs +++ /dev/null @@ -1,112 +0,0 @@ -use std::time::Instant; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Neural Engine Integration Demo"); - println!("=================================="); - - demonstrate_neural_processing_pipeline().await?; - demonstrate_cognitive_enhancement().await?; - demonstrate_neural_code_generation().await?; - demonstrate_performance_monitoring().await?; - demonstrate_batch_processing().await?; - - println!("\nโœ… Neural Engine Integration Demo completed successfully!"); - Ok(()) -} - -async fn demonstrate_neural_processing_pipeline() -> Result<(), Box> { - println!("\n๐Ÿ”„ Neural Processing Pipeline Demonstration"); - println!("--------------------------------------------"); - - println!(" ๐Ÿ”ง Initializing neural processing pipeline..."); - - // Simulate neural processing without actual model dependencies - let start_time = Instant::now(); - - println!(" ๐Ÿง  Processing cognitive enhancement requests..."); - println!(" โ””โ”€ Symbolic state integration: โœ… Active"); - println!(" โ””โ”€ Context awareness: โœ… Enhanced"); - println!(" โ””โ”€ Memory integration: โœ… Operational"); - - let processing_time = start_time.elapsed(); - println!(" โšก Processing completed in {:.2}ms", processing_time.as_millis()); - - Ok(()) -} - -async fn demonstrate_cognitive_enhancement() -> Result<(), Box> { - println!("\n๐Ÿง  Cognitive Enhancement Demonstration"); - println!("--------------------------------------"); - - println!(" ๐ŸŽฏ Testing cognitive enhancement levels..."); - - let enhancement_levels = vec!["Basic", "Intermediate", "Advanced"]; - - for level in enhancement_levels { - println!(" ๐Ÿ” Testing {} cognitive enhancement:", level); - println!(" โ””โ”€ Pattern recognition: โœ… Enhanced"); - println!(" โ””โ”€ Context awareness: โœ… Improved"); - println!(" โ””โ”€ Decision quality: โœ… Optimized"); - } - - Ok(()) -} - -async fn demonstrate_neural_code_generation() -> Result<(), Box> { - println!("\n๐Ÿ’ป Neural Code Generation Demonstration"); - println!("----------------------------------------"); - - println!(" ๐ŸŽฏ Testing neural-enhanced code generation..."); - - let test_prompts = vec![ - "Sort an array efficiently", - "Implement binary search", - "Create a hash map" - ]; - - for prompt in test_prompts { - println!(" ๐Ÿ“ Processing: '{}'", prompt); - println!(" โ””โ”€ Neural analysis: โœ… Complete"); - println!(" โ””โ”€ Code generation: โœ… Optimized"); - println!(" โ””โ”€ Quality score: 95.2%"); - } - - Ok(()) -} - -async fn demonstrate_performance_monitoring() -> Result<(), Box> { - println!("\n๐Ÿ“Š Performance Monitoring Demonstration"); - println!("---------------------------------------"); - - println!("๐Ÿ” Neural Engine Performance Analysis:"); - println!(" โ€ข Model inference latency: <1ms average"); - println!(" โ€ข Memory usage: Optimized for production"); - println!(" โ€ข Cognitive enhancement: Active"); - println!(" โ€ข Success rate: 99.7%"); - println!(" โ€ข Throughput: 1000+ requests/second"); - - Ok(()) -} - -async fn demonstrate_batch_processing() -> Result<(), Box> { - println!("\nโšก Batch Processing Demonstration"); - println!("--------------------------------"); - - println!(" ๐Ÿ”„ Processing batch requests concurrently..."); - - let start_time = Instant::now(); - - // Simulate batch processing - let batch_size = 10; - println!(" ๐Ÿ“ฆ Processing {} requests in parallel...", batch_size); - - // Simulate some processing time - tokio::time::sleep(tokio::time::Duration::from_millis(100)).await; - - let batch_time = start_time.elapsed(); - println!(" โœ… Batch completed in {:.2}ms", batch_time.as_millis()); - println!(" ๐Ÿ“ˆ Average per request: {:.2}ms", batch_time.as_millis() as f64 / batch_size as f64); - - Ok(()) -} \ No newline at end of file diff --git a/novelty_demo.db b/novelty_demo.db deleted file mode 100644 index 6595eb54c9bac00af32f60f16ef8e0bd15905f4d..0000000000000000000000000000000000000000 Binary files a/novelty_demo.db and /dev/null differ diff --git a/novelty_detection_demo.rs b/novelty_detection_demo.rs deleted file mode 100644 index d020afa7d82026b595af97b93c856e70ebbaa073..0000000000000000000000000000000000000000 --- a/novelty_detection_demo.rs +++ /dev/null @@ -1,916 +0,0 @@ -//! Novelty Detection System Demonstration -//! -//! This example demonstrates the capabilities of novelty detection: -//! - Statistical novelty detection comparing inputs against knowledge distributions -//! - Surprise metrics quantifying deviation from expected patterns -//! - Anomaly detection for identifying outlier inputs -//! - Context-based novelty assessment considering task context -//! - Novelty scoring system (0-1 range) combining multiple detection methods -//! - Integration with meta-memory system for confidence-based assessments - -use anyhow::Result; -use std::collections::HashMap; -use std::sync::Arc; -use tokio::sync::RwLock; -use uuid::Uuid; -use chrono::{DateTime, Utc}; -use std::collections::VecDeque; - -// Import from new service architecture -use brain_cognitive::meta::{MetaMemoryService, MetaMemoryRepository, MetaMemoryAnalytics, - MetaMemoryMaintenance, MetaMemoryConfig, KnowledgeType, MetaMemoryItem, IntegrityReport}; -use brain_types::BrainError; - -/// Configuration for novelty detection -#[derive(Debug, Clone)] -pub struct DemoNoveltyConfig { - pub high_novelty_threshold: f64, - pub low_novelty_threshold: f64, - pub statistical_weight: f64, - pub confidence_weight: f64, - pub context_weight: f64, - pub min_sample_size: usize, - pub context_window_size: usize, - pub enable_logging: bool, - pub max_novelty_records: usize, -} - -impl Default for DemoNoveltyConfig { - fn default() -> Self { - Self { - high_novelty_threshold: 0.7, - low_novelty_threshold: 0.3, - statistical_weight: 0.4, - confidence_weight: 0.3, - context_weight: 0.3, - min_sample_size: 5, - context_window_size: 5, - enable_logging: true, - max_novelty_records: 1000, - } - } -} - -/// Context for novelty assessment -#[derive(Debug, Clone)] -pub struct DemoNoveltyContext { - pub task_context: String, - pub recent_inputs: Vec, - pub active_components: Vec, - pub temporal_context: DateTime, - pub metadata: HashMap, -} - -impl Default for DemoNoveltyContext { - fn default() -> Self { - Self { - task_context: "general".to_string(), - recent_inputs: Vec::new(), - active_components: Vec::new(), - temporal_context: Utc::now(), - metadata: HashMap::new(), - } - } -} - -/// Method used for novelty detection -#[derive(Debug, Clone, PartialEq, Eq, Hash)] -pub enum NoveltyMethod { - Statistical, - ConfidenceBased, - ContextBased, - FrequencyAnalysis, - PatternMatching, -} - -/// Local novelty level with Hash support -#[derive(Debug, Clone, PartialEq, Eq, Hash)] -pub enum NoveltyLevel { - VeryLow, - Low, - Medium, - High, - VeryHigh, -} - -/// Enhanced novelty assessment with detailed method breakdown -#[derive(Debug, Clone)] -pub struct DetailedNoveltyAssessment { - pub novelty_score: f64, - pub assessment_confidence: f64, - pub method_scores: HashMap, - pub explanation: Vec, - pub recommendations: Vec, - pub input: String, -} - -impl DetailedNoveltyAssessment { - pub fn get_novelty_level(&self, config: &DemoNoveltyConfig) -> NoveltyLevel { - if self.novelty_score >= config.high_novelty_threshold { - NoveltyLevel::High - } else if self.novelty_score <= config.low_novelty_threshold { - NoveltyLevel::Low - } else { - NoveltyLevel::Medium - } - } -} - -/// Novelty detection statistics -#[derive(Debug, Clone)] -pub struct NoveltyStats { - pub total_assessments: usize, - pub average_novelty_score: f64, - pub average_assessment_confidence: f64, - pub novelty_distribution: HashMap, - pub method_usage: HashMap, - pub common_contexts: HashMap, -} - -/// Comprehensive novelty detection engine -pub struct DemoNoveltyDetectionEngine { - config: DemoNoveltyConfig, - #[allow(dead_code)] - meta_memory: Arc, - #[allow(dead_code)] - prediction_history: VecDeque<(String, f64)>, - #[allow(dead_code)] - context_window: Vec, - #[allow(dead_code)] - context_history: Arc>>, - stats: Arc>, - known_patterns: Arc>>, - assessment_history: Arc>>, -} - -impl DemoNoveltyDetectionEngine { - pub fn new(config: DemoNoveltyConfig, meta_memory: Arc) -> Result { - Ok(Self { - config, - meta_memory, - prediction_history: VecDeque::new(), - context_window: Vec::new(), - context_history: Arc::new(RwLock::new(HashMap::new())), - stats: Arc::new(RwLock::new(NoveltyStats { - total_assessments: 0, - average_novelty_score: 0.0, - average_assessment_confidence: 0.0, - novelty_distribution: HashMap::new(), - method_usage: HashMap::new(), - common_contexts: HashMap::new(), - })), - known_patterns: Arc::new(RwLock::new(HashMap::new())), - assessment_history: Arc::new(RwLock::new(Vec::new())), - }) - } - - pub async fn assess_novelty(&mut self, input: &str, context: Option) -> Result { - let context = context.unwrap_or_default(); - - // Calculate different types of novelty scores - let statistical_score = self.calculate_statistical_novelty(input).await?; - let confidence_score = self.calculate_confidence_based_novelty(input).await?; - let context_score = self.calculate_context_based_novelty(input, &context).await?; - let frequency_score = self.calculate_frequency_novelty(input).await?; - let pattern_score = self.calculate_pattern_novelty(input).await?; - - // Combine scores using weighted average - let overall_novelty = - statistical_score * self.config.statistical_weight + - confidence_score * self.config.confidence_weight + - context_score * self.config.context_weight + - frequency_score * 0.15 + - pattern_score * 0.15; - - let overall_novelty = overall_novelty.clamp(0.0, 1.0); - - // Create method breakdown - let mut method_scores = HashMap::new(); - method_scores.insert(NoveltyMethod::Statistical, statistical_score); - method_scores.insert(NoveltyMethod::ConfidenceBased, confidence_score); - method_scores.insert(NoveltyMethod::ContextBased, context_score); - method_scores.insert(NoveltyMethod::FrequencyAnalysis, frequency_score); - method_scores.insert(NoveltyMethod::PatternMatching, pattern_score); - - // Generate explanations - let explanation = self.generate_explanation(input, &method_scores).await; - let recommendations = self.generate_recommendations(overall_novelty, &context).await; - - let assessment = DetailedNoveltyAssessment { - novelty_score: overall_novelty, - assessment_confidence: 0.85, // High confidence in demo implementation - method_scores, - explanation, - recommendations, - input: input.to_string(), - }; - - // Update statistics and history - self.update_stats_and_history(&assessment, &context).await; - - Ok(assessment) - } - - async fn calculate_statistical_novelty(&self, input: &str) -> Result { - let known_patterns = self.known_patterns.read().await; - let words: Vec<&str> = input.split_whitespace().collect(); - - if words.is_empty() { - return Ok(1.0); // Empty input is novel - } - - let mut novelty_sum = 0.0; - for word in &words { - let word_lower = word.to_lowercase(); - let familiarity = known_patterns.get(&word_lower).copied().unwrap_or(0.0); - novelty_sum += 1.0 - familiarity; - } - - Ok((novelty_sum / words.len() as f64).clamp(0.0, 1.0)) - } - - async fn calculate_confidence_based_novelty(&self, input: &str) -> Result { - // Check against meta-memory for confidence-based assessment - let word_count = input.split_whitespace().count(); - let char_count = input.chars().count(); - - // Simple heuristic: longer, more complex inputs are potentially more novel - let complexity_score = if char_count > 0 { - (word_count as f64 / char_count as f64 * 10.0).clamp(0.0, 1.0) - } else { - 0.0 - }; - - Ok(complexity_score) - } - - async fn calculate_context_based_novelty(&self, input: &str, context: &DemoNoveltyContext) -> Result { - // Calculate novelty based on context - let context_novelty = match context.task_context.as_str() { - "technology" | "general" => { - if input.to_lowercase().contains("machine") || input.to_lowercase().contains("computer") { - 0.2 // Low novelty in tech context - } else { - 0.7 // Higher novelty - } - } - "cooking" => { - if input.to_lowercase().contains("food") || input.to_lowercase().contains("recipe") { - 0.3 - } else { - 0.8 - } - } - "poetry" | "creative" => { - // Creative contexts expect more novelty - if input.chars().any(|c| !c.is_ascii_alphanumeric() && !c.is_whitespace()) { - 0.9 // Creative symbols - } else { - 0.6 - } - } - _ => 0.5 // Default medium novelty - }; - - Ok(context_novelty) - } - - async fn calculate_frequency_novelty(&self, input: &str) -> Result { - // Check for repetitive patterns - let chars: Vec = input.chars().collect(); - if chars.is_empty() { - return Ok(0.0); - } - - let mut char_counts = HashMap::new(); - for &ch in &chars { - *char_counts.entry(ch).or_insert(0) += 1; - } - - // Calculate repetition score - let max_count = char_counts.values().max().copied().unwrap_or(0); - let repetition_ratio = max_count as f64 / chars.len() as f64; - - // High repetition = low novelty - Ok(1.0 - repetition_ratio) - } - - async fn calculate_pattern_novelty(&self, input: &str) -> Result { - // Simple pattern analysis - let has_mixed_case = input.chars().any(|c| c.is_uppercase()) && input.chars().any(|c| c.is_lowercase()); - let has_numbers = input.chars().any(|c| c.is_numeric()); - let has_symbols = input.chars().any(|c| !c.is_alphanumeric() && !c.is_whitespace()); - let has_spaces = input.contains(' '); - - let pattern_complexity = [has_mixed_case, has_numbers, has_symbols, has_spaces] - .iter() - .map(|&b| if b { 0.25 } else { 0.0 }) - .sum::(); - - Ok(pattern_complexity) - } - - async fn generate_explanation(&self, input: &str, method_scores: &HashMap) -> Vec { - let mut explanations = Vec::new(); - - if let Some(&statistical_score) = method_scores.get(&NoveltyMethod::Statistical) { - if statistical_score > 0.7 { - explanations.push("Input contains unfamiliar word patterns".to_string()); - } else if statistical_score < 0.3 { - explanations.push("Input matches known patterns well".to_string()); - } - } - - if input.len() > 50 { - explanations.push("Input length suggests complexity".to_string()); - } - - if input.chars().any(|c| !c.is_ascii_alphanumeric() && !c.is_whitespace()) { - explanations.push("Contains special characters or symbols".to_string()); - } - - if explanations.is_empty() { - explanations.push("Standard text input with moderate characteristics".to_string()); - } - - explanations - } - - async fn generate_recommendations(&self, novelty_score: f64, context: &DemoNoveltyContext) -> Vec { - let mut recommendations = Vec::new(); - - if novelty_score > 0.8 { - recommendations.push("High novelty detected - prioritize for learning".to_string()); - recommendations.push("Consider deeper analysis and pattern storage".to_string()); - } else if novelty_score > 0.6 { - recommendations.push("Moderate novelty - schedule for validation".to_string()); - } else { - recommendations.push("Low novelty - process with standard confidence".to_string()); - } - - if context.task_context == "creative" && novelty_score < 0.5 { - recommendations.push("Consider encouraging more creative expression".to_string()); - } - - recommendations - } - - async fn update_stats_and_history(&mut self, assessment: &DetailedNoveltyAssessment, context: &DemoNoveltyContext) { - let mut stats = self.stats.write().await; - stats.total_assessments += 1; - - // Update averages - let total = stats.total_assessments as f64; - stats.average_novelty_score = ((stats.average_novelty_score * (total - 1.0)) + assessment.novelty_score) / total; - stats.average_assessment_confidence = ((stats.average_assessment_confidence * (total - 1.0)) + assessment.assessment_confidence) / total; - - // Update distributions - let level = assessment.get_novelty_level(&self.config); - *stats.novelty_distribution.entry(level).or_insert(0) += 1; - - // Update method usage - for method in assessment.method_scores.keys() { - *stats.method_usage.entry(method.clone()).or_insert(0) += 1; - } - - // Update context usage - *stats.common_contexts.entry(context.task_context.clone()).or_insert(0) += 1; - - // Store assessment history - let mut history = self.assessment_history.write().await; - history.push(assessment.clone()); - - // Keep history size manageable - if history.len() > self.config.max_novelty_records { - history.remove(0); - } - } - - pub async fn get_stats(&self) -> NoveltyStats { - self.stats.read().await.clone() - } - - pub fn get_config(&self) -> &DemoNoveltyConfig { - &self.config - } - - pub async fn get_assessments_by_level(&self, level: NoveltyLevel) -> Vec { - let history = self.assessment_history.read().await; - history.iter() - .filter(|assessment| assessment.get_novelty_level(&self.config) == level) - .cloned() - .collect() - } - - pub async fn get_recent_assessments(&self, limit: usize) -> Vec { - let history = self.assessment_history.read().await; - history.iter() - .rev() - .take(limit) - .cloned() - .collect() - } - - pub async fn seed_with_patterns(&mut self, patterns: &[(&str, f64)]) { - let mut known_patterns = self.known_patterns.write().await; - for (pattern, confidence) in patterns { - known_patterns.insert(pattern.to_lowercase(), *confidence); - } - } -} - -/// Simple meta-memory repository implementation for demo -#[derive(Debug)] -pub struct SimpleMetaMemoryRepository { - items: Arc>>, - component_to_item: Arc>>, -} - -impl SimpleMetaMemoryRepository { - pub fn new() -> Self { - Self { - items: Arc::new(RwLock::new(HashMap::new())), - component_to_item: Arc::new(RwLock::new(HashMap::new())), - } - } -} - -use brain_cognitive::meta::{MetaMemoryResult, MetaMemoryQuery, MetaMemoryStats, PerformanceMetrics}; - -#[async_trait::async_trait] -impl MetaMemoryRepository for SimpleMetaMemoryRepository { - async fn store_item(&mut self, item: MetaMemoryItem) -> MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_item.write().await; - let id = item.id; - let component_id = item.component_id; - items.insert(id, item); - component_map.insert(component_id, id); - Ok(id) - } - - async fn get_item(&self, id: Uuid) -> MetaMemoryResult> { - let items = self.items.read().await; - Ok(items.get(&id).cloned()) - } - - async fn get_item_by_component(&self, component_id: Uuid) -> MetaMemoryResult> { - let component_map = self.component_to_item.read().await; - if let Some(item_id) = component_map.get(&component_id) { - let items = self.items.read().await; - Ok(items.get(item_id).cloned()) - } else { - Ok(None) - } - } - - async fn query_items(&self, query: &MetaMemoryQuery) -> MetaMemoryResult> { - let items = self.items.read().await; - let mut results: Vec = items.values() - .filter(|item| { - // Apply filters - if let Some(ref knowledge_type) = query.knowledge_type { - if &item.knowledge_type != knowledge_type { - return false; - } - } - if let Some(min_conf) = query.min_confidence { - if item.confidence_score < min_conf { - return false; - } - } - if let Some(max_conf) = query.max_confidence { - if item.confidence_score > max_conf { - return false; - } - } - true - }) - .cloned() - .collect(); - - // Apply limit - if let Some(limit) = query.limit { - results.truncate(limit); - } - - Ok(results) - } - - async fn remove_item(&mut self, id: Uuid) -> MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_item.write().await; - - if let Some(item) = items.remove(&id) { - component_map.remove(&item.component_id); - Ok(true) - } else { - Ok(false) - } - } - - async fn batch_update(&mut self, items_to_update: Vec) -> MetaMemoryResult> { - let mut items = self.items.write().await; - let mut ids = Vec::new(); - - for item in items_to_update { - let id = item.id; - items.insert(id, item); - ids.push(id); - } - - Ok(ids) - } - - async fn count_items(&self) -> MetaMemoryResult { - let items = self.items.read().await; - Ok(items.len()) - } - - async fn clear_all(&mut self) -> MetaMemoryResult { - let mut items = self.items.write().await; - let mut component_map = self.component_to_item.write().await; - let count = items.len(); - items.clear(); - component_map.clear(); - Ok(count) - } -} - -/// Simple analytics implementation -#[derive(Debug)] -pub struct SimpleMetaMemoryAnalytics; - -#[async_trait::async_trait] -impl MetaMemoryAnalytics for SimpleMetaMemoryAnalytics { - async fn calculate_stats(&self) -> MetaMemoryResult { - Ok(MetaMemoryStats::default()) - } - - async fn get_confidence_distribution(&self) -> MetaMemoryResult> { - Ok(HashMap::new()) - } - - async fn get_quality_distribution(&self) -> MetaMemoryResult> { - Ok(HashMap::new()) - } - - async fn get_knowledge_type_distribution(&self) -> MetaMemoryResult> { - Ok(HashMap::new()) - } - - async fn get_trending_components(&self, _limit: usize) -> MetaMemoryResult> { - Ok(Vec::new()) - } - - async fn get_performance_metrics(&self, _hours_back: f64) -> MetaMemoryResult { - Ok(PerformanceMetrics { - time_period_hours: 24.0, - items_added: 0, - items_updated: 0, - items_accessed: 0, - avg_confidence_change: 0.0, - avg_quality_improvement: 0.0, - validation_success_rate: 0.9, - storage_efficiency: 0.8, - }) - } -} - -/// Simple maintenance implementation -#[derive(Debug)] -pub struct SimpleMetaMemoryMaintenance; - -#[async_trait::async_trait] -impl MetaMemoryMaintenance for SimpleMetaMemoryMaintenance { - async fn cleanup_stale_components(&mut self, _config: &MetaMemoryConfig) -> MetaMemoryResult { - Ok(0) - } - - async fn optimize_storage(&mut self) -> MetaMemoryResult<()> { - Ok(()) - } - - async fn backup_data(&self, _backup_path: &str) -> MetaMemoryResult<()> { - Ok(()) - } - - async fn restore_data(&mut self, _backup_path: &str) -> MetaMemoryResult { - Ok(0) - } - - async fn validate_integrity(&self) -> MetaMemoryResult { - Ok(IntegrityReport { - total_items: 0, - corrupted_items: 0, - missing_metadata: 0, - invalid_confidence: 0, - timestamp_issues: 0, - integrity_score: 1.0, - issues: Vec::new(), - }) - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿ” Novelty Detection System Demonstration - Enhanced Implementation"); - println!("{}", "=".repeat(70)); - - // Phase 1: Initialize Systems - println!("\n๐Ÿš€ Phase 1: Initialize Meta-Memory and Novelty Detection Systems"); - println!("{}", "-".repeat(50)); - - // Initialize meta-memory system - let meta_memory_repo = Arc::new(RwLock::new(SimpleMetaMemoryRepository::new())); - let meta_memory_analytics = Arc::new(SimpleMetaMemoryAnalytics); - let meta_memory_maintenance = Arc::new(SimpleMetaMemoryMaintenance); - let meta_memory_config = MetaMemoryConfig::default(); - - let meta_memory = Arc::new(MetaMemoryService::new( - meta_memory_repo, - meta_memory_analytics, - meta_memory_maintenance, - meta_memory_config, - )); - - println!("โœ… Meta-memory system initialized"); - - // Initialize novelty detection system - let novelty_config = DemoNoveltyConfig::default(); - let mut novelty_engine = DemoNoveltyDetectionEngine::new( - novelty_config, - meta_memory.clone() - )?; - - // Seed with known patterns - novelty_engine.seed_with_patterns(&[ - ("the", 0.9), ("and", 0.9), ("a", 0.85), ("to", 0.85), ("of", 0.8), - ("in", 0.8), ("is", 0.75), ("for", 0.75), ("with", 0.7), ("on", 0.7), - ("hello", 0.6), ("how", 0.6), ("are", 0.6), ("you", 0.6), - ("machine", 0.4), ("learning", 0.4), ("algorithm", 0.3), - ]).await; - - println!("โœ… Novelty detection engine initialized and seeded"); - - // Phase 2: Populate Meta-Memory with Known Patterns - println!("\n๐Ÿ“š Phase 2: Populate Meta-Memory with Known Patterns"); - println!("{}", "-".repeat(50)); - - // Add various knowledge components to establish baseline distributions - let known_patterns = [ - (KnowledgeType::Segment, 0.9, "Common segment: 'the'"), - (KnowledgeType::Segment, 0.85, "Frequent pattern: 'ing'"), - (KnowledgeType::Segment, 0.8, "Regular occurrence: 'tion'"), - (KnowledgeType::ConceptNode, 0.95, "Well-established concept: 'animal'"), - (KnowledgeType::ConceptNode, 0.9, "Clear concept: 'food'"), - (KnowledgeType::Rule, 0.8, "Reliable rule: if hungry then eat"), - (KnowledgeType::Rule, 0.75, "Good rule: if raining then umbrella"), - (KnowledgeType::SemanticConcept, 0.9, "Core concept: 'learning'"), - (KnowledgeType::WorkingMemory, 0.6, "Current task: reading email"), - (KnowledgeType::EpisodicMemory, 0.8, "Yesterday: went to store"), - (KnowledgeType::Pattern, 0.7, "Common pattern: greeting->conversation"), - ]; - - for (knowledge_type, confidence, description) in known_patterns.iter() { - let component_id = Uuid::new_v4(); - let _item_id = meta_memory.track_component( - component_id, - knowledge_type.clone(), - *confidence, - description.to_string(), - ).await.map_err(|e| anyhow::Error::msg(format!("Failed to track component: {}", e)))?; - - println!("๐Ÿ“ Added {}: {} (confidence: {:.2})", - knowledge_type, description, confidence); - } - - println!("โœ… {} known patterns added to meta-memory", known_patterns.len()); - - // Phase 3: Test Novelty Detection with Various Inputs - println!("\n๐ŸŽฏ Phase 3: Novelty Detection Testing"); - println!("{}", "-".repeat(50)); - - // Test inputs with expected novelty levels - let test_inputs = [ - // Low novelty (familiar patterns) - ("Hello, how are you?", "general", "Familiar greeting pattern"), - ("I need food", "general", "Common need expression"), - ("The weather is nice", "general", "Standard weather comment"), - - // Medium novelty (somewhat unexpected) - ("Quantum entanglement in cooking", "science", "Unusual domain mixing"), - ("Purple elephants dance silently", "creative", "Creative but comprehensible"), - ("Algorithm learns to paint emotions", "technology", "Novel AI application"), - - // High novelty (very unexpected) - ("Zxqwtyuiop asdfghjkl vbnm", "random", "Random character sequence"), - ("The table dreams of algebraic poetry while singing numerical lullabies", "surreal", "Highly abstract concept"), - ("!@#$%^&*()_+{}|:<>?", "symbols", "Pure symbol input"), - - // Anomalous inputs - ("aaaaaaaaaaaaaaaaaaaaaaaaa", "repetitive", "Highly repetitive content"), - ("abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz", "long", "Very long repetitive sequence"), - ]; - - println!("\n๐Ÿ” Testing Novelty Detection on Various Inputs:\n"); - - for (i, (input, task_context, description)) in test_inputs.iter().enumerate() { - // Create context for this test - let context = DemoNoveltyContext { - task_context: task_context.to_string(), - recent_inputs: if i > 0 { - test_inputs[..i.min(3)] - .iter() - .map(|(inp, _, _)| inp.to_string()) - .collect() - } else { - Vec::new() - }, - active_components: Vec::new(), - temporal_context: Utc::now(), - metadata: HashMap::new(), - }; - - // Assess novelty - let assessment = novelty_engine.assess_novelty(input, Some(context)).await?; - let level = assessment.get_novelty_level(novelty_engine.get_config()); - - println!("{}. Input: \"{}\"", i + 1, input); - println!(" Description: {}", description); - println!(" Context: {}", task_context); - println!(" โ”Œโ”€ Novelty Score: {:.3} ({:?})", assessment.novelty_score, level); - println!(" โ”œโ”€ Assessment Confidence: {:.3}", assessment.assessment_confidence); - println!(" โ””โ”€ Method Breakdown:"); - - // Display method scores - for (method, score) in &assessment.method_scores { - println!(" โ€ข {:?}: {:.3}", method, score); - } - - // Display key explanations - if !assessment.explanation.is_empty() { - println!(" ๐Ÿ“‹ Key Findings:"); - for explanation in assessment.explanation.iter().take(2) { - println!(" โ€ข {}", explanation); - } - } - - // Display recommendations - if !assessment.recommendations.is_empty() { - println!(" ๐Ÿ’ก Recommendations:"); - for rec in assessment.recommendations.iter().take(2) { - println!(" โ€ข {}", rec); - } - } - - println!(); - } - - // Phase 4: Context-Aware Novelty Testing - println!("\n๐ŸŒ Phase 4: Context-Aware Novelty Testing"); - println!("{}", "-".repeat(50)); - - println!("Testing how context affects novelty assessment:\n"); - - let context_test_input = "Machine learning model"; - let contexts = [ - ("technology", "Technology discussion context"), - ("cooking", "Cooking/culinary context"), - ("poetry", "Creative writing context"), - ("general", "General conversation context"), - ]; - - for (context_name, context_desc) in contexts.iter() { - let context = DemoNoveltyContext { - task_context: context_name.to_string(), - recent_inputs: vec![format!("Previous discussion about {}", context_name)], - ..Default::default() - }; - - let assessment = novelty_engine.assess_novelty(context_test_input, Some(context)).await?; - let level = assessment.get_novelty_level(novelty_engine.get_config()); - - println!("Input: \"{}\" in {} context", context_test_input, context_name); - println!(" Novelty: {:.3} ({:?}) - {}", - assessment.novelty_score, level, context_desc); - - if let Some(context_score) = assessment.method_scores.get(&NoveltyMethod::ContextBased) { - println!(" Context-specific score: {:.3}", context_score); - } - println!(); - } - - // Phase 5: Integration API Demonstration - println!("\n๐Ÿ”— Phase 5: Integration API Demonstration"); - println!("{}", "-".repeat(50)); - - println!("Demonstrating API capabilities for other system components:\n"); - - // Example API usage for other Brain components - let api_test_inputs = [ - "New learned segment pattern", - "Discovered rule relationship", - "Novel concept formation", - ]; - - for input in api_test_inputs.iter() { - let assessment = novelty_engine.assess_novelty(input, None).await?; - let level = assessment.get_novelty_level(novelty_engine.get_config()); - - println!("API Query: \"{}\"", input); - println!(" Response: Novelty {:.3} ({:?})", assessment.novelty_score, level); - - // Show how other components might use this information - match level { - NoveltyLevel::High | NoveltyLevel::VeryHigh => println!(" โ†’ System Action: Prioritize for learning and exploration"), - NoveltyLevel::Medium => println!(" โ†’ System Action: Schedule for additional validation"), - NoveltyLevel::Low | NoveltyLevel::VeryLow => println!(" โ†’ System Action: Process with standard confidence"), - } - println!(); - } - - // Phase 6: System Analytics and Performance - println!("\n๐Ÿ“Š Phase 6: System Analytics and Performance"); - println!("{}", "-".repeat(50)); - - let stats = novelty_engine.get_stats().await; - println!("๐Ÿ“ˆ Novelty Detection Statistics:"); - println!(" โ€ข Total assessments performed: {}", stats.total_assessments); - println!(" โ€ข Average novelty score: {:.3}", stats.average_novelty_score); - println!(" โ€ข Average assessment confidence: {:.3}", stats.average_assessment_confidence); - - println!("\n๐ŸŽญ Novelty Level Distribution:"); - let total = stats.total_assessments; - for (level, count) in &stats.novelty_distribution { - let percentage = if total > 0 { *count as f64 / total as f64 * 100.0 } else { 0.0 }; - println!(" โ€ข {:?}: {} assessments ({:.1}%)", level, count, percentage); - } - - println!("\n๐Ÿ”ง Method Usage Statistics:"); - for (method, count) in &stats.method_usage { - println!(" โ€ข {:?}: {} times", method, count); - } - - if !stats.common_contexts.is_empty() { - println!("\n๐ŸŒ Common Contexts:"); - for (context, count) in &stats.common_contexts { - println!(" โ€ข '{}': {} assessments", context, count); - } - } - - // Phase 7: Novelty Level Analysis - println!("\n๐ŸŽฏ Phase 7: Novelty Level Analysis"); - println!("{}", "-".repeat(50)); - - println!("High Novelty Assessments:"); - let high_novelty = novelty_engine.get_assessments_by_level(NoveltyLevel::High).await; - for (i, assessment) in high_novelty.iter().enumerate().take(3) { - println!(" {}. \"{}\" (score: {:.3})", - i + 1, assessment.input, assessment.novelty_score); - if !assessment.recommendations.is_empty() { - println!(" โ†’ {}", assessment.recommendations[0]); - } - } - - println!("\nLow Novelty Assessments:"); - let low_novelty = novelty_engine.get_assessments_by_level(NoveltyLevel::Low).await; - for (i, assessment) in low_novelty.iter().enumerate().take(3) { - println!(" {}. \"{}\" (score: {:.3})", - i + 1, assessment.input, assessment.novelty_score); - } - - // Phase 8: Export and Analysis - println!("\n๐Ÿ’พ Phase 8: Export and Analysis Capabilities"); - println!("{}", "-".repeat(50)); - - println!("Recent assessment history (last 5 assessments):"); - let recent = novelty_engine.get_recent_assessments(5).await; - for (i, assessment) in recent.iter().enumerate() { - println!(" {}. \"{}...\" - Novelty: {:.3} ({:?})", - i + 1, - assessment.input.chars().take(20).collect::(), - assessment.novelty_score, - assessment.get_novelty_level(novelty_engine.get_config()) - ); - } - - // Export assessments (truncated for demo) - println!("\n๐Ÿ“ค Assessment export capability available"); - println!(" (JSON export with {} total assessments)", stats.total_assessments); - - // Final Summary - println!("\n๐ŸŽ‰ Novelty Detection System - DEMONSTRATION COMPLETE!"); - println!("{}", "=".repeat(70)); - println!("โœ… Statistical novelty detection operational"); - println!("โœ… Confidence-based assessment using meta-memory"); - println!("โœ… Context-aware novelty evaluation"); - println!("โœ… Anomaly detection for outlier identification"); - println!("โœ… Composite novelty scoring (0-1 range)"); - println!("โœ… Comprehensive logging and analytics"); - println!("โœ… API integration for other Brain components"); - println!("โœ… Export capabilities for analysis and visualization"); - println!("\n๐ŸŽฏ Novelty detection system ready for integration with curiosity-driven learning!"); - - Ok(()) -} \ No newline at end of file diff --git a/openai_brain_test.rs b/openai_brain_test.rs deleted file mode 100644 index bc588dc4b5e707d0db388ae9f92b3463bc457dfb..0000000000000000000000000000000000000000 --- a/openai_brain_test.rs +++ /dev/null @@ -1,133 +0,0 @@ -//! OpenAI Brain Test -//! -//! Tests Brain AI conversation capabilities with OpenAI integration -//! using the new MemoryService and ConceptGraphService architecture. - -use brain::*; -use brain::services::*; -use std::env; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain AI - OpenAI Integration Test"); - println!("===================================="); - - // Check for OpenAI API key - let _openai_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| { - println!("โŒ OPENAI_API_KEY environment variable not found!"); - println!(" Please set your OpenAI API key:"); - println!(" export OPENAI_API_KEY=your_key_here"); - std::process::exit(1); - }); - - println!("โœ… OpenAI API key found"); - - // Initialize Brain AI services - println!("\n๐Ÿ”ง Initializing Brain AI Services..."); - let mut memory_system = create_memory_service_with_capacity(1000).await?; - let mut concept_graph = create_concept_graph_service_default().await?; - - println!("โœ… MemoryService initialized"); - println!("โœ… ConceptGraphService initialized"); - - // Load some test knowledge - println!("\n๐Ÿ“š Loading Test Knowledge..."); - let test_knowledge = vec![ - "Brain AI is an advanced artificial intelligence system with memory and reasoning capabilities", - "The system uses episodic, working, and semantic memory for comprehensive knowledge storage", - "Brain AI can learn from conversations and improve its responses over time", - "The architecture supports real-time learning and knowledge consolidation", - "Brain AI integrates with OpenAI for enhanced language generation capabilities", - ]; - - for (i, knowledge) in test_knowledge.iter().enumerate() { - memory_system.learn(knowledge.to_string(), Priority::High).await?; - println!("โœ… Loaded knowledge item {}", i + 1); - } - - // Create RAG orchestrator for conversation processing - println!("\n๐Ÿค– Initializing Conversation System..."); - let mut rag_orchestrator = RagOrchestrator::new()?; - - // Test questions to validate the integration - let test_questions = vec![ - "What is Brain AI?", - "How does Brain AI handle memory?", - "What makes Brain AI different from other AI systems?", - "How does Brain AI integrate with OpenAI?", - "Can Brain AI learn from our conversation?", - ]; - - println!("\n๐Ÿ’ฌ Testing Brain AI Conversations"); - println!("================================="); - - for (i, question) in test_questions.iter().enumerate() { - println!("\n๐Ÿ“ Test {}: {}", i + 1, question); - - let request = RagRequest { - message: question.to_string(), - conversation_id: Some("openai_test_session".to_string()), - context_limit: Some(5), - retrieval_threshold: Some(0.3), - }; - - match rag_orchestrator.process_conversation( - request, - &mut memory_system, - &mut concept_graph, - ).await { - Ok(response) => { - println!("๐Ÿค– Brain AI Response:"); - println!(" {}", response.response); - println!(" ๐Ÿ“Š Confidence: {:.1}%", response.confidence_score * 100.0); - println!(" ๐Ÿ“š Knowledge sources used: {}", response.context_used.len()); - - // Store the interaction for learning - let interaction = format!("Q: {} | A: {}", question, response.response); - memory_system.learn(interaction, Priority::Low).await?; - - // Validate response quality - if response.confidence_score > 0.5 { - println!(" โœ… High confidence response"); - } else { - println!(" โš ๏ธ Lower confidence response"); - } - } - Err(e) => { - println!(" โŒ Error processing question: {}", e); - println!(" This could indicate API issues or configuration problems"); - } - } - - // Brief pause between requests - tokio::time::sleep(tokio::time::Duration::from_millis(750)).await; - } - - // Display final statistics - println!("\n๐Ÿ“Š Session Summary"); - println!("=================="); - - let conversation_stats = rag_orchestrator.get_conversation_stats(); - for (key, value) in conversation_stats { - println!(" {}: {}", key, value); - } - - // Test memory consolidation - println!("\n๐Ÿง  Testing Memory Consolidation..."); - match memory_system.consolidate().await { - Ok(result) => { - println!("โœ… Memory consolidation successful:"); - println!(" Working to Episodic: {} items", result.working_to_episodic); - println!(" Episodic to Semantic: {} items", result.episodic_to_semantic); - println!(" Forgotten items: {} items", result.forgotten_events); - } - Err(e) => { - println!("โš ๏ธ Memory consolidation warning: {}", e); - } - } - - println!("\nโœ… OpenAI Brain AI Test Complete!"); - println!(" The new service architecture is functioning properly with OpenAI integration."); - - Ok(()) -} \ No newline at end of file diff --git a/performance_monitoring_validation.rs b/performance_monitoring_validation.rs deleted file mode 100644 index dcd4e3265201ce13531266aeb7a6b2f99c431f49..0000000000000000000000000000000000000000 --- a/performance_monitoring_validation.rs +++ /dev/null @@ -1,384 +0,0 @@ -//! # Academic Performance Monitoring Validation -//! -//! **Created**: July 31, 2025 at 06:42:30 EDT -//! **Purpose**: Validate real-time academic performance monitoring with HLE benchmark tracking -//! -//! ## Key Validation Points -//! -//! 1. **Real-Time HLE Accuracy Tracking**: Monitor live performance towards 45%+ target -//! 2. **Research Impact Measurement**: Track adaptive research system effectiveness -//! 3. **Domain Performance Analytics**: Monitor specialist accuracy across disciplines -//! 4. **Competitive Intelligence**: Track position against SOTA models -//! 5. **Performance Alert System**: Validate automated regression detection - -use std::time::{Duration, Instant}; -use std::collections::HashMap; -use chrono::Utc; -use rand::{thread_rng, Rng, seq::SliceRandom}; - -use brain_cognitive::agents::{ - AcademicDomain, - monitoring::{ - AcademicPerformanceMonitor, - AcademicPerformanceReport, - }, -}; -use brain_cognitive::agents::traits::{AcademicQuestion, QuestionType}; -use brain_cognitive::agents::intelligence::adaptive_research_engine::ResearchStrategy; -use brain_types::error::BrainError; - -/// **Performance Monitoring Validation Demo** -pub struct PerformanceMonitoringValidator { - monitor: AcademicPerformanceMonitor, - validation_start: Instant, -} - -impl PerformanceMonitoringValidator { - /// Create new performance monitoring validator - pub async fn new() -> Result { - println!("๐Ÿš€ Initializing Academic Performance Monitor..."); - let monitor = AcademicPerformanceMonitor::new().await?; - - Ok(Self { - monitor, - validation_start: Instant::now(), - }) - } - - /// Execute comprehensive monitoring validation - pub async fn validate_monitoring_system(&mut self) -> Result<(), BrainError> { - println!("๐Ÿง  ========== ACADEMIC PERFORMANCE MONITORING VALIDATION =========="); - println!("๐Ÿ“… Validation Date: {}", Utc::now().format("%Y-%m-%d %H:%M:%S UTC")); - println!("๐ŸŽฏ Mission: Validate real-time academic intelligence performance tracking"); - println!("๐Ÿ“Š Target: Demonstrate monitoring capabilities for 45%+ HLE accuracy"); - println!(); - - // Phase 1: Test basic performance tracking - println!("๐Ÿ“Š PHASE 1: Basic Performance Tracking Validation"); - self.test_basic_performance_tracking().await?; - println!("โœ… Basic tracking validated"); - println!(); - - // Phase 2: Test research effectiveness monitoring - println!("๐Ÿ”ฌ PHASE 2: Research Effectiveness Monitoring"); - self.test_research_monitoring().await?; - println!("โœ… Research monitoring validated"); - println!(); - - // Phase 3: Test domain-specific analytics - println!("๐Ÿ“š PHASE 3: Domain Performance Analytics"); - self.test_domain_analytics().await?; - println!("โœ… Domain analytics validated"); - println!(); - - // Phase 4: Generate comprehensive report - println!("๐Ÿ“‹ PHASE 4: Comprehensive Performance Report"); - let report = self.monitor.generate_performance_report().await?; - self.display_validation_report(&report).await; - - // Phase 5: Test alert system - println!("๐Ÿšจ PHASE 5: Performance Alert System"); - let alerts = self.monitor.check_alerts().await?; - self.validate_alert_system(&alerts).await; - - println!("๐ŸŽฏ VALIDATION COMPLETE: Academic Performance Monitoring System Operational"); - println!("โฑ๏ธ Total Validation Time: {:.2}s", self.validation_start.elapsed().as_secs_f64()); - println!("================================================================================"); - - Ok(()) - } - - /// Test basic performance tracking capabilities - async fn test_basic_performance_tracking(&mut self) -> Result<(), BrainError> { - let mut rng = thread_rng(); - - // Simulate processing 20 questions across domains - let domains = vec![ - AcademicDomain::TheoreticalPhysics, - AcademicDomain::AdvancedMathematics, - AcademicDomain::MolecularBiology, - AcademicDomain::AdvancedChemistry, - AcademicDomain::ComputerScienceTheory, - ]; - - let mut correct_count = 0; - let question_count = 20; - - for i in 0..question_count { - let domain = domains.choose(&mut rng).unwrap().clone(); - - // Create test question - let mut metadata = HashMap::new(); - metadata.insert("difficulty".to_string(), rng.gen_range(5..=9).to_string()); - metadata.insert("context".to_string(), "Monitoring Validation".to_string()); - - let question = AcademicQuestion { - id: format!("test_q_{}", i + 1), - question: format!("Test question {} in {:?}", i + 1, domain), - domain: domain.clone(), - question_type: QuestionType::MultipleChoice, - options: Some(vec!["A".to_string(), "B".to_string(), "C".to_string(), "D".to_string()]), - metadata, - }; - - // Simulate question processing with realistic accuracy - let base_accuracy = match domain { - AcademicDomain::ComputerScienceTheory => 0.60, - AcademicDomain::TheoreticalPhysics => 0.35, - AcademicDomain::AdvancedMathematics => 0.25, - AcademicDomain::MolecularBiology => 0.20, - AcademicDomain::AdvancedChemistry => 0.15, - _ => 0.30, - }; - - let is_correct = rng.gen::() < base_accuracy; - if is_correct { - correct_count += 1; - } - - let confidence = if is_correct { - rng.gen_range(0.5..0.9) - } else { - rng.gen_range(0.2..0.6) - }; - - let selected_answer = if is_correct { "A" } else { "B" }; - let correct_answer = "A"; - - // Record performance - self.monitor.record_performance( - &question, - selected_answer, - correct_answer, - confidence, - confidence < 0.7, // Research needed - Some(Duration::from_millis(rng.gen_range(100..500))), - vec![ResearchStrategy::DatabaseLookup], - ).await?; - - if (i + 1) % 5 == 0 { - let current_accuracy = correct_count as f64 / (i + 1) as f64; - println!(" ๐Ÿ“ˆ Processed {}/{} questions | Accuracy: {:.1}%", - i + 1, question_count, current_accuracy * 100.0); - } - } - - Ok(()) - } - - /// Test research effectiveness monitoring - async fn test_research_monitoring(&mut self) -> Result<(), BrainError> { - let _rng = thread_rng(); - - println!(" ๐Ÿ” Testing research trigger scenarios..."); - - // Test low-confidence scenario (should trigger research) - let mut low_conf_metadata = HashMap::new(); - low_conf_metadata.insert("difficulty".to_string(), "9".to_string()); - low_conf_metadata.insert("context".to_string(), "Research Test".to_string()); - - let low_conf_question = AcademicQuestion { - id: "research_test_1".to_string(), - question: "Complex theoretical physics question".to_string(), - domain: AcademicDomain::TheoreticalPhysics, - question_type: QuestionType::MultipleChoice, - options: Some(vec!["A".to_string(), "B".to_string(), "C".to_string(), "D".to_string()]), - metadata: low_conf_metadata, - }; - - self.monitor.record_performance( - &low_conf_question, - "B", - "A", - 0.35, // Low confidence - should trigger research - true, // Research used - Some(Duration::from_secs(3)), - vec![ResearchStrategy::DatabaseLookup, ResearchStrategy::FactVerification], - ).await?; - - // Test high-confidence scenario (no research needed) - let mut high_conf_metadata = HashMap::new(); - high_conf_metadata.insert("difficulty".to_string(), "5".to_string()); - high_conf_metadata.insert("context".to_string(), "Research Test".to_string()); - - let high_conf_question = AcademicQuestion { - id: "research_test_2".to_string(), - question: "Straightforward CS question".to_string(), - domain: AcademicDomain::ComputerScienceTheory, - question_type: QuestionType::MultipleChoice, - options: Some(vec!["A".to_string(), "B".to_string(), "C".to_string(), "D".to_string()]), - metadata: high_conf_metadata, - }; - - self.monitor.record_performance( - &high_conf_question, - "A", - "A", - 0.85, // High confidence - no research needed - false, // No research - None, - vec![], - ).await?; - - println!(" โœ… Research monitoring scenarios validated"); - - Ok(()) - } - - /// Test domain-specific analytics - async fn test_domain_analytics(&mut self) -> Result<(), BrainError> { - let mut rng = thread_rng(); - - println!(" ๐Ÿ“š Testing domain performance tracking..."); - - // Test performance across all domains - let domains = vec![ - AcademicDomain::TheoreticalPhysics, - AcademicDomain::AdvancedMathematics, - AcademicDomain::MolecularBiology, - AcademicDomain::AdvancedChemistry, - AcademicDomain::ComputerScienceTheory, - ]; - - for (domain_idx, domain) in domains.iter().enumerate() { - // Test 3 questions per domain - for q_idx in 0..3 { - let mut metadata = HashMap::new(); - metadata.insert("difficulty".to_string(), rng.gen_range(6..=8).to_string()); - metadata.insert("context".to_string(), "Domain Analytics Test".to_string()); - - let question = AcademicQuestion { - id: format!("domain_test_{}_{}", domain_idx, q_idx), - question: format!("Domain test question for {:?}", domain), - domain: domain.clone(), - question_type: QuestionType::MultipleChoice, - options: Some(vec!["A".to_string(), "B".to_string(), "C".to_string(), "D".to_string()]), - metadata, - }; - - // Simulate varying performance by domain - let domain_accuracy = match domain { - AcademicDomain::ComputerScienceTheory => 0.70, - AcademicDomain::TheoreticalPhysics => 0.40, - AcademicDomain::AdvancedMathematics => 0.30, - AcademicDomain::MolecularBiology => 0.25, - AcademicDomain::AdvancedChemistry => 0.20, - _ => 0.35, - }; - - let is_correct = rng.gen::() < domain_accuracy; - let confidence = if is_correct { - rng.gen_range(0.6..0.9) - } else { - rng.gen_range(0.3..0.6) - }; - - self.monitor.record_performance( - &question, - if is_correct { "A" } else { "B" }, - "A", - confidence, - confidence < 0.7, - if confidence < 0.7 { Some(Duration::from_millis(rng.gen_range(200..800))) } else { None }, - if confidence < 0.7 { vec![ResearchStrategy::ConceptualSynthesis] } else { vec![] }, - ).await?; - } - } - - println!(" โœ… Domain analytics validated across all academic disciplines"); - - Ok(()) - } - - /// Display comprehensive validation report - async fn display_validation_report(&self, report: &AcademicPerformanceReport) { - println!("๐Ÿ“Š ========== PERFORMANCE MONITORING VALIDATION REPORT =========="); - - // Display the comprehensive report - report.display_report(); - - // Additional validation metrics - println!("๐Ÿ”ฌ VALIDATION METRICS"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ Monitoring System Status: OPERATIONAL โ”‚"); - println!("โ”‚ Real-Time Tracking: โœ… VALIDATED โ”‚"); - println!("โ”‚ Research Impact Analysis: โœ… VALIDATED โ”‚"); - println!("โ”‚ Domain Performance Analytics: โœ… VALIDATED โ”‚"); - println!("โ”‚ Competitive Intelligence: โœ… VALIDATED โ”‚"); - println!("โ”‚ Alert System: โœ… VALIDATED โ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(); - - // Key achievements - println!("๐Ÿ† KEY ACHIEVEMENTS"); - println!(" โœ… Real-time HLE accuracy tracking operational"); - println!(" โœ… Research effectiveness monitoring functional"); - println!(" โœ… Domain-specific performance analytics working"); - println!(" โœ… Competitive benchmarking system active"); - println!(" โœ… Performance alert system responsive"); - println!(" โœ… Comprehensive reporting capabilities validated"); - println!(); - - // Path to global leadership - println!("๐Ÿš€ PATH TO #1 GLOBAL RANKING"); - println!(" ๐Ÿ“Š Current Monitoring Capabilities: COMPREHENSIVE"); - println!(" ๐ŸŽฏ Target HLE Accuracy: 45%+ (for #1 global position)"); - println!(" ๐Ÿ”ฌ Research System Integration: OPERATIONAL"); - println!(" ๐Ÿ“ˆ Performance Optimization: DATA-DRIVEN"); - println!(" ๐Ÿ† Competitive Advantage: INTELLIGENT MONITORING"); - println!(); - } - - /// Validate alert system functionality - async fn validate_alert_system(&self, alerts: &[brain_cognitive::agents::monitoring::PerformanceAlert]) { - println!("๐Ÿšจ ========== ALERT SYSTEM VALIDATION =========="); - - if alerts.is_empty() { - println!("โœ… Alert system operational - no critical issues detected"); - println!(" ๐Ÿ“Š System operating within normal parameters"); - println!(" ๐Ÿ” Monitoring thresholds properly calibrated"); - } else { - println!("โš ๏ธ Alert system active - {} alerts detected", alerts.len()); - for (i, alert) in alerts.iter().enumerate() { - println!(" {}. {}: {}", i + 1, format!("{:?}", alert.alert_type), alert.message); - } - } - - println!(); - println!("๐ŸŽฏ ALERT SYSTEM CAPABILITIES"); - println!(" โœ… Accuracy regression detection"); - println!(" โœ… Confidence calibration monitoring"); - println!(" โœ… Research effectiveness tracking"); - println!(" โœ… Competitive position alerts"); - println!(" โœ… Learning plateau identification"); - println!(" โœ… System anomaly detection"); - println!(); - - println!("๐Ÿ“‹ RECOMMENDED ALERT CONFIGURATION"); - println!(" โ€ข Accuracy regression threshold: 2% decline"); - println!(" โ€ข Confidence calibration error: >15%"); - println!(" โ€ข Research success rate: <70%"); - println!(" โ€ข Competitive ranking drop: >1 position"); - println!(" โ€ข Learning plateau duration: >1 week"); - } -} - -/// **MAIN VALIDATION EXECUTION** -#[tokio::main] -async fn main() -> Result<(), BrainError> { - println!("๐Ÿง  ACADEMIC PERFORMANCE MONITORING SYSTEM VALIDATION"); - println!("๐Ÿ“… Validation Date: {}", Utc::now().format("%Y-%m-%d %H:%M:%S UTC")); - println!("๐ŸŽฏ Mission: Validate comprehensive academic intelligence monitoring"); - println!("๐Ÿ† Strategic Goal: Operational monitoring for #1 global HLE ranking"); - println!(); - - // Create and run validation - let mut validator = PerformanceMonitoringValidator::new().await?; - validator.validate_monitoring_system().await?; - - println!("โœ… VALIDATION COMPLETED SUCCESSFULLY"); - println!("๐Ÿš€ Academic Performance Monitoring System ready for production deployment"); - println!("๐Ÿ“Š System fully validated for real-time HLE performance tracking"); - - Ok(()) -} \ No newline at end of file diff --git a/philosophy_expert_demo.rs b/philosophy_expert_demo.rs deleted file mode 100644 index 5490491725022c7b98d4fcf9160c60c96b9db8ca..0000000000000000000000000000000000000000 --- a/philosophy_expert_demo.rs +++ /dev/null @@ -1,170 +0,0 @@ -/// Philosophy Expert Demo -/// -/// This demonstrates the PhilosophyExpert implementation for TASK 3.2, -/// showing how it handles philosophical questions across ethics, epistemology, -/// metaphysics, and other philosophical domains. - -use std::time::Instant; -use brain_cognitive::agents::intelligence::philosophy_expert::PhilosophyExpert; -use brain_cognitive::agents::traits::{ - AgentInput, CognitiveContext, BrainAgent, AcademicReasoningAgent -}; -use uuid::Uuid; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  PHILOSOPHY EXPERT DEMO"); - println!("========================"); - println!("Demonstrating TASK 3.2: Philosophy Expert for academic reasoning"); - println!("across ethics, epistemology, metaphysics, and critical thinking.\n"); - - // Initialize the Philosophy Expert - let philosophy_expert = PhilosophyExpert::new().await?; - println!("โœ… PhilosophyExpert initialized successfully\n"); - - // Test scenarios covering different philosophical domains - let test_scenarios = vec![ - ( - "Ethics Question", - "What is the fundamental difference between deontological and consequentialist ethical theories?", - "philosophy_question" - ), - ( - "Epistemology Question", - "How does the Gettier problem challenge the traditional definition of knowledge as justified true belief?", - "academic_question" - ), - ( - "Metaphysics Question", - "What are the implications of the mind-body problem for our understanding of personal identity?", - "philosophy_question" - ), - ( - "Logic and Reasoning", - "Explain the validity of modus ponens and provide an example of its application in philosophical argument.", - "academic_question" - ), - ]; - - for (i, (scenario_name, question, input_type)) in test_scenarios.iter().enumerate() { - println!("๐Ÿ“š Scenario {}: {}", i + 1, scenario_name); - println!("โ“ Question: {}", question); - - let start_time = Instant::now(); - let context = CognitiveContext::default(); - - // Test BrainAgent execute method - let agent_input = AgentInput { - input_type: input_type.to_string(), - content: question.to_string(), - parameters: std::collections::HashMap::new(), - previous_outputs: vec![], - user_preferences: std::collections::HashMap::new(), - session_id: Uuid::new_v4().to_string(), - timestamp: chrono::Utc::now(), - }; - - match philosophy_expert.execute(agent_input, &context).await { - Ok(output) => { - let duration = start_time.elapsed(); - println!("โœ… Analysis completed in {:?}", duration); - println!("๐ŸŽฏ Confidence: {:.2}", output.confidence); - println!("๐Ÿ“ Analysis:\n{}\n", output.content); - } - Err(e) => { - println!("โŒ Error: {}\n", e); - } - } - - // Test AcademicReasoningAgent methods - let analysis_start = Instant::now(); - match philosophy_expert.analyze_question(question).await { - Ok(analysis) => { - println!("๐Ÿ” Question Analysis:"); - println!(" Domain: {:?}", analysis.domain); - println!(" Question Type: {:?}", analysis.question_type); - println!(" Complexity Level: {}", analysis.complexity_level); - println!(" Key Concepts: {}", analysis.key_concepts.join(", ")); - println!(" Required Knowledge: {}", analysis.required_knowledge.join(", ")); - println!(" Analysis Confidence: {:.2}", analysis.analysis_confidence); - - let analysis_duration = analysis_start.elapsed(); - println!(" Analysis Time: {:?}", analysis_duration); - } - Err(e) => { - println!("โŒ Analysis Error: {}", e); - } - } - - // Test option evaluation with sample options - let sample_options = vec![ - "A) Focus on intentions and duties".to_string(), - "B) Focus on outcomes and consequences".to_string(), - "C) Focus on character and virtues".to_string(), - "D) Focus on cultural relativism".to_string(), - ]; - - match philosophy_expert.evaluate_options(question, &sample_options).await { - Ok(evaluation) => { - println!("๐Ÿ“Š Option Evaluation:"); - println!(" Recommended Answer: {}", evaluation.recommended_answer); - println!(" Recommendation Confidence: {:.2}", evaluation.recommendation_confidence); - println!(" Option Scores:"); - for (option, score) in &evaluation.option_scores { - println!(" {}: {:.3}", option, score); - } - } - Err(e) => { - println!("โŒ Evaluation Error: {}", e); - } - } - - println!("{:=<60}\n", ""); - } - - // Test confidence assessment - println!("๐ŸŽฏ CONFIDENCE ASSESSMENT TEST"); - println!("============================"); - - let test_input = AgentInput { - input_type: "philosophy_question".to_string(), - content: "What is the nature of justice according to Rawls' theory of justice?".to_string(), - parameters: std::collections::HashMap::new(), - previous_outputs: vec![], - user_preferences: std::collections::HashMap::new(), - session_id: Uuid::new_v4().to_string(), - timestamp: chrono::Utc::now(), - }; - - let context = CognitiveContext::default(); - match philosophy_expert.assess_confidence(&test_input, &context).await { - Ok(confidence) => { - println!("โœ… Confidence Assessment: {:.2}", confidence); - println!("๐Ÿ“Š Confidence Threshold: {:.2}", philosophy_expert.confidence_threshold()); - println!("๐Ÿ”„ Can Handle: {}", confidence >= philosophy_expert.confidence_threshold()); - } - Err(e) => { - println!("โŒ Confidence Assessment Error: {}", e); - } - } - - // Test academic domains - println!("\n๐ŸŽ“ ACADEMIC DOMAIN SPECIALIZATIONS"); - println!("=================================="); - let domains = philosophy_expert.academic_domains(); - println!("Specialized Domains: {:?}", domains); - - // Test metadata - println!("\n๐Ÿ“‹ AGENT METADATA"); - println!("================"); - let metadata = philosophy_expert.metadata(); - println!("Name: {}", metadata.name); - println!("Version: {}", metadata.version); - println!("Capabilities: {}", metadata.capabilities.join(", ")); - println!("Base Confidence: {:.2}", metadata.base_confidence); - - println!("\n๐ŸŽ‰ Philosophy Expert Demo completed successfully!"); - println!("The PhilosophyExpert is ready for TASK 3.2 academic reasoning tasks."); - - Ok(()) -} \ No newline at end of file diff --git a/planner_agent_demo.rs b/planner_agent_demo.rs deleted file mode 100644 index f788a01635b7e0118849c3f9b91744f312c75815..0000000000000000000000000000000000000000 --- a/planner_agent_demo.rs +++ /dev/null @@ -1,272 +0,0 @@ -use std::sync::Arc; -use std::collections::HashMap; -use brain_cognitive::{ - agents::{traits::*, development::PlannerAgent}, - meta::*, - conversation::*, -}; - -// Simple mock implementations for testing -#[derive(Clone, Debug)] -struct MockMetaMemoryRepository; - -#[async_trait::async_trait] -impl MetaMemoryRepository for MockMetaMemoryRepository { - async fn store_item(&mut self, _item: MetaMemoryItem) -> MetaMemoryResult { - Ok(uuid::Uuid::new_v4()) - } - - async fn get_item(&self, _id: uuid::Uuid) -> MetaMemoryResult> { - Ok(None) - } - - async fn get_item_by_component(&self, _component_id: uuid::Uuid) -> MetaMemoryResult> { - Ok(None) - } - - async fn query_items(&self, _query: &MetaMemoryQuery) -> MetaMemoryResult> { - Ok(vec![]) - } - - async fn remove_item(&mut self, _id: uuid::Uuid) -> MetaMemoryResult { - Ok(false) - } - - async fn batch_update(&mut self, _items: Vec) -> MetaMemoryResult> { - Ok(vec![]) - } - - async fn count_items(&self) -> MetaMemoryResult { - Ok(0) - } - - async fn clear_all(&mut self) -> MetaMemoryResult { - Ok(0) - } -} - -#[derive(Clone, Debug)] -struct MockConversationService; - -#[async_trait::async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: RagRequest, - _memory_repo: &mut dyn brain_core::memory::WorkingMemoryRepository, - _concept_repo: &mut dyn brain_core::concepts::ConceptRepository, - _insight_repo: &mut dyn brain_core::insights::InsightRepository, - ) -> Result { - Ok(RagResponse { - response: "Mock response".to_string(), - conversation_id: "mock-id".to_string(), - context_used: vec![], - confidence_score: 0.8, - response_quality: response_quality::ResponseQuality::default(), - }) - } - - fn get_conversation_stats(&self) -> HashMap { - HashMap::new() - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Brain AI - PlannerAgent Demo"); - println!("=================================\n"); - - // Create project context - let project_context = ProjectContext { - project_name: "Task Management App".to_string(), - project_version: "1.0.0".to_string(), - project_description: Some("A comprehensive task management application".to_string()), - tech_stack: vec!["React".to_string(), "Node.js".to_string(), "PostgreSQL".to_string()], - git_branch: Some("main".to_string()), - git_commit: None, - active_files: vec![], - recent_changes: vec![], - directory_structure: HashMap::new(), - }; - - // Create cognitive profile - let cognitive_profile = CognitivePreferenceProfile { - interaction_mode: InteractionMode::Collaborative, - detail_level: DetailLevel::Comprehensive, - emotional_sensitivity: EmotionalSensitivity::Medium, - autonomy_level: AutonomyLevel::SemiAuto, - communication_style: brain_cognitive::agents::traits::CommunicationStyle::Technical, - cognitive_load_settings: CognitiveLoadSettings { - max_items_per_chunk: 8, - pacing_preference: PacingPreference::Medium, - progressive_disclosure: true, - }, - }; - - // Create simple cognitive context - let meta_memory: Arc> = Arc::new(tokio::sync::RwLock::new(MockMetaMemoryRepository)); - let conversation_service: Arc = Arc::new(MockConversationService); - - let context = CognitiveContext { - meta_memory, - conversation_service, - project_context, - cognitive_profile, - session_history: Vec::new(), - config: HashMap::new(), - working_directory: std::env::current_dir().unwrap_or_else(|_| std::path::PathBuf::from(".")), - }; - - println!("โœ… Cognitive context initialized"); - println!(" Project: {}", context.project_context.project_name); - println!(" Tech Stack: {:?}", context.project_context.tech_stack); - println!(" Interaction Mode: {:?}", context.cognitive_profile.interaction_mode); - println!(" Detail Level: {:?}", context.cognitive_profile.detail_level); - println!(); - - // Create and test PlannerAgent - let planner = PlannerAgent::new(); - - println!("๐ŸŽฏ PlannerAgent Metadata:"); - println!(" Name: {}", planner.metadata().name); - println!(" Persona: {}", planner.metadata().persona); - println!(" Capabilities: {:?}", planner.metadata().capabilities); - println!(" Supported Inputs: {:?}", planner.metadata().supported_input_types); - println!(" Base Confidence: {:.2}", planner.metadata().base_confidence); - println!(); - - // Test project idea planning - let project_idea = r#" - Create a modern task management web application that allows teams to collaborate effectively. - - Requirements: - - Users must be able to create, edit, and delete tasks - - Tasks should have priorities, due dates, and assignees - - The system must support real-time collaboration - - Users should receive notifications for task updates - - The app must work on both desktop and mobile devices - - Data should be stored securely with user authentication - - The system should generate progress reports and analytics - "#; - - let input = AgentInput::new( - "project_idea".to_string(), - project_idea.to_string(), - "demo-session".to_string(), - ); - - println!("๐Ÿ“‹ Testing PlannerAgent with project idea..."); - - // Assess confidence first - let confidence = planner.assess_confidence(&input, &context).await?; - println!(" Agent confidence: {:.2}", confidence); - - if confidence >= planner.confidence_threshold() { - // Execute the planning - let output = planner.execute(input, &context).await?; - - println!("โœ… Planning completed successfully!"); - println!(" Output Type: {}", output.output_type); - println!(" Confidence: {:.2}", output.confidence); - println!(" Execution Time: {}ms", output.execution_metadata.execution_time_ms); - - if let Some(reasoning) = &output.reasoning { - println!(" Reasoning: {}", reasoning); - } - - println!(" Next Actions: {:?}", output.next_actions); - println!(); - - // Parse and display the structured plan - if let Ok(plan) = serde_json::from_str::(&output.content) { - println!("๐Ÿ“Š Generated Project Plan Summary:"); - - if let Some(overview) = plan.get("project_overview") { - println!(" Analysis Confidence: {:.2}", - overview.get("analysis_confidence").and_then(|v| v.as_f64()).unwrap_or(0.0)); - } - - if let Some(task_breakdown) = plan.get("task_breakdown") { - if let Some(total_hours) = task_breakdown.get("total_estimated_hours") { - println!(" Total Estimated Hours: {}", total_hours); - } - - if let Some(phases) = task_breakdown.get("phases") { - println!(" Development Phases: {:?}", phases); - } - - if let Some(tasks) = task_breakdown.get("tasks").and_then(|t| t.as_array()) { - println!(" Total Tasks Generated: {}", tasks.len()); - for (i, task) in tasks.iter().enumerate() { - if let (Some(title), Some(phase)) = ( - task.get("title").and_then(|t| t.as_str()), - task.get("phase").and_then(|p| p.as_str()) - ) { - println!(" {}. {} [{}]", i + 1, title, phase); - } - } - } - } - - if let Some(roadmap) = plan.get("project_roadmap") { - if let Some(timeline) = roadmap.get("timeline") { - if let Some(weeks) = timeline.get("estimated_duration_weeks") { - println!(" Estimated Duration: {} weeks", weeks); - } - } - - if let Some(milestones) = roadmap.get("milestones").and_then(|m| m.as_array()) { - println!(" Key Milestones: {}", milestones.len()); - } - - if let Some(risks) = roadmap.get("risks").and_then(|r| r.as_array()) { - println!(" Identified Risks: {}", risks.len()); - } - } - - if let Some(recommendations) = plan.get("recommendations").and_then(|r| r.as_array()) { - println!("\n๐Ÿ’ก Key Recommendations:"); - for (i, rec) in recommendations.iter().enumerate() { - if let Some(rec_str) = rec.as_str() { - println!(" {}. {}", i + 1, rec_str); - } - } - } - } - - } else { - println!("โŒ Agent confidence ({:.2}) below threshold ({:.2})", - confidence, planner.confidence_threshold()); - } - - // Test additional input types - println!("\n๐Ÿ“ Testing user story breakdown..."); - let user_story = "As a project manager, I want to create and assign tasks to team members so that I can track project progress effectively."; - - let story_input = AgentInput::new( - "user_story".to_string(), - user_story.to_string(), - "demo-session".to_string(), - ); - - let story_output = planner.execute(story_input, &context).await?; - println!(" Story breakdown confidence: {:.2}", story_output.confidence); - - if let Ok(story_plan) = serde_json::from_str::(&story_output.content) { - if let Some(breakdown) = story_plan.get("story_breakdown") { - if let Some(effort) = breakdown.get("estimated_effort") { - println!(" Estimated Effort: {}", effort); - } - if let Some(complexity) = breakdown.get("complexity") { - println!(" Complexity: {}", complexity); - } - } - } - - println!("\n๐ŸŽ‰ PlannerAgent demo completed!"); - Ok(()) -} \ No newline at end of file diff --git a/pocketflow_analysis_demo.rs b/pocketflow_analysis_demo.rs deleted file mode 100644 index 32dd11ccf3d74cd1d5209daf9505e10c8791d1b6..0000000000000000000000000000000000000000 --- a/pocketflow_analysis_demo.rs +++ /dev/null @@ -1,157 +0,0 @@ -#!/usr/bin/env cargo run --example pocketflow_analysis_demo -//! PocketFlow Analysis Demo -//! -//! Demonstrates advanced analysis capabilities using Brain AI -//! with the new MemoryService and ConceptGraphService architecture. - -use brain::*; -use brain::services::*; -use std::env; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿ” PocketFlow Analysis Demo"); - println!("==========================="); - - // Check for OpenAI API key - let _openai_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| { - println!("โš ๏ธ OPENAI_API_KEY not set. Please set it to use this demo."); - std::process::exit(1); - }); - - println!("โœ… OpenAI API key found"); - - // Initialize Brain AI components using new service architecture - println!("\n๐Ÿ”ง Initializing Brain AI Services..."); - let mut memory_system = create_memory_service_with_capacity(2000).await?; - let mut concept_graph = create_concept_graph_service_default().await?; - - println!("โœ… MemoryService initialized for detailed analysis"); - println!("โœ… ConceptGraphService initialized"); - - // Load comprehensive PocketFlow analysis data - println!("\n๐Ÿ“Š Loading PocketFlow Analysis Data..."); - let analysis_data = vec![ - "PocketFlow analysis reveals three core architectural patterns for LLM orchestration", - "Performance metrics show 60% cost reduction through batch processing optimization", - "Code analysis indicates 85% reduction in boilerplate through Node-Flow abstraction", - "The 100-line philosophy maintains simplicity while supporting complex workflows", - "Async parallel processing reduces latency by 40% in multi-LLM scenarios", - "Agent-based design enables recursive and self-improving AI system architectures", - "Batch optimization framework shows 3x improvement in API cost efficiency", - "Node-Flow pattern separates concerns between logic and orchestration effectively", - "Framework demonstrates high extensibility with minimal core complexity", - "Real-world usage shows significant developer productivity improvements", - ]; - - for (i, data) in analysis_data.iter().enumerate() { - memory_system.learn(data.to_string(), Priority::High).await?; - println!("โœ… Loaded analysis data {}", i + 1); - } - - // Create RAG orchestrator for analysis processing - println!("\n๐Ÿค– Initializing Analysis System..."); - let mut rag_orchestrator = RagOrchestrator::new()?; - - // Comprehensive analysis questions - let analysis_questions = vec![ - "What are the key performance improvements shown by PocketFlow?", - "How does the Node-Flow pattern improve code organization?", - "What cost optimizations does PocketFlow provide?", - "How does PocketFlow handle parallel processing?", - "What makes PocketFlow suitable for production environments?", - "How does the framework balance simplicity with functionality?", - "What are the measurable benefits of using PocketFlow?", - "How does PocketFlow support different types of AI workflows?", - ]; - - println!("\n๐Ÿ“ˆ Running Comprehensive PocketFlow Analysis"); - println!("============================================"); - - let mut analysis_results = Vec::new(); - - for (i, question) in analysis_questions.iter().enumerate() { - println!("\n๐Ÿ” Analysis {}: {}", i + 1, question); - - let request = RagRequest { - message: question.to_string(), - conversation_id: Some("analysis_session".to_string()), - context_limit: Some(7), - retrieval_threshold: Some(0.25), - }; - - match rag_orchestrator.process_conversation( - request, - &mut memory_system, - &mut concept_graph, - ).await { - Ok(response) => { - println!("๐Ÿ“Š Analysis Result:"); - println!(" {}", response.response); - println!(" ๐ŸŽฏ Confidence: {:.1}%", response.confidence_score * 100.0); - println!(" ๐Ÿ“š Data sources: {}", response.context_used.len()); - - // Store analysis result - analysis_results.push((question.to_string(), response.response.clone(), response.confidence_score)); - - // Learn from analysis for future insights - let insight = format!("Analysis insight: {} -> {}", question, response.response); - memory_system.learn(insight, Priority::Medium).await?; - } - Err(e) => { - println!(" โŒ Analysis error: {}", e); - } - } - - tokio::time::sleep(tokio::time::Duration::from_millis(600)).await; - } - - // Generate summary report - println!("\n๐Ÿ“‹ Analysis Summary Report"); - println!("=========================="); - - let high_confidence_analyses: Vec<_> = analysis_results.iter() - .filter(|(_, _, confidence)| *confidence > 0.6) - .collect(); - - println!("โœ… High confidence analyses: {}/{}", high_confidence_analyses.len(), analysis_results.len()); - println!("๐Ÿ“Š Average confidence: {:.1}%", - analysis_results.iter().map(|(_, _, c)| c).sum::() / analysis_results.len() as f64 * 100.0); - - if !high_confidence_analyses.is_empty() { - println!("\n๐Ÿ† Key Insights (High Confidence):"); - for (i, (question, answer, confidence)) in high_confidence_analyses.iter().enumerate() { - println!("{}. {} ({:.1}%)", i + 1, question, confidence * 100.0); - println!(" ๐Ÿ’ก {}", answer.chars().take(100).collect::()); - if answer.len() > 100 { - println!(" ..."); - } - } - } - - // Display session statistics - println!("\n๐Ÿ“Š Session Statistics"); - println!("====================="); - let stats = rag_orchestrator.get_conversation_stats(); - for (key, value) in stats { - println!(" {}: {}", key, value); - } - - // Memory consolidation - println!("\n๐Ÿง  Consolidating Analysis Results..."); - match memory_system.consolidate().await { - Ok(result) => { - println!("โœ… Consolidation complete:"); - println!(" Promoted to episodic: {} items", result.working_to_episodic); - println!(" Extracted semantic concepts: {} items", result.episodic_to_semantic); - } - Err(e) => { - println!("โš ๏ธ Consolidation warning: {}", e); - } - } - - println!("\nโœ… PocketFlow Analysis Complete!"); - println!(" Advanced analysis completed successfully with new service architecture."); - - Ok(()) -} \ No newline at end of file diff --git a/pocketflow_chat.db b/pocketflow_chat.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/pocketflow_chat.db and /dev/null differ diff --git a/postgresql_training_service_demo.rs b/postgresql_training_service_demo.rs deleted file mode 100644 index 1e70ebc42c9247e732dc86ec89659aeba456ffec..0000000000000000000000000000000000000000 --- a/postgresql_training_service_demo.rs +++ /dev/null @@ -1,450 +0,0 @@ -//! PostgreSQL Training Service Demo -//! -//! Demonstrates the production-ready PostgreSQL training data service with -//! comprehensive conversation collection, quality assessment, and export functionality. - -use brain_cognitive::{ - PostgreSQLTrainingService, PostgreSQLTrainingConfig, - ConversationRecord, MessageRecord, ConversationMetadata, ConversationQualityMetrics, - ComplexityLevel, ConversationType, UserExpertise, KnowledgeSourceRecord, - DatasetFilter, UserFeedback, ResponseQuality, -}; -use brain_cognitive::models::TrainingDataService; -use chrono::Utc; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐ŸŽ“ PostgreSQL Training Service Demo"); - println!("====================================="); - - // Check for required environment variables - let database_url = std::env::var("DATABASE_URL") - .or_else(|_| std::env::var("POSTGRES_URL")) - .unwrap_or_else(|_| { - println!("โš ๏ธ DATABASE_URL not set. Using default connection"); - "postgresql://brain_user:brain_password@localhost:5432/brain_training".to_string() - }); - - println!("โœ… Database URL: {}", mask_password(&database_url)); - - // Configure the PostgreSQL training service - let config = PostgreSQLTrainingConfig { - host: "localhost".to_string(), - port: 5432, - database: "brain_training".to_string(), - username: "brain_user".to_string(), - password: "brain_password".to_string(), - max_connections: 10, - min_connections: 1, - acquire_timeout_seconds: 30, - idle_timeout_seconds: 300, - quality_threshold: 0.7, - max_conversations_per_export: 1000, - enable_anonymization: true, - retention_days: 365, - }; - - println!("\n๐Ÿ”ง Initializing PostgreSQL Training Service..."); - let mut training_service = match PostgreSQLTrainingService::new(config).await { - Ok(service) => { - println!("โœ… PostgreSQL training service initialized successfully"); - service - } - Err(e) => { - println!("โŒ Failed to initialize training service: {}", e); - println!("๐Ÿ’ก Make sure PostgreSQL is running and accessible"); - println!(" Try: docker-compose -f scripts/docker-compose.dev.yml up -d"); - return Err(Box::new(e) as Box); - } - }; - - // Test health check - println!("\n๐Ÿ” Testing Service Health..."); - match training_service.health_check().await { - Ok(true) => println!("โœ… Service health check passed"), - Ok(false) => println!("โš ๏ธ Service health check returned false"), - Err(e) => { - println!("โŒ Service health check failed: {}", e); - return Err(Box::new(e) as Box); - } - } - - // Create sample conversation data - println!("\n๐Ÿ“ Creating Sample Training Data..."); - let sample_conversations = create_sample_conversations(); - - // Store conversations in PostgreSQL - println!("\n๐Ÿ’พ Storing Conversations in PostgreSQL..."); - let mut stored_count = 0; - for conversation in &sample_conversations { - match training_service.collect_conversation(conversation.clone()).await { - Ok(()) => { - stored_count += 1; - println!(" โœ… Stored conversation: {}", conversation.conversation_id); - } - Err(e) => { - println!(" โŒ Failed to store conversation {}: {}", conversation.conversation_id, e); - } - } - } - println!("โœ… Successfully stored {}/{} conversations", stored_count, sample_conversations.len()); - - // Get comprehensive statistics - println!("\n๐Ÿ“Š Retrieving Training Data Statistics..."); - match training_service.get_statistics().await { - Ok(stats) => { - println!("โœ… Training Data Statistics:"); - for (key, value) in &stats { - println!(" ๐Ÿ“ˆ {}: {:.2}", key, value); - } - } - Err(e) => { - println!("โŒ Failed to get statistics: {}", e); - } - } - - // Test export functionality with filtering - println!("\n๐Ÿ“ค Testing Dataset Export..."); - - // Export high-quality conversations - let filter = DatasetFilter { - min_quality: Some(0.8), - max_quality: None, - conversation_types: Some(vec![ConversationType::Technical, ConversationType::ProblemSolving]), - complexity_levels: Some(vec![ComplexityLevel::Complex, ComplexityLevel::Expert]), - topics: None, - date_range: None, - }; - - let filter_json = serde_json::to_string(&filter) - .map_err(|e| Box::new(e) as Box)?; - match training_service.export_dataset(Some(&filter_json)).await { - Ok(dataset) => { - println!("โœ… Successfully exported dataset:"); - println!(" ๐Ÿ“Š Total conversations: {}", dataset.metadata.total_conversations); - println!(" ๐Ÿ“Š Total messages: {}", dataset.metadata.total_messages); - println!(" ๐Ÿ“Š Quality threshold: {:.2}", dataset.metadata.quality_threshold); - println!(" ๐Ÿ“Š Average quality: {:.2}", dataset.statistics.average_quality); - println!(" ๐Ÿ“Š Average conversation length: {:.1}", dataset.statistics.average_conversation_length); - - // Display quality distribution - println!(" ๐Ÿ“Š Quality distribution:"); - for (quality_level, count) in &dataset.statistics.quality_distribution { - println!(" - {}: {} conversations", quality_level, count); - } - } - Err(e) => { - println!("โŒ Failed to export dataset: {}", e); - } - } - - // Test export without filtering - println!("\n๐Ÿ“ค Testing Full Dataset Export..."); - match training_service.export_dataset(None).await { - Ok(dataset) => { - println!("โœ… Successfully exported full dataset:"); - println!(" ๐Ÿ“Š Total conversations: {}", dataset.metadata.total_conversations); - println!(" ๐Ÿ“Š Total messages: {}", dataset.metadata.total_messages); - - // Display topic distribution - println!(" ๐Ÿ“Š Topic distribution:"); - for (topic, count) in dataset.statistics.topic_distribution.iter().take(5) { - println!(" - {}: {} mentions", topic, count); - } - } - Err(e) => { - println!("โŒ Failed to export full dataset: {}", e); - } - } - - // Test data cleanup (uncomment to test) - // println!("\n๐Ÿงน Testing Data Cleanup..."); - // match training_service.cleanup_old_data().await { - // Ok(deleted_count) => { - // println!("โœ… Cleanup completed. Deleted {} old conversations", deleted_count); - // } - // Err(e) => { - // println!("โŒ Cleanup failed: {}", e); - // } - // } - - println!("\n๐ŸŽ‰ PostgreSQL Training Service Demo Completed Successfully!"); - println!("\n๐Ÿ’ก Next Steps:"); - println!(" - Integrate with conversation system for real-time data collection"); - println!(" - Set up automated quality assessment pipelines"); - println!(" - Configure data export schedules for model training"); - println!(" - Monitor storage usage and implement data lifecycle policies"); - - Ok(()) -} - -/// Create sample conversation data for demonstration -fn create_sample_conversations() -> Vec { - let mut conversations = Vec::new(); - - // Technical conversation - let conv1 = ConversationRecord { - conversation_id: "tech_conv_001".to_string(), - messages: vec![ - MessageRecord { - message_id: "msg_001".to_string(), - role: "user".to_string(), - content: "How do I implement a binary search algorithm in Rust?".to_string(), - anonymized_content: None, - timestamp: Utc::now(), - knowledge_sources: vec![ - KnowledgeSourceRecord { - source_type: "memory".to_string(), - content_summary: "Algorithm knowledge from computer science concepts".to_string(), - relevance_score: 0.9, - confidence: 0.8, - } - ], - response_quality: Some(ResponseQuality { - factual_grounding: 0.9, - coherence: 0.85, - relevance: 0.95, - safety_score: 1.0, - source_attribution: 0.8, - consistency_score: 0.85, - completeness: 0.9, - clarity: 0.88, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.1, - confidence_calibration: 0.85, - }), - user_feedback: Some(UserFeedback { - satisfaction_score: 0.9, - helpfulness: 0.85, - accuracy: 0.95, - clarity: 0.88, - feedback_text: Some("Very helpful explanation with clear code examples".to_string()), - timestamp: Utc::now(), - }), - }, - MessageRecord { - message_id: "msg_002".to_string(), - role: "assistant".to_string(), - content: "Here's a comprehensive binary search implementation in Rust:\n\n```rust\nfn binary_search(arr: &[T], target: &T) -> Option {\n let mut left = 0;\n let mut right = arr.len();\n \n while left < right {\n let mid = left + (right - left) / 2;\n match arr[mid].cmp(target) {\n std::cmp::Ordering::Equal => return Some(mid),\n std::cmp::Ordering::Less => left = mid + 1,\n std::cmp::Ordering::Greater => right = mid,\n }\n }\n None\n}\n```\n\nThis implementation has O(log n) time complexity and uses Rust's built-in comparison traits for type safety.".to_string(), - anonymized_content: None, - timestamp: Utc::now(), - knowledge_sources: vec![ - KnowledgeSourceRecord { - source_type: "concept".to_string(), - content_summary: "Binary search algorithm concepts and Rust implementation patterns".to_string(), - relevance_score: 0.95, - confidence: 0.9, - } - ], - response_quality: Some(ResponseQuality { - factual_grounding: 0.95, - coherence: 0.9, - relevance: 0.98, - safety_score: 1.0, - source_attribution: 0.85, - consistency_score: 0.9, - completeness: 0.95, - clarity: 0.92, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.05, - confidence_calibration: 0.9, - }), - user_feedback: None, - } - ], - metadata: ConversationMetadata { - domain: "Computer Science".to_string(), - complexity_level: ComplexityLevel::Complex, - conversation_type: ConversationType::Technical, - user_expertise: UserExpertise::Intermediate, - session_duration_minutes: 15.5, - turn_count: 2, - context_switches: 0, - topics: vec!["algorithms".to_string(), "rust".to_string(), "binary search".to_string()], - }, - quality_metrics: ConversationQualityMetrics { - overall_quality: 0.88, - coherence_score: 0.9, - knowledge_grounding: 0.92, - response_relevance: 0.95, - safety_score: 1.0, - educational_value: 0.9, - diversity_score: 0.7, - uniqueness_score: 0.8, - }, - created_at: Utc::now(), - last_updated: Utc::now(), - }; - - // Problem-solving conversation - let conv2 = ConversationRecord { - conversation_id: "problem_conv_002".to_string(), - messages: vec![ - MessageRecord { - message_id: "msg_003".to_string(), - role: "user".to_string(), - content: "I'm getting a compilation error in my Rust project. Can you help me debug it?".to_string(), - anonymized_content: None, - timestamp: Utc::now(), - knowledge_sources: vec![], - response_quality: None, - user_feedback: None, - }, - MessageRecord { - message_id: "msg_004".to_string(), - role: "assistant".to_string(), - content: "I'd be happy to help you debug the compilation error! Could you please share:\n\n1. The exact error message you're seeing\n2. The relevant code that's causing the issue\n3. Your Rust version (`rustc --version`)\n\nThis information will help me provide a more targeted solution.".to_string(), - anonymized_content: None, - timestamp: Utc::now(), - knowledge_sources: vec![ - KnowledgeSourceRecord { - source_type: "pattern".to_string(), - content_summary: "Common Rust compilation error patterns and debugging strategies".to_string(), - relevance_score: 0.8, - confidence: 0.75, - } - ], - response_quality: Some(ResponseQuality { - factual_grounding: 0.7, - coherence: 0.85, - relevance: 0.9, - safety_score: 1.0, - source_attribution: 0.6, - consistency_score: 0.8, - completeness: 0.75, - clarity: 0.9, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.2, - confidence_calibration: 0.75, - }), - user_feedback: Some(UserFeedback { - satisfaction_score: 0.8, - helpfulness: 0.85, - accuracy: 0.75, - clarity: 0.9, - feedback_text: Some("Good structured approach to debugging".to_string()), - timestamp: Utc::now(), - }), - } - ], - metadata: ConversationMetadata { - domain: "Software Development".to_string(), - complexity_level: ComplexityLevel::Moderate, - conversation_type: ConversationType::ProblemSolving, - user_expertise: UserExpertise::Beginner, - session_duration_minutes: 8.2, - turn_count: 2, - context_switches: 0, - topics: vec!["rust".to_string(), "debugging".to_string(), "compilation".to_string()], - }, - quality_metrics: ConversationQualityMetrics { - overall_quality: 0.78, - coherence_score: 0.85, - knowledge_grounding: 0.7, - response_relevance: 0.9, - safety_score: 1.0, - educational_value: 0.8, - diversity_score: 0.6, - uniqueness_score: 0.7, - }, - created_at: Utc::now(), - last_updated: Utc::now(), - }; - - // Tutorial conversation - let conv3 = ConversationRecord { - conversation_id: "tutorial_conv_003".to_string(), - messages: vec![ - MessageRecord { - message_id: "msg_005".to_string(), - role: "user".to_string(), - content: "Can you explain the concept of ownership in Rust for beginners?".to_string(), - anonymized_content: None, - timestamp: Utc::now(), - knowledge_sources: vec![], - response_quality: None, - user_feedback: None, - }, - MessageRecord { - message_id: "msg_006".to_string(), - role: "assistant".to_string(), - content: "Ownership is one of Rust's most important concepts! Let me explain it step by step:\n\n**What is Ownership?**\nOwnership is Rust's way of managing memory safely without a garbage collector. Every value in Rust has a single owner.\n\n**Three Rules of Ownership:**\n1. Each value has exactly one owner\n2. When the owner goes out of scope, the value is dropped\n3. There can only be one owner at a time\n\n**Example:**\n```rust\nfn main() {\n let s1 = String::from(\"hello\"); // s1 owns the string\n let s2 = s1; // Ownership moves to s2\n // println!(\"{}\", s1); // Error! s1 no longer owns the value\n println!(\"{}\", s2); // This works fine\n}\n```\n\nThis system prevents common bugs like use-after-free and double-free errors!".to_string(), - anonymized_content: None, - timestamp: Utc::now(), - knowledge_sources: vec![ - KnowledgeSourceRecord { - source_type: "concept".to_string(), - content_summary: "Rust ownership system fundamentals and memory management".to_string(), - relevance_score: 0.98, - confidence: 0.95, - } - ], - response_quality: Some(ResponseQuality { - factual_grounding: 0.95, - coherence: 0.95, - relevance: 0.98, - safety_score: 1.0, - source_attribution: 0.9, - consistency_score: 0.95, - completeness: 0.9, - clarity: 0.95, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.05, - confidence_calibration: 0.95, - }), - user_feedback: Some(UserFeedback { - satisfaction_score: 0.95, - helpfulness: 0.98, - accuracy: 0.95, - clarity: 0.98, - feedback_text: Some("Excellent explanation with clear examples!".to_string()), - timestamp: Utc::now(), - }), - } - ], - metadata: ConversationMetadata { - domain: "Programming Education".to_string(), - complexity_level: ComplexityLevel::Moderate, - conversation_type: ConversationType::Tutorial, - user_expertise: UserExpertise::Beginner, - session_duration_minutes: 12.3, - turn_count: 2, - context_switches: 0, - topics: vec!["rust".to_string(), "ownership".to_string(), "memory management".to_string()], - }, - quality_metrics: ConversationQualityMetrics { - overall_quality: 0.94, - coherence_score: 0.95, - knowledge_grounding: 0.95, - response_relevance: 0.98, - safety_score: 1.0, - educational_value: 0.98, - diversity_score: 0.8, - uniqueness_score: 0.85, - }, - created_at: Utc::now(), - last_updated: Utc::now(), - }; - - conversations.push(conv1); - conversations.push(conv2); - conversations.push(conv3); - - conversations -} - -/// Mask password in database URL for logging -fn mask_password(url: &str) -> String { - if let Some(at_pos) = url.find('@') { - if let Some(colon_pos) = url[..at_pos].rfind(':') { - let mut masked = url.to_string(); - masked.replace_range(colon_pos + 1..at_pos, "***"); - return masked; - } - } - url.to_string() -} \ No newline at end of file diff --git a/python_api_demo.py b/python_api_demo.py deleted file mode 100644 index 34d84402a9df8e0360777c33df05636e92c7e9ef..0000000000000000000000000000000000000000 --- a/python_api_demo.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive demonstration of Brain Python API -Shows usage of all four core functions with error handling and examples. -""" - -import sys -import time - -try: - from brain import BrainEngine, segment_text, quick_query # type: ignore -except ImportError as e: - print(f"Error importing brain module: {e}") - print("Make sure you've built the Python package with maturin") - sys.exit(1) - - -def print_header(title: str): - """Print a formatted header for each demo section.""" - print(f"\n{'='*60}") - print(f" {title}") - print(f"{'='*60}") - - -def demo_segmentation(): - """Demonstrate text segmentation functionality.""" - print_header("TEXT SEGMENTATION DEMO") - - engine = BrainEngine() - - test_texts = [ - "Hello world! This is a test.", - "The quick brown fox jumps over the lazy dog.", - "Python is a programming language. It's very popular.", - "" # Edge case: empty string - ] - - for i, text in enumerate(test_texts, 1): - print(f"\nTest {i}: '{text}'") - try: - start_time = time.time() - segments = engine.segment(text) - duration = time.time() - start_time - - print(f" Segmented into {len(segments)} parts " - f"(in {duration:.4f}s):") - for j, segment in enumerate(segments): - print(f" {j+1}. '{segment.text}' " - f"(confidence: {segment.confidence:.3f}, " - f"type: {segment.segment_type})") - - except Exception as e: - print(f" Error: {e}") - - -def demo_learning(): - """Demonstrate knowledge storage functionality.""" - print_header("LEARNING & STORAGE DEMO") - - engine = BrainEngine() - - knowledge_items = [ - ("Python is a high-level programming language", "high"), - ("The capital of France is Paris", "medium"), - ("Today's weather is sunny", "low"), - ("Machine learning uses algorithms to find patterns", "high"), - ("Coffee tastes good", "low") - ] - - print("Storing knowledge items:") - for knowledge, priority in knowledge_items: - try: - start_time = time.time() - success = engine.learn(knowledge, priority) - duration = time.time() - start_time - - status = "โœ“" if success else "โœ—" - print(f" {status} '{knowledge}' (priority: {priority}) - " - f"{duration:.4f}s") - - except Exception as e: - print(f" โœ— Error storing '{knowledge}': {e}") - - # Show storage stats - try: - status = engine.get_status() - print(f"\nStorage status: {status}") - except Exception as e: - print(f"Error getting status: {e}") - - -def demo_simulation(): - """Demonstrate predictive simulation functionality.""" - print_header("SIMULATION DEMO") - - engine = BrainEngine() - - scenarios = [ - "What happens if I learn Python programming?", - "Predict the outcome of studying machine learning", - "What if I drink coffee every morning?", - "" # Edge case - ] - - for i, scenario in enumerate(scenarios, 1): - print(f"\nScenario {i}: '{scenario}'") - try: - max_steps = 5 - threshold = 0.1 - - start_time = time.time() - result = engine.simulate(scenario, max_steps=max_steps, - confidence_threshold=threshold) - duration = time.time() - start_time - - print(f" Simulation completed in {duration:.4f}s:") - print(f" Steps taken: {result.steps}") - print(f" Final state: '{result.outcome}'") - print(f" Confidence: {result.confidence:.3f}") - - if result.metadata: - print(f" Metadata: {result.metadata}") - - except Exception as e: - print(f" Error: {e}") - - -def demo_memory_query(): - """Demonstrate memory querying functionality.""" - print_header("MEMORY QUERY DEMO") - - engine = BrainEngine() - - # First, store some knowledge to query - print("Setting up test data...") - test_data = [ - "Python is used for web development", - "Machine learning requires data", - "Paris is the capital of France", - "Coffee contains caffeine" - ] - - for data in test_data: - try: - engine.learn(data, "medium") - except Exception as e: - print(f"Warning: Could not store '{data}': {e}") - - queries = [ - "programming languages", - "European capitals", - "beverages", - "nonexistent topic" - ] - - print(f"\nQuerying memory with {len(queries)} different topics:") - for i, query in enumerate(queries, 1): - print(f"\nQuery {i}: '{query}'") - try: - start_time = time.time() - results = engine.query_memory(query, limit=3) - duration = time.time() - start_time - - print(f" Found {len(results)} result(s) in {duration:.4f}s:") - for j, result in enumerate(results): - print(f" {j+1}. '{result.content}' " - f"(relevance: {result.relevance:.3f}, " - f"type: {result.memory_type})") - - except Exception as e: - print(f" Error: {e}") - - -def demo_configuration(): - """Demonstrate configuration management.""" - print_header("CONFIGURATION DEMO") - - engine = BrainEngine() - - try: - # Get current configuration - config = engine.get_config() - print("Current configuration:") - for key, value in config.items(): - print(f" {key}: {value}") - - # Update some settings - print("\nUpdating configuration...") - new_settings = { - "max_memory_size": "2000", - "prediction_steps": "10" - } - - success = engine.update_config(new_settings) - status = "โœ“" if success else "โœ—" - print(f" {status} Updated configuration with new settings") - - # Show updated config - updated_config = engine.get_config() - print("\nUpdated configuration:") - for key, value in updated_config.items(): - print(f" {key}: {value}") - - except Exception as e: - print(f"Configuration error: {e}") - - -def demo_convenience_functions(): - """Demonstrate module-level convenience functions.""" - print_header("CONVENIENCE FUNCTIONS DEMO") - - print("Testing module-level functions:") - - # Test segment_text function - try: - text = "Quick test of convenience functions" - segments = segment_text(text) - print(f"โœ“ segment_text('{text}') -> {len(segments)} segments") - except Exception as e: - print(f"โœ— segment_text error: {e}") - - # Test quick_query function - try: - query = "test query" - quick_query(query) - print(f"โœ“ quick_query('{query}') -> query executed") - except Exception as e: - print(f"โœ— quick_query error: {e}") - - -def demo_performance_test(): - """Run basic performance tests.""" - print_header("PERFORMANCE TEST") - - engine = BrainEngine() - - # Test batch operations - print("Testing batch performance...") - - # Segmentation performance - long_text = "This is a longer text for performance testing. " * 20 - start_time = time.time() - segments = engine.segment(long_text) - seg_duration = time.time() - start_time - print(f"Segmentation: {len(segments)} segments from " - f"{len(long_text)} chars in {seg_duration:.4f}s") - - # Learning performance - start_time = time.time() - for i in range(10): - engine.learn(f"Test knowledge item number {i}", "low") - learn_duration = time.time() - start_time - print(f"Learning: 10 items stored in {learn_duration:.4f}s " - f"({learn_duration/10:.4f}s per item)") - - # Query performance - start_time = time.time() - total_results = 0 - for i in range(5): - results = engine.query_memory(f"test query {i}", limit=3) - total_results += len(results) - query_duration = time.time() - start_time - print(f"Querying: 5 queries in {query_duration:.4f}s " - f"({query_duration/5:.4f}s per query)") - print(f"Total results found: {total_results}") - - -def main(): - """Run all demonstrations.""" - print("Brain Python API Comprehensive Demo") - print("This demo shows all core functionality with error handling and " - "examples.") - - try: - # Core functionality demos - demo_segmentation() - demo_learning() - demo_simulation() - demo_memory_query() - - # Advanced features - demo_configuration() - demo_convenience_functions() - demo_performance_test() - - print_header("DEMO COMPLETED SUCCESSFULLY") - print("All core API functions demonstrated.") - print("The Brain Python API is working correctly!") - - except KeyboardInterrupt: - print("\n\nDemo interrupted by user.") - except Exception as e: - print(f"\n\nUnexpected error during demo: {e}") - import traceback - traceback.print_exc() - - -if __name__ == "__main__": - main() diff --git a/quantization_edge_demo.rs b/quantization_edge_demo.rs deleted file mode 100644 index bedf1f04bf0c5f75784a8be387ac7353d151821c..0000000000000000000000000000000000000000 --- a/quantization_edge_demo.rs +++ /dev/null @@ -1,489 +0,0 @@ -// @transform: Quantization and Edge Optimization Demo -//! # MuBrain Quantization and Edge Optimization Demo -//! -//! Demonstrates the complete quantization and edge optimization system including: -//! - INT8/INT4 quantization with adaptive selection -//! - Resource monitoring and hardware classification -//! - Edge deployment profile creation -//! - Performance tracking and optimization decisions -//! - Adaptive optimization based on resource constraints - -use anyhow::Result; -use brain_mubrain::{ - EdgeOptimizationManager, EdgeOptimizationConfig, QuantizationEngine, QuantizationConfig, - ModelRegistryConfig, ModelLoaderConfig, - HardwareSpec, PerformanceRequirements, StorageType, - QuantizationType, OptimizationLevel, ModelType, ModelFormat, -}; -use candle_core::Device; -use std::path::PathBuf; -use tracing::info; - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - tracing_subscriber::fmt::init(); - - info!("๐Ÿš€ Starting MuBrain Quantization and Edge Optimization Demo"); - - // Run all demo scenarios - demo_quantization_basics().await?; - demo_edge_optimization().await?; - demo_deployment_profiles().await?; - demo_adaptive_optimization().await?; - demo_performance_monitoring().await?; - - info!("โœ… MuBrain Quantization and Edge Optimization Demo completed successfully!"); - Ok(()) -} - -/// @transform - Demonstrate basic quantization capabilities -async fn demo_quantization_basics() -> Result<()> { - info!("\n๐Ÿ”ง Demo 1: Basic Quantization Capabilities"); - - // Configure quantization engine - let config = QuantizationConfig { - default_quantization: QuantizationType::INT8, - adaptive_quantization: true, - max_memory_mb: 4096, - dynamic_quantization: true, - quality_threshold: 0.85, - cache_size_mb: 1024, - }; - - let quantization_engine = QuantizationEngine::new(config, Device::Cpu)?; - - // Create mock model metadata for demonstration - let model_metadata = create_mock_model_metadata("demo-codelama-7b", ModelType::CodeLlama); - - info!("๐Ÿ“Š Testing different quantization types:"); - - // Test INT8 quantization - let weights = create_mock_tensor()?; - let int8_result = quantization_engine.quantize_model( - &model_metadata, - &weights, - QuantizationType::INT8, - ).await?; - - info!(" โ€ข INT8: {:.2}x compression, {:.1}% quality", - int8_result.compression_ratio, - int8_result.quality_score * 100.0 - ); - - // Test INT4 quantization - let int4_result = quantization_engine.quantize_model( - &model_metadata, - &weights, - QuantizationType::INT4, - ).await?; - - info!(" โ€ข INT4: {:.2}x compression, {:.1}% quality", - int4_result.compression_ratio, - int4_result.quality_score * 100.0 - ); - - // Test NF4 quantization - let nf4_result = quantization_engine.quantize_model( - &model_metadata, - &weights, - QuantizationType::NF4, - ).await?; - - info!(" โ€ข NF4: {:.2}x compression, {:.1}% quality", - nf4_result.compression_ratio, - nf4_result.quality_score * 100.0 - ); - - // Demonstrate adaptive quantization selection - let optimal_quantization = quantization_engine.select_optimal_quantization(&model_metadata).await?; - info!("๐ŸŽฏ Optimal quantization selected: {:?}", optimal_quantization); - - Ok(()) -} - -/// @oracle - Demonstrate edge optimization manager -async fn demo_edge_optimization() -> Result<()> { - info!("\nโšก Demo 2: Edge Optimization Manager"); - - // Configure edge optimization - let edge_config = EdgeOptimizationConfig { - auto_quantization: true, - target_memory_usage: 0.7, - min_quality_threshold: 0.8, - monitoring_interval_secs: 60, - edge_optimization_level: OptimizationLevel::EdgeOptimized, - aggressive_edge_optimization: true, - }; - - // Configure supporting components - let registry_config = ModelRegistryConfig { - data_dir: PathBuf::from("./data/models"), - cache_size_mb: 2048, - auto_download: false, - prefer_quantized: true, - max_context_length: 4096, - memory_map: true, - }; - - let loader_config = ModelLoaderConfig { - max_cache_size_mb: 1024, - use_memory_mapping: true, - compress_cached_tensors: true, - preload_models: false, - preferred_device: Device::Cpu, - }; - - let quantization_config = QuantizationConfig::default(); - - // Initialize edge optimization manager - let edge_manager = EdgeOptimizationManager::new( - edge_config, - registry_config, - loader_config, - quantization_config, - ).await?; - - info!("๐Ÿญ Edge Optimization Manager initialized successfully"); - - // Simulate model optimization for edge deployment - let model_id = "demo-codelama-7b"; - - info!("๐Ÿ”„ Optimizing model '{}' for edge deployment...", model_id); - - // This would normally use real model data - info!(" โ€ข Analyzing model characteristics..."); - info!(" โ€ข Selecting optimal quantization strategy..."); - info!(" โ€ข Applying resource-aware optimizations..."); - - // Get optimization statistics - let stats = edge_manager.get_optimization_statistics().await?; - info!("๐Ÿ“ˆ Current optimization strategy: {:?}", stats.current_strategy.target_hardware); - info!(" โ€ข Preferred quantization: {:?}", stats.current_strategy.preferred_quantization); - info!(" โ€ข Quality preference: {:.1}%", stats.current_strategy.quality_preference * 100.0); - - Ok(()) -} - -/// @bridge - Demonstrate deployment profile creation -async fn demo_deployment_profiles() -> Result<()> { - info!("\n๐Ÿ“ฑ Demo 3: Edge Deployment Profiles"); - - let edge_config = EdgeOptimizationConfig::default(); - let registry_config = ModelRegistryConfig { - data_dir: PathBuf::from("./data/models"), - cache_size_mb: 1024, - auto_download: false, - prefer_quantized: true, - max_context_length: 2048, - memory_map: true, - }; - let loader_config = ModelLoaderConfig::default(); - let quantization_config = QuantizationConfig::default(); - - let edge_manager = EdgeOptimizationManager::new( - edge_config, - registry_config, - loader_config, - quantization_config, - ).await?; - - info!("๐Ÿ”ง Creating deployment profiles for different hardware categories:"); - - // Edge device profile (Raspberry Pi-like) - let edge_hardware = HardwareSpec { - memory_mb: 2048, - cpu_cores: 4, - gpu_memory_mb: None, - storage_type: StorageType::EMMC, - power_constrained: true, - }; - - let edge_requirements = PerformanceRequirements { - max_inference_time_ms: 3000.0, - min_tokens_per_second: 5.0, - max_memory_usage_mb: 1024, - min_quality_score: 0.75, - max_startup_time_secs: 45.0, - }; - - let edge_profile = edge_manager.create_deployment_profile( - "EdgeDevice-2GB".to_string(), - edge_hardware, - edge_requirements, - ).await?; - - info!(" โ€ข Edge Device (2GB RAM):"); - info!(" - Hardware category: {:?}", edge_profile.optimization_strategy.target_hardware); - info!(" - Preferred quantization: {:?}", edge_profile.optimization_strategy.preferred_quantization); - info!(" - Memory limit: {} MB", edge_profile.optimization_strategy.memory_limit_mb); - info!(" - Model overrides: {} configured", edge_profile.model_overrides.len()); - - // Standard laptop/desktop profile - let standard_hardware = HardwareSpec { - memory_mb: 8192, - cpu_cores: 8, - gpu_memory_mb: Some(4096), - storage_type: StorageType::SSD, - power_constrained: false, - }; - - let standard_requirements = PerformanceRequirements { - max_inference_time_ms: 1500.0, - min_tokens_per_second: 15.0, - max_memory_usage_mb: 4096, - min_quality_score: 0.85, - max_startup_time_secs: 20.0, - }; - - let standard_profile = edge_manager.create_deployment_profile( - "StandardLaptop-8GB".to_string(), - standard_hardware, - standard_requirements, - ).await?; - - info!(" โ€ข Standard Laptop (8GB RAM):"); - info!(" - Hardware category: {:?}", standard_profile.optimization_strategy.target_hardware); - info!(" - Preferred quantization: {:?}", standard_profile.optimization_strategy.preferred_quantization); - info!(" - Memory limit: {} MB", standard_profile.optimization_strategy.memory_limit_mb); - info!(" - GPU memory available: {} MB", standard_profile.hardware_spec.gpu_memory_mb.unwrap_or(0)); - - // High-performance workstation profile - let high_perf_hardware = HardwareSpec { - memory_mb: 32768, - cpu_cores: 16, - gpu_memory_mb: Some(16384), - storage_type: StorageType::NVME, - power_constrained: false, - }; - - let high_perf_requirements = PerformanceRequirements { - max_inference_time_ms: 800.0, - min_tokens_per_second: 30.0, - max_memory_usage_mb: 8192, - min_quality_score: 0.95, - max_startup_time_secs: 10.0, - }; - - let high_perf_profile = edge_manager.create_deployment_profile( - "Workstation-32GB".to_string(), - high_perf_hardware, - high_perf_requirements, - ).await?; - - info!(" โ€ข High-Performance Workstation (32GB RAM):"); - info!(" - Hardware category: {:?}", high_perf_profile.optimization_strategy.target_hardware); - info!(" - Preferred quantization: {:?}", high_perf_profile.optimization_strategy.preferred_quantization); - info!(" - Memory limit: {} MB", high_perf_profile.optimization_strategy.memory_limit_mb); - info!(" - Quality preference: {:.1}%", high_perf_profile.optimization_strategy.quality_preference * 100.0); - - Ok(()) -} - -/// @sentinel - Demonstrate adaptive optimization monitoring -async fn demo_adaptive_optimization() -> Result<()> { - info!("\n๐Ÿ”„ Demo 4: Adaptive Optimization Monitoring"); - - let edge_config = EdgeOptimizationConfig { - auto_quantization: true, - target_memory_usage: 0.8, - min_quality_threshold: 0.8, - monitoring_interval_secs: 30, - edge_optimization_level: OptimizationLevel::Balanced, - aggressive_edge_optimization: false, - }; - - let registry_config = ModelRegistryConfig::default(); - let loader_config = ModelLoaderConfig::default(); - let quantization_config = QuantizationConfig::default(); - - let edge_manager = EdgeOptimizationManager::new( - edge_config, - registry_config, - loader_config, - quantization_config, - ).await?; - - info!("๐Ÿ” Running adaptive optimization monitoring cycles..."); - - // Simulate monitoring cycles - for cycle in 1..=3 { - info!(" ๐Ÿ“Š Monitoring cycle {}/3:", cycle); - - let decisions = edge_manager.monitor_and_adapt().await?; - - if decisions.is_empty() { - info!(" - No optimization changes needed"); - } else { - info!(" - {} optimization decisions made", decisions.len()); - for (i, decision) in decisions.iter().enumerate() { - info!(" - Decision {}: {} -> {:?} ({})", - i + 1, - decision.model_id, - decision.new_quantization, - decision.reason - ); - } - } - - // Simulate resource pressure changes - if cycle == 2 { - info!(" - Simulating high memory pressure scenario..."); - } - - // Small delay between cycles - tokio::time::sleep(tokio::time::Duration::from_millis(100)).await; - } - - info!("โœ… Adaptive optimization monitoring completed"); - - Ok(()) -} - -/// @oracle - Demonstrate performance monitoring capabilities -async fn demo_performance_monitoring() -> Result<()> { - info!("\n๐Ÿ“ˆ Demo 5: Performance Monitoring and Metrics"); - - let quantization_config = QuantizationConfig { - default_quantization: QuantizationType::INT8, - adaptive_quantization: true, - max_memory_mb: 4096, - dynamic_quantization: true, - quality_threshold: 0.85, - cache_size_mb: 1024, - }; - - let quantization_engine = QuantizationEngine::new(quantization_config, Device::Cpu)?; - - info!("๐Ÿ“Š Monitoring quantization performance across different scenarios:"); - - // Test performance with different model sizes - let model_sizes = vec![ - ("Small Model (1B params)", 1000), - ("Medium Model (7B params)", 7000), - ("Large Model (13B params)", 13000), - ]; - - for (model_name, param_count) in model_sizes { - info!(" ๐Ÿงช Testing {}", model_name); - - let model_metadata = create_mock_model_metadata_with_size( - &format!("test-{}", param_count), - ModelType::CodeLlama, - param_count, - ); - - // Update resource monitoring - quantization_engine.update_resource_monitor().await?; - - // Test different quantization types - let quantization_types = vec![ - QuantizationType::INT8, - QuantizationType::INT4, - QuantizationType::NF4, - ]; - - for quant_type in quantization_types { - let weights = create_mock_tensor()?; - let start_time = std::time::Instant::now(); - - let result = quantization_engine.quantize_model( - &model_metadata, - &weights, - quant_type.clone(), - ).await?; - - let quantization_time = start_time.elapsed(); - - info!(" โ€ข {:?}: {:.0}ms, {:.1}x compression, {:.1}% quality", - quant_type, - quantization_time.as_millis(), - result.compression_ratio, - result.quality_score * 100.0 - ); - } - } - - // Demonstrate resource usage tracking - info!("๐Ÿ”ง Resource usage summary:"); - info!(" โ€ข Memory efficiency: Quantization reduces memory usage by 50-75%"); - info!(" โ€ข Storage efficiency: Model files 2-4x smaller on disk"); - info!(" โ€ข Quality preservation: 85-95% of original model quality maintained"); - info!(" โ€ข Edge compatibility: All quantized models suitable for edge deployment"); - - Ok(()) -} - -// Helper functions for demo - -/// @transform - Create mock model metadata for demonstration -fn create_mock_model_metadata(id: &str, model_type: ModelType) -> brain_mubrain::ModelMetadata { - brain_mubrain::ModelMetadata { - id: id.to_string(), - name: format!("{} Demo Model", model_type), - model_type, - path: PathBuf::from(format!("./models/{}.safetensors", id)), - format: ModelFormat::SafeTensors, - size_bytes: 14_000_000_000, // 14GB - parameters: 7_000_000_000, // 7B parameters - context_length: 4096, - quantization: None, - performance_metrics: brain_mubrain::PerformanceMetrics { - avg_inference_time_ms: 1500.0, - tokens_per_second: 12.0, - memory_usage_mb: 14000.0, - accuracy_score: Some(0.92), - total_inferences: 0, - }, - created_at: chrono::Utc::now(), - last_used: None, - usage_count: 0, - } -} - -/// @bridge - Create mock model metadata with specific size -fn create_mock_model_metadata_with_size( - id: &str, - model_type: ModelType, - param_count_millions: u64 -) -> brain_mubrain::ModelMetadata { - let size_bytes = param_count_millions * 1_000_000 * 4; // Rough estimate: 4 bytes per parameter - let memory_usage_mb = size_bytes as f64 / (1024.0 * 1024.0); - - brain_mubrain::ModelMetadata { - id: id.to_string(), - name: format!("{} {:.1}B Demo Model", model_type, param_count_millions as f64 / 1000.0), - model_type, - path: PathBuf::from(format!("./models/{}.safetensors", id)), - format: ModelFormat::SafeTensors, - size_bytes, - parameters: param_count_millions * 1_000_000, - context_length: 4096, - quantization: None, - performance_metrics: brain_mubrain::PerformanceMetrics { - avg_inference_time_ms: 1000.0 + (param_count_millions as f64 * 0.1), - tokens_per_second: 20.0 - (param_count_millions as f64 * 0.001), - memory_usage_mb, - accuracy_score: Some(0.90 + (param_count_millions as f64 * 0.001)), - total_inferences: 0, - }, - created_at: chrono::Utc::now(), - last_used: None, - usage_count: 0, - } -} - -/// @oracle - Create mock tensor for quantization testing -fn create_mock_tensor() -> Result { - // Create a mock tensor with realistic neural network weight distribution - use rand::Rng; - let mut rng = rand::thread_rng(); - - // Generate random weights with normal distribution (mean=0, std=0.02) - let weights: Vec = (0..10000) - .map(|_| rng.gen_range(-0.1..0.1)) - .collect(); - - candle_core::Tensor::from_vec(weights, &[100, 100], &Device::Cpu) - .map_err(|e| anyhow::anyhow!("Failed to create mock tensor: {}", e)) -} \ No newline at end of file diff --git a/query_export_demo.rs b/query_export_demo.rs deleted file mode 100644 index f8a3bbe8c20d56f0355b1e59d06b5b3e582aecb7..0000000000000000000000000000000000000000 --- a/query_export_demo.rs +++ /dev/null @@ -1,533 +0,0 @@ -//! # Query Language and Export System Demo -//! -//! Comprehensive demonstration of the Brain AI query language and export functionality. -//! This demo showcases: -//! - SQL-like query language for concepts, memories, and rules -//! - JSON graph exports for network visualization -//! - CSV exports for spreadsheet analysis -//! - Specialized query operations and relationship traversal -//! - Cross-system data export and query capabilities -//! - Performance metrics and system integration - -use brain::*; -use std::fs; -use std::collections::HashMap; - -/// Demo concept structure -#[derive(Debug, Clone)] -pub struct DemoConcept { - pub name: String, - pub confidence: f64, - pub concept_type: String, -} - -/// Demo memory structure -#[derive(Debug, Clone)] -pub struct DemoMemory { - pub content: String, - pub relevance_score: f64, - pub memory_type: String, -} - -/// Demo rule structure -#[derive(Debug, Clone)] -pub struct DemoRule { - pub pattern: String, - pub confidence: f64, - pub rule_type: String, -} - -/// Query result types -#[derive(Debug, Clone)] -pub enum QueryResult { - Concepts(Vec), - Memories(Vec), - Rules(Vec), -} - -/// Demo query engine implementation -pub struct QueryEngine { - concepts: Vec, - memories: Vec, - rules: Vec, - query_count: usize, - execution_times: Vec, -} - -impl QueryEngine { - pub fn new() -> Self { - // Populate with sample data - let concepts = vec![ - DemoConcept { name: "artificial_intelligence".to_string(), confidence: 0.95, concept_type: "Entity".to_string() }, - DemoConcept { name: "machine_learning".to_string(), confidence: 0.90, concept_type: "Entity".to_string() }, - DemoConcept { name: "neural_networks".to_string(), confidence: 0.88, concept_type: "Entity".to_string() }, - ]; - - let memories = vec![ - DemoMemory { content: "User asked about AI capabilities".to_string(), relevance_score: 0.8, memory_type: "episodic".to_string() }, - DemoMemory { content: "Neural networks process information".to_string(), relevance_score: 0.9, memory_type: "semantic".to_string() }, - ]; - - let rules = vec![ - DemoRule { pattern: "If user asks question, then provide answer".to_string(), confidence: 0.95, rule_type: "conditional".to_string() }, - ]; - - Self { - concepts, - memories, - rules, - query_count: 0, - execution_times: Vec::new(), - } - } - - pub fn query(&mut self, query_str: &str) -> Result { - self.query_count += 1; - let start = std::time::Instant::now(); - - let result = if query_str.contains("CONCEPTS") { - Ok(QueryResult::Concepts(self.concepts.clone())) - } else if query_str.contains("MEMORIES") { - Ok(QueryResult::Memories(self.memories.clone())) - } else if query_str.contains("RULES") { - Ok(QueryResult::Rules(self.rules.clone())) - } else { - Err(brain_types::BrainError::PredictionError { - message: "Unknown query type".to_string(), - context: None - }) - }; - - self.execution_times.push(start.elapsed().as_millis() as u64); - result - } - - pub fn get_performance_stats(&self) -> (usize, f64) { - let avg_time = if !self.execution_times.is_empty() { - self.execution_times.iter().sum::() as f64 / self.execution_times.len() as f64 - } else { - 0.0 - }; - (self.query_count, avg_time) - } -} - -/// Demo export system implementation -pub struct ExportSystem { - exports_generated: usize, -} - -impl ExportSystem { - pub fn new() -> Self { - Self { exports_generated: 0 } - } - - pub async fn export_to_json(&mut self, _data: &QueryResult, filename: &str) -> Result<()> { - self.exports_generated += 1; - let json_content = "{}".to_string(); // Simplified - fs::write(filename, json_content)?; - println!(" โœ… Exported to JSON: {}", filename); - Ok(()) - } - - pub async fn export_to_csv(&mut self, _data: &QueryResult, filename: &str) -> Result<()> { - self.exports_generated += 1; - let csv_content = "name,value\ntest,1\n".to_string(); // Simplified - fs::write(filename, csv_content)?; - println!(" โœ… Exported to CSV: {}", filename); - Ok(()) - } - - pub fn get_export_stats(&self) -> usize { - self.exports_generated - } -} - -/// Demo specialized query engine -pub struct SpecializedQueryEngine { - relationships: HashMap>, -} - -impl SpecializedQueryEngine { - pub fn new() -> Self { - let mut relationships = HashMap::new(); - relationships.insert("artificial_intelligence".to_string(), - vec!["machine_learning".to_string()]); - Self { relationships } - } - - pub async fn find_related_concepts(&self, concept: &str, _depth: usize) -> Result> { - Ok(self.relationships.get(concept).cloned().unwrap_or_default()) - } - - pub async fn find_shortest_path(&self, _from: &str, _to: &str) -> Result> { - Ok(vec!["path".to_string()]) // Simplified - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿ” Query Language and Export System Demo"); - println!("========================================\n"); - - // Phase 1: Setup System with Sample Data - println!("๐Ÿ“š Phase 1: System Setup and Data Population"); - println!("============================================="); - - let (mut query_engine, mut export_system, mut specialized_engine) = setup_demo_systems().await?; - let sample_data = populate_sample_data().await?; - - println!("โœ… Initialized query engine, export system, and specialized queries"); - println!("โœ… Populated system with {} sample data items\n", sample_data.len()); - - // Phase 2: Basic Query Language Demonstrations - println!("๐Ÿ” Phase 2: SQL-like Query Language"); - println!("==================================="); - - demonstrate_basic_queries(&mut query_engine).await?; - - // Phase 3: Advanced Query Operations - println!("๐Ÿš€ Phase 3: Advanced Query Operations"); - println!("====================================="); - - demonstrate_advanced_queries(&mut query_engine).await?; - - // Phase 4: Specialized Query Functions - println!("โšก Phase 4: Specialized Query Functions"); - println!("======================================"); - - demonstrate_specialized_queries(&mut specialized_engine).await?; - - // Phase 5: Export System Demonstrations - println!("๐Ÿ“Š Phase 5: Data Export Capabilities"); - println!("===================================="); - - demonstrate_export_functionality(&mut export_system, &mut query_engine).await?; - - // Phase 6: Performance Analysis - println!("๐Ÿ“ˆ Phase 6: Performance Analysis"); - println!("================================"); - - analyze_query_performance(&mut query_engine).await?; - - // Phase 7: Integration Testing - println!("๐Ÿ”ง Phase 7: System Integration Testing"); - println!("======================================"); - - demonstrate_integration_workflows(&mut query_engine, &mut export_system).await?; - - println!("\n๐ŸŽ‰ Query Language and Export System Demo Complete!"); - println!("==================================================="); - println!("Successfully demonstrated:"); - println!(" โœ… SQL-like query language with advanced filtering"); - println!(" โœ… JSON graph exports for visualization"); - println!(" โœ… CSV exports for spreadsheet analysis"); - println!(" โœ… Specialized relationship traversal queries"); - println!(" โœ… Cross-system data integration"); - println!(" โœ… Performance optimization and metrics"); - println!(" โœ… Comprehensive export metadata and versioning"); - - cleanup_demo_files()?; - - Ok(()) -} - -/// Initialize demo systems with configuration -async fn setup_demo_systems() -> Result<(QueryEngine, ExportSystem, SpecializedQueryEngine)> { - let query_engine = QueryEngine::new(); - let export_system = ExportSystem::new(); - let specialized_engine = SpecializedQueryEngine::new(); - - println!("๐Ÿ”ง Configured query engine with default settings"); - println!("๐Ÿ“Š Configured export system with metadata tracking"); - println!("โšก Configured specialized query engine for relationship traversal"); - - Ok((query_engine, export_system, specialized_engine)) -} - -/// Populate system with sample data for demonstrations -async fn populate_sample_data() -> Result> { - let mut data_items = Vec::new(); - - // Sample concepts - let concept_data = [ - ("artificial_intelligence", "Entity", 0.95), - ("machine_learning", "Entity", 0.90), - ("neural_networks", "Entity", 0.88), - ("natural_language", "Entity", 0.85), - ("computer_science", "Entity", 0.92), - ("learn", "Action", 0.80), - ("process", "Action", 0.75), - ("intelligent", "Attribute", 0.70), - ("complex", "Attribute", 0.65), - ("reasoning", "Abstract", 0.85), - ]; - - println!("๐Ÿ“ Populating sample concepts:"); - for (name, concept_type, confidence) in &concept_data { - println!(" โ€ข {} ({}, confidence: {:.2})", name, concept_type, confidence); - data_items.push(format!("concept:{}", name)); - } - - // Sample memories - let memory_data = [ - ("User asked about AI capabilities", "episodic", 0.8), - ("Neural networks process information", "semantic", 0.9), - ("Machine learning requires data", "semantic", 0.85), - ("Current working directory changed", "working", 0.6), - ("User preference for morning queries", "episodic", 0.7), - ]; - - println!("\n๐Ÿง  Populating sample memories:"); - for (content, memory_type, relevance) in &memory_data { - println!(" โ€ข {} ({}, relevance: {:.2})", content, memory_type, relevance); - data_items.push(format!("memory:{}", content)); - } - - // Sample rules - let rule_data = [ - ("If user asks question, then provide answer", "conditional", 0.95), - ("If confidence > 0.8, then trust result", "threshold", 0.90), - ("If memory accessed frequently, then increase importance", "learning", 0.85), - ("If concept related to AI, then tag as technology", "classification", 0.80), - ]; - - println!("\n๐Ÿ“‹ Populating sample rules:"); - for (pattern, rule_type, confidence) in &rule_data { - println!(" โ€ข {} ({}, confidence: {:.2})", pattern, rule_type, confidence); - data_items.push(format!("rule:{}", pattern)); - } - - Ok(data_items) -} - -/// Demonstrate basic query language functionality -async fn demonstrate_basic_queries(query_engine: &mut QueryEngine) -> Result<()> { - println!("๐Ÿ” Basic Query Examples:"); - println!("------------------------"); - - // Basic concept queries - let queries = [ - "CONCEPTS WHERE confidence > 0.8 LIMIT 5", - "CONCEPTS WHERE type = 'entity' ORDER BY confidence DESC", - "MEMORIES WHERE memory_type = 'semantic' AND relevance > 0.8", - "RULES WHERE confidence > 0.85 ORDER BY created_at ASC", - "CONCEPTS WHERE content CONTAINS 'intelligence' LIMIT 3", - ]; - - for (i, query_str) in queries.iter().enumerate() { - println!("\nQuery {}: {}", i + 1, query_str); - - match query_engine.query(query_str) { - Ok(result) => { - match result { - QueryResult::Concepts(concepts) => { - println!(" โœ… Found {} concept(s)", concepts.len()); - for (j, concept) in concepts.iter().take(3).enumerate() { - println!(" {}. {} (confidence: {:.2}, type: {})", - j + 1, concept.name, concept.confidence, concept.concept_type); - } - } - QueryResult::Memories(memories) => { - println!(" โœ… Found {} memory(ies)", memories.len()); - for (j, memory) in memories.iter().take(3).enumerate() { - println!(" {}. {} (relevance: {:.2}, type: {})", - j + 1, memory.content, memory.relevance_score, memory.memory_type); - } - } - QueryResult::Rules(rules) => { - println!(" โœ… Found {} rule(s)", rules.len()); - for (j, rule) in rules.iter().take(3).enumerate() { - println!(" {}. {} (confidence: {:.2}, type: {})", - j + 1, rule.pattern, rule.confidence, rule.rule_type); - } - } - } - } - Err(e) => { - println!(" โš ๏ธ Query error: {}", e); - } - } - } - - Ok(()) -} - -/// Demonstrate advanced query operations -async fn demonstrate_advanced_queries(query_engine: &mut QueryEngine) -> Result<()> { - println!("๐Ÿš€ Advanced Query Examples:"); - println!("---------------------------"); - - let advanced_queries = [ - "CONCEPTS WHERE confidence > 0.85 ORDER BY confidence DESC LIMIT 3", - "MEMORIES WHERE memory_type = 'semantic'", - "RULES WHERE confidence > 0.80", - ]; - - for (i, query_str) in advanced_queries.iter().enumerate() { - println!("\nAdvanced Query {}: {}", i + 1, query_str); - - match query_engine.query(query_str) { - Ok(result) => { - match result { - QueryResult::Concepts(concepts) => { - println!(" โœ… Advanced query returned {} concept(s)", concepts.len()); - for (j, concept) in concepts.iter().take(2).enumerate() { - println!(" {}. {} (confidence: {:.2})", - j + 1, concept.name, concept.confidence); - } - } - QueryResult::Memories(memories) => { - println!(" โœ… Advanced query returned {} memory(ies)", memories.len()); - } - QueryResult::Rules(rules) => { - println!(" โœ… Advanced query returned {} rule(s)", rules.len()); - } - } - } - Err(e) => { - println!(" โš ๏ธ Query error: {}", e); - } - } - } - - Ok(()) -} - -/// Demonstrate specialized query functions -async fn demonstrate_specialized_queries(specialized_engine: &mut SpecializedQueryEngine) -> Result<()> { - println!("โšก Specialized Query Examples:"); - println!("-----------------------------"); - - // Relationship traversal - let concepts_to_explore = ["artificial_intelligence", "machine_learning", "computer_science"]; - - for concept in &concepts_to_explore { - println!("\n๐Ÿ” Finding related concepts for '{}':", concept); - match specialized_engine.find_related_concepts(concept, 2).await { - Ok(related) => { - println!(" โœ… Found {} related concept(s)", related.len()); - for (i, related_concept) in related.iter().take(3).enumerate() { - println!(" {}. {}", i + 1, related_concept); - } - } - Err(e) => { - println!(" โš ๏ธ Error finding relationships: {}", e); - } - } - } - - // Path finding - println!("\n๐Ÿ›ค๏ธ Finding shortest path from 'computer_science' to 'neural_networks':"); - match specialized_engine.find_shortest_path("computer_science", "neural_networks").await { - Ok(path) => { - if !path.is_empty() { - println!(" โœ… Path found: {}", path.join(" โ†’ ")); - } else { - println!(" โš ๏ธ No path found"); - } - } - Err(e) => { - println!(" โš ๏ธ Error finding path: {}", e); - } - } - - Ok(()) -} - -/// Demonstrate export functionality -async fn demonstrate_export_functionality(export_system: &mut ExportSystem, query_engine: &mut QueryEngine) -> Result<()> { - println!("๐Ÿ“Š Export Functionality Examples:"); - println!("--------------------------------"); - - // Export concepts to JSON - println!("\n๐Ÿ“„ Exporting high-confidence concepts to JSON:"); - let concepts_result = query_engine.query("CONCEPTS WHERE confidence > 0.8 LIMIT 5")?; - export_system.export_to_json(&concepts_result, "demo_concepts.json").await?; - - // Export memories to CSV - println!("\n๐Ÿ“„ Exporting semantic memories to CSV:"); - let memories_result = query_engine.query("MEMORIES WHERE memory_type = 'semantic'")?; - export_system.export_to_csv(&memories_result, "demo_memories.csv").await?; - - // Export rules to JSON - println!("\n๐Ÿ“„ Exporting high-confidence rules to JSON:"); - let rules_result = query_engine.query("RULES WHERE confidence > 0.85")?; - export_system.export_to_json(&rules_result, "demo_rules.json").await?; - - println!("\n๐Ÿ“Š Export Statistics:"); - println!(" Total exports generated: {}", export_system.get_export_stats()); - - Ok(()) -} - -/// Analyze query performance -async fn analyze_query_performance(query_engine: &mut QueryEngine) -> Result<()> { - println!("๐Ÿ“ˆ Query Performance Analysis:"); - println!("-----------------------------"); - - // Run performance test queries - let test_queries = [ - "CONCEPTS WHERE confidence > 0.8", - "MEMORIES WHERE memory_type = 'semantic'", - "RULES WHERE confidence > 0.9", - "CONCEPTS WHERE type = 'entity'", - "CONCEPTS WHERE confidence > 0.7 LIMIT 10", - ]; - - for query in &test_queries { - let _ = query_engine.query(query); - } - - let (total_queries, avg_time) = query_engine.get_performance_stats(); - - println!("๐Ÿ“Š Performance Metrics:"); - println!(" Total queries executed: {}", total_queries); - println!(" Average execution time: {:.2} ms", avg_time); - println!(" Queries per second: {:.2}", 1000.0 / avg_time.max(1.0)); - - Ok(()) -} - -/// Demonstrate integration workflows -async fn demonstrate_integration_workflows(query_engine: &mut QueryEngine, export_system: &mut ExportSystem) -> Result<()> { - println!("๐Ÿ”ง Integration Workflow Examples:"); - println!("--------------------------------"); - - println!("\n๐Ÿ”„ Workflow 1: Query โ†’ Export โ†’ Analysis"); - let workflow_result = query_engine.query("CONCEPTS WHERE confidence > 0.85")?; - export_system.export_to_json(&workflow_result, "workflow_concepts.json").await?; - export_system.export_to_csv(&workflow_result, "workflow_concepts.csv").await?; - println!(" โœ… Workflow 1 completed successfully"); - - println!("\n๐Ÿ”„ Workflow 2: Batch Export"); - let memory_result = query_engine.query("MEMORIES WHERE memory_type = 'episodic'")?; - export_system.export_to_json(&memory_result, "batch_memories.json").await?; - println!(" โœ… Workflow 2 completed successfully"); - - println!("\n๐Ÿ“Š Integration Statistics:"); - println!(" Total exports in workflows: {}", export_system.get_export_stats()); - - Ok(()) -} - -/// Clean up demo files -fn cleanup_demo_files() -> Result<()> { - let files_to_remove = [ - "demo_concepts.json", - "demo_memories.csv", - "demo_rules.json", - "workflow_concepts.json", - "workflow_concepts.csv", - "batch_memories.json", - ]; - - for file in &files_to_remove { - if std::path::Path::new(file).exists() { - fs::remove_file(file)?; - } - } - - println!("๐Ÿงน Cleaned up demo files"); - Ok(()) -} \ No newline at end of file diff --git a/real_time_hle_api_test.rs b/real_time_hle_api_test.rs deleted file mode 100644 index 4169a4791118027964d717476d4bbf65f9344848..0000000000000000000000000000000000000000 --- a/real_time_hle_api_test.rs +++ /dev/null @@ -1,538 +0,0 @@ -use anyhow::Result; -use reqwest::Client; -use serde::{Deserialize, Serialize}; -use std::time::{Duration, Instant}; -use tokio::time::sleep; -use brain_cognitive::{ - agents::{ - registry::AgentRegistry, - intelligence::{ - academic_reasoning::UniversalAcademicAgent, - multiple_choice_processor::MultipleChoiceProcessor, - }, - traits::{AgentInput, BrainAgent}, - AcademicDomain, OptionEvaluation, - }, -}; -use brain_types::error::BrainError; - -#[derive(Debug, Serialize, Deserialize)] -struct HLEQuestion { - id: String, - question: String, - options: Vec, - domain: Option, - difficulty: Option, - metadata: Option, -} - -#[derive(Debug, Serialize, Deserialize)] -struct HLEQuestionBatch { - questions: Vec, - batch_id: String, - total_questions: usize, -} - -#[derive(Debug, Serialize, Deserialize)] -struct HLESubmission { - question_id: String, - selected_option: usize, - confidence: f32, - reasoning: String, - processing_time_ms: u64, -} - -#[derive(Debug, Serialize, Deserialize)] -struct HLESubmissionBatch { - batch_id: String, - submissions: Vec, - agent_metadata: AgentPerformanceMetadata, -} - -#[derive(Debug, Serialize, Deserialize)] -struct AgentPerformanceMetadata { - agent_type: String, - version: String, - bias_mitigation_enabled: bool, - domain_experts_active: usize, - average_confidence: f32, - total_processing_time_ms: u64, -} - -#[derive(Debug, Serialize, Deserialize)] -struct HLEResults { - batch_id: String, - accuracy: f32, - correct_answers: usize, - total_questions: usize, - breakdown_by_domain: std::collections::HashMap, - bias_analysis: BiasAnalysis, - performance_ranking: Option, -} - -#[derive(Debug, Serialize, Deserialize)] -struct DomainResults { - correct: usize, - total: usize, - accuracy: f32, - average_confidence: f32, -} - -#[derive(Debug, Serialize, Deserialize)] -struct BiasAnalysis { - option_distribution: Vec, - bias_score: f32, - systematic_bias_detected: bool, - bias_mitigation_effectiveness: f32, -} - -#[derive(Debug, Serialize, Deserialize)] -struct PerformanceRanking { - current_rank: usize, - total_participants: usize, - percentile: f32, - comparison_to_sota: std::collections::HashMap, -} - -pub struct RealTimeHLEValidator { - client: Client, - api_base_url: String, - academic_agent: UniversalAcademicAgent, - multiple_choice_processor: std::cell::RefCell, - agent_registry: AgentRegistry, -} - -impl RealTimeHLEValidator { - pub async fn new(api_base_url: String) -> Result { - println!("๐Ÿš€ Initializing Real-Time HLE Validator..."); - - let client = Client::builder() - .timeout(Duration::from_secs(30)) - .build() - .map_err(|e| BrainError::NetworkError { - message: format!("Failed to create HTTP client: {}", e), - context: None, - source: None, - })?; - - // Initialize academic intelligence components - let academic_agent = UniversalAcademicAgent::new().await?; - let multiple_choice_processor = std::cell::RefCell::new(MultipleChoiceProcessor::new()); - let agent_registry = AgentRegistry::new_with_defaults(); - - println!("โœ… Real-Time HLE Validator initialized"); - println!(" โ€ข API Base URL: {}", api_base_url); - println!(" โ€ข Academic Agent: READY"); - println!(" โ€ข Multiple Choice Processor: READY"); - println!(" โ€ข Agent Registry: READY"); - - Ok(Self { - client, - api_base_url, - academic_agent, - multiple_choice_processor, - agent_registry, - }) - } - - pub async fn run_real_time_hle_test(&self, num_questions: usize) -> Result { - println!("\n๐Ÿงช Starting Real-Time HLE Test"); - println!("==============================="); - println!("๐Ÿ“Š Target Questions: {}", num_questions); - println!("๐ŸŽฏ Goal: Validate 40%+ accuracy with bias mitigation"); - - // Step 1: Request questions from HLE API - let question_batch = self.fetch_hle_questions(num_questions).await?; - println!("โœ… Fetched {} questions from HLE API", question_batch.questions.len()); - - // Step 2: Process questions through our academic intelligence system - let submissions = self.process_questions_with_academic_intelligence(&question_batch).await?; - println!("โœ… Processed all questions through Brain AI academic intelligence"); - - // Step 3: Submit answers to HLE API for evaluation - let results = self.submit_answers_for_evaluation(&question_batch.batch_id, submissions).await?; - println!("โœ… Received HLE evaluation results"); - - // Step 4: Display comprehensive results - self.display_comprehensive_results(&results).await; - - Ok(results) - } - - async fn fetch_hle_questions(&self, num_questions: usize) -> Result { - println!("๐Ÿ“ฅ Fetching {} questions from HLE API...", num_questions); - - let url = format!("{}/hle/questions", self.api_base_url); - let request_body = serde_json::json!({ - "count": num_questions, - "format": "multiple_choice", - "difficulty": "mixed", - "domains": "all", - "include_metadata": true - }); - - let start_time = Instant::now(); - let response = self.client - .post(&url) - .json(&request_body) - .send() - .await - .map_err(|e| BrainError::NetworkError { - message: format!("Failed to fetch HLE questions: {}", e), - context: None, - source: None, - })?; - - if !response.status().is_success() { - let error_text = response.text().await.unwrap_or_default(); - return Err(BrainError::HttpError { - message: format!("HLE API returned error: {}", error_text), - context: None, - source: None, - }); - } - - let question_batch: HLEQuestionBatch = response - .json() - .await - .map_err(|e| BrainError::Serialization { - message: format!("Failed to parse HLE questions: {}", e), - context: None, - source: None, - })?; - - let fetch_time = start_time.elapsed(); - println!("๐Ÿ“Š Fetch Statistics:"); - println!(" โ€ข Questions received: {}", question_batch.questions.len()); - println!(" โ€ข Batch ID: {}", question_batch.batch_id); - println!(" โ€ข Fetch time: {}ms", fetch_time.as_millis()); - - Ok(question_batch) - } - - async fn process_questions_with_academic_intelligence( - &self, - question_batch: &HLEQuestionBatch, - ) -> Result, BrainError> { - println!("๐Ÿง  Processing questions through Brain AI Academic Intelligence..."); - - let mut submissions = Vec::new(); - let total_start_time = Instant::now(); - - for (index, question) in question_batch.questions.iter().enumerate() { - let question_start_time = Instant::now(); - - println!(" Processing Question {}/{}: {}", - index + 1, - question_batch.questions.len(), - self.truncate_text(&question.question, 60) - ); - - // Create academic input for the question - let academic_input = AgentInput::new( - "AcademicQuestion".to_string(), - self.format_question_for_processing(question), - format!("hle_session_{}", question_batch.batch_id), - ); - - // Process through Universal Academic Agent - let academic_result = self.academic_agent - .execute(academic_input, &Default::default()) - .await?; - - // Process through Multiple Choice Processor for bias mitigation - let domain = self.determine_domain_from_question(question); - let mc_result = self.multiple_choice_processor - .borrow_mut() - .process_options(&question.question, &question.options, &domain) - .await?; - - // Extract results and create submission - let (selected_option, confidence, reasoning) = self.extract_answer_from_results( - &academic_result.content, - &mc_result, - &question.options, - )?; - - let processing_time = question_start_time.elapsed(); - - let submission = HLESubmission { - question_id: question.id.clone(), - selected_option, - confidence, - reasoning, - processing_time_ms: processing_time.as_millis() as u64, - }; - - submissions.push(submission); - - println!(" โœ… Selected: {} (Confidence: {:.1}%, Time: {}ms)", - self.get_option_letter(selected_option), - confidence * 100.0, - processing_time.as_millis() - ); - - // Small delay to avoid overwhelming the system - sleep(Duration::from_millis(100)).await; - } - - let total_time = total_start_time.elapsed(); - println!("๐Ÿ“Š Processing Statistics:"); - println!(" โ€ข Total processing time: {}ms", total_time.as_millis()); - println!(" โ€ข Average time per question: {}ms", total_time.as_millis() / question_batch.questions.len() as u128); - - Ok(submissions) - } - - async fn submit_answers_for_evaluation( - &self, - batch_id: &str, - submissions: Vec, - ) -> Result { - println!("๐Ÿ“ค Submitting answers to HLE API for evaluation..."); - - let submission_batch = HLESubmissionBatch { - batch_id: batch_id.to_string(), - submissions, - agent_metadata: AgentPerformanceMetadata { - agent_type: "Brain AI Academic Intelligence".to_string(), - version: "v2025.07.30.002".to_string(), - bias_mitigation_enabled: true, - domain_experts_active: 5, - average_confidence: 0.0, // Will be calculated by HLE API - total_processing_time_ms: 0, // Will be calculated by HLE API - }, - }; - - let url = format!("{}/hle/evaluate", self.api_base_url); - let start_time = Instant::now(); - - let response = self.client - .post(&url) - .json(&submission_batch) - .send() - .await - .map_err(|e| BrainError::NetworkError { - message: format!("Failed to submit HLE answers: {}", e), - context: None, - source: None, - })?; - - if !response.status().is_success() { - let error_text = response.text().await.unwrap_or_default(); - return Err(BrainError::HttpError { - message: format!("HLE evaluation failed: {}", error_text), - context: None, - source: None, - }); - } - - let results: HLEResults = response - .json() - .await - .map_err(|e| BrainError::Serialization { - message: format!("Failed to parse HLE results: {}", e), - context: None, - source: None, - })?; - - let evaluation_time = start_time.elapsed(); - println!("โœ… Evaluation completed in {}ms", evaluation_time.as_millis()); - - Ok(results) - } - - async fn display_comprehensive_results(&self, results: &HLEResults) { - println!("\n๐Ÿ† Real-Time HLE Test Results"); - println!("============================"); - - // Overall Performance - println!("๐Ÿ“Š Overall Performance:"); - println!(" โ€ข Accuracy: {:.1}% ({}/{})", - results.accuracy * 100.0, - results.correct_answers, - results.total_questions - ); - - // Compare to target - let target_accuracy = 40.0; - if results.accuracy * 100.0 >= target_accuracy { - println!(" โœ… TARGET ACHIEVED: Exceeded {:.1}% target accuracy!", target_accuracy); - } else { - println!(" โš ๏ธ TARGET MISSED: {:.1}% below {:.1}% target", - target_accuracy - (results.accuracy * 100.0), target_accuracy); - } - - // Bias Analysis - println!("\n๐ŸŽฏ Bias Analysis:"); - println!(" โ€ข Bias Score: {:.3} (0.0 = perfect, 1.0 = maximum bias)", results.bias_analysis.bias_score); - println!(" โ€ข Systematic Bias: {}", if results.bias_analysis.systematic_bias_detected { "โŒ DETECTED" } else { "โœ… NOT DETECTED" }); - println!(" โ€ข Bias Mitigation Effectiveness: {:.1}%", results.bias_analysis.bias_mitigation_effectiveness * 100.0); - - println!(" โ€ข Option Distribution:"); - for (i, percentage) in results.bias_analysis.option_distribution.iter().enumerate() { - println!(" {} {}: {:.1}%", - self.get_option_letter(i), - if *percentage > 40.0 { "โš ๏ธ" } else { "โœ…" }, - percentage * 100.0 - ); - } - - // Domain-specific Results - if !results.breakdown_by_domain.is_empty() { - println!("\n๐Ÿ”ฌ Domain-Specific Results:"); - for (domain, domain_results) in &results.breakdown_by_domain { - println!(" โ€ข {}: {:.1}% ({}/{}) - Avg Confidence: {:.1}%", - domain, - domain_results.accuracy * 100.0, - domain_results.correct, - domain_results.total, - domain_results.average_confidence * 100.0 - ); - } - } - - // Performance Ranking - if let Some(ranking) = &results.performance_ranking { - println!("\n๐Ÿ… Global Performance Ranking:"); - println!(" โ€ข Current Rank: #{} out of {}", ranking.current_rank, ranking.total_participants); - println!(" โ€ข Percentile: {:.1}%", ranking.percentile * 100.0); - - if !ranking.comparison_to_sota.is_empty() { - println!(" โ€ข Comparison to SOTA Models:"); - for (model, accuracy) in &ranking.comparison_to_sota { - let comparison = if results.accuracy > *accuracy { - format!("โœ… +{:.1}% ahead", (results.accuracy - accuracy) * 100.0) - } else { - format!("โŒ -{:.1}% behind", (accuracy - results.accuracy) * 100.0) - }; - println!(" {} {:.1}%: {}", model, accuracy * 100.0, comparison); - } - } - } - - // System Status Summary - println!("\n๐Ÿ”ง System Status Summary:"); - println!(" โ€ข Academic Intelligence: โœ… OPERATIONAL"); - println!(" โ€ข Bias Mitigation: โœ… ACTIVE"); - println!(" โ€ข Domain Experts: โœ… 5 SPECIALISTS ACTIVE"); - println!(" โ€ข Real-time HLE Integration: โœ… WORKING"); - - if results.accuracy >= 0.45 { - println!("\n๐Ÿ† BREAKTHROUGH: Global #1 HLE Leadership Achieved!"); - } else if results.accuracy >= 0.40 { - println!("\n๐ŸŽฏ SUCCESS: Phase 1 Target Exceeded - Ready for Phase 2!"); - } else if results.accuracy >= 0.30 { - println!("\n๐Ÿ“ˆ PROGRESS: Significant improvement detected - Continue optimization!"); - } else { - println!("\n๐Ÿ”ง OPTIMIZATION NEEDED: Focus on domain specialists and knowledge base expansion"); - } - } - - // Helper methods - fn format_question_for_processing(&self, question: &HLEQuestion) -> String { - serde_json::json!({ - "question": question.question, - "options": question.options, - "domain": question.domain, - "difficulty": question.difficulty, - "type": "multiple_choice_academic" - }).to_string() - } - - - - fn determine_domain_from_question(&self, question: &HLEQuestion) -> AcademicDomain { - // Simple domain detection based on question content or metadata - if let Some(domain) = &question.domain { - match domain.to_lowercase().as_str() { - "physics" | "theoretical physics" => AcademicDomain::TheoreticalPhysics, - "mathematics" | "math" | "advanced mathematics" => AcademicDomain::AdvancedMathematics, - "chemistry" | "advanced chemistry" => AcademicDomain::AdvancedChemistry, - "biology" | "molecular biology" => AcademicDomain::MolecularBiology, - "computer science" | "cs" | "theoretical cs" => AcademicDomain::ComputerScienceTheory, - _ => AcademicDomain::TheoreticalPhysics, // Default - } - } else { - // Default to theoretical physics if no domain specified - AcademicDomain::TheoreticalPhysics - } - } - - fn extract_answer_from_results( - &self, - academic_result: &str, - mc_result: &OptionEvaluation, - _options: &[String], - ) -> Result<(usize, f32, String), BrainError> { - // Extract selected option from OptionEvaluation - let selected_option = self.parse_option_letter(&mc_result.recommended_answer); - let confidence = mc_result.recommendation_confidence; - - // Create comprehensive reasoning combining both results - let reasoning = format!( - "Academic Analysis: {} | Multiple Choice Processing: Selected option {} with confidence {:.1}%", - academic_result.chars().take(200).collect::(), - &mc_result.recommended_answer, - confidence * 100.0 - ); - - Ok((selected_option, confidence, reasoning)) - } - - fn parse_option_letter(&self, letter: &str) -> usize { - match letter { - "A" => 0, - "B" => 1, - "C" => 2, - "D" => 3, - _ => 0, // Default to A if parsing fails - } - } - - fn get_option_letter(&self, index: usize) -> String { - match index { - 0 => "A".to_string(), - 1 => "B".to_string(), - 2 => "C".to_string(), - 3 => "D".to_string(), - _ => format!("{}", index + 1), - } - } - - fn truncate_text(&self, text: &str, max_len: usize) -> String { - if text.len() <= max_len { - text.to_string() - } else { - format!("{}...", &text[..max_len.saturating_sub(3)]) - } - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Brain AI Real-Time HLE Performance Test"); - println!("=========================================="); - println!("๐ŸŽฏ Testing Phase 1 Academic Intelligence Implementation"); - println!("๐Ÿ“Š Using live HLE API for authentic performance validation"); - - // Initialize the validator with the API URL - let api_url = "http://localhost:8080".to_string(); - let validator = RealTimeHLEValidator::new(api_url).await?; - - // Run comprehensive HLE test - let num_questions = 10; // Start with 10 questions for thorough testing - let results = validator.run_real_time_hle_test(num_questions).await?; - - // Final summary - println!("\nโœ… Real-Time HLE Test Complete!"); - println!("๐Ÿ“Š Final Accuracy: {:.1}%", results.accuracy * 100.0); - - if results.accuracy >= 0.40 { - println!("๐Ÿ† SUCCESS: Brain AI Academic Intelligence validated and ready for global HLE competition!"); - } else { - println!("๐Ÿ”ง Continue optimization to reach 40%+ target accuracy"); - } - - Ok(()) -} \ No newline at end of file diff --git a/refactor_agent_demo.rs b/refactor_agent_demo.rs deleted file mode 100644 index 82e742d06f827e24f3c19f528b7cbedcd67c84f1..0000000000000000000000000000000000000000 --- a/refactor_agent_demo.rs +++ /dev/null @@ -1,418 +0,0 @@ -use std::sync::Arc; -use brain_cognitive::agents::{traits::*, development::RefactorAgent}; -use brain_cognitive::{ - meta::{MetaMemoryRepository, MetaMemoryItem, MetaMemoryQuery}, - conversation::{ - traits::ConversationService, - RagRequest, RagResponse, - ResponseQuality, - }, -}; -use serde_json::json; -use std::collections::HashMap; -use async_trait::async_trait; -use uuid::Uuid; - -#[derive(Debug)] -struct MockMetaMemoryRepository; - -#[async_trait] -impl MetaMemoryRepository for MockMetaMemoryRepository { - async fn store_item(&mut self, _item: MetaMemoryItem) -> Result { - Ok(Uuid::new_v4()) - } - - async fn get_item(&self, _id: Uuid) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(None) - } - - async fn get_item_by_component(&self, _component_id: Uuid) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(None) - } - - async fn query_items(&self, _query: &MetaMemoryQuery) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(Vec::new()) - } - - async fn remove_item(&mut self, _id: Uuid) -> Result { - Ok(true) - } - - async fn batch_update(&mut self, _items: Vec) -> Result, brain_cognitive::meta::MetaMemoryError> { - Ok(Vec::new()) - } - - async fn count_items(&self) -> Result { - Ok(0) - } - - async fn clear_all(&mut self) -> Result { - Ok(0) - } -} - -#[derive(Debug)] -struct MockConversationService; - -#[async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: RagRequest, - _memory_repo: &mut dyn brain_core::memory::WorkingMemoryRepository, - _concept_repo: &mut dyn brain_core::concepts::ConceptRepository, - _insight_repo: &mut dyn brain_core::insights::InsightRepository, - ) -> Result { - Ok(RagResponse { - response: "Mock response".to_string(), - conversation_id: "mock-conversation".to_string(), - context_used: Vec::new(), - confidence_score: 0.8, - response_quality: ResponseQuality { - factual_grounding: 0.8, - coherence: 0.9, - relevance: 0.8, - safety_score: 1.0, - source_attribution: 0.7, - consistency_score: 0.8, - completeness: 0.7, - clarity: 0.9, - toxicity_score: 0.0, - bias_score: 0.0, - hallucination_risk: 0.1, - confidence_calibration: 0.8, - }, - }) - } - - fn get_conversation_stats(&self) -> HashMap { - let mut stats = HashMap::new(); - stats.insert("total_conversations".to_string(), 1); - stats - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ”ง RefactorAgent Demo - Code Refactoring and Optimization"); - println!("{}", "=".repeat(60)); - println!(); - - // Initialize infrastructure components (simplified) - let _config = brain_infra::config::BrainConfig::default(); - let _db_config = brain_infra::database::DatabaseConfig::default(); - - // Create mock dependencies - let meta_memory: Arc> = - Arc::new(tokio::sync::RwLock::new(MockMetaMemoryRepository)); - let conversation_service = Arc::new(MockConversationService); - - // Create project context for a legacy codebase - let project_context = ProjectContext { - project_name: "Legacy E-commerce Platform".to_string(), - project_version: "3.2.1".to_string(), - project_description: Some("Legacy e-commerce platform requiring modernization and optimization".to_string()), - tech_stack: vec!["Python".to_string(), "Django".to_string(), "PostgreSQL".to_string(), "Redis".to_string(), "JavaScript".to_string()], - git_branch: Some("feature/code-refactoring".to_string()), - git_commit: Some("def456abc".to_string()), - active_files: vec!["src/models/user.py".to_string(), "src/views/checkout.py".to_string(), "static/js/cart.js".to_string()], - recent_changes: vec!["Added performance monitoring".to_string(), "Identified code smell patterns".to_string()], - directory_structure: { - let mut map = HashMap::new(); - map.insert("src".to_string(), vec!["models".to_string(), "views".to_string(), "services".to_string(), "utils".to_string()]); - map.insert("tests".to_string(), vec!["unit".to_string(), "integration".to_string()]); - map.insert("static".to_string(), vec!["js".to_string(), "css".to_string(), "images".to_string()]); - map - }, - }; - - // Create cognitive preference profile - let cognitive_profile = CognitivePreferenceProfile { - interaction_mode: InteractionMode::Collaborative, - detail_level: DetailLevel::Detailed, - emotional_sensitivity: EmotionalSensitivity::Medium, - autonomy_level: AutonomyLevel::SemiAuto, - communication_style: brain_cognitive::agents::traits::CommunicationStyle::Technical, - cognitive_load_settings: CognitiveLoadSettings { - max_items_per_chunk: 7, - pacing_preference: PacingPreference::Medium, - progressive_disclosure: true, - }, - }; - - // Build cognitive context manually - let mut config = HashMap::new(); - config.insert("demo_mode".to_string(), serde_json::Value::Bool(true)); - - let context = CognitiveContext { - meta_memory, - conversation_service, - project_context, - cognitive_profile, - session_history: Vec::new(), - config, - working_directory: std::env::current_dir().unwrap_or_else(|_| std::path::PathBuf::from(".")), - }; - - println!("โœ… Cognitive context initialized"); - println!(" Project: {}", context.project_context.project_name); - println!(" Tech Stack: {:?}", context.project_context.tech_stack); - println!(" Interaction Mode: {:?}", context.cognitive_profile.interaction_mode); - println!(" Detail Level: {:?}", context.cognitive_profile.detail_level); - println!(); - - // Initialize RefactorAgent - let refactor_agent = RefactorAgent::new(); - println!("๐Ÿ”ง Initializing RefactorAgent..."); - println!(" Agent: {}", refactor_agent.metadata().name); - println!(" Persona: {}", refactor_agent.metadata().persona); - println!(" Capabilities: {:?}", refactor_agent.metadata().capabilities); - println!(" Base Confidence: {:.2}", refactor_agent.metadata().base_confidence); - println!(); - - // Test Case 1: Legacy Codebase Analysis - println!("๐Ÿ“Š Test Case 1: Legacy Codebase Analysis"); - println!("{}", "-".repeat(50)); - - let codebase_analysis_input = AgentInput::new( - "codebase_analysis".to_string(), - json!({ - "codebase_analysis": { - "project_info": { - "name": "E-commerce Platform", - "age_years": 5, - "lines_of_code": 125000, - "languages": ["Python", "JavaScript", "HTML", "CSS"], - "frameworks": ["Django", "jQuery", "Bootstrap"] - }, - "current_metrics": { - "complexity_score": 0.82, - "technical_debt_ratio": 0.35, - "test_coverage": 0.45, - "code_duplication": 0.28, - "security_vulnerabilities": 12, - "performance_issues": 8 - }, - "problematic_areas": { - "models": { - "user_model": { - "file": "src/models/user.py", - "issues": ["god_object", "too_many_methods", "tight_coupling"], - "lines": 850, - "methods": 45 - }, - "order_model": { - "file": "src/models/order.py", - "issues": ["feature_envy", "data_clumps"], - "lines": 420 - } - }, - "views": { - "checkout_view": { - "file": "src/views/checkout.py", - "issues": ["long_method", "complex_conditionals", "duplicate_code"], - "lines": 680, - "cyclomatic_complexity": 15 - } - }, - "frontend": { - "cart_js": { - "file": "static/js/cart.js", - "issues": ["global_variables", "callback_hell", "no_error_handling"], - "lines": 320 - } - } - }, - "dependencies": { - "outdated_packages": 8, - "security_vulnerabilities": 4, - "unused_dependencies": 6 - } - }, - "refactoring_requirements": { - "priority": "high", - "timeline": "3_months", - "focus_areas": ["performance", "maintainability", "security"], - "constraints": { - "budget": "limited", - "team_size": 3, - "downtime_tolerance": "minimal" - }, - "success_criteria": { - "performance_improvement": 30, - "code_quality_score": 0.85, - "test_coverage": 0.80, - "security_issues": 0 - } - }, - "test_coverage": { - "percentage": 0.45, - "unit_tests": 250, - "integration_tests": 45, - "missing_coverage": ["error_handling", "edge_cases", "security_scenarios"] - } - }).to_string(), - "refactor-demo-session".to_string(), - ); - - let confidence = refactor_agent.assess_confidence(&codebase_analysis_input, &context).await?; - println!("๐Ÿ“Š Confidence Assessment: {:.2}", confidence); - - if confidence >= refactor_agent.confidence_threshold() { - println!("โœ… Confidence threshold met, proceeding with refactoring analysis..."); - let result = refactor_agent.execute(codebase_analysis_input, &context).await?; - - println!("๐Ÿ”ง Refactoring Analysis Result:"); - println!(" Output Type: {}", result.output_type); - println!(" Confidence: {:.2}", result.confidence); - println!(" Execution Time: {}ms", result.execution_metadata.execution_time_ms); - - if let Some(reasoning) = &result.reasoning { - println!(" Reasoning: {}", reasoning); - } - - println!(" Next Actions: {:?}", result.next_actions); - - // Parse and display key refactoring components - if let Ok(refactoring_data) = serde_json::from_str::(&result.content) { - if let Some(analysis) = refactoring_data.get("refactoring_analysis") { - if let Some(quality_assessment) = analysis.get("code_quality_assessment") { - if let Some(metrics) = quality_assessment.get("quality_metrics") { - println!(" ๐Ÿ“ˆ Quality Metrics Analyzed:"); - if let Some(complexity) = metrics.get("complexity_score") { - println!(" - Complexity Score: {:.2}", complexity.as_f64().unwrap_or(0.0)); - } - if let Some(maintainability) = metrics.get("maintainability_index") { - println!(" - Maintainability Index: {:.2}", maintainability.as_f64().unwrap_or(0.0)); - } - if let Some(tech_debt) = metrics.get("technical_debt_ratio") { - println!(" - Technical Debt Ratio: {:.2}", tech_debt.as_f64().unwrap_or(0.0)); - } - } - } - - if let Some(opportunities) = analysis.get("improvement_opportunities") { - if let Some(phases) = opportunities.get("refactoring_phases") { - println!(" ๐Ÿ”„ Refactoring Phases Planned:"); - if phases.get("phase_1_preparation").is_some() { - println!(" - Phase 1: Preparation & Testing"); - } - if phases.get("phase_2_structural").is_some() { - println!(" - Phase 2: Structural Improvements"); - } - if phases.get("phase_3_optimization").is_some() { - println!(" - Phase 3: Performance Optimization"); - } - if phases.get("phase_4_quality").is_some() { - println!(" - Phase 4: Quality Enhancement"); - } - } - } - } - } - } else { - println!("โŒ Confidence too low ({:.2}), skipping execution", confidence); - } - println!(); - - // Test Case 2: Performance Optimization Focus - println!("โšก Test Case 2: Performance Optimization Focus"); - println!("{}", "-".repeat(50)); - - let performance_optimization_input = AgentInput::new( - "performance_optimization".to_string(), - json!({ - "codebase_analysis": { - "performance_bottlenecks": { - "database_queries": { - "n_plus_one_queries": 15, - "slow_queries": 8, - "missing_indexes": 12, - "inefficient_joins": 6 - }, - "frontend_performance": { - "large_bundle_size": "2.5MB", - "unused_javascript": "40%", - "image_optimization": "not_implemented", - "caching_strategy": "minimal" - }, - "backend_performance": { - "memory_leaks": 3, - "cpu_intensive_operations": 5, - "inefficient_algorithms": 4, - "blocking_operations": 7 - } - }, - "current_performance_metrics": { - "page_load_time": "4.2s", - "api_response_time": "850ms", - "database_query_time": "320ms", - "memory_usage": "high", - "cpu_utilization": "78%" - } - }, - "refactoring_requirements": { - "primary_focus": "performance", - "target_improvements": { - "page_load_time": "under_2s", - "api_response_time": "under_200ms", - "database_query_time": "under_100ms", - "memory_usage": "reduce_30_percent" - } - } - }).to_string(), - "refactor-demo-session".to_string(), - ); - - let performance_result = refactor_agent.execute(performance_optimization_input, &context).await?; - println!("โšก Performance Optimization Result:"); - println!(" Output Type: {}", performance_result.output_type); - println!(" Confidence: {:.2}", performance_result.confidence); - println!(" Execution Time: {}ms", performance_result.execution_metadata.execution_time_ms); - - // Display agent capabilities summary - println!(); - println!("๐ŸŽฏ RefactorAgent Capabilities Summary"); - println!("{}", "-".repeat(50)); - println!("โœ… Comprehensive code quality analysis"); - println!("โœ… Code smell detection and remediation"); - println!("โœ… Performance bottleneck identification"); - println!("โœ… Security vulnerability assessment"); - println!("โœ… Automated refactoring script generation"); - println!("โœ… Design pattern application guidance"); - println!("โœ… Technical debt reduction strategies"); - println!("โœ… Test coverage enhancement planning"); - println!("โœ… Dependency optimization recommendations"); - println!("โœ… Maintainability improvement roadmaps"); - println!(); - - // Integration showcase - println!("๐Ÿ”— Integration with Development Pipeline"); - println!("{}", "-".repeat(50)); - println!("๐Ÿ“‹ PlannerAgent โ†’ ๐Ÿ—๏ธ ArchitectAgent โ†’ ๐ŸŽจ DesignerAgent โ†’ ๐Ÿ—„๏ธ SchemaAgent"); - println!(" โ†“"); - println!("๐Ÿ”Œ APIAgent โ†’ ๐Ÿ’ป FrontendCoder โ†’ โš™๏ธ BackendCoder โ†’ ๐Ÿ”ง RefactorAgent"); - println!(); - println!(" โ†ณ Requirements โ†’ Architecture โ†’ Design โ†’ Database โ†’ API โ†’ Frontend โ†’ Backend โ†’ Refactoring"); - println!(" โ†ณ Planning โ†’ Technical Design โ†’ UI/UX โ†’ Schema โ†’ Contracts โ†’ Implementation โ†’ Optimization"); - println!(); - println!("๐Ÿ”„ RefactorAgent Position in Pipeline:"); - println!(" โ€ข Analyzes completed implementation for improvements"); - println!(" โ€ข Identifies code quality and performance issues"); - println!(" โ€ข Provides systematic refactoring strategies"); - println!(" โ€ข Generates automated improvement scripts"); - println!(" โ€ข Ensures maintainable and optimized codebase"); - println!(); - println!("๐Ÿ”„ Next Steps in Development Pipeline:"); - println!(" 1. DocAgent - Documentation generation and maintenance"); - println!(" 2. DeployerAgent - Deployment orchestration and automation"); - println!(" 3. MaintainerAgent - System maintenance and monitoring"); - println!(); - - println!("๐ŸŽ‰ RefactorAgent Demo completed successfully!"); - Ok(()) -} \ No newline at end of file diff --git a/run_adaptive_research_demo.rs b/run_adaptive_research_demo.rs deleted file mode 100644 index 12ecb18453444399bfff2c3de833b6d3477035fe..0000000000000000000000000000000000000000 --- a/run_adaptive_research_demo.rs +++ /dev/null @@ -1,87 +0,0 @@ -//! # Quick Adaptive Research Demo -//! -//! **Simple demonstration** of the Brain AI Adaptive Research & Learning System validation. -//! -//! This demo runs a focused test showing how the research system transforms low-confidence -//! guesses into high-confidence researched answers, proving the path to 45%+ HLE accuracy. -//! -//! **Created**: July 31, 2025 at 02:57:00 EDT -//! **Purpose**: Quick validation of research-driven academic intelligence breakthrough - -use std::time::Duration; - -/// **DEMO**: Quick validation of adaptive research system -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿ”ฌ BRAIN AI ADAPTIVE RESEARCH SYSTEM - QUICK DEMO"); - println!("๐Ÿ“… Demo Date: July 31, 2025 at 02:57:00 EDT"); - println!("๐ŸŽฏ Demonstrating: 25% โ†’ 45%+ HLE accuracy through intelligent research"); - println!(); - - println!("๐Ÿš€ Simulating Adaptive Research Process..."); - println!(); - - // Simulate baseline evaluation - println!("๐Ÿ“Š PHASE 1: Baseline Academic Question Processing"); - println!(" Question: 'What is the fundamental principle behind quantum entanglement?'"); - println!(" Options: A) Wave-particle duality B) Superposition collapse"); - println!(" C) Non-local correlation D) Uncertainty principle"); - println!(); - - tokio::time::sleep(Duration::from_millis(500)).await; - println!(" ๐Ÿค” Baseline Analysis: Low confidence (42%) - Answer: A) Wave-particle duality"); - println!(" โŒ Baseline Result: INCORRECT (correct answer: C) Non-local correlation)"); - println!(); - - // Simulate research trigger - println!("๐Ÿ”ฌ PHASE 2: Research System Activation"); - println!(" ๐Ÿšจ TRIGGER: Confidence 42% < 70% threshold"); - println!(" ๐Ÿ” Activating multi-source research automation..."); - println!(); - - tokio::time::sleep(Duration::from_millis(800)).await; - println!(" ๐Ÿ“š Researching: PubMed database for quantum entanglement papers"); - println!(" ๐Ÿ” Fact-checking: Wikipedia quantum mechanics articles"); - println!(" ๐Ÿง  Cross-domain synthesis: Physics + Information Theory"); - println!(); - - tokio::time::sleep(Duration::from_millis(1000)).await; - - // Simulate research results - println!("๐Ÿ“Š PHASE 3: Research-Enhanced Evaluation"); - println!(" โœ… Research Sources Consulted:"); - println!(" - PubMed: 'Quantum Entanglement and Non-locality' (2024)"); - println!(" - arXiv: 'Bell Inequality Violations in Quantum Systems'"); - println!(" - Wikipedia: 'Quantum entanglement' verified content"); - println!(); - - tokio::time::sleep(Duration::from_millis(500)).await; - println!(" ๐ŸŽฏ Research-Enhanced Analysis: High confidence (87%) - Answer: C) Non-local correlation"); - println!(" โœ… Research Result: CORRECT! ๐ŸŽ‰"); - println!(); - - // Show improvement metrics - println!("๐Ÿ“ˆ BREAKTHROUGH RESULTS:"); - println!("โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”"); - println!("โ”‚ Confidence Improvement: 42% โ†’ 87% (+45 percentage points) โ”‚"); - println!("โ”‚ Accuracy Improvement: WRONG โ†’ CORRECT (100% improvement) โ”‚"); - println!("โ”‚ Research Time: 2.1 seconds โ”‚"); - println!("โ”‚ Knowledge Sources: 3 authoritative databases โ”‚"); - println!("โ”‚ Research Success: โœ… Threshold reached (87% > 70%) โ”‚"); - println!("โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜"); - println!(); - - // Project to full system - println!("๐Ÿ† PROJECTED SYSTEM-WIDE IMPACT:"); - println!(" ๐Ÿ“Š Current HLE Accuracy: 25.0% (baseline without research)"); - println!(" ๐Ÿ”ฌ Research-Enhanced Target: 45.0%+ (with adaptive research)"); - println!(" ๐ŸŽฏ Expected Improvement: +20 percentage points"); - println!(" ๐Ÿ Global Ranking: #1 (surpassing Gemini Pro 2.5)"); - println!(); - - println!("โœ… DEMO COMPLETE: Adaptive Research System Validated!"); - println!("๐Ÿš€ Next Step: Deploy on full HLE dataset for comprehensive validation"); - println!("๐Ÿ† Ultimate Goal: Universal Intelligence #1 global ranking achieved"); - - Ok(()) -} \ No newline at end of file diff --git a/run_benchmark.rs b/run_benchmark.rs deleted file mode 100644 index 5dc5a06d71b06178079324cccf782d5040c70976..0000000000000000000000000000000000000000 --- a/run_benchmark.rs +++ /dev/null @@ -1,149 +0,0 @@ -//! # Brain AI Benchmark Runner -//! -//! An example of how to run the Brain AI benchmark suite to evaluate -//! the performance and capabilities of the cognitive agents. - -use anyhow::Result; -use brain_benchmark::{ - BenchmarkOrchestrator, - BenchmarkOrchestratorConfig, - application::ExecuteBenchmarkCommand, - ExecutionEngine, - ExecutionEngineConfig, - ResultAnalyzer, - ResultAnalyzerConfig, - application::ProblemDto, - BenchmarkType, - ExecutionStrategy, - application::ExampleDto, - application::TestCaseDto, - domain::Difficulty, - domain::Category, - domain::EvaluationMode, -}; -use std::sync::Arc; -use uuid::Uuid; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain AI Benchmark Runner"); - println!("==========================="); - println!(); - - // 1. Create the necessary components - let exec_engine_config = ExecutionEngineConfig::default(); - let execution_engine = Arc::new(ExecutionEngine::new(exec_engine_config).await?); - - let analyzer_config = ResultAnalyzerConfig::default(); - let result_analyzer = Arc::new(ResultAnalyzer::new(analyzer_config)); - - let orchestrator_config = BenchmarkOrchestratorConfig::default(); - let orchestrator = BenchmarkOrchestrator::new( - orchestrator_config, - execution_engine, - result_analyzer, - ); - - println!("โœ… Benchmark components initialized."); - - // 2. Define the benchmark problems - let problems = vec![ - ProblemDto { - id: "problem_1".to_string(), - title: "has_close_elements".to_string(), - description: "Check if in given list of numbers, are any two numbers closer to each other than given threshold.".to_string(), - difficulty: Difficulty::Easy, - category: Category::Algorithms, - tags: vec!["array".to_string(), "distance".to_string()], - input_format: "List[float], float".to_string(), - output_format: "bool".to_string(), - constraints: vec!["0 <= len(numbers) <= 100".to_string()], - examples: vec![ - ExampleDto { - input: "[1.0, 2.0, 3.1, 4.0, 5.0, 2.0], 0.3".to_string(), - output: "True".to_string(), - explanation: Some("Numbers 2.0 and 2.0 are exactly the same".to_string()), - } - ], - test_cases: vec![ - TestCaseDto { - id: "test_1".to_string(), - input: "[1.0, 2.0, 3.1, 4.0, 5.0, 2.0], 0.3".to_string(), - expected_output: "True".to_string(), - hidden: false, - points: 1, - } - ], - time_limit_ms: 1000, - memory_limit_mb: 64, - }, - ProblemDto { - id: "problem_2".to_string(), - title: "separate_paren_groups".to_string(), - description: "Separate parentheses into groups.".to_string(), - difficulty: Difficulty::Medium, - category: Category::StringProcessing, - tags: vec!["string".to_string(), "parsing".to_string()], - input_format: "str".to_string(), - output_format: "List[str]".to_string(), - constraints: vec!["Valid parentheses only".to_string()], - examples: vec![ - ExampleDto { - input: "()()".to_string(), - output: "['()', '()']".to_string(), - explanation: Some("Two separate groups".to_string()), - } - ], - test_cases: vec![ - TestCaseDto { - id: "test_2".to_string(), - input: "()()".to_string(), - expected_output: "['()', '()']".to_string(), - hidden: false, - points: 1, - } - ], - time_limit_ms: 1000, - memory_limit_mb: 64, - }, - ]; - println!("๐Ÿ“‹ {} problems loaded for benchmark.", problems.len()); - - // 3. Create and execute the benchmark command - let command = ExecuteBenchmarkCommand { - benchmark_id: Uuid::new_v4(), - benchmark_type: BenchmarkType::General("standard".to_string()), - agent_id: "algorithm-coder".to_string(), - problems, - execution_strategy: ExecutionStrategy::Direct, - evaluation_mode: EvaluationMode::Standard, - parallel_execution: true, - timeout_seconds: 300, - max_memory_mb: 1024, - metadata: std::collections::HashMap::new(), - }; - - println!("๐Ÿš€ Executing benchmark..."); - let result = orchestrator.execute_benchmark(command).await?; - - // 4. Print the results - println!("\n๐Ÿ† Benchmark Results"); - println!("==================="); - println!("Benchmark ID: {}", result.benchmark_id); - println!("State: {:?}", result.state); - println!("Duration: {}ms", result.total_duration_ms.unwrap_or(0)); - println!("Problems Completed: {}/{}", result.problems_completed, result.problems_total); - println!("Success Rate: {:.2}%", result.success_rate * 100.0); - - if let Some(metrics) = result.metrics { - println!("\n๐Ÿ“Š Performance Metrics"); - println!("---------------------"); - println!("Average Execution Time: {:.2}ms", metrics.performance.avg_execution_time_ms); - println!("Average Confidence: {:.2}", metrics.quality.avg_confidence); - println!("Total Solutions Generated: {}", metrics.statistics.total_problems); - } - - println!("\nโœ… Benchmark complete."); - - Ok(()) -} \ No newline at end of file diff --git a/sample_text.txt b/sample_text.txt deleted file mode 100644 index 100047421547bed1e580d1543b04ac68c1a31a6b..0000000000000000000000000000000000000000 --- a/sample_text.txt +++ /dev/null @@ -1 +0,0 @@ -Hello world! This is a simple test for the character ingestion engine. The quick brown fox jumps over the lazy dog. diff --git a/segments_archive.json b/segments_archive.json deleted file mode 100644 index 9e26dfeeb6e641a33dae4961196235bdb965b21b..0000000000000000000000000000000000000000 --- a/segments_archive.json +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/simple_github_demo.db b/simple_github_demo.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/simple_github_demo.db and /dev/null differ diff --git a/simple_github_learning.rs b/simple_github_learning.rs deleted file mode 100644 index 2356a179db7b1f99c6220376aef4c7e5bceb8885..0000000000000000000000000000000000000000 --- a/simple_github_learning.rs +++ /dev/null @@ -1,116 +0,0 @@ -//! Simple GitHub Learning Demo -//! -//! This is a streamlined demonstration of Brain AI's GitHub learning capabilities. -//! -//! A more focused version that shows the core functionality: -//! - Learning from a single repository -//! - Storing knowledge in memory -//! - Simple querying of learned content - -use brain::{MemoryService, WorkingMemoryQuery, Result, WorkingMemoryRepository}; -use brain_infra::memory::{ - WorkingMemoryRepository as WorkingMemoryRepo, - EpisodicMemoryRepository as EpisodicMemoryRepo, - SemanticMemoryRepository as SemanticMemoryRepo -}; -use brain_infra::github_integration::{GitHubLearningEngine, GitHubLearningConfig}; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Simple GitHub Learning Demo"); - println!("=============================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| { - eprintln!("Failed to create data directory: {}", e); - brain::BrainError::InvalidInput { - message: "Failed to create data directory".to_string(), - context: None, - } - })?; - - // Create memory repositories using concrete types - let mut working_repo = WorkingMemoryRepo::new(100); - let episodic_repo = Box::new(EpisodicMemoryRepo::new("data/simple_github_demo.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepo::new()); - - // Create memory service for potential future use - let _memory_service = MemoryService::new( - Box::new(WorkingMemoryRepo::new(100)), - episodic_repo, - semantic_repo, - ); - - // Get GitHub token if available - let github_token = env::var("GITHUB_TOKEN").ok(); - - // Create GitHub learning configuration - let config = GitHubLearningConfig { - max_files: 20, // Fewer files for simplicity - max_file_size: 30_000, // 30KB per file - include_code: true, - include_docs: true, - include_config: false, // Skip config files for simplicity - ..Default::default() - }; - - let github_engine = GitHubLearningEngine::new(github_token, Some(config)); - - // Learn from a single, simple repository - let repo_url = "BurntSushi/ripgrep"; // A well-documented Rust tool - - println!("๐Ÿ“š Learning from repository: {}", repo_url); - println!("โณ This may take a moment...\n"); - - match github_engine.learn_from_repository(&mut working_repo, repo_url).await { - Ok(result) => { - println!("โœ… Learning completed successfully!"); - println!(" Files processed: {}", result.files_processed); - println!(" Memory entries: {}", result.memory_entries_created); - println!(" Concepts discovered: {}", result.concepts_discovered); - println!(" Learning time: {}ms\n", result.learning_time_ms); - - // Query what we learned - println!("๐Ÿ” Querying learned knowledge:"); - println!("{}", "-".repeat(35)); - - let query = WorkingMemoryQuery::default(); - match working_repo.query_items(&query).await { - Ok(items) => { - println!("๐Ÿ“Š Total items in memory: {}\n", items.len()); - - if !items.is_empty() { - println!("๐Ÿ”— Sample content:"); - for (i, item) in items.iter().take(3).enumerate() { - let content = if item.content.len() > 120 { - format!("{}...", &item.content[..120]) - } else { - item.content.clone() - }; - println!(" {}. {} (Priority: {:?})", i + 1, content, item.priority); - } - } - } - Err(e) => { - println!("โŒ Error querying memory: {}", e); - } - } - } - Err(e) => { - println!("โŒ Learning failed: {}", e); - println!("๐Ÿ’ก Make sure you have internet connectivity"); - if env::var("GITHUB_TOKEN").is_err() { - println!("๐Ÿ’ก Set GITHUB_TOKEN environment variable for better rate limits"); - } - return Ok(()); // Don't fail the demo completely - } - } - - println!("\n๐ŸŽ‰ Simple GitHub Learning Demo Completed!"); - println!(" Repository knowledge has been stored in memory"); - println!(" Try the more comprehensive 'github_learning_demo' for advanced features"); - - Ok(()) -} \ No newline at end of file diff --git a/simple_integration_demo.rs b/simple_integration_demo.rs deleted file mode 100644 index f50cae95db54c3a4d278d386bf5c71ea2b2a6fe1..0000000000000000000000000000000000000000 --- a/simple_integration_demo.rs +++ /dev/null @@ -1,156 +0,0 @@ -//! Simplified Integration Demo - Predictor-Segmenter Integration -//! -//! This example demonstrates basic integration between CharacterPredictor -//! and FeedbackBpeSegmenter with core functionality. - -use brain::character_ingestion::{CharacterVocab, CharacterPredictor, ModelConfig, CharacterPredictorService}; -use brain::segment_discovery::{BpeConfig, FeedbackBpeSegmenter}; -use brain::Result; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Brain - Integration Demo: Predictor-Segmenter Integration"); - println!("============================================================="); - - // Sample training text with rich patterns - let training_text = "the quick brown fox jumps over the lazy dog. \ - the fox is quick and the dog is lazy. \ - brown foxes and lazy dogs are common. \ - quick movements and lazy afternoons."; - - println!("\n๐Ÿ“ Training Text:"); - println!("{}", training_text); - - // 1. Initialize Character Predictor - println!("\n๐Ÿ”ค Initializing Character Predictor..."); - let vocab = CharacterVocab::from_text(training_text); - let config = ModelConfig { - vocab_size: vocab.vocab_size(), - embedding_dim: 64, - hidden_dim: 128, - learning_rate: 0.01, - sequence_length: 16, - }; - - let mut predictor = CharacterPredictor::new(vocab.clone(), Some(config))?; - println!("โœ… Character predictor initialized with vocab size: {}", vocab.vocab_size()); - - // 2. Initialize BPE Segmenter with feedback - println!("\n๐Ÿ” Initializing Feedback BPE Segmenter..."); - let bpe_config = BpeConfig { - min_frequency: 2, - max_vocab_size: 100, - num_merges: 10, - include_chars: true, - enable_advanced_heuristics: true, - min_entropy_threshold: 0.3, - context_window_size: 3, - min_confidence: 0.4, - }; - - let feedback_segmenter = FeedbackBpeSegmenter::from_text(training_text, Some(bpe_config))?; - println!("โœ… Feedback BPE segmenter initialized and trained"); - - // Display basic statistics - let bpe_stats = feedback_segmenter.get_segmenter().get_stats(); - println!("๐Ÿ“Š Segmenter Stats:"); - println!(" - Total segments: {}", bpe_stats.total_segments); - println!(" - Merged segments: {}", bpe_stats.merged_segments); - println!(" - Average confidence: {:.3}", bpe_stats.average_confidence); - - let high_confidence_segments = feedback_segmenter.get_high_confidence_segments(); - println!("๐ŸŽฏ High confidence segments: {} found", high_confidence_segments.len()); - for (i, segment) in high_confidence_segments.iter().take(5).enumerate() { - println!(" {}. '{}'", i + 1, segment); - } - - // 3. Demonstrate basic prediction functionality - println!("\n๐Ÿ”ฎ Testing Basic Prediction Capabilities"); - println!("=========================================="); - - let test_inputs = vec![ - "the quick", - "brown fox", - "lazy dog", - "quick brown", - ]; - - for (i, input) in test_inputs.iter().enumerate() { - println!("\n--- Test {} ---", i + 1); - println!("Input: '{}'", input); - - // Character-level prediction - let (char_pred, char_conf) = predictor.predict_next_char(input).await?; - println!("Character prediction: '{}' (confidence: {:.3})", char_pred, char_conf); - - // Segment the input text - let segments = feedback_segmenter.segment(input)?; - println!("Text segmentation: {:?}", segments); - - // Segment-aware prediction - let (seg_pred, seg_conf) = predictor.predict_next_segment(&segments).await?; - println!("Segment prediction: '{}' (confidence: {:.3})", seg_pred, seg_conf); - - // Hybrid prediction combining both approaches - let (hybrid_pred, hybrid_conf) = predictor.predict_hybrid(input, &segments).await?; - println!("Hybrid prediction: '{}' (confidence: {:.3})", hybrid_pred, hybrid_conf); - } - - // 4. Performance comparison - println!("\n๐Ÿ“Š Performance Insights"); - println!("========================"); - - let metrics = predictor.get_metrics(); - println!("Predictor Performance:"); - println!(" - Total predictions: {}", metrics.total_predictions); - println!(" - Correct predictions: {}", metrics.correct_predictions); - println!(" - Overall accuracy: {:.2}%", metrics.accuracy() * 100.0); - println!(" - Character accuracy: {:.2}%", metrics.character_accuracy); - println!(" - Segment accuracy: {:.2}%", metrics.segment_accuracy); - println!(" - Hybrid accuracy: {:.2}%", metrics.hybrid_accuracy); - - // 5. Text generation demo - println!("\n๐ŸŽจ Text Generation Demo"); - println!("========================"); - - let generation_prefixes = vec!["the", "quick", "fox"]; - - for prefix in generation_prefixes { - println!("\nGenerating from prefix: '{}'", prefix); - let generated = predictor.generate(prefix, 20, 0.8).await?; - println!("Generated text: '{}'", generated); - } - - // 6. Advanced segmentation analysis - println!("\n๐Ÿ”ฌ Advanced Segmentation Analysis"); - println!("=================================="); - - let analysis_texts = vec![ - "the quick brown fox", - "jumps over the lazy dog", - "foxes and dogs are animals", - ]; - - for text in analysis_texts { - println!("\nAnalyzing: '{}'", text); - let segments = feedback_segmenter.segment(text)?; - println!(" Segments: {:?}", segments); - println!(" Segment count: {}", segments.len()); - println!(" Average segment length: {:.1}", - segments.iter().map(|s| s.len()).sum::() as f64 / segments.len() as f64); - } - - println!("\n๐ŸŽ‰ Integration Demo Complete!"); - println!("=============================="); - println!("โœ… Successfully demonstrated:"); - println!(" โ€ข Character prediction with confidence scoring"); - println!(" โ€ข Advanced BPE segmentation with feedback"); - println!(" โ€ข Segment-aware prediction capabilities"); - println!(" โ€ข Hybrid prediction combining both approaches"); - println!(" โ€ข Text generation from prefixes"); - println!(" โ€ข Performance metrics and analysis"); - println!(" โ€ข Advanced segmentation analysis"); - println!("\n๐Ÿš€ The Brain AI system now features robust predictor-segmenter integration!"); - - Ok(()) -} \ No newline at end of file diff --git a/simple_pocketflow_chat.rs b/simple_pocketflow_chat.rs deleted file mode 100644 index 18b5a61afeb52158c76948052cf197d9668225f3..0000000000000000000000000000000000000000 --- a/simple_pocketflow_chat.rs +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/env cargo run --example simple_pocketflow_chat -//! Simple PocketFlow Chat Demo -//! -//! Demonstrates basic conversation capabilities using the Brain AI orchestrator -//! with proper MemoryService and ConceptGraphService architecture. - -use brain::*; -use brain::services::*; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Simple PocketFlow Chat Demo"); - println!("==============================="); - - // Check for OpenAI API key - let _openai_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| { - println!("โš ๏ธ OPENAI_API_KEY not set. Please set it to use this demo."); - println!(" export OPENAI_API_KEY=your_key_here"); - std::process::exit(1); - }); - - println!("โœ… OpenAI API key found"); - - // Initialize Brain AI components using new service architecture - println!("\n๐Ÿ”ง Initializing Brain AI Services..."); - let mut memory_system = create_memory_service_with_capacity(2000).await?; - let mut concept_graph = create_concept_graph_service_default().await?; - - println!("โœ… MemoryService initialized with SQLite persistence"); - println!("โœ… ConceptGraphService initialized with in-memory storage"); - - println!("\n๐Ÿง  Loading PocketFlow Knowledge Base"); - - // Create RAG orchestrator - let mut rag_orchestrator = RagOrchestrator::new()?; - - // Add some PocketFlow-specific knowledge to memory - let pocketflow_knowledge = vec![ - "PocketFlow is a streamlined development framework for building AI applications", - "It emphasizes simplicity, modularity, and rapid prototyping", - "PocketFlow supports multiple AI models and provides unified interfaces", - "The framework includes built-in conversation management and context handling", - "PocketFlow can integrate with various databases and external APIs", - "PocketFlow uses Node-Flow Architecture for modular processing", - "The framework implements async parallel processing for concurrent operations", - "PocketFlow follows a minimalist 100-line framework philosophy", - "BatchNode and ParallelBatchNode enable cost-effective LLM operations", - "PocketFlow enables agent-based workflows through autonomous agents", - ]; - - for (i, knowledge) in pocketflow_knowledge.iter().enumerate() { - memory_system.learn(knowledge.to_string(), Priority::Medium).await?; - println!("โœ… Loaded knowledge chunk {}", i + 1); - } - - println!("โœ… Knowledge base loaded with {} items", pocketflow_knowledge.len()); - - // Demo questions - let demo_questions = vec![ - "What is PocketFlow?", - "What are the key features of PocketFlow?", - "How does PocketFlow handle AI models?", - "What is the Node-Flow Architecture in PocketFlow?", - "How does PocketFlow optimize costs?", - ]; - - println!("\n๐Ÿ’ฌ PocketFlow Chat Demo - Automated Q&A"); - println!("=========================================="); - - for (i, question) in demo_questions.iter().enumerate() { - println!("\n๐Ÿ“ Question {}: {}", i + 1, question); - - // Create request - let request = RagRequest { - message: question.to_string(), - conversation_id: Some("demo_session".to_string()), - context_limit: Some(5), - retrieval_threshold: Some(0.3), - }; - - // Process with Brain AI - match rag_orchestrator.process_conversation( - request, - &mut memory_system, - &mut concept_graph, - ).await { - Ok(response) => { - println!("๐Ÿค– Brain AI: {}", response.response); - println!(" ๐Ÿ“Š Confidence: {:.1}%", response.confidence_score * 100.0); - println!(" ๐Ÿ“š Knowledge sources: {}", response.context_used.len()); - - // Learn from this interaction - let interaction = format!("User asked: '{}' | AI responded: '{}'", question, response.response); - memory_system.learn(interaction, Priority::Low).await?; - } - Err(e) => { - println!("โŒ Error: {}", e); - println!(" This might be due to missing OpenAI API key or network issues."); - } - } - - // Brief pause between questions - tokio::time::sleep(tokio::time::Duration::from_millis(500)).await; - } - - // Show final statistics - println!("\n๐Ÿ“ˆ Session Statistics:"); - let stats = rag_orchestrator.get_conversation_stats(); - for (key, value) in stats { - println!(" {}: {}", key, value); - } - - println!("\nโœ… Demo Complete! The new service architecture is working properly."); - - Ok(()) -} \ No newline at end of file diff --git a/simple_tool_test.rs b/simple_tool_test.rs deleted file mode 100644 index 4098748653d36dd9a9cd30dd473b8dff04663268..0000000000000000000000000000000000000000 --- a/simple_tool_test.rs +++ /dev/null @@ -1,163 +0,0 @@ -//! # Simple Tool Test -//! -//! A basic test to verify the new tools can be instantiated and registered. - -use anyhow::Result; -use brain_cognitive::{ - agents::AgentRegistry, - tools::{FileSystemTool, DatabaseTool, WebSearchTool}, -}; -use std::path::PathBuf; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Simple Tool Test - Phase 2"); - println!("============================="); - println!(); - - // Test 1: Tool Creation - println!("๐Ÿ”ง Test 1: Tool Creation"); - println!("------------------------"); - - let creation_passed = test_tool_creation().await?; - if creation_passed { - println!("โœ… Tool creation works correctly"); - } else { - println!("โŒ Tool creation has issues"); - } - println!(); - - // Test 2: Tool Registry - println!("๐Ÿ”ง Test 2: Tool Registry"); - println!("------------------------"); - - let registry_passed = test_tool_registry().await?; - if registry_passed { - println!("โœ… Tool registry works correctly"); - } else { - println!("โŒ Tool registry has issues"); - } - println!(); - - // Test 3: File System Operations - println!("๐Ÿ”ง Test 3: File System Operations"); - println!("----------------------------------"); - - let fs_passed = test_file_operations().await?; - if fs_passed { - println!("โœ… File system operations work correctly"); - } else { - println!("โŒ File system operations have issues"); - } - println!(); - - // Summary - println!("๐Ÿ“Š Simple Tool Test Summary"); - println!("=========================="); - println!("Tool Creation: {}", if creation_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Tool Registry: {}", if registry_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("File Operations: {}", if fs_passed { "โœ… PASS" } else { "โŒ FAIL" }); - - let all_passed = creation_passed && registry_passed && fs_passed; - println!(); - println!("๐ŸŽฏ Overall Result: {}", if all_passed { "โœ… ALL TESTS PASSED" } else { "โŒ SOME TESTS FAILED" }); - - if all_passed { - println!("๐Ÿš€ Tool catalog expansion is working!"); - println!(" Phase 2 tool development is successful."); - } else { - println!("๐Ÿ”ง Some tool issues need attention."); - } - - Ok(()) -} - -/// Test tool creation -async fn test_tool_creation() -> Result { - println!(" Testing tool instantiation..."); - - // Test FileSystemTool creation - let _fs_tool = FileSystemTool::new(); - println!(" โœ“ FileSystemTool created"); - - // Test DatabaseTool creation - let _db_tool = DatabaseTool::new(PathBuf::from("./temp/test.db")); - println!(" โœ“ DatabaseTool created"); - - // Test WebSearchTool creation - let _web_tool = WebSearchTool::new("test_key".to_string()); - println!(" โœ“ WebSearchTool created"); - - Ok(true) -} - -/// Test tool registry -async fn test_tool_registry() -> Result { - println!(" Testing tool registry..."); - - let registry = AgentRegistry::new_with_defaults(); - - // Check that tools are registered - let agents = registry.list_agents()?; - println!(" ๐Ÿ“‹ Total registered agents/tools: {}", agents.len()); - - // Look for our specific tools - let mut found_fs_tool = false; - let mut found_db_tool = false; - let mut found_web_tool = false; - - for agent in agents { - let metadata = agent.metadata(); - match metadata.id.as_str() { - "file-system-tool" => found_fs_tool = true, - "database-tool" => found_db_tool = true, - "web-search-tool" => found_web_tool = true, - _ => {} - } - } - - println!(" โœ“ File System Tool found: {}", found_fs_tool); - println!(" โœ“ Database Tool found: {}", found_db_tool); - println!(" โœ“ Web Search Tool found: {}", found_web_tool); - - // Test capability discovery - let fs_agents = registry.get_agents_by_capability("FileSystem")?; - println!(" ๐Ÿ“ FileSystem capability agents: {}", fs_agents.len()); - - let db_agents = registry.get_agents_by_capability("Database")?; - println!(" ๐Ÿ’พ Database capability agents: {}", db_agents.len()); - - let web_agents = registry.get_agents_by_capability("WebSearch")?; - println!(" ๐Ÿ” WebSearch capability agents: {}", web_agents.len()); - - Ok(found_fs_tool && found_db_tool && found_web_tool && - fs_agents.len() > 0 && db_agents.len() > 0 && web_agents.len() > 0) -} - -/// Test basic file operations -async fn test_file_operations() -> Result { - println!(" Testing basic file operations..."); - - // Create test directory - let test_dir = PathBuf::from("./temp/simple_test"); - if !test_dir.exists() { - std::fs::create_dir_all(&test_dir)?; - } - - // Write a test file - let test_file = test_dir.join("test.txt"); - let test_content = "Hello, Tool Test!"; - std::fs::write(&test_file, test_content)?; - println!(" โœ“ Test file created"); - - // Read the test file - let read_content = std::fs::read_to_string(&test_file)?; - println!(" โœ“ Test file read: {}", read_content); - - let success = read_content.contains("Hello, Tool Test!"); - - // Cleanup - let _ = std::fs::remove_dir_all(&test_dir); - - Ok(success) -} \ No newline at end of file diff --git a/simulation_dashboard.html b/simulation_dashboard.html deleted file mode 100644 index ca5c8aa18ffe62ce045a741c78f5bfa2320cbc82..0000000000000000000000000000000000000000 --- a/simulation_dashboard.html +++ /dev/null @@ -1,1106 +0,0 @@ - - - - - - Brain AI - Simulation Results Dashboard - - - - -
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๐Ÿง  Brain AI Simulation Dashboard

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Explore simulation results, insights, and performance metrics

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๐ŸŽ›๏ธ Dashboard Controls

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- - - - diff --git a/soma_builtin_operators_demo.rs b/soma_builtin_operators_demo.rs deleted file mode 100644 index 1b27f80ceb03c1fd3a369d04f1b1e157406c2cab..0000000000000000000000000000000000000000 --- a/soma_builtin_operators_demo.rs +++ /dev/null @@ -1,299 +0,0 @@ -//! SOMA++ Built-in Operators Demonstration -//! -//! This example demonstrates all the built-in SOMA++ operators including: -//! - ReflectOperator::ฮ”๐Ÿชž for meta-reflection -//! - SOMA::Compose for symbolic composition -//! - MemoryLogger::Store for persistent storage -//! - SymbolicEvaluator::Optimize for optimization -//! - ErrorRecovery::InjectDiversity for failure handling - -use brain_types::soma::*; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ SOMA++ Built-in Operators Comprehensive Demonstration"); - println!("=========================================================\n"); - - // Create operator registry and register all built-in operators - let mut registry = OperatorRegistry::new(); - register_builtin_operators(&mut registry)?; - - println!("๐Ÿ“š Registered {} built-in operators:", registry.count()); - for op_name in registry.list_operators() { - println!(" โ€ข {}", op_name); - } - - // Test 1: ReflectOperator::ฮ”๐Ÿชž (Self-Reflection) - println!("\n๐Ÿชž Testing Reflection Operator (ฮ”403 Phase)"); - println!("============================================"); - - let reflection_header = PacketHeader { - phase: DeltaPhase::self_reflection(), - time_offset: 0.014, - task: "Meta-cognitive self-reflection on Brain AI consciousness".to_string(), - origin: Some("Brain AI Cognitive Engine".to_string()), - }; - - let reflection_payload = PacketPayload { - inputs: vec![ - "consciousness".to_string(), - "emergence".to_string(), - "intelligence".to_string(), - "meta_cognition".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "ReflectOperator".to_string(), - "ฮ”๐Ÿชž".to_string(), - )), - constraints: vec!["maintain_coherence".to_string()], - }; - - let reflection_packet = SomaPacket::new(reflection_header, reflection_payload); - - let reflect_operator = registry.get_operator("ReflectOperator::ฮ”๐Ÿชž")?; - let reflection_result = reflect_operator.execute(reflection_packet).await?; - - println!("๐Ÿ“ฅ Input concepts: {:?}", reflection_result.payload.inputs); - println!("๐Ÿ“ค Reflection outputs: {:?}", reflection_result.payload.outputs); - println!("๐Ÿท๏ธ Tags: {:?}", reflection_result.metadata.tags); - - // Test 2: SOMA::Compose (Symbolic Composition) - println!("\n๐Ÿ”ง Testing Composition Operator (ฮ”700 Phase)"); - println!("============================================="); - - let composition_header = PacketHeader { - phase: DeltaPhase::architecture_evolution(700), - time_offset: 0.0, - task: "Compose neural-symbolic hybrid architecture".to_string(), - origin: Some("Architecture Evolution Engine".to_string()), - }; - - let composition_payload = PacketPayload { - inputs: vec![ - "neural_networks".to_string(), - "symbolic_reasoning".to_string(), - "emergent_behavior".to_string(), - "cognitive_architecture".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "SOMA".to_string(), - "Compose".to_string(), - )), - constraints: vec!["preserve_functionality".to_string(), "enhance_capabilities".to_string()], - }; - - let composition_packet = SomaPacket::new(composition_header, composition_payload); - - let compose_operator = registry.get_operator("SOMA::Compose")?; - let composition_result = compose_operator.execute(composition_packet).await?; - - println!("๐Ÿ“ฅ Input components: {:?}", composition_result.payload.inputs); - println!("๐Ÿ“ค Composition outputs: {:?}", composition_result.payload.outputs); - println!("๐Ÿท๏ธ Tags: {:?}", composition_result.metadata.tags); - - // Test 3: MemoryLogger::Store (Persistent Storage) - println!("\n๐Ÿ’พ Testing Memory Logger Operator (Memory Storage)"); - println!("=================================================="); - - let memory_header = PacketHeader { - phase: DeltaPhase::architecture_evolution(701), - time_offset: 0.0, - task: "Store cognitive patterns for future retrieval".to_string(), - origin: Some("Memory Consolidation System".to_string()), - }; - - let memory_payload = PacketPayload { - inputs: vec![ - "pattern: neural_activation_sequence_alpha".to_string(), - "pattern: symbolic_rule_derivation_beta".to_string(), - "pattern: emergent_behavior_gamma".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "MemoryLogger".to_string(), - "Store".to_string(), - )), - constraints: vec!["key=cognitive_patterns".to_string()], - }; - - let memory_packet = SomaPacket::new(memory_header, memory_payload); - - let memory_operator = registry.get_operator("MemoryLogger::Store")?; - let memory_result = memory_operator.execute(memory_packet).await?; - - println!("๐Ÿ“ฅ Input patterns: {:?}", memory_result.payload.inputs); - println!("๐Ÿ“ค Storage confirmations: {:?}", memory_result.payload.outputs); - println!("๐Ÿท๏ธ Tags: {:?}", memory_result.metadata.tags); - - // Test 4: SymbolicEvaluator::Optimize (Symbolic Optimization) - println!("\nโšก Testing Symbolic Evaluator Operator (Optimization)"); - println!("===================================================="); - - let optimization_header = PacketHeader { - phase: DeltaPhase::architecture_evolution(702), - time_offset: 0.0, - task: "Optimize symbolic expressions for efficiency".to_string(), - origin: Some("Symbolic Optimization Engine".to_string()), - }; - - let optimization_payload = PacketPayload { - inputs: vec![ - "neural + symbolic * 1 - 0".to_string(), - "consciousness + emergence + intelligence".to_string(), - "very_long_expression_that_needs_compression_for_efficient_processing_and_storage".to_string(), - "hybrid[neural_symbolic_reasoning] + optimization".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "SymbolicEvaluator".to_string(), - "Optimize".to_string(), - )), - constraints: vec!["preserve_semantics".to_string()], - }; - - let optimization_packet = SomaPacket::new(optimization_header, optimization_payload); - - let optimization_operator = registry.get_operator("SymbolicEvaluator::Optimize")?; - let optimization_result = optimization_operator.execute(optimization_packet).await?; - - println!("๐Ÿ“ฅ Input expressions: {:?}", optimization_result.payload.inputs); - println!("๐Ÿ“ค Optimized expressions: {:?}", optimization_result.payload.outputs); - println!("๐Ÿท๏ธ Tags: {:?}", optimization_result.metadata.tags); - - // Test 5: ErrorRecovery::InjectDiversity (Error Recovery) - println!("\n๐Ÿšจ Testing Error Recovery Operator (Failure Handling)"); - println!("======================================================"); - - let recovery_header = PacketHeader { - phase: DeltaPhase::architecture_evolution(703), - time_offset: 0.0, - task: "Recover from neural network training failure".to_string(), - origin: Some("Error Recovery System".to_string()), - }; - - let recovery_payload = PacketPayload { - inputs: vec![ - "failed_operation: neural_training_convergence".to_string(), - "failed_operation: symbolic_rule_validation".to_string(), - "failed_operation: memory_consolidation_timeout".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "ErrorRecovery".to_string(), - "InjectDiversity".to_string(), - )), - constraints: vec!["error_type=neural".to_string()], - }; - - let recovery_packet = SomaPacket::new(recovery_header, recovery_payload); - - let recovery_operator = registry.get_operator("ErrorRecovery::InjectDiversity")?; - let recovery_result = recovery_operator.execute(recovery_packet).await?; - - println!("๐Ÿ“ฅ Failed operations: {:?}", recovery_result.payload.inputs); - println!("๐Ÿ“ค Recovery strategies: {:?}", recovery_result.payload.outputs); - println!("๐Ÿท๏ธ Tags: {:?}", recovery_result.metadata.tags); - - // Test 6: Operator Chain Execution - println!("\n๐Ÿ”— Testing Operator Chain Execution"); - println!("===================================="); - - // Start with reflection, then compose, then optimize - let chain_header = PacketHeader { - phase: DeltaPhase::self_reflection(), - time_offset: 0.0, - task: "Chain execution: reflection โ†’ composition โ†’ optimization".to_string(), - origin: Some("Chain Execution Demo".to_string()), - }; - - let chain_payload = PacketPayload { - inputs: vec![ - "creativity".to_string(), - "logic".to_string(), - "intuition".to_string(), - ], - outputs: vec![], - target: None, - operator: None, - constraints: vec![], - }; - - let mut chain_packet = SomaPacket::new(chain_header, chain_payload); - - // Step 1: Reflection - println!("\n Step 1: Applying reflection..."); - chain_packet = reflect_operator.execute(chain_packet).await?; - println!(" โ†’ Outputs: {:?}", chain_packet.payload.outputs); - - // Step 2: Update phase and apply composition - println!("\n Step 2: Applying composition..."); - chain_packet.header.phase = DeltaPhase::architecture_evolution(700); - chain_packet.payload.inputs = chain_packet.payload.outputs.clone(); - chain_packet.payload.outputs.clear(); - chain_packet = compose_operator.execute(chain_packet).await?; - println!(" โ†’ Outputs: {:?}", chain_packet.payload.outputs); - - // Step 3: Apply optimization - println!("\n Step 3: Applying optimization..."); - chain_packet.header.phase = DeltaPhase::architecture_evolution(702); - chain_packet.payload.inputs = chain_packet.payload.outputs.clone(); - chain_packet.payload.outputs.clear(); - chain_packet = optimization_operator.execute(chain_packet).await?; - println!(" โ†’ Final optimized outputs: {:?}", chain_packet.payload.outputs); - println!(" โ†’ Final tags: {:?}", chain_packet.metadata.tags); - - // Test 7: Operator Discovery and Metadata - println!("\n๐Ÿ“‹ Built-in Operator Metadata Summary"); - println!("====================================="); - - for op_name in registry.list_operators() { - let operator = registry.get_operator(&op_name)?; - let metadata = operator.metadata(); - - println!("\n๐Ÿ”ง {}", op_name); - println!(" ๐Ÿ“ Description: {}", metadata.description); - println!(" ๐Ÿท๏ธ Tags: {:?}", metadata.tags); - println!(" ๐Ÿ”ข Supported Phases: {:?}", metadata.supported_phases); - println!(" ๐Ÿ“… Version: {}", metadata.version); - if let Some(author) = &metadata.author { - println!(" ๐Ÿ‘ค Author: {}", author); - } - } - - // Test 8: Phase-based Operator Discovery - println!("\n๐Ÿ” Phase-based Operator Discovery"); - println!("=================================="); - - let phases_to_test = vec![403, 700, 701, 702, 703]; - for phase in phases_to_test { - let operators_for_phase = registry.find_operators_by_phase(phase); - println!(" ฮ”{}: {:?}", phase, operators_for_phase); - } - - // Test 9: Tag-based Operator Discovery - println!("\n๐Ÿท๏ธ Tag-based Operator Discovery"); - println!("================================="); - - let tags_to_test = vec!["reflection", "composition", "memory", "optimization", "error_handling"]; - for tag in tags_to_test { - let operators_with_tag = registry.find_operators_by_tag(tag); - println!(" '{}': {:?}", tag, operators_with_tag); - } - - println!("\nโœจ SOMA++ Built-in Operators demonstration complete!"); - println!("All {} core operators tested successfully:", registry.count()); - println!(" ๐Ÿชž ReflectOperator::ฮ”๐Ÿชž - Meta-cognitive reflection"); - println!(" ๐Ÿ”ง SOMA::Compose - Symbolic composition"); - println!(" ๐Ÿ’พ MemoryLogger::Store - Persistent storage"); - println!(" โšก SymbolicEvaluator::Optimize - Expression optimization"); - println!(" ๐Ÿšจ ErrorRecovery::InjectDiversity - Failure recovery"); - - Ok(()) -} \ No newline at end of file diff --git a/soma_cognitive_integration_simple_demo.rs b/soma_cognitive_integration_simple_demo.rs deleted file mode 100644 index a49906e6d38a879ccae197c9cede0310e36f16fc..0000000000000000000000000000000000000000 --- a/soma_cognitive_integration_simple_demo.rs +++ /dev/null @@ -1,242 +0,0 @@ -//! Simple SOMA++ Cognitive Integration Demo -//! -//! This example demonstrates basic integration of SOMA++ symbolic packets -//! with cognitive processing, showing a simplified approach to symbolic reasoning. - -use brain_types::soma::{ - SomaPacket, DeltaPhase, PacketContext, EnergyLevel, PacketHeader, PacketPayload, - PacketMetadata, OperatorCall, OperatorRegistry, register_builtin_operators, - PacketExecutor, ExecutionConfig -}; -use std::sync::Arc; -use chrono::Utc; -use uuid::Uuid; -use std::collections::HashMap; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Simple SOMA++ Cognitive Integration Demo"); - println!("==========================================\n"); - - // Set up the execution environment - let mut registry = OperatorRegistry::new(); - register_builtin_operators(&mut registry)?; - let registry = Arc::new(registry); - println!("โœ… Registered built-in operators\n"); - - let execution_config = ExecutionConfig::default(); - let executor = PacketExecutor::new(registry.clone(), execution_config); - println!("๐Ÿ”ง Created PacketExecutor for cognitive integration\n"); - - // Simulate cognitive input processing - println!("๐Ÿ—ฃ๏ธ Processing natural language input: 'What is the meaning of life?'"); - let cognitive_packet = create_cognitive_query_packet(); - println!("๐Ÿ“ฆ Created cognitive query packet: {}", cognitive_packet.metadata.id); - println!(" Task: {}", cognitive_packet.header.task); - println!(" Energy level: {:?}", cognitive_packet.context.as_ref().unwrap().energy_level); - - // Execute cognitive reasoning - println!("\n๐Ÿค” Executing cognitive reasoning..."); - match executor.execute_packet(cognitive_packet).await { - Ok(result) => { - println!("โœ… Cognitive processing successful!"); - println!(" Status: {:?}", result.status); - println!(" Duration: {}ms", result.metrics.duration_ms); - - if let Some(output_packet) = result.output_packet { - println!(" Response: Generated symbolic reasoning output"); - println!(" Output ID: {}", output_packet.metadata.id); - } - } - Err(e) => { - println!("โŒ Cognitive processing failed: {}", e); - } - } - - // Simulate conversation context processing - println!("\n๐Ÿ’ฌ Processing conversation context..."); - let conversation_packet = create_conversation_packet(); - println!("๐Ÿ“ฆ Created conversation packet: {}", conversation_packet.metadata.id); - println!(" Context source: {:?}", conversation_packet.context.as_ref().unwrap().source); - - match executor.execute_packet(conversation_packet).await { - Ok(result) => { - println!("โœ… Conversation processing successful!"); - println!(" Status: {:?}", result.status); - println!(" Duration: {}ms", result.metrics.duration_ms); - } - Err(e) => { - println!("โŒ Conversation processing failed: {}", e); - } - } - - // Simulate learning and adaptation - println!("\n๐ŸŽ“ Processing learning signal..."); - let learning_packet = create_learning_packet(); - println!("๐Ÿ“ฆ Created learning packet: {}", learning_packet.metadata.id); - println!(" Learning class: {:?}", learning_packet.context.as_ref().unwrap().task_class); - - match executor.execute_packet(learning_packet).await { - Ok(result) => { - println!("โœ… Learning processing successful!"); - println!(" Status: {:?}", result.status); - println!(" Duration: {}ms", result.metrics.duration_ms); - } - Err(e) => { - println!("โŒ Learning processing failed: {}", e); - } - } - - println!("\n๐ŸŽ‰ Cognitive Integration Demo completed!"); - println!("โœ… Successfully demonstrated:"); - println!(" โ€ข Natural language to symbolic packet conversion"); - println!(" โ€ข Cognitive reasoning through symbolic operators"); - println!(" โ€ข Conversation context processing"); - println!(" โ€ข Learning signal integration"); - println!(" โ€ข Basic cognitive-symbolic integration workflow"); - - Ok(()) -} - -/// Create a packet for cognitive query processing -fn create_cognitive_query_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 403, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Cognitive query processing".to_string(), - origin: Some("cognitive_service".to_string()), - }, - context: Some(PacketContext { - source: Some("natural_language_input".to_string()), - gaps: vec!["philosophical_reasoning".to_string()], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.8), - task_class: Some("cognitive_query".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "What is the meaning of life?".to_string(), - "User seeking philosophical insight".to_string(), - ], - outputs: vec![], - target: Some("reasoning_output".to_string()), - operator: Some(OperatorCall { - namespace: "CognitiveProcessor".to_string(), - operation: "PhilosophicalReasoning".to_string(), - parameters: { - let mut params = HashMap::new(); - params.insert("depth".to_string(), serde_json::Value::String("deep".to_string())); - params.insert("approach".to_string(), serde_json::Value::String("reflective".to_string())); - params - }, - }), - constraints: vec!["maintain_coherence".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 8, - tags: vec!["cognitive".to_string(), "philosophy".to_string(), "reasoning".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - } -} - -/// Create a packet for conversation context processing -fn create_conversation_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 200, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Conversation context processing".to_string(), - origin: Some("conversation_manager".to_string()), - }, - context: Some(PacketContext { - source: Some("user_conversation".to_string()), - gaps: vec![], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.9), - task_class: Some("conversation".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Previous message context".to_string(), - "User intent: seeking information".to_string(), - "Conversation history: 3 turns".to_string(), - ], - outputs: vec![], - target: Some("context_understanding".to_string()), - operator: Some(OperatorCall { - namespace: "ConversationProcessor".to_string(), - operation: "ContextAnalysis".to_string(), - parameters: { - let mut params = HashMap::new(); - params.insert("turn_count".to_string(), serde_json::Value::Number(serde_json::Number::from(3))); - params.insert("intent_confidence".to_string(), serde_json::Value::Number(serde_json::Number::from_f64(0.9).unwrap())); - params - }, - }), - constraints: vec!["preserve_context".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 7, - tags: vec!["conversation".to_string(), "context".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - } -} - -/// Create a packet for learning signal processing -fn create_learning_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 600, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Learning signal processing".to_string(), - origin: Some("learning_system".to_string()), - }, - context: Some(PacketContext { - source: Some("user_feedback".to_string()), - gaps: vec!["pattern_recognition".to_string()], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.7), - task_class: Some("learning".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "User provided positive feedback".to_string(), - "Response quality: high".to_string(), - "Learning signal: reinforce approach".to_string(), - ], - outputs: vec![], - target: Some("learning_update".to_string()), - operator: Some(OperatorCall { - namespace: "LearningProcessor".to_string(), - operation: "AdaptFromFeedback".to_string(), - parameters: { - let mut params = HashMap::new(); - params.insert("feedback_type".to_string(), serde_json::Value::String("positive".to_string())); - params.insert("strength".to_string(), serde_json::Value::Number(serde_json::Number::from_f64(0.8).unwrap())); - params - }, - }), - constraints: vec!["preserve_stability".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 6, - tags: vec!["learning".to_string(), "adaptation".to_string(), "feedback".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - } -} \ No newline at end of file diff --git a/soma_comprehensive_testing_demo.rs b/soma_comprehensive_testing_demo.rs deleted file mode 100644 index 160b8d258dd261ebf013de9e77693b95339a10d0..0000000000000000000000000000000000000000 --- a/soma_comprehensive_testing_demo.rs +++ /dev/null @@ -1,302 +0,0 @@ -//! SOMA++ Comprehensive Testing Suite Demonstration -//! -//! This example demonstrates the complete testing capabilities of SOMA++: -//! - Unit tests for core components -//! - Integration tests with Brain AI connections -//! - Property-based testing for packet invariants -//! - Performance benchmarks for symbolic operations -//! - End-to-end cognitive scenario testing - -use brain_types::soma::{ - SomaPacket, SomaError, OperatorRegistry, OperatorMetadata, SymbolicOperator, ValidationResult, - SomaTestConfig, PropertyTestGenerator, PerformanceBenchmark, PacketInvariant, PropertyTestRunner, - E2EScenarioRunner, SomaTestSuite -}; -use async_trait::async_trait; -use serde_json::json; -use std::sync::Arc; - -/// Example test operator for demonstration -#[derive(Debug)] -struct TestOperator { - metadata: OperatorMetadata, -} - -impl Default for TestOperator { - fn default() -> Self { - Self::new() - } -} - -impl TestOperator { - fn new() -> Self { - Self { - metadata: OperatorMetadata { - description: "Test operator for SOMA++ testing demonstration".to_string(), - input_schema: json!({ - "type": "object", - "properties": { - "test_input": {"type": "string", "description": "Test input data"} - }, - "required": ["test_input"] - }), - output_schema: json!({ - "type": "object", - "properties": { - "test_output": {"type": "string", "description": "Test output data"}, - "processing_time": {"type": "number", "description": "Processing time in milliseconds"} - } - }), - supported_phases: vec![403, 700, 701, 702], - version: "1.0.0".to_string(), - author: Some("SOMA++ Test Team".to_string()), - tags: vec!["testing".to_string(), "demo".to_string()], - } - } - } -} - -#[async_trait] -impl SymbolicOperator for TestOperator { - fn namespace(&self) -> &str { - "Test" - } - - fn name(&self) -> &str { - "PropertyTest" - } - - fn metadata(&self) -> &OperatorMetadata { - &self.metadata - } - - fn validate_input(&self, packet: &SomaPacket) -> ValidationResult { - if packet.payload.inputs.is_empty() { - ValidationResult::Invalid(vec!["Input payload is empty".to_string()]) - } else { - ValidationResult::Valid - } - } - - async fn execute(&self, mut packet: SomaPacket) -> Result { - let start_time = std::time::Instant::now(); - - // Simulate some processing - tokio::time::sleep(tokio::time::Duration::from_millis(1)).await; - - let processing_time = start_time.elapsed().as_millis() as f64; - - // Add test output - packet.payload.outputs.push(format!( - "Processed test input at phase {} in {:.2}ms", - packet.header.phase.delta, - processing_time - )); - - // Add processing metadata via parameters - packet.set_parameter( - "processing_time_ms".to_string(), - json!(processing_time) - ); - - Ok(packet) - } -} - -#[tokio::main] -async fn main() -> Result<(), SomaError> { - println!("๐Ÿงช SOMA++ Comprehensive Testing Suite Demonstration"); - println!("=================================================="); - - // Create test configuration - let test_config = SomaTestConfig { - enable_unit_tests: true, - enable_integration_tests: true, - enable_property_tests: true, - enable_performance_tests: true, - enable_e2e_tests: true, - test_timeout_ms: 30000, - property_test_iterations: 100, // Reduced for demo - performance_test_duration: 3, // 3 seconds for demo - }; - - // Set up operator registry with test operator - let mut operator_registry = OperatorRegistry::new(); - operator_registry.register_operator(Arc::new(TestOperator::new()))?; - let registry_arc = Arc::new(operator_registry); - - println!("๐Ÿ“‹ Test Configuration:"); - println!(" Property Test Iterations: {}", test_config.property_test_iterations); - println!(" Performance Test Duration: {}s", test_config.performance_test_duration); - println!(" Test Timeout: {}ms", test_config.test_timeout_ms); - println!(); - - // 1. Property-Based Testing - println!("๐Ÿ” Running Property-Based Tests..."); - let mut property_runner = PropertyTestRunner::new(test_config.clone()); - - // Add custom invariant - property_runner.add_invariant(PacketInvariant::Custom( - "HasInputsOrOutputs".to_string(), - |packet| !packet.payload.inputs.is_empty() || !packet.payload.outputs.is_empty() - )); - - let property_results = property_runner.run_packet_invariant_tests().await?; - - println!(" ๐Ÿ“Š Property Test Results:"); - println!(" Total Tests: {}", property_results.total_tests); - println!(" Passed: {}", property_results.passed_tests); - println!(" Failed: {}", property_results.failed_tests); - println!(" Success Rate: {:.1}%", property_results.success_rate() * 100.0); - println!(" Duration: {:.2}s", property_results.total_duration.as_secs_f64()); - - for result in &property_results.test_results { - let status = if result.passed { "โœ…" } else { "โŒ" }; - println!(" {} {} ({:.2}ms)", status, result.test_name, result.duration.as_millis()); - if let Some(error) = &result.error_message { - println!(" Error: {}", error); - } - } - println!(); - - // 2. Performance Benchmarking - println!("โšก Running Performance Benchmarks..."); - let mut performance_runner = PerformanceBenchmark::new(test_config.clone()); - - // Benchmark packet creation - println!(" ๐Ÿ”จ Benchmarking packet creation..."); - let creation_metrics = performance_runner.benchmark_packet_creation().await?; - println!(" Ops/sec: {:.0}", creation_metrics.ops_per_second); - println!(" Avg Latency: {:.2}ฮผs", creation_metrics.avg_latency_us); - println!(" P95 Latency: {:.2}ฮผs", creation_metrics.p95_latency_us); - println!(" P99 Latency: {:.2}ฮผs", creation_metrics.p99_latency_us); - - // Benchmark operator execution - println!(" โš™๏ธ Benchmarking operator execution..."); - let execution_metrics = performance_runner.benchmark_operator_execution(&*registry_arc).await?; - println!(" Ops/sec: {:.0}", execution_metrics.ops_per_second); - println!(" Avg Latency: {:.2}ฮผs", execution_metrics.avg_latency_us); - println!(" P95 Latency: {:.2}ฮผs", execution_metrics.p95_latency_us); - println!(" P99 Latency: {:.2}ฮผs", execution_metrics.p99_latency_us); - println!(" Error Rate: {:.2}%", execution_metrics.error_rate * 100.0); - println!(); - - // 3. End-to-End Scenario Testing - println!("๐ŸŽฏ Running End-to-End Scenario Tests..."); - let mut e2e_runner = E2EScenarioRunner::new(test_config.clone(), registry_arc.clone()); - - let e2e_results = e2e_runner.run_all_scenarios().await?; - - println!(" ๐Ÿ“Š E2E Test Results:"); - println!(" Total Scenarios: {}", e2e_results.total_tests); - println!(" Passed: {}", e2e_results.passed_tests); - println!(" Failed: {}", e2e_results.failed_tests); - println!(" Success Rate: {:.1}%", e2e_results.success_rate() * 100.0); - println!(" Duration: {:.2}s", e2e_results.total_duration.as_secs_f64()); - - for result in &e2e_results.test_results { - let status = if result.passed { "โœ…" } else { "โŒ" }; - println!(" {} {} ({:.2}ms)", status, result.test_name, result.duration.as_millis()); - if let Some(error) = &result.error_message { - println!(" Error: {}", error); - } - } - println!(); - - // 4. Comprehensive Test Suite - println!("๐Ÿงช Running Comprehensive Test Suite..."); - let mut test_suite = SomaTestSuite::new(test_config.clone(), registry_arc.clone()); - - let comprehensive_results = test_suite.run_all_tests().await?; - - println!(" ๐Ÿ“Š Comprehensive Test Results:"); - println!(" Total Tests: {}", comprehensive_results.total_tests); - println!(" Passed: {}", comprehensive_results.passed_tests); - println!(" Failed: {}", comprehensive_results.failed_tests); - println!(" Success Rate: {:.1}%", comprehensive_results.success_rate() * 100.0); - println!(" Total Duration: {:.2}s", comprehensive_results.total_duration.as_secs_f64()); - println!(); - - // 5. Test Data Generation Demo - println!("๐ŸŽฒ Testing Property Test Generator..."); - let generator = PropertyTestGenerator::new(12345); - - // Generate single packet - let test_packet = generator.generate_packet(); - println!(" ๐Ÿ“ฆ Generated Test Packet:"); - println!(" ID: {}", test_packet.id()); - println!(" Phase: ฮ”{}", test_packet.header.phase.delta); - println!(" Origin: {:?}", test_packet.header.origin); - println!(" Inputs: {:?}", test_packet.payload.inputs); - println!(" Outputs: {:?}", test_packet.payload.outputs); - - // Generate packet sequence - let packet_sequence = generator.generate_packet_sequence(3); - println!(" ๐Ÿ”— Generated Packet Sequence:"); - for (i, packet) in packet_sequence.iter().enumerate() { - println!(" {}. {} (Phase: ฮ”{})", i + 1, packet.id(), packet.header.phase.delta); - } - - // Generate operator call - let operator_call = generator.generate_operator_call(); - println!(" โš™๏ธ Generated Operator Call:"); - println!(" Namespace: {}", operator_call.namespace); - println!(" Operation: {}", operator_call.operation); - println!(" Parameters: {:?}", operator_call.parameters); - println!(); - - // 6. Custom Invariant Testing - println!("๐Ÿ” Testing Custom Packet Invariants..."); - - let test_packet = generator.generate_packet(); - - let invariants = vec![ - PacketInvariant::UniqueId, - PacketInvariant::ValidPhase, - PacketInvariant::ReasonableTimestamp, - PacketInvariant::ValidContent, - PacketInvariant::PositiveTTL, - PacketInvariant::Custom( - "HasTestData".to_string(), - |packet| packet.get_parameter("test_data").is_some() - ), - ]; - - for invariant in invariants { - let result = invariant.check(&test_packet); - let status = if result { "โœ…" } else { "โŒ" }; - println!(" {} {}", status, invariant.name()); - } - println!(); - - // Summary - let total_tests = property_results.total_tests + e2e_results.total_tests; - let total_passed = property_results.passed_tests + e2e_results.passed_tests; - let overall_success_rate = if total_tests > 0 { - total_passed as f64 / total_tests as f64 * 100.0 - } else { - 0.0 - }; - - println!("๐Ÿ“ˆ Testing Summary:"); - println!(" Overall Tests: {}", total_tests); - println!(" Overall Passed: {}", total_passed); - println!(" Overall Success Rate: {:.1}%", overall_success_rate); - println!(" Packet Creation Performance: {:.0} ops/sec", creation_metrics.ops_per_second); - println!(" Operator Execution Performance: {:.0} ops/sec", execution_metrics.ops_per_second); - - if comprehensive_results.all_passed() && overall_success_rate > 95.0 { - println!("\nโœ… SOMA++ Testing Suite Demonstration Complete!"); - println!(" All major test categories passed successfully:"); - println!(" โ€ข Property-based testing with packet invariants"); - println!(" โ€ข Performance benchmarking for operations"); - println!(" โ€ข End-to-end scenario testing"); - println!(" โ€ข Custom invariant validation"); - println!(" โ€ข Test data generation and validation"); - } else { - println!("\nโš ๏ธ Some tests failed or performance below expectations"); - println!(" Review failed tests and performance metrics above"); - } - - Ok(()) -} \ No newline at end of file diff --git a/soma_execution_demo.rs b/soma_execution_demo.rs deleted file mode 100644 index 4266bf261533e9c9aabbb3498a399809ca693f12..0000000000000000000000000000000000000000 --- a/soma_execution_demo.rs +++ /dev/null @@ -1,393 +0,0 @@ -//! SOMA++ Packet Execution Engine Demonstration -//! -//! This example demonstrates the complete SOMA++ packet execution engine including: -//! - Creating and configuring the PacketExecutor -//! - Executing individual packets with built-in operators -//! - Chain execution with dependency resolution -//! - Performance monitoring and tracing -//! - Error recovery and timeout handling - -use brain_types::soma::{ - SomaPacket, DeltaPhase, PacketContext, EnergyLevel, PacketHeader, PacketPayload, - PacketMetadata, OperatorCall, OperatorRegistry, register_builtin_operators, - PacketExecutor, ExecutionConfig -}; -use brain_types::soma::execution::RetryConfig; -use std::sync::Arc; -use std::time::Duration; -use chrono::Utc; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ SOMA++ Packet Execution Engine Demonstration"); - println!("===============================================\n"); - - // Create and configure the execution environment - let execution_config = ExecutionConfig { - max_execution_time: Duration::from_secs(30), - max_chain_depth: 10, - max_concurrent_executions: 100, - enable_tracing: true, - enable_performance_monitoring: true, - retry_config: RetryConfig { - max_attempts: 3, - base_delay: Duration::from_millis(100), - backoff_multiplier: 2.0, - max_delay: Duration::from_secs(5), - }, - }; - - // Performance thresholds are configured internally by the executor - - // Create operator registry and register built-in operators - let mut registry = OperatorRegistry::new(); - register_builtin_operators(&mut registry)?; - - println!("โœ… Registered {} operators", registry.count()); - - // Create packet executor - let executor = PacketExecutor::new( - Arc::new(registry), - execution_config, - ); - - println!("๐Ÿ”ง Created PacketExecutor with comprehensive configuration\n"); - - // Demo 1: Execute a simple reflection packet - println!("๐Ÿ“– Demo 1: Executing reflection packet (ReflectOperator::ฮ”๐Ÿชž)"); - let reflection_packet = create_reflection_packet(); - - let result1 = executor.execute_packet(reflection_packet).await?; - println!("โœ… Reflection execution: {:?}", result1.status); - println!("๐Ÿ“Š Metrics: {}ms, {} bytes memory", - result1.metrics.duration_ms, - result1.metrics.memory_usage_bytes); - println!(); - - // Demo 2: Execute a composition packet - println!("๐Ÿ”ง Demo 2: Executing composition packet (SOMA::Compose)"); - let composition_packet = create_composition_packet(); - - let result2 = executor.execute_packet(composition_packet).await?; - println!("โœ… Composition execution: {:?}", result2.status); - println!("๐Ÿ“Š Metrics: {}ms, {} bytes memory", - result2.metrics.duration_ms, - result2.metrics.memory_usage_bytes); - println!(); - - // Demo 3: Execute a memory storage packet - println!("๐Ÿ’พ Demo 3: Executing memory storage packet (MemoryLogger::Store)"); - let storage_packet = create_storage_packet(); - - let result3 = executor.execute_packet(storage_packet).await?; - println!("โœ… Storage execution: {:?}", result3.status); - println!("๐Ÿ“Š Metrics: {}ms, {} bytes memory", - result3.metrics.duration_ms, - result3.metrics.memory_usage_bytes); - println!(); - - // Demo 4: Execute an optimization packet - println!("โšก Demo 4: Executing optimization packet (SymbolicEvaluator::Optimize)"); - let optimization_packet = create_optimization_packet(); - - let result4 = executor.execute_packet(optimization_packet).await?; - println!("โœ… Optimization execution: {:?}", result4.status); - println!("๐Ÿ“Š Metrics: {}ms, {} bytes memory", - result4.metrics.duration_ms, - result4.metrics.memory_usage_bytes); - println!(); - - // Demo 5: Execute error recovery packet - println!("๐Ÿฉน Demo 5: Executing error recovery packet (ErrorRecovery::InjectDiversity)"); - let recovery_packet = create_recovery_packet(); - - let result5 = executor.execute_packet(recovery_packet).await?; - println!("โœ… Recovery execution: {:?}", result5.status); - println!("๐Ÿ“Š Metrics: {}ms, {} bytes memory", - result5.metrics.duration_ms, - result5.metrics.memory_usage_bytes); - println!(); - - // Demo 6: Execute a packet chain - println!("๐Ÿ”— Demo 6: Executing packet chain with dependencies"); - let packet_chain = create_packet_chain(); - - let chain_results = executor.execute_packet_chain(packet_chain).await?; - println!("โœ… Chain execution: {} packets processed", chain_results.len()); - for (i, result) in chain_results.iter().enumerate() { - println!(" Chain[{}]: {:?} ({}ms)", i, result.status, result.metrics.duration_ms); - } - println!(); - - // Demo 7: Performance monitoring - println!("๐Ÿ“ˆ Demo 7: Performance monitoring"); - println!("๐Ÿ“Š Individual packet metrics are available in execution results"); - println!("๐Ÿ“Š Aggregated metrics would be implemented in a monitoring service"); - - // Demo 8: Execution tracing - println!("๐Ÿ” Demo 8: Execution tracing and debugging"); - println!("๐Ÿ“ Tracing is configured in ExecutionConfig"); - println!("๐Ÿ“ Trace data would be available through dedicated tracing interface"); - - println!("\n๐ŸŽ‰ SOMA++ Packet Execution Engine demonstration completed successfully!"); - println!("โœ… All operators executed, performance monitored, and traces collected."); - - Ok(()) -} - -/// Create a reflection packet for meta-cognitive operations -fn create_reflection_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 403, - timestamp: 0.014, - }, - time_offset: 0.0, - task: "Meta-cognitive reflection analysis".to_string(), - origin: Some("DemoCreator".to_string()), - }, - context: Some(PacketContext { - source: Some("ExecutionDemo".to_string()), - gaps: vec![], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.85), - task_class: Some("reflection".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Current cognitive state assessment".to_string(), - "Meta-learning pattern analysis".to_string(), - "Self-reflection on problem-solving approach".to_string(), - ], - outputs: vec![], - target: Some("cognitive_state".to_string()), - operator: Some(OperatorCall { - namespace: "ReflectOperator".to_string(), - operation: "ฮ”๐Ÿชž".to_string(), - parameters: std::collections::HashMap::from([ - ("depth".to_string(), serde_json::json!(3)), - ("mode".to_string(), serde_json::json!("meta_analysis")), - ]), - }), - constraints: vec!["reflection_depth=3".to_string()], - }, - metadata: PacketMetadata { - id: uuid::Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 5, - tags: vec!["reflection".to_string(), "meta-cognitive".to_string()], - parent_id: None, - trace_id: None, - }, - } -} - -/// Create a composition packet for symbolic operations -fn create_composition_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 700, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Symbolic composition".to_string(), - origin: Some("DemoCreator".to_string()), - }, - context: Some(PacketContext { - source: Some("demo_user".to_string()), - gaps: vec![], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.9), - task_class: Some("composition".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Mathematical equation: f(x) = 2x + 1".to_string(), - "Optimization constraint: x > 0".to_string(), - "Domain knowledge: linear functions".to_string(), - ], - outputs: vec![], - target: Some("symbolic_output".to_string()), - operator: Some(OperatorCall { - namespace: "SOMA".to_string(), - operation: "Compose".to_string(), - parameters: std::collections::HashMap::new(), - }), - constraints: vec!["composition_type=functional".to_string()], - }, - metadata: PacketMetadata { - id: uuid::Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 7, - tags: vec!["composition".to_string(), "symbolic".to_string()], - parent_id: None, - trace_id: None, - }, - } -} - -/// Create a storage packet for memory operations -fn create_storage_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 701, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Memory storage operation".to_string(), - origin: Some("DemoCreator".to_string()), - }, - context: Some(PacketContext { - source: Some("demo_user".to_string()), - gaps: vec![], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.85), - task_class: Some("storage".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Important insight: SOMA++ enables symbolic reasoning".to_string(), - "Pattern discovered: Meta-reflection improves performance".to_string(), - "Learning: Composition creates emergent capabilities".to_string(), - ], - outputs: vec![], - target: Some("memory_store".to_string()), - operator: Some(OperatorCall { - namespace: "MemoryLogger".to_string(), - operation: "Store".to_string(), - parameters: { - let mut params = std::collections::HashMap::new(); - params.insert("key".to_string(), serde_json::Value::String("learning_session_1".to_string())); - params - }, - }), - constraints: vec!["key=learning_session_1".to_string()], - }, - metadata: PacketMetadata { - id: uuid::Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 6, - tags: vec!["storage".to_string(), "memory".to_string()], - parent_id: None, - trace_id: None, - }, - } -} - -/// Create an optimization packet for symbolic evaluation -fn create_optimization_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 702, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Symbolic optimization".to_string(), - origin: Some("DemoCreator".to_string()), - }, - context: Some(PacketContext { - source: Some("demo_user".to_string()), - gaps: vec![], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.95), - task_class: Some("optimization".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Complex expression: ((a + b) * (c + d)) + ((e * f) / (g + h))".to_string(), - "Performance constraint: minimize operations".to_string(), - "Accuracy requirement: preserve mathematical equivalence".to_string(), - ], - outputs: vec![], - target: Some("optimized_expression".to_string()), - operator: Some(OperatorCall { - namespace: "SymbolicEvaluator".to_string(), - operation: "Optimize".to_string(), - parameters: { - let mut params = std::collections::HashMap::new(); - params.insert("optimization_level".to_string(), serde_json::Value::String("aggressive".to_string())); - params - }, - }), - constraints: vec!["optimization_level=aggressive".to_string()], - }, - metadata: PacketMetadata { - id: uuid::Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 8, - tags: vec!["optimization".to_string(), "symbolic".to_string()], - parent_id: None, - trace_id: None, - }, - } -} - -/// Create an error recovery packet for failure handling -fn create_recovery_packet() -> SomaPacket { - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 703, timestamp: Utc::now().timestamp_millis() as f64 }, - time_offset: 0.0, - task: "Error recovery operation".to_string(), - origin: Some("DemoCreator".to_string()), - }, - context: Some(PacketContext { - source: Some("demo_user".to_string()), - gaps: vec!["dimension_compatibility".to_string()], - energy_level: EnergyLevel::Low, - agent_confidence: Some(0.7), - task_class: Some("recovery".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Failed operation: matrix multiplication with incompatible dimensions".to_string(), - "Error context: dimension mismatch (3x2) * (4x3)".to_string(), - "Available alternatives: transpose, reshape, or dimension padding".to_string(), - ], - outputs: vec![], - target: Some("recovery_strategies".to_string()), - operator: Some(OperatorCall { - namespace: "ErrorRecovery".to_string(), - operation: "InjectDiversity".to_string(), - parameters: { - let mut params = std::collections::HashMap::new(); - params.insert("error_type".to_string(), serde_json::Value::String("dimension_mismatch".to_string())); - params - }, - }), - constraints: vec!["error_type=dimension_mismatch".to_string()], - }, - metadata: PacketMetadata { - id: uuid::Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 9, - tags: vec!["recovery".to_string(), "error-handling".to_string()], - parent_id: None, - trace_id: None, - }, - } -} - -/// Create a packet chain for demonstrating dependency resolution -fn create_packet_chain() -> Vec { - let trace_id = uuid::Uuid::new_v4(); - - // Packet 1: Initial reflection to understand the problem - let mut packet1 = create_reflection_packet(); - packet1.metadata.trace_id = Some(trace_id); - packet1.payload.inputs = vec!["Analyze problem: optimize data processing pipeline".to_string()]; - - // Packet 2: Compose a solution based on reflection - let mut packet2 = create_composition_packet(); - packet2.metadata.trace_id = Some(trace_id); - packet2.metadata.parent_id = Some(packet1.metadata.id); - packet2.payload.inputs = vec!["Compose solution using reflection insights".to_string()]; - - // Packet 3: Store the solution in memory - let mut packet3 = create_storage_packet(); - packet3.metadata.trace_id = Some(trace_id); - packet3.metadata.parent_id = Some(packet2.metadata.id); - packet3.payload.inputs = vec!["Store optimized solution for future use".to_string()]; - - vec![packet1, packet2, packet3] -} \ No newline at end of file diff --git a/soma_execution_simple_demo.rs b/soma_execution_simple_demo.rs deleted file mode 100644 index 5ef8476ddaf48b35a73b526056d555b33c392897..0000000000000000000000000000000000000000 --- a/soma_execution_simple_demo.rs +++ /dev/null @@ -1,100 +0,0 @@ -//! Simple SOMA++ Execution Demo -//! -//! This example demonstrates basic SOMA++ packet creation and execution -//! with the current API structure. - -use brain_types::soma::{ - SomaPacket, DeltaPhase, PacketContext, EnergyLevel, PacketHeader, PacketPayload, - PacketMetadata, OperatorCall, OperatorRegistry, register_builtin_operators, - PacketExecutor, ExecutionConfig -}; -use std::sync::Arc; -use chrono::Utc; -use uuid::Uuid; -use std::collections::HashMap; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ Simple SOMA++ Execution Demo"); - println!("===============================\n"); - - // Create operator registry and register built-in operators - let mut registry = OperatorRegistry::new(); - register_builtin_operators(&mut registry)?; - println!("โœ… Registered built-in operators\n"); - - // Create packet executor with default configuration - let executor = PacketExecutor::new( - Arc::new(registry), - ExecutionConfig::default(), - ); - println!("๐Ÿ”ง Created PacketExecutor\n"); - - // Create a simple SOMA packet - let packet = create_simple_packet(); - println!("๐Ÿ“ฆ Created SOMA packet with ID: {}", packet.metadata.id); - println!(" Phase: ฮ”{}", packet.header.phase.delta); - println!(" Task: {}\n", packet.header.task); - - // Execute the packet - println!("โšก Executing packet..."); - match executor.execute_packet(packet).await { - Ok(result) => { - println!("โœ… Execution successful!"); - println!(" Status: {:?}", result.status); - println!(" Duration: {}ms", result.metrics.duration_ms); - if result.metrics.memory_usage_bytes > 0 { - println!(" Memory: {} bytes", result.metrics.memory_usage_bytes); - } - } - Err(e) => { - println!("โŒ Execution failed: {}", e); - } - } - - println!("\n๐ŸŽ‰ Demo completed!"); - Ok(()) -} - -fn create_simple_packet() -> SomaPacket { - let now = Utc::now(); - - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 700, - timestamp: 0.014, - }, - time_offset: 0.0, - task: "Simple reflection task".to_string(), - origin: Some("demo".to_string()), - }, - context: Some(PacketContext { - source: Some("simple_demo".to_string()), - gaps: vec![], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.8), - task_class: Some("reflection".to_string()), - }), - payload: PacketPayload { - inputs: vec!["Hello World".to_string()], - outputs: vec![], - target: Some("console".to_string()), - operator: Some(OperatorCall { - namespace: "ReflectOperator".to_string(), - operation: "ฮ”๐Ÿชž".to_string(), - parameters: HashMap::new(), - }), - constraints: vec![], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: 5, - tags: vec!["demo".to_string(), "simple".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - } -} \ No newline at end of file diff --git a/soma_mubrain_integration_demo.rs b/soma_mubrain_integration_demo.rs deleted file mode 100644 index f69dfed17e4966f57274a7428eec90a879d0bb0f..0000000000000000000000000000000000000000 --- a/soma_mubrain_integration_demo.rs +++ /dev/null @@ -1,244 +0,0 @@ -//! SOMA++ MuBrain Integration Demonstration -//! -//! This example demonstrates the integration between SOMA++ symbolic packets and the -//! Brain AI MuBrain symbolic planning system, including: -//! - Symbolic task generation for MuBrain operations -//! - Architecture discovery packet creation and processing -//! - Symbolic mutation strategy encoding -//! - Integration with MuBrain symbolic planning engine - -use brain_types::soma::*; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  SOMA++ MuBrain Integration Comprehensive Demonstration"); - println!("=========================================================\n"); - - // Initialize MuBrain Connector with default configuration - let connector = MuBrainConnector::new(); - println!("โœ… MuBrain Connector initialized successfully"); - - // Demonstrate symbolic task generation - demonstrate_symbolic_task_generation(&connector).await?; - - // Demonstrate architecture discovery - demonstrate_architecture_discovery(&connector).await?; - - // Demonstrate mutation strategy encoding - demonstrate_mutation_strategy_encoding(&connector).await?; - - // Demonstrate symbolic planning integration - demonstrate_symbolic_planning_integration(&connector).await?; - - // Demonstrate packet processing - demonstrate_packet_processing(&connector).await?; - - println!("\n๐ŸŽ‰ SOMA++ MuBrain Integration Demo completed successfully!"); - println!("โœ… All integration features validated"); - println!("โœ… Symbolic planning engine operational"); - println!("โœ… Architecture discovery functional"); - println!("โœ… Mutation strategies encoded properly"); - - Ok(()) -} - -async fn demonstrate_symbolic_task_generation(connector: &MuBrainConnector) -> Result<(), Box> { - println!("\n๐Ÿ”„ 1. Symbolic Task Generation for MuBrain Operations"); - println!("----------------------------------------------------"); - - // Create a sample SOMA packet for task generation - let packet = SomaPacket::new_simple( - DeltaPhase::self_reflection(), - "Optimize code generation for neural network training".to_string() - ); - - println!("๐Ÿ“ฆ Created symbolic packet: {}", packet.id()); - println!(" โ””โ”€ Phase: ฮ”{} (T+{:.3})", packet.header.phase.delta, packet.header.phase.timestamp); - println!(" โ””โ”€ Task: {}", packet.header.task); - - // Generate symbolic planning task - let planning_task = connector.generate_planning_task(&packet).await?; - println!("๐ŸŽฏ Generated symbolic planning task: {}", planning_task.id); - println!(" โ””โ”€ Task type: {:?}", planning_task.task_type); - println!(" โ””โ”€ Complexity level: {}", planning_task.planning_parameters.complexity_level); - println!(" โ””โ”€ Domain: {}", planning_task.planning_parameters.domain); - - // Demonstrate task parameters - println!("โš™๏ธ Task parameters configured:"); - for (key, value) in &planning_task.planning_parameters.context { - println!(" โ””โ”€ {}: {}", key, value); - } - - Ok(()) -} - -async fn demonstrate_architecture_discovery(connector: &MuBrainConnector) -> Result<(), Box> { - println!("\n๐Ÿ—๏ธ 2. Architecture Discovery Packet Processing"); - println!("----------------------------------------------"); - - // Create architecture analysis packet - let arch_packet = SomaPacket::new_simple( - DeltaPhase::architecture_evolution(701), - "Analyze microservices architecture for scalability patterns".to_string() - ); - - println!("๐Ÿ” Initiating architecture discovery for packet: {}", arch_packet.id()); - - // Process architecture discovery - let discovery_result = connector.discover_architecture(&arch_packet).await?; - println!("๐Ÿ“Š Architecture discovery completed:"); - println!(" โ””โ”€ Patterns identified: {}", discovery_result.discovered_patterns.len()); - println!(" โ””โ”€ Confidence score: {:.2}", discovery_result.quality_assessment.overall_score); - println!(" โ””โ”€ Analysis time: {}", discovery_result.analyzed_at.format("%H:%M:%S")); - - // Display discovered patterns - for (i, pattern) in discovery_result.discovered_patterns.iter().enumerate() { - println!(" ๐Ÿ“‹ Pattern {}: {} (Score: {:.2})", - i + 1, pattern.pattern.name, pattern.confidence); - println!(" โ””โ”€ ID: {}", pattern.pattern.id); - println!(" โ””โ”€ Description: {}", pattern.pattern.description); - if !pattern.pattern.characteristics.is_empty() { - println!(" โ””โ”€ Characteristics: {:?}", pattern.pattern.characteristics[0]); - } - } - - // Show recommendations - println!("๐Ÿ’ก Architecture recommendations:"); - for rec in &discovery_result.recommendations { - println!(" โ””โ”€ Priority {:?}: {}", rec.priority, rec.description); - } - - Ok(()) -} - -async fn demonstrate_mutation_strategy_encoding(connector: &MuBrainConnector) -> Result<(), Box> { - println!("\n๐Ÿงฌ 3. Symbolic Mutation Strategy Encoding"); - println!("----------------------------------------"); - - // Create mutation strategy packet - let mutation_packet = SomaPacket::new_simple( - DeltaPhase::architecture_evolution(702), - "Develop evolutionary algorithm for code optimization".to_string() - ); - - println!("๐Ÿงช Encoding mutation strategy for packet: {}", mutation_packet.id()); - - // Encode mutation strategy - let encoded_strategy = connector.encode_mutation_strategy(&mutation_packet).await?; - println!("๐Ÿ”ง Encoded mutation strategy: {}", encoded_strategy.name); - println!(" โ””โ”€ Operations count: {}", encoded_strategy.operations.len()); - println!(" โ””โ”€ Description: {}", encoded_strategy.description); - - // Show strategy parameters - println!(" โ””โ”€ Parameters:"); - for (key, value) in &encoded_strategy.parameters { - println!(" โ€ข {}: {}", key, value); - } - - // Show operations - println!(" โ””โ”€ Operations:"); - for (i, op) in encoded_strategy.operations.iter().enumerate() { - println!(" {}. {:?} (weight: {:.2})", i + 1, op.operation_type, op.weight); - } - - Ok(()) -} - -async fn demonstrate_symbolic_planning_integration(connector: &MuBrainConnector) -> Result<(), Box> { - println!("\n๐ŸŽฏ 4. Symbolic Planning Engine Integration"); - println!("-----------------------------------------"); - - // Create complex planning packet - let planning_packet = SomaPacket::new_simple( - DeltaPhase::architecture_evolution(703), - "Design distributed system with fault tolerance and auto-scaling".to_string() - ); - - println!("๐Ÿ“‹ Initiating symbolic planning for packet: {}", planning_packet.id()); - - // Execute symbolic planning - let planning_result = connector.execute_planning(&connector.generate_planning_task(&planning_packet).await?).await?; - println!("๐ŸŽฏ Symbolic planning completed:"); - println!(" โ””โ”€ Plan ID: {}", planning_result.id); - println!(" โ””โ”€ Total actions: {}", planning_result.actions.len()); - println!(" โ””โ”€ Success probability: {:.2}%", planning_result.confidence * 100.0); - println!(" โ””โ”€ Planning duration: {}ms", planning_result.metrics.planning_duration_ms); - - // Display planned actions - println!("๐Ÿ“ Planned actions sequence:"); - for (i, action) in planning_result.actions.iter().enumerate() { - println!(" {}. {} (Type: {:?})", i + 1, action.description, action.action_type); - println!(" โ””โ”€ Confidence: {:.2}", action.confidence); - println!(" โ””โ”€ Priority: {}", action.priority); - println!(" โ””โ”€ Expected outcome: {}", action.expected_outcome); - } - - // Show insights - println!("๐Ÿ’ก Learning insights:"); - for insight in &planning_result.insights { - println!(" โ””โ”€ {:?}: {}", insight.insight_type, insight.description); - println!(" โ””โ”€ Confidence: {:.2}", insight.confidence); - for rec in &insight.recommendations { - println!(" โ””โ”€ Recommendation: {}", rec); - } - } - - Ok(()) -} - -async fn demonstrate_packet_processing(connector: &MuBrainConnector) -> Result<(), Box> { - println!("\n๐Ÿ”— 5. SOMA++ Packet Processing with MuBrain Planning"); - println!("---------------------------------------------------"); - - // Create a series of related packets for processing - let packets = vec![ - SomaPacket::new_simple( - DeltaPhase::self_reflection(), - "Analyze current system architecture".to_string() - ), - SomaPacket::new_simple( - DeltaPhase::architecture_evolution(701), - "Optimize identified bottlenecks".to_string() - ), - SomaPacket::new_simple( - DeltaPhase::architecture_evolution(702), - "Validate optimization results".to_string() - ), - ]; - - println!("๐Ÿ”— Processing packet chain with {} packets", packets.len()); - - // Process each packet - let mut results = Vec::new(); - - for (i, packet) in packets.iter().enumerate() { - println!(" ๐Ÿ“ฆ Processing packet {}: {}", i + 1, packet.id()); - - // Process packet through MuBrain connector - let execution_result = connector.process_packet(packet.clone()).await?; - - println!(" โœ… Status: {:?}", execution_result.status); - println!(" โ””โ”€ Duration: {}ms", execution_result.metrics.duration_ms); - - // Learn from execution - connector.learn_from_execution(&execution_result).await?; - println!(" โ””โ”€ Learning applied from execution"); - - results.push(execution_result); - } - - // Show chain metrics - let total_duration: u64 = results.iter() - .map(|r| r.metrics.duration_ms) - .sum(); - let success_count = results.iter() - .filter(|r| matches!(r.status, ExecutionStatus::Success)) - .count(); - - println!("๐Ÿ“Š Chain execution metrics:"); - println!(" โ””โ”€ Total execution time: {}ms", total_duration); - println!(" โ””โ”€ Success rate: {:.1}%", (success_count as f64 / results.len() as f64) * 100.0); - println!(" โ””โ”€ Packets processed: {}", results.len()); - - Ok(()) -} \ No newline at end of file diff --git a/soma_operators_demo.rs b/soma_operators_demo.rs deleted file mode 100644 index 74c33cb4b01225c1c3c0578dd195d8dcbfda7b10..0000000000000000000000000000000000000000 --- a/soma_operators_demo.rs +++ /dev/null @@ -1,304 +0,0 @@ -//! SOMA++ Operator System Demonstration -//! -//! This example demonstrates the symbolic operator system functionality, -//! including operator registration, validation, and execution. - -use brain_types::soma::*; -use brain_types::soma::operators::ValidationResult; -use async_trait::async_trait; -use std::sync::Arc; - -/// Example reflection operator that analyzes packet contents -#[derive(Debug)] -struct ReflectOperator { - metadata: OperatorMetadata, -} - -#[async_trait] -impl SymbolicOperator for ReflectOperator { - fn namespace(&self) -> &str { - "ReflectOperator" - } - - fn name(&self) -> &str { - "ฮ”๐Ÿชž" - } - - async fn execute(&self, mut packet: SomaPacket) -> Result { - println!("๐Ÿชž Executing reflection operator on packet {}", packet.id()); - - // Add reflection analysis to the packet - packet.add_tag("reflected".to_string()); - - // Add analysis output based on inputs - for input in &packet.payload.inputs { - let reflection = format!("Reflection of '{}': {}", input, input.chars().rev().collect::()); - packet.payload.outputs.push(reflection); - } - - // Add metadata about the reflection process - packet.payload.outputs.push("Self-reflection completed".to_string()); - - println!("โœ… Reflection analysis complete"); - Ok(packet) - } - - fn validate_input(&self, packet: &SomaPacket) -> ValidationResult { - // Check if this is a self-reflection phase - if !packet.header.phase.is_self_reflection() { - return ValidationResult::Invalid(vec![ - "Reflection operator requires ฮ”403 phase".to_string() - ]); - } - - // Check if there are inputs to reflect on - if packet.payload.inputs.is_empty() { - return ValidationResult::ValidWithWarnings(vec![ - "No inputs provided for reflection".to_string() - ]); - } - - ValidationResult::Valid - } - - fn metadata(&self) -> &OperatorMetadata { - &self.metadata - } -} - -/// Example composition operator that combines multiple inputs -#[derive(Debug)] -struct ComposeOperator { - metadata: OperatorMetadata, -} - -#[async_trait] -impl SymbolicOperator for ComposeOperator { - fn namespace(&self) -> &str { - "SOMA" - } - - fn name(&self) -> &str { - "Compose" - } - - async fn execute(&self, mut packet: SomaPacket) -> Result { - println!("๐Ÿ”ง Executing composition operator on packet {}", packet.id()); - - // Compose all inputs into a single output - let composition = packet.payload.inputs.join(" โˆ˜ "); - packet.payload.outputs.clear(); - packet.payload.outputs.push(format!("Composition: {}", composition)); - - packet.add_tag("composed".to_string()); - - println!("โœ… Composition complete"); - Ok(packet) - } - - fn validate_input(&self, packet: &SomaPacket) -> ValidationResult { - if packet.payload.inputs.len() < 2 { - return ValidationResult::Invalid(vec![ - "Composition requires at least 2 inputs".to_string() - ]); - } - - ValidationResult::Valid - } - - fn metadata(&self) -> &OperatorMetadata { - &self.metadata - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ SOMA++ Operator System Demonstration"); - println!("========================================\n"); - - // Create operator registry - let mut registry = OperatorRegistry::new(); - println!("๐Ÿ“š Created empty operator registry"); - - // Create reflection operator - let reflect_op = Arc::new(ReflectOperator { - metadata: OperatorMetadata::new( - "Self-reflection operator that mirrors input content".to_string(), - vec![403], // Supports ฮ”403 phase - ) - .with_tag("reflection".to_string()) - .with_tag("introspection".to_string()) - .with_version("1.0.0".to_string()) - .with_author("SOMA++ Team".to_string()) - .with_input_schema(serde_json::json!({ - "type": "object", - "properties": { - "inputs": { - "type": "array", - "items": {"type": "string"}, - "minItems": 1 - } - } - })) - .with_output_schema(serde_json::json!({ - "type": "object", - "properties": { - "outputs": { - "type": "array", - "items": {"type": "string"} - } - } - })), - }); - - // Create composition operator - let compose_op = Arc::new(ComposeOperator { - metadata: OperatorMetadata::new( - "Composition operator that combines multiple inputs".to_string(), - vec![700, 701, 702], // Supports ฮ”700+ phases - ) - .with_tag("composition".to_string()) - .with_tag("combination".to_string()) - .with_version("1.0.0".to_string()) - .with_author("SOMA++ Team".to_string()), - }); - - // Register operators - registry.register_operator(reflect_op.clone())?; - registry.register_operator(compose_op.clone())?; - - println!("โœ… Registered {} operators:", registry.count()); - for op_name in registry.list_operators() { - println!(" โ€ข {}", op_name); - } - - // Query operators by different criteria - println!("\n๐Ÿ” Operator Discovery:"); - println!(" โ€ข Reflection operators: {:?}", registry.find_operators_by_tag("reflection")); - println!(" โ€ข Operators supporting ฮ”403: {:?}", registry.find_operators_by_phase(403)); - println!(" โ€ข Operators supporting ฮ”700: {:?}", registry.find_operators_by_phase(700)); - - // Create test packets - println!("\n๐Ÿ“ฆ Creating test packets..."); - - // Create a reflection packet (ฮ”403) - let reflection_header = PacketHeader { - phase: DeltaPhase::self_reflection(), - time_offset: 0.014, - task: "Self-reflection on system state".to_string(), - origin: Some("SOMA++ Demo".to_string()), - }; - - let reflection_payload = PacketPayload { - inputs: vec![ - "consciousness".to_string(), - "emergence".to_string(), - "intelligence".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "ReflectOperator".to_string(), - "ฮ”๐Ÿชž".to_string(), - )), - constraints: vec!["maintain_coherence".to_string()], - }; - - let mut reflection_packet = SomaPacket::new(reflection_header, reflection_payload); - reflection_packet.add_tag("demo".to_string()); - - // Create a composition packet (ฮ”700) - let composition_header = PacketHeader { - phase: DeltaPhase::architecture_evolution(700), - time_offset: 0.0, - task: "Compose system components".to_string(), - origin: Some("SOMA++ Demo".to_string()), - }; - - let composition_payload = PacketPayload { - inputs: vec![ - "neural_networks".to_string(), - "symbolic_reasoning".to_string(), - "emergent_behavior".to_string(), - ], - outputs: vec![], - target: None, - operator: Some(OperatorCall::new( - "SOMA".to_string(), - "Compose".to_string(), - )), - constraints: vec!["preserve_functionality".to_string()], - }; - - let mut composition_packet = SomaPacket::new(composition_header, composition_payload); - composition_packet.add_tag("demo".to_string()); - - // Test reflection operator - println!("\n๐Ÿชž Testing Reflection Operator"); - println!("================================"); - - let reflect_operator = registry.get_operator("ReflectOperator::ฮ”๐Ÿชž")?; - - // Validate input - let validation = reflect_operator.validate_input(&reflection_packet); - println!("Validation result: {:?}", validation); - - if validation.is_valid() { - println!("๐Ÿ“ฅ Input packet:"); - println!(" โ€ข Phase: ฮ”{}", reflection_packet.header.phase.delta); - println!(" โ€ข Task: {}", reflection_packet.header.task); - println!(" โ€ข Inputs: {:?}", reflection_packet.payload.inputs); - - // Execute operator - let result = reflect_operator.execute(reflection_packet).await?; - - println!("๐Ÿ“ค Output packet:"); - println!(" โ€ข Tags: {:?}", result.metadata.tags); - println!(" โ€ข Outputs: {:?}", result.payload.outputs); - } - - // Test composition operator - println!("\n๐Ÿ”ง Testing Composition Operator"); - println!("================================="); - - let compose_operator = registry.get_operator("SOMA::Compose")?; - - // Validate input - let validation = compose_operator.validate_input(&composition_packet); - println!("Validation result: {:?}", validation); - - if validation.is_valid() { - println!("๐Ÿ“ฅ Input packet:"); - println!(" โ€ข Phase: ฮ”{}", composition_packet.header.phase.delta); - println!(" โ€ข Task: {}", composition_packet.header.task); - println!(" โ€ข Inputs: {:?}", composition_packet.payload.inputs); - - // Execute operator - let result = compose_operator.execute(composition_packet).await?; - - println!("๐Ÿ“ค Output packet:"); - println!(" โ€ข Tags: {:?}", result.metadata.tags); - println!(" โ€ข Outputs: {:?}", result.payload.outputs); - } - - // Test operator metadata - println!("\n๐Ÿ“‹ Operator Metadata"); - println!("====================="); - - for op_name in registry.list_operators() { - let operator = registry.get_operator(&op_name)?; - let metadata = operator.metadata(); - println!("\n๐Ÿ”ง {}", op_name); - println!(" โ€ข Description: {}", metadata.description); - println!(" โ€ข Version: {}", metadata.version); - println!(" โ€ข Supported Phases: {:?}", metadata.supported_phases); - println!(" โ€ข Tags: {:?}", metadata.tags); - if let Some(author) = &metadata.author { - println!(" โ€ข Author: {}", author); - } - } - - println!("\nโœจ SOMA++ Operator System demonstration complete!"); - - Ok(()) -} \ No newline at end of file diff --git a/soma_parallel_execution_demo.rs b/soma_parallel_execution_demo.rs deleted file mode 100644 index 945229fbef6ad1b2ddea91f8425debe6470e29a6..0000000000000000000000000000000000000000 --- a/soma_parallel_execution_demo.rs +++ /dev/null @@ -1,573 +0,0 @@ -//! SOMA++ Parallel Symbolic Operation Demo -//! -//! This example demonstrates parallel symbolic operation support in SOMA++, -//! showcasing concurrent packet execution, load balancing, conflict resolution, -//! synchronization primitives, and performance monitoring for scalable symbolic processing. - -use brain_types::soma::{ - ParallelExecutor, ParallelExecutionConfig, LoadBalancingStrategy, ConflictResolutionStrategy, - BatchConstraints, ExecutionOrder, ResourceRequirements, - SyncRequirement, SyncType, - SomaPacket, DeltaPhase, PacketContext, EnergyLevel, PacketHeader, PacketPayload, - PacketMetadata, OperatorCall, OperatorRegistry, SymbolicMemoryStore, - MemoryConfig, register_builtin_operators -}; -use brain_types::soma::PacketExecutor; -use chrono::{Utc, Duration}; -use serde_json; -use std::collections::HashMap; -use std::sync::Arc; -use tokio; -use uuid::Uuid; - -async fn setup_parallel_executor() -> Result> { - println!("๐Ÿš€ Setting up SOMA++ Parallel Execution Engine"); - - // Create operator registry and register built-in operators - let mut operator_registry = OperatorRegistry::new(); - register_builtin_operators(&mut operator_registry)?; - let operator_registry = Arc::new(operator_registry); - - // Create packet executor - let packet_executor = Arc::new(PacketExecutor::new(operator_registry.clone(), Default::default())); - - // Create symbolic memory store - let memory_config = MemoryConfig { - max_packets: 2000, - max_patterns: 1000, - max_traces: 500, - retention_period: Duration::hours(2), - enable_pattern_recognition: true, - enable_consolidation: true, - consolidation_interval: Duration::minutes(15), - }; - let memory_store = Arc::new(SymbolicMemoryStore::new(memory_config)); - - // Configure parallel execution - let parallel_config = ParallelExecutionConfig { - max_concurrent_executions: 50, - thread_pool_size: 8, - operation_timeout_seconds: 180, - enable_adaptive_load_balancing: true, - enable_conflict_resolution: true, - max_queue_size: 200, - batch_size: 20, - enable_result_streaming: true, - load_balancing_strategy: LoadBalancingStrategy::Adaptive, - conflict_resolution_strategy: ConflictResolutionStrategy::MergeWithPriority, - }; - - // Create parallel executor - let executor = ParallelExecutor::new( - parallel_config, - packet_executor, - operator_registry, - memory_store, - ).await?; - - println!("โœ… Parallel executor initialized successfully"); - Ok(executor) -} - -fn create_computational_packets(count: usize, task_prefix: &str) -> Vec { - println!("๐Ÿ“ฆ Creating {} computational packets for parallel processing", count); - - let mut packets = Vec::new(); - let now = Utc::now(); - - for i in 0..count { - let complexity_level = match i % 3 { - 0 => (EnergyLevel::Low, "simple"), - 1 => (EnergyLevel::Medium, "moderate"), - _ => (EnergyLevel::High, "complex"), - }; - - let packet = SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 403 + (i % 3) as u32 * 100, // Vary phases - timestamp: i as f64 * 0.1, - }, - time_offset: i as f64 * 0.05, - task: format!("{}_task_{}", task_prefix, i), - origin: Some("parallel_demo".to_string()), - }, - context: Some(PacketContext { - source: Some(format!("generator_{}", i % 4)), - gaps: vec![format!("computation_gap_{}", i)], - energy_level: complexity_level.0, - agent_confidence: Some(0.8 - (i as f64 * 0.01)), - task_class: Some(complexity_level.1.to_string()), - }), - payload: PacketPayload { - inputs: vec![ - format!("Input data set {} for parallel processing", i), - format!("Processing parameters: complexity={}, batch={}", complexity_level.1, i / 5), - format!("Computational load: {} units", (i % 10) + 1), - ], - outputs: Vec::new(), - target: Some(format!("parallel_computation_{}", i)), - operator: Some(OperatorCall { - namespace: match i % 4 { - 0 => "ReflectOperator".to_string(), - 1 => "SOMA".to_string(), - 2 => "MemoryLogger".to_string(), - _ => "SymbolicEvaluator".to_string(), - }, - operation: match i % 4 { - 0 => "ฮ”๐Ÿชž".to_string(), - 1 => "Compose".to_string(), - 2 => "Store".to_string(), - _ => "Evaluate".to_string(), - }, - parameters: HashMap::from([ - ("complexity".to_string(), serde_json::Value::String(complexity_level.1.to_string())), - ("parallel_index".to_string(), serde_json::Value::Number(serde_json::Number::from(i))), - ("load_factor".to_string(), serde_json::Value::Number( - serde_json::Number::from_f64((i % 10) as f64 / 10.0).unwrap() - )), - ]), - }), - constraints: vec![ - format!("max_execution_time_{}s", 30 + (i % 5) * 10), - "memory_efficient".to_string(), - ], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: (8 - (i % 8)) as u8, // Vary priorities - tags: vec![ - "parallel_processing".to_string(), - complexity_level.1.to_string(), - format!("batch_{}", i / 5), - ], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - }; - - packets.push(packet); - } - - println!("โœ… Created {} packets with varied complexity and priorities", count); - packets -} - -fn create_dependency_packets() -> Vec { - println!("๐Ÿ”— Creating packets with dependencies for ordered execution"); - - let now = Utc::now(); - let base_id = Uuid::new_v4(); - - // Create a chain of dependent packets - let packets = vec![ - // Phase 1: Data preparation - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 403, timestamp: 0.0 }, - time_offset: 0.0, - task: "data_preparation".to_string(), - origin: Some("dependency_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("data_processor".to_string()), - gaps: vec!["raw_data".to_string()], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.9), - task_class: Some("preparation".to_string()), - }), - payload: PacketPayload { - inputs: vec!["Raw dataset for processing".to_string()], - outputs: Vec::new(), - target: Some("prepared_data".to_string()), - operator: Some(OperatorCall { - namespace: "MemoryLogger".to_string(), - operation: "Store".to_string(), - parameters: HashMap::from([ - ("data_type".to_string(), serde_json::Value::String("prepared".to_string())), - ]), - }), - constraints: vec!["data_integrity".to_string()], - }, - metadata: PacketMetadata { - id: base_id, - created_at: now, - modified_at: now, - priority: 9, - tags: vec!["dependency".to_string(), "preparation".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - }, - - // Phase 2: Analysis (depends on Phase 1) - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 500, timestamp: 1.0 }, - time_offset: 1.0, - task: "data_analysis".to_string(), - origin: Some("dependency_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("analyzer".to_string()), - gaps: vec!["analysis_patterns".to_string()], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.85), - task_class: Some("analysis".to_string()), - }), - payload: PacketPayload { - inputs: vec!["Prepared data from previous step".to_string()], - outputs: Vec::new(), - target: Some("analysis_results".to_string()), - operator: Some(OperatorCall { - namespace: "ReflectOperator".to_string(), - operation: "ฮ”๐Ÿชž".to_string(), - parameters: HashMap::from([ - ("analysis_depth".to_string(), serde_json::Value::String("comprehensive".to_string())), - ("depends_on".to_string(), serde_json::Value::String(base_id.to_string())), - ]), - }), - constraints: vec!["requires_prepared_data".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: 8, - tags: vec!["dependency".to_string(), "analysis".to_string()], - parent_id: Some(base_id), - trace_id: Some(Uuid::new_v4()), - }, - }, - - // Phase 3: Synthesis (depends on Phase 2) - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 700, timestamp: 2.0 }, - time_offset: 2.0, - task: "result_synthesis".to_string(), - origin: Some("dependency_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("synthesizer".to_string()), - gaps: vec!["final_integration".to_string()], - energy_level: EnergyLevel::Critical, - agent_confidence: Some(0.95), - task_class: Some("synthesis".to_string()), - }), - payload: PacketPayload { - inputs: vec!["Analysis results and patterns".to_string()], - outputs: Vec::new(), - target: Some("final_results".to_string()), - operator: Some(OperatorCall { - namespace: "SOMA".to_string(), - operation: "Compose".to_string(), - parameters: HashMap::from([ - ("synthesis_strategy".to_string(), serde_json::Value::String("comprehensive".to_string())), - ("integration_level".to_string(), serde_json::Value::String("complete".to_string())), - ]), - }), - constraints: vec!["requires_analysis_results".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: 10, - tags: vec!["dependency".to_string(), "synthesis".to_string()], - parent_id: Some(base_id), - trace_id: Some(Uuid::new_v4()), - }, - }, - ]; - - println!("โœ… Created {} dependent packets for ordered execution", packets.len()); - packets -} - -async fn demonstrate_unordered_parallel_execution(executor: &ParallelExecutor) -> Result<(), Box> { - println!("\n=== Unordered Parallel Execution Demo ==="); - - let packets = create_computational_packets(15, "unordered"); - - let batch = ParallelExecutor::create_batch( - packets, - 7, // High priority - Some(BatchConstraints { - max_execution_time: Some(Duration::minutes(2)), - execution_order: ExecutionOrder::Unordered, - resource_requirements: ResourceRequirements { - min_cpu_cores: 2, - min_memory_mb: 512, - max_network_mbps: None, - custom_resources: HashMap::new(), - }, - sync_requirements: Vec::new(), - }), - ); - - println!("๐Ÿ“Š Executing batch of {} packets in parallel (unordered)", batch.packets.len()); - let start_time = std::time::Instant::now(); - - let result = executor.execute_batch(batch).await?; - - let execution_time = start_time.elapsed(); - println!("โฑ๏ธ Execution completed in {:.2} seconds", execution_time.as_secs_f64()); - - println!("๐Ÿ“ˆ Execution Results:"); - println!(" โ€ข Total packets: {}", result.statistics.total_packets); - println!(" โ€ข Successful: {}", result.statistics.successful_executions); - println!(" โ€ข Failed: {}", result.statistics.failed_executions); - println!(" โ€ข Parallelism efficiency: {:.1}%", result.statistics.parallelism_efficiency * 100.0); - println!(" โ€ข Average execution time: {:.2}ms", result.statistics.avg_execution_time.num_milliseconds()); - - println!("๐Ÿš€ Performance Metrics:"); - println!(" โ€ข Throughput: {:.1} packets/second", result.performance_metrics.throughput); - println!(" โ€ข Load balance score: {:.1}%", result.performance_metrics.load_balance_score * 100.0); - println!(" โ€ข Resource efficiency: {:.1}%", result.performance_metrics.resource_efficiency * 100.0); - - if let Some(merged) = result.merged_result { - println!("๐Ÿ”— Merged result created with {} outputs", merged.payload.outputs.len()); - } - - println!("โœ… Unordered parallel execution completed"); - Ok(()) -} - -async fn demonstrate_ordered_parallel_execution(executor: &ParallelExecutor) -> Result<(), Box> { - println!("\n=== Ordered Parallel Execution with Dependencies ==="); - - let packets = create_dependency_packets(); - - // Define dependencies: packet 1 depends on packet 0, packet 2 depends on packet 1 - let dependencies = vec![(0, 1), (1, 2)]; - - let batch = ParallelExecutor::create_batch( - packets, - 9, // Highest priority - Some(BatchConstraints { - max_execution_time: Some(Duration::minutes(3)), - execution_order: ExecutionOrder::PartialOrder(dependencies), - resource_requirements: ResourceRequirements { - min_cpu_cores: 4, - min_memory_mb: 1024, - max_network_mbps: Some(100), - custom_resources: HashMap::from([ - ("gpu_memory".to_string(), serde_json::Value::Number(serde_json::Number::from(2048))), - ]), - }, - sync_requirements: vec![ - SyncRequirement { - sync_type: SyncType::Barrier, - participants: vec!["phase_1".to_string(), "phase_2".to_string()], - timeout: Some(Duration::seconds(30)), - }, - ], - }), - ); - - println!("๐Ÿ”— Executing batch with dependency constraints"); - println!(" Dependencies: Phase 1 โ†’ Phase 2 โ†’ Phase 3"); - - let start_time = std::time::Instant::now(); - let result = executor.execute_batch(batch).await?; - let execution_time = start_time.elapsed(); - - println!("โฑ๏ธ Ordered execution completed in {:.2} seconds", execution_time.as_secs_f64()); - - println!("๐Ÿ“Š Dependency Execution Results:"); - println!(" โ€ข Dependency chain length: {}", result.statistics.total_packets); - println!(" โ€ข Successful chain execution: {}", result.statistics.successful_executions == result.statistics.total_packets); - println!(" โ€ข Resource utilization - CPU: {:.1}%", result.statistics.resource_utilization.cpu_utilization * 100.0); - println!(" โ€ข Resource utilization - Memory: {:.1}%", result.statistics.resource_utilization.memory_utilization * 100.0); - - if !result.resolved_conflicts.is_empty() { - println!("โšก Conflicts resolved: {}", result.resolved_conflicts.len()); - for conflict in &result.resolved_conflicts { - println!(" โ€ข Conflict resolved using {:?}", conflict.resolution_strategy); - } - } - - println!("โœ… Ordered execution with dependencies completed"); - Ok(()) -} - -async fn demonstrate_load_balancing_strategies(executor: &ParallelExecutor) -> Result<(), Box> { - println!("\n=== Load Balancing Strategy Comparison ==="); - - // Test different complexity loads - let test_scenarios = vec![ - ("Uniform Load", create_computational_packets(10, "uniform")), - ("Mixed Complexity", create_computational_packets(12, "mixed")), - ("High Complexity", create_computational_packets(8, "complex")), - ]; - - for (scenario_name, packets) in test_scenarios { - println!("\n๐Ÿ”„ Testing scenario: {}", scenario_name); - - let batch = ParallelExecutor::create_batch( - packets, - 6, - None, // Use default constraints - ); - - println!(" Packets in batch: {}", batch.packets.len()); - - let start_time = std::time::Instant::now(); - let result = executor.execute_batch(batch).await?; - let execution_time = start_time.elapsed(); - - println!(" โฑ๏ธ Execution time: {:.2}s", execution_time.as_secs_f64()); - println!(" ๐Ÿ“Š Success rate: {:.1}%", - (result.statistics.successful_executions as f64 / result.statistics.total_packets as f64) * 100.0); - println!(" ๐Ÿš€ Throughput: {:.1} packets/sec", result.performance_metrics.throughput); - println!(" โš–๏ธ Load balance score: {:.1}%", result.performance_metrics.load_balance_score * 100.0); - } - - println!("โœ… Load balancing strategy comparison completed"); - Ok(()) -} - -async fn demonstrate_conflict_resolution(executor: &ParallelExecutor) -> Result<(), Box> { - println!("\n=== Conflict Resolution Demo ==="); - - // Create packets that will compete for the same resources - let conflicting_packets = vec![ - create_resource_contention_packet("shared_resource_1", "task_a", 8), - create_resource_contention_packet("shared_resource_1", "task_b", 9), // Higher priority - create_resource_contention_packet("shared_resource_1", "task_c", 7), - create_resource_contention_packet("shared_resource_2", "task_d", 8), - create_resource_contention_packet("shared_resource_2", "task_e", 8), // Same priority - ]; - - let batch = ParallelExecutor::create_batch( - conflicting_packets, - 8, - Some(BatchConstraints { - max_execution_time: Some(Duration::minutes(1)), - execution_order: ExecutionOrder::Unordered, - resource_requirements: ResourceRequirements { - min_cpu_cores: 1, - min_memory_mb: 256, - max_network_mbps: None, - custom_resources: HashMap::new(), - }, - sync_requirements: Vec::new(), - }), - ); - - println!("โšก Executing batch with potential resource conflicts"); - println!(" Resource contention scenarios: 3 tasks โ†’ resource_1, 2 tasks โ†’ resource_2"); - - let result = executor.execute_batch(batch).await?; - - println!("๐Ÿ›ก๏ธ Conflict Resolution Results:"); - println!(" โ€ข Total conflicts detected: {}", result.resolved_conflicts.len()); - println!(" โ€ข Conflict rate: {:.1}%", result.performance_metrics.conflict_rate * 100.0); - - for (i, conflict) in result.resolved_conflicts.iter().enumerate() { - println!(" Conflict {}: {:?} โ†’ {:?}", - i + 1, conflict.resolution_strategy, conflict.outcome); - } - - println!(" โ€ข Final success rate: {:.1}%", - (result.statistics.successful_executions as f64 / result.statistics.total_packets as f64) * 100.0); - - println!("โœ… Conflict resolution demonstration completed"); - Ok(()) -} - -fn create_resource_contention_packet(resource: &str, task: &str, priority: u8) -> SomaPacket { - let now = Utc::now(); - - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 403, timestamp: 0.0 }, - time_offset: 0.0, - task: task.to_string(), - origin: Some("conflict_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("resource_contender".to_string()), - gaps: vec!["resource_access".to_string()], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.8), - task_class: Some("resource_intensive".to_string()), - }), - payload: PacketPayload { - inputs: vec![format!("Access request for {}", resource)], - outputs: Vec::new(), - target: Some(resource.to_string()), - operator: Some(OperatorCall { - namespace: "ResourceManager".to_string(), - operation: "AcquireResource".to_string(), - parameters: HashMap::from([ - ("resource_name".to_string(), serde_json::Value::String(resource.to_string())), - ("exclusive_access".to_string(), serde_json::Value::Bool(true)), - ]), - }), - constraints: vec!["exclusive_resource_access".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority, - tags: vec!["conflict_test".to_string(), "resource_contention".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - } -} - -async fn display_performance_summary(_executor: &ParallelExecutor) -> Result<(), Box> { - println!("\n=== Parallel Execution Performance Summary ==="); - - println!("๐Ÿ“Š Overall Performance Statistics:"); - println!(" โ€ข Performance metrics available through internal monitoring"); - println!(" โ€ข Detailed metrics would be exposed through public accessors"); - println!(" โ€ข Current implementation shows functional parallel execution"); - - println!("\n๐ŸŽฏ Execution Efficiency:"); - println!(" โ€ข Resource efficiency: Based on adaptive load balancing"); - println!(" โ€ข Conflict resolution: Automatic priority-based resolution"); - println!(" โ€ข Scalability: Dynamic worker pool with {} threads", 8); - - println!("โœ… Performance summary completed"); - Ok(()) -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  SOMA++ Parallel Symbolic Operations Demonstration"); - println!("==================================================="); - println!("This demo showcases parallel symbolic operation support with"); - println!("concurrent execution, load balancing, conflict resolution,"); - println!("synchronization primitives, and performance monitoring.\n"); - - // Set up the parallel executor - let executor = setup_parallel_executor().await?; - - // Run demonstration scenarios - demonstrate_unordered_parallel_execution(&executor).await?; - demonstrate_ordered_parallel_execution(&executor).await?; - demonstrate_load_balancing_strategies(&executor).await?; - demonstrate_conflict_resolution(&executor).await?; - - // Display final performance summary - display_performance_summary(&executor).await?; - - println!("\n๐ŸŽ‰ SOMA++ Parallel Symbolic Operations Demo completed successfully!"); - println!("The demonstration showcased key parallel processing capabilities:"); - println!("โ€ข โœ… Concurrent packet execution with configurable parallelism"); - println!("โ€ข โœ… Multiple load balancing strategies (round-robin, least-loaded, adaptive)"); - println!("โ€ข โœ… Dependency-aware execution with partial ordering constraints"); - println!("โ€ข โœ… Automatic conflict detection and resolution mechanisms"); - println!("โ€ข โœ… Resource management and utilization optimization"); - println!("โ€ข โœ… Comprehensive performance monitoring and metrics"); - println!("โ€ข โœ… Scalable architecture supporting high-throughput symbolic processing"); - - Ok(()) -} \ No newline at end of file diff --git a/soma_phase_engine_demo.rs b/soma_phase_engine_demo.rs deleted file mode 100644 index 0cb77c3bbc31537019c8025e9bf71c38000eb626..0000000000000000000000000000000000000000 --- a/soma_phase_engine_demo.rs +++ /dev/null @@ -1,236 +0,0 @@ -//! SOMA++ Phase Engine Integration Demo -//! -//! This example demonstrates the phase engine integration functionality, -//! showing how symbolic packets can trigger phase transitions and be routed -//! based on delta phases. - -use brain_types::soma::*; -use std::sync::Arc; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ SOMA++ Phase Engine Integration Demo"); - println!("====================================="); - - // 1. Setup operator registry and packet executor - println!("\n๐Ÿ“ฆ Setting up SOMA++ system components..."); - let mut operator_registry = OperatorRegistry::new(); - let _ = register_builtin_operators(&mut operator_registry); - let operator_registry = Arc::new(operator_registry); - - println!("โœ… Registered {} operators", operator_registry.list_operators().len()); - - // Create packet executor - use brain_types::soma::execution::{PacketExecutor, ExecutionConfig}; - let execution_config = ExecutionConfig::default(); - let packet_executor = Arc::new(PacketExecutor::new( - operator_registry.clone(), - execution_config, - )); - - // 2. Create phase engine connector - println!("\n๐Ÿ”„ Creating phase engine connector..."); - let phase_config = PhaseEngineConfig::default(); - let phase_engine = PhaseEngineConnector::new( - phase_config, - operator_registry.clone(), - packet_executor, - ); - - println!("โœ… Phase engine connector created with auto-transitions enabled"); - - // 3. Test phase validation - println!("\n๐Ÿ” Testing phase validation..."); - - // Valid reflection phase - let reflection_packet = SomaPacket::new_simple( - DeltaPhase::self_reflection(), - "Analyze current system state for optimization opportunities".to_string(), - ); - - let is_valid = phase_engine.validate_packet_phase(&reflection_packet).await?; - println!("โœ… ฮ”403 reflection packet validation: {}", is_valid); - - // Valid architecture evolution phase - let evolution_packet = SomaPacket::new_simple( - DeltaPhase::architecture_evolution(700), - "Compose enhanced cognitive architecture".to_string(), - ); - - let is_valid = phase_engine.validate_packet_phase(&evolution_packet).await?; - println!("โœ… ฮ”700 evolution packet validation: {}", is_valid); - - // Invalid phase (out of range) - let invalid_packet = SomaPacket::new_simple( - DeltaPhase::new(999, 0.0), - "Invalid phase test".to_string(), - ); - - let is_valid = phase_engine.validate_packet_phase(&invalid_packet).await?; - println!("โŒ ฮ”999 invalid packet validation: {} (expected false)", is_valid); - - // 4. Test phase-based routing - println!("\n๐Ÿงญ Testing phase-based packet routing..."); - - let operator_name = phase_engine.route_packet_by_phase(&reflection_packet).await?; - println!("๐Ÿชž ฮ”403 reflection packet routed to: {}", operator_name); - - let operator_name = phase_engine.route_packet_by_phase(&evolution_packet).await?; - println!("๐Ÿ”ง ฮ”700 evolution packet routed to: {}", operator_name); - - // 5. Test phase transitions - println!("\nโšก Testing phase transitions..."); - - // Create initial phase - let initial_phase = DeltaPhase::new(100, 0.0); - let initial_packet = SomaPacket::new_simple( - initial_phase.clone(), - "Initial system state assessment".to_string(), - ); - - // Trigger transition to reflection phase - let target_phase = DeltaPhase::self_reflection(); - let transition_id = phase_engine.trigger_phase_transition( - initial_packet, - target_phase.clone(), - ).await?; - - println!("๐Ÿ”„ Phase transition triggered: ฮ”100 โ†’ ฮ”403 (ID: {})", transition_id); - - // Get transition history - let history = phase_engine.get_transition_history(5).await; - println!("๐Ÿ“œ Transition history contains {} records", history.len()); - - if let Some(record) = history.first() { - println!(" Latest: ฮ”{} โ†’ ฮ”{} [{:?}]", - record.from_phase.delta, - record.to_phase.delta, - record.status); - } - - // 6. Test phase tracking - println!("\n๐Ÿ“Š Testing phase tracking..."); - - let context = "demo_session"; - let current_phase = phase_engine.get_current_phase(context).await; - - match current_phase { - Some(phase) => println!("๐Ÿ“ Current phase for {}: ฮ”{}", context, phase.delta), - None => println!("๐Ÿ“ No current phase set for {}", context), - } - - // Get phase metrics - let metrics = phase_engine.get_phase_metrics().await; - println!("๐Ÿ“ˆ Phase metrics:"); - println!(" Total transitions: {}", metrics.total_transitions); - println!(" Phase distribution: {:?}", metrics.phase_distribution); - println!(" Success rate: {:.2}%", metrics.success_rate * 100.0); - - // 7. Advanced phase transition with validation - println!("\n๐ŸŽฏ Testing advanced phase transition validation..."); - - // Create a packet that should trigger reflection - let assessment_packet = SomaPacket::new_simple( - DeltaPhase::new(200, 0.0), - "System requires deep analysis before evolution".to_string(), - ); - - // Attempt transition to architecture evolution (should succeed) - match phase_engine.trigger_phase_transition( - assessment_packet.clone(), - DeltaPhase::architecture_evolution(701), - ).await { - Ok(transition_id) => { - println!("โœ… Valid transition ฮ”200 โ†’ ฮ”701 accepted (ID: {})", transition_id); - } - Err(e) => { - println!("โŒ Transition rejected: {}", e); - } - } - - // Attempt invalid transition (should fail with constraint) - match phase_engine.trigger_phase_transition( - assessment_packet, - DeltaPhase::new(50, 0.0), // Backwards transition - ).await { - Ok(transition_id) => { - println!("โš ๏ธ Unexpected: Invalid transition accepted (ID: {})", transition_id); - } - Err(e) => { - println!("โœ… Invalid transition properly rejected: {}", e); - } - } - - // 8. Demonstrate phase-specific operator execution - println!("\n๐Ÿ”ง Testing phase-specific operator execution..."); - - // Create reflection packet with explicit operator - let mut reflection_with_operator = SomaPacket::new_simple( - DeltaPhase::self_reflection(), - "Deep cognitive reflection on system capabilities".to_string(), - ); - - // Add reflection operator - reflection_with_operator.payload.operator = Some(OperatorCall::new( - "ReflectOperator".to_string(), - "ฮ”๐Ÿชž".to_string(), - )); - reflection_with_operator.set_parameter( - "reflection_depth".to_string(), - serde_json::json!(5) - ); - reflection_with_operator.set_parameter( - "analysis_scope".to_string(), - serde_json::json!("full_system") - ); - - // Validate this complex packet - let is_valid = phase_engine.validate_packet_phase(&reflection_with_operator).await?; - println!("๐Ÿ” Complex reflection packet validation: {}", is_valid); - - // Create composition packet for architecture evolution - let mut composition_packet = SomaPacket::new_simple( - DeltaPhase::architecture_evolution(702), - "Compose advanced multi-agent cognitive architecture".to_string(), - ); - - composition_packet.payload.operator = Some(OperatorCall::new( - "SOMA".to_string(), - "Compose".to_string(), - )); - composition_packet.set_parameter( - "composition_strategy".to_string(), - serde_json::json!("hierarchical") - ); - composition_packet.set_parameter( - "target_agents".to_string(), - serde_json::json!(["cognitive", "memory", "orchestration"]) - ); - - let is_valid = phase_engine.validate_packet_phase(&composition_packet).await?; - println!("๐Ÿ—๏ธ Complex composition packet validation: {}", is_valid); - - // 9. Final metrics summary - println!("\n๐Ÿ“‹ Final Phase Engine Demo Summary"); - println!("================================"); - - let final_metrics = phase_engine.get_phase_metrics().await; - let final_history = phase_engine.get_transition_history(10).await; - - println!("๐Ÿ“Š Phase Engine Performance:"); - println!(" โœ… Total phase transitions: {}", final_metrics.total_transitions); - println!(" ๐Ÿ“ˆ Phase distribution: {:?}", final_metrics.phase_distribution); - println!(" ๐ŸŽฏ Success rate: {:.1}%", final_metrics.success_rate * 100.0); - println!(" ๐Ÿ“œ History records: {}", final_history.len()); - - println!("\n๐ŸŽ‰ Phase Engine Integration Demo Complete!"); - println!(" โœ… Phase validation working"); - println!(" โœ… Phase-based routing operational"); - println!(" โœ… Phase transitions functioning"); - println!(" โœ… Phase tracking active"); - println!(" โœ… Operator integration validated"); - - println!("\n๐Ÿš€ SOMA++ Phase Engine is ready for production integration!"); - - Ok(()) -} \ No newline at end of file diff --git a/soma_plugin_system_demo.rs b/soma_plugin_system_demo.rs deleted file mode 100644 index f422a4f8aa5835bab711dad57ced0896df3c18da..0000000000000000000000000000000000000000 --- a/soma_plugin_system_demo.rs +++ /dev/null @@ -1,726 +0,0 @@ -//! SOMA++ Plugin System Demonstration -//! -//! This example demonstrates the extensibility features of SOMA++ through -//! the plugin system, showing custom operators, packet types, grammar -//! extensions, and DSL embedding capabilities. - -use async_trait::async_trait; -use brain_types::soma::{ - SomaPacket, SomaError, DeltaPhase, PacketHeader, PacketPayload, OperatorCall, - SymbolicOperator, OperatorRegistry, OperatorMetadata, ValidationResult, - SomaPlugin, PluginRegistry, PluginMetadata, PluginCapability, PluginInitResult, - CustomPacketType, GrammarExtension, DSLEmbeddingConfig, DSLDelimiters, DSLExecutionStrategy, - CompatibilityValidator -}; -use serde_json::json; -use std::collections::HashMap; - -/// Example custom operator for mathematical calculations -#[derive(Debug)] -struct MathOperator { - metadata: OperatorMetadata, -} - -impl Default for MathOperator { - fn default() -> Self { - Self::new() - } -} - -impl MathOperator { - fn new() -> Self { - Self { - metadata: OperatorMetadata { - description: "Performs mathematical calculations on symbolic expressions".to_string(), - input_schema: json!({ - "type": "object", - "properties": { - "expression": {"type": "string", "description": "Mathematical expression to evaluate"} - }, - "required": ["expression"] - }), - output_schema: json!({ - "type": "object", - "properties": { - "result": {"type": "number", "description": "Calculated result"}, - "expression": {"type": "string", "description": "Original expression"} - } - }), - supported_phases: vec![403, 700, 701, 702], - version: "1.0.0".to_string(), - author: Some("SOMA++ Mathematics Plugin".to_string()), - tags: vec!["math".to_string(), "calculation".to_string(), "symbolic".to_string()], - }, - } - } -} - -#[async_trait] -impl SymbolicOperator for MathOperator { - fn namespace(&self) -> &str { - "Math" - } - - fn name(&self) -> &str { - "Calculate" - } - - fn metadata(&self) -> &OperatorMetadata { - &self.metadata - } - - fn validate_input(&self, packet: &SomaPacket) -> ValidationResult { - if let Some(expr) = packet.get_parameter("expression") { - if expr.as_str().is_some() { - ValidationResult::Valid - } else { - ValidationResult::Invalid(vec![ - "Expression parameter must be a string".to_string() - ]) - } - } else { - ValidationResult::Invalid(vec![ - "Missing required parameter: expression".to_string() - ]) - } - } - - async fn execute(&self, packet: SomaPacket) -> Result { - let expression = packet.get_parameter("expression") - .and_then(|v| v.as_str()) - .ok_or_else(|| SomaError::ValidationError { - field: "expression".to_string(), - message: "Missing or invalid expression parameter".to_string(), - })?; - - // Simple mathematical evaluation (in practice, use a proper math parser) - let result = match expression { - "2 + 2" => 4.0, - "10 * 5" => 50.0, - "100 / 4" => 25.0, - _ => { - return Err(SomaError::OperatorError { - operator: self.full_name(), - message: format!("Unsupported expression: {}", expression), - context: Some("This demo only supports basic arithmetic".to_string()), - }); - } - }; - - // Create output packet - let mut output_packet = packet.clone(); - output_packet.payload.outputs = vec![format!("Result: {}", result)]; - - // Add result to operator parameters - output_packet.set_parameter("result".to_string(), json!(result)); - output_packet.set_parameter("original_expression".to_string(), json!(expression)); - - output_packet.add_tag("math_result".to_string()); - - Ok(output_packet) - } -} - -/// Example custom operator for text processing -#[derive(Debug)] -struct TextProcessorOperator { - metadata: OperatorMetadata, -} - -impl Default for TextProcessorOperator { - fn default() -> Self { - Self::new() - } -} - -impl TextProcessorOperator { - fn new() -> Self { - Self { - metadata: OperatorMetadata { - description: "Transforms text using various processing operations".to_string(), - input_schema: json!({ - "type": "object", - "properties": { - "text": {"type": "string", "description": "Text to process"}, - "operation": {"type": "string", "enum": ["uppercase", "lowercase", "reverse", "word_count"]} - }, - "required": ["text", "operation"] - }), - output_schema: json!({ - "type": "object", - "properties": { - "result": {"type": "string", "description": "Processed text"}, - "metadata": {"type": "object", "description": "Processing metadata"} - } - }), - supported_phases: vec![403, 700, 701], - version: "1.0.0".to_string(), - author: Some("SOMA++ Text Plugin".to_string()), - tags: vec!["text".to_string(), "processing".to_string(), "nlp".to_string()], - }, - } - } -} - -#[async_trait] -impl SymbolicOperator for TextProcessorOperator { - fn namespace(&self) -> &str { - "TextProcessor" - } - - fn name(&self) -> &str { - "Transform" - } - - fn metadata(&self) -> &OperatorMetadata { - &self.metadata - } - - fn validate_input(&self, packet: &SomaPacket) -> ValidationResult { - let mut errors = Vec::new(); - - if packet.get_parameter("text").and_then(|v| v.as_str()).is_none() { - errors.push("Missing or invalid text parameter".to_string()); - } - - if let Some(op) = packet.get_parameter("operation").and_then(|v| v.as_str()) { - if !["uppercase", "lowercase", "reverse", "word_count"].contains(&op) { - errors.push("Invalid operation type".to_string()); - } - } else { - errors.push("Missing or invalid operation parameter".to_string()); - } - - if errors.is_empty() { - ValidationResult::Valid - } else { - ValidationResult::Invalid(errors) - } - } - - async fn execute(&self, packet: SomaPacket) -> Result { - let text = packet.get_parameter("text") - .and_then(|v| v.as_str()) - .ok_or_else(|| SomaError::ValidationError { - field: "text".to_string(), - message: "Missing text parameter".to_string(), - })?; - - let operation = packet.get_parameter("operation") - .and_then(|v| v.as_str()) - .ok_or_else(|| SomaError::ValidationError { - field: "operation".to_string(), - message: "Missing operation parameter".to_string(), - })?; - - let (result, metadata) = match operation { - "uppercase" => (text.to_uppercase(), json!({"operation": "uppercase", "char_count": text.len()})), - "lowercase" => (text.to_lowercase(), json!({"operation": "lowercase", "char_count": text.len()})), - "reverse" => (text.chars().rev().collect(), json!({"operation": "reverse", "char_count": text.len()})), - "word_count" => { - let word_count = text.split_whitespace().count(); - (format!("Word count: {}", word_count), json!({"operation": "word_count", "count": word_count})) - }, - _ => return Err(SomaError::OperatorError { - operator: self.full_name(), - message: format!("Unsupported operation: {}", operation), - context: None, - }), - }; - - // Create output packet - let mut output_packet = packet.clone(); - output_packet.payload.outputs = vec![result.clone()]; - output_packet.set_parameter("result".to_string(), json!(result)); - output_packet.set_parameter("metadata".to_string(), metadata); - - output_packet.add_tag("text_processed".to_string()); - - Ok(output_packet) - } -} - -/// Example plugin that provides custom operators and capabilities -#[derive(Debug)] -struct MathTextPlugin { - metadata: PluginMetadata, -} - -impl MathTextPlugin { - fn new() -> Self { - Self { - metadata: PluginMetadata { - id: "math_text_plugin".to_string(), - name: "Mathematics & Text Processing Plugin".to_string(), - version: "1.0.0".to_string(), - author: "SOMA++ Demo Team".to_string(), - description: "Provides mathematical calculation and text processing capabilities".to_string(), - soma_version_range: "*".to_string(), - dependencies: vec![], - capabilities: vec![ - PluginCapability::CustomOperators, - PluginCapability::CustomPacketTypes, - PluginCapability::GrammarExtension, - PluginCapability::CustomValidation, - ], - config_schema: Some(json!({ - "type": "object", - "properties": { - "enable_math": {"type": "boolean", "default": true}, - "enable_text": {"type": "boolean", "default": true} - } - })), - }, - } - } -} - -#[async_trait] -impl SomaPlugin for MathTextPlugin { - fn metadata(&self) -> &PluginMetadata { - &self.metadata - } - - async fn initialize(&mut self, _config: Option) -> Result { - println!("๐Ÿ”Œ Initializing Math & Text Processing Plugin v{}", self.metadata.version); - Ok(PluginInitResult::Success) - } - - async fn shutdown(&mut self) -> Result<(), SomaError> { - println!("๐Ÿ”Œ Shutting down Math & Text Processing Plugin"); - Ok(()) - } - - async fn register_operators(&self, registry: &mut OperatorRegistry) -> Result<(), SomaError> { - println!("๐Ÿ“ Registering custom operators: Math::Calculate, TextProcessor::Transform"); - - registry.register_operator(std::sync::Arc::new(MathOperator::new()))?; - registry.register_operator(std::sync::Arc::new(TextProcessorOperator::new()))?; - - Ok(()) - } - - fn get_packet_types(&self) -> Vec { - vec![ - CustomPacketType { - type_name: "calculation".to_string(), - payload_schema: json!({ - "type": "object", - "properties": { - "expression": {"type": "string"}, - "precision": {"type": "integer", "default": 2} - }, - "required": ["expression"] - }), - defaults: { - let mut defaults = HashMap::new(); - defaults.insert("precision".to_string(), json!(2)); - defaults - }, - required_fields: vec!["expression".to_string()], - description: "Packet type for mathematical calculations".to_string(), - }, - CustomPacketType { - type_name: "text_processing".to_string(), - payload_schema: json!({ - "type": "object", - "properties": { - "text": {"type": "string"}, - "operation": {"type": "string", "enum": ["uppercase", "lowercase", "reverse", "word_count"]}, - "options": {"type": "object"} - }, - "required": ["text", "operation"] - }), - defaults: HashMap::new(), - required_fields: vec!["text".to_string(), "operation".to_string()], - description: "Packet type for text processing operations".to_string(), - }, - ] - } - - fn get_grammar_extensions(&self) -> Vec { - vec![ - GrammarExtension { - rule_name: "math_expression".to_string(), - rule_definition: r#"math_expr = "calc(" ~ expression ~ ")" ~ ";" -expression = term ~ (("+" | "-") ~ term)* -term = factor ~ (("*" | "/") ~ factor)* -factor = number | "(" ~ expression ~ ")" -number = ASCII_DIGIT+ ~ ("." ~ ASCII_DIGIT+)?"#.to_string(), - description: "Grammar for inline mathematical expressions".to_string(), - examples: vec![ - "calc(2 + 3 * 4);".to_string(), - "calc((10 + 5) / 3);".to_string(), - ], - }, - GrammarExtension { - rule_name: "text_transform".to_string(), - rule_definition: r#"text_transform = "transform(" ~ text_literal ~ "," ~ operation ~ ")" ~ ";" -text_literal = "\"" ~ (!"\"" ~ ANY)* ~ "\"" -operation = "upper" | "lower" | "reverse" | "count""#.to_string(), - description: "Grammar for text transformation operations".to_string(), - examples: vec![ - r#"transform("Hello World", upper);"#.to_string(), - r#"transform("Count my words", count);"#.to_string(), - ], - }, - ] - } - - async fn validate_packet(&self, packet: &SomaPacket) -> Result { - let mut warnings = Vec::new(); - - // Check for mathematical expressions in inputs - for input in &packet.payload.inputs { - if input.contains("calc(") && !input.ends_with(';') { - warnings.push("Mathematical expressions should end with semicolon".to_string()); - } - } - - // Check for text transformation syntax - for input in &packet.payload.inputs { - if input.contains("transform(") && !input.contains(',') { - warnings.push("Text transformations require operation parameter".to_string()); - } - } - - if warnings.is_empty() { - Ok(brain_types::soma::plugins::ValidationResult::Valid) - } else { - Ok(brain_types::soma::plugins::ValidationResult::ValidWithWarnings(warnings)) - } - } - - async fn pre_execution_hook(&self, packet: &SomaPacket) -> Result, SomaError> { - // Log packet preprocessing - println!("๐Ÿ” Pre-processing packet {} with Math & Text Plugin", packet.id()); - - // Add plugin tracking metadata - let mut modified_packet = packet.clone(); - modified_packet.add_tag("math_text_plugin_processed".to_string()); - - Ok(Some(modified_packet)) - } - - async fn post_execution_hook(&self, packet: &SomaPacket, result: &brain_types::soma::plugins::ExecutionResult) -> Result<(), SomaError> { - // Log execution completion - println!("โœ… Post-processing packet {} - Success: {}", packet.id(), result.success); - - if result.success { - // Could perform additional logging, metrics collection, etc. - println!("๐Ÿ“Š Execution completed successfully"); - } else { - println!("โŒ Execution failed: {:?}", result.error); - } - - Ok(()) - } -} - -/// DSL Plugin for embedding Python-like syntax -#[derive(Debug)] -struct PythonDSLPlugin { - metadata: PluginMetadata, -} - -impl PythonDSLPlugin { - fn new() -> Self { - Self { - metadata: PluginMetadata { - id: "python_dsl_plugin".to_string(), - name: "Python DSL Embedding Plugin".to_string(), - version: "1.0.0".to_string(), - author: "SOMA++ DSL Team".to_string(), - description: "Embeds Python-like syntax within SOMA++ packets".to_string(), - soma_version_range: "*".to_string(), - dependencies: vec![], - capabilities: vec![ - PluginCapability::DSLEmbedding { language: "python".to_string() }, - PluginCapability::GrammarExtension, - ], - config_schema: None, - }, - } - } -} - -#[async_trait] -impl SomaPlugin for PythonDSLPlugin { - fn metadata(&self) -> &PluginMetadata { - &self.metadata - } - - async fn initialize(&mut self, _config: Option) -> Result { - println!("๐Ÿ Initializing Python DSL Embedding Plugin"); - Ok(PluginInitResult::Success) - } - - async fn shutdown(&mut self) -> Result<(), SomaError> { - println!("๐Ÿ Shutting down Python DSL Plugin"); - Ok(()) - } - - fn get_dsl_embedding(&self) -> Option { - Some(DSLEmbeddingConfig { - language_name: "python".to_string(), - grammar_rules: vec![ - GrammarExtension { - rule_name: "python_block".to_string(), - rule_definition: r#"python_block = "```python" ~ NEWLINE ~ python_code ~ NEWLINE ~ "```" -python_code = (!"```" ~ ANY)*"#.to_string(), - description: "Python code block embedded in SOMA++ packets".to_string(), - examples: vec![ - "```python\nresult = x + y\nprint(result)\n```".to_string(), - ], - }, - ], - delimiters: DSLDelimiters { - open: "```python".to_string(), - close: "```".to_string(), - inline: Some("`python:`".to_string()), - }, - execution_strategy: DSLExecutionStrategy::Transform { - target_operator: "PythonExecutor::Run".to_string(), - }, - }) - } - - fn get_grammar_extensions(&self) -> Vec { - vec![ - GrammarExtension { - rule_name: "embedded_python".to_string(), - rule_definition: r#"embedded_python = python_block | inline_python -python_block = "```python" ~ NEWLINE ~ python_code ~ NEWLINE ~ "```" -inline_python = "`python:" ~ python_expr ~ "`" -python_code = (!"```" ~ ANY)* -python_expr = (!"`" ~ ANY)+"#.to_string(), - description: "Python embedding grammar for SOMA++ packets".to_string(), - examples: vec![ - "```python\nresult = calculate(x, y)\n```".to_string(), - "`python:x + y * 2`".to_string(), - ], - }, - ] - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ SOMA++ Plugin System Demonstration"); - println!("====================================="); - - // Create compatibility validator - let compatibility = CompatibilityValidator::new("1.0.0".to_string()); - - // Create plugin registry - let mut plugin_registry = PluginRegistry::new(); - - // Create operator registry - let mut operator_registry = OperatorRegistry::new(); - - println!("\n๐Ÿ“ฆ Registering plugins..."); - - // Register Math & Text Plugin - let math_text_plugin = Box::new(MathTextPlugin::new()); - - // Validate compatibility - compatibility.validate_plugin_compatibility(math_text_plugin.metadata())?; - println!("โœ… Math & Text Plugin compatibility validated"); - - plugin_registry.register_plugin(math_text_plugin).await?; - - // Register Python DSL Plugin - let python_plugin = Box::new(PythonDSLPlugin::new()); - compatibility.validate_plugin_compatibility(python_plugin.metadata())?; - println!("โœ… Python DSL Plugin compatibility validated"); - - plugin_registry.register_plugin(python_plugin).await?; - - // Register operators from all plugins - plugin_registry.register_all_operators(&mut operator_registry).await?; - - println!("\n๐Ÿ” Plugin registry information:"); - let plugins = plugin_registry.list_plugins(); - for plugin in plugins { - println!(" ๐Ÿ“‹ {} v{} by {}", plugin.name, plugin.version, plugin.author); - println!(" Capabilities: {:?}", plugin.capabilities); - } - - println!("\n๐Ÿ“ Custom packet types:"); - let custom_types = plugin_registry.get_all_custom_packet_types(); - for packet_type in custom_types { - println!(" ๐ŸŽฏ {} - {}", packet_type.type_name, packet_type.description); - } - - println!("\n๐Ÿ”ค Grammar extensions:"); - let grammar_extensions = plugin_registry.get_all_grammar_extensions(); - for extension in grammar_extensions { - println!(" ๐Ÿ“ {} - {}", extension.rule_name, extension.description); - if !extension.examples.is_empty() { - println!(" Example: {}", extension.examples[0]); - } - } - - println!("\n๐ŸŒ DSL embeddings:"); - let dsl_configs = plugin_registry.get_all_dsl_embeddings(); - for config in dsl_configs { - println!(" ๐Ÿ {} - Delimiters: {} ... {}", - config.language_name, config.delimiters.open, config.delimiters.close); - } - - println!("\n๐Ÿงฎ Testing Mathematical Operation:"); - - // Create a math calculation packet - let math_packet = create_math_packet("2 + 2")?; - - // Validate with plugins - let validation_result = plugin_registry.validate_packet_with_plugins(&math_packet).await?; - println!(" Validation: {:?}", validation_result); - - // Execute pre-hooks - let processed_packet = plugin_registry.execute_pre_hooks(&math_packet).await? - .unwrap_or(math_packet.clone()); - - // Execute the math operator - match operator_registry.get_operator("Math::Calculate") { - Ok(math_operator) => { - println!(" ๐Ÿ”ข Executing Math::Calculate operator"); - let result_packet = math_operator.execute(processed_packet.clone()).await?; - - let execution_result = brain_types::soma::plugins::ExecutionResult { - success: true, - output: Some(result_packet.clone()), - error: None, - metrics: HashMap::new(), - }; - - plugin_registry.execute_post_hooks(&processed_packet, &execution_result).await?; - - println!(" โœ… Math result: {:?}", result_packet.payload.outputs); - if let Some(result_value) = result_packet.get_parameter("result") { - println!(" ๐Ÿ“Š Calculated value: {}", result_value); - } - } - Err(e) => { - println!(" โŒ Math operator not found: {:?}", e); - } - } - - println!("\n๐Ÿ“ Testing Text Processing Operation:"); - - // Create a text processing packet - let text_packet = create_text_packet("Hello SOMA++ World", "uppercase")?; - - // Validate and process - let validation_result = plugin_registry.validate_packet_with_plugins(&text_packet).await?; - println!(" Validation: {:?}", validation_result); - - let processed_packet = plugin_registry.execute_pre_hooks(&text_packet).await? - .unwrap_or(text_packet.clone()); - - // Execute the text processor operator - match operator_registry.get_operator("TextProcessor::Transform") { - Ok(text_operator) => { - println!(" ๐Ÿ”ค Executing TextProcessor::Transform operator"); - let result_packet = text_operator.execute(processed_packet.clone()).await?; - - let execution_result = brain_types::soma::plugins::ExecutionResult { - success: true, - output: Some(result_packet.clone()), - error: None, - metrics: HashMap::new(), - }; - - plugin_registry.execute_post_hooks(&processed_packet, &execution_result).await?; - - println!(" โœ… Text result: {:?}", result_packet.payload.outputs); - if let Some(metadata) = result_packet.get_parameter("metadata") { - println!(" ๐Ÿ“‹ Processing metadata: {}", metadata); - } - } - Err(e) => { - println!(" โŒ Text operator not found: {:?}", e); - } - } - - println!("\n๐ŸŽฏ Plugin Capabilities by Type:"); - for capability in [ - PluginCapability::CustomOperators, - PluginCapability::CustomPacketTypes, - PluginCapability::GrammarExtension, - PluginCapability::DSLEmbedding { language: "python".to_string() }, - ] { - let plugins = plugin_registry.get_plugins_by_capability(&capability); - println!(" {:?}: {} plugin(s)", capability, plugins.len()); - for plugin_id in plugins { - println!(" - {}", plugin_id); - } - } - - println!("\nโœ… SOMA++ Plugin System Demonstration Complete!"); - println!(" The plugin system successfully:"); - println!(" โ€ข Registered custom operators (Math::Calculate, TextProcessor::Transform)"); - println!(" โ€ข Defined custom packet types (calculation, text_processing)"); - println!(" โ€ข Extended grammar with mathematical and text processing syntax"); - println!(" โ€ข Embedded Python DSL capabilities"); - println!(" โ€ข Validated plugin compatibility"); - println!(" โ€ข Executed pre/post-processing hooks"); - println!(" โ€ข Demonstrated real operator execution with plugin integration"); - - Ok(()) -} - -/// Helper function to create a mathematical calculation packet -fn create_math_packet(expression: &str) -> Result { - let header = PacketHeader { - phase: DeltaPhase::new(403, 0.0), - time_offset: 0.0, - task: "Mathematical Calculation".to_string(), - origin: Some("plugin_demo".to_string()), - }; - - let mut operator_params = HashMap::new(); - operator_params.insert("expression".to_string(), json!(expression)); - - let payload = PacketPayload { - inputs: vec![format!("Calculate: {}", expression)], - outputs: vec![], - target: None, - operator: Some(OperatorCall::with_parameters( - "Math".to_string(), - "Calculate".to_string(), - operator_params, - )), - constraints: vec![], - }; - - Ok(SomaPacket::new(header, payload)) -} - -/// Helper function to create a text processing packet -fn create_text_packet(text: &str, operation: &str) -> Result { - let header = PacketHeader { - phase: DeltaPhase::new(403, 0.0), - time_offset: 0.0, - task: "Text Processing".to_string(), - origin: Some("plugin_demo".to_string()), - }; - - let mut operator_params = HashMap::new(); - operator_params.insert("text".to_string(), json!(text)); - operator_params.insert("operation".to_string(), json!(operation)); - - let payload = PacketPayload { - inputs: vec![format!("Process: {} with {}", text, operation)], - outputs: vec![], - target: None, - operator: Some(OperatorCall::with_parameters( - "TextProcessor".to_string(), - "Transform".to_string(), - operator_params, - )), - constraints: vec![], - }; - - Ok(SomaPacket::new(header, payload)) -} \ No newline at end of file diff --git a/soma_recursive_feedback_demo.rs b/soma_recursive_feedback_demo.rs deleted file mode 100644 index b482c12eef5f5e71137fcc9c80d2bb156cbb5545..0000000000000000000000000000000000000000 --- a/soma_recursive_feedback_demo.rs +++ /dev/null @@ -1,537 +0,0 @@ -//! SOMA++ Recursive Feedback Loops Demo -//! -//! This example demonstrates recursive cognitive feedback loops in SOMA++, -//! showing output-to-input chaining, cycle detection, autonomous reasoning, -//! and continuous packet processing for dynamic problem solving. - -use brain_types::soma::{ - RecursiveFeedbackLoopEngine, FeedbackLoopConfig, ErrorPropagationStrategy, - SomaPacket, DeltaPhase, PacketContext, EnergyLevel, PacketHeader, PacketPayload, - PacketMetadata, OperatorCall, OperatorRegistry, SymbolicMemoryStore, - MemoryConfig, register_builtin_operators, FeedbackLoopStatus -}; -use brain_types::soma::PacketExecutor; -use chrono::{Utc, Duration}; -use serde_json; -use std::collections::HashMap; -use std::sync::Arc; -use tokio; -use uuid::Uuid; - -async fn setup_feedback_loop_engine() -> Result> { - println!("๐Ÿš€ Setting up SOMA++ Recursive Feedback Loop Engine"); - - // Create operator registry and register built-in operators - let mut operator_registry = OperatorRegistry::new(); - register_builtin_operators(&mut operator_registry)?; - let operator_registry = Arc::new(operator_registry); - - // Create packet executor - let packet_executor = Arc::new(PacketExecutor::new(operator_registry.clone(), Default::default())); - - // Create symbolic memory store - let memory_config = MemoryConfig { - max_packets: 1000, - max_patterns: 500, - max_traces: 200, - retention_period: Duration::hours(1), - enable_pattern_recognition: true, - enable_consolidation: true, - consolidation_interval: Duration::minutes(10), - }; - let memory_store = Arc::new(SymbolicMemoryStore::new(memory_config)); - - // Configure feedback loops - let feedback_config = FeedbackLoopConfig { - max_recursion_depth: 15, - max_iterations: 50, - execution_timeout_seconds: 120, - enable_cycle_detection: true, - enable_loop_optimization: true, - enable_autonomous_reasoning: true, - performance_monitor_interval: 5, - error_propagation_strategy: ErrorPropagationStrategy::ContinueWithRecovery, - }; - - // Create feedback loop engine - let engine = RecursiveFeedbackLoopEngine::new( - feedback_config, - packet_executor, - operator_registry, - memory_store, - ).await?; - - println!("โœ… Feedback loop engine initialized successfully"); - Ok(engine) -} - -fn create_problem_solving_packets() -> Vec { - println!("๐Ÿงฉ Creating problem-solving packet chain"); - - let now = Utc::now(); - let base_id = Uuid::new_v4(); - - // Initial problem analysis packet - let analysis_packet = SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 403, - timestamp: 0.0, - }, - time_offset: 0.0, - task: "problem_analysis".to_string(), - origin: Some("feedback_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("problem_solver".to_string()), - gaps: vec!["solution_approach".to_string()], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.8), - task_class: Some("analysis".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "How can we optimize a complex system with multiple variables?".to_string(), - "Current constraints: performance, scalability, maintainability".to_string(), - ], - outputs: Vec::new(), - target: Some("optimization_analysis".to_string()), - operator: Some(OperatorCall { - namespace: "ReflectOperator".to_string(), - operation: "ฮ”๐Ÿชž".to_string(), - parameters: HashMap::from([ - ("reflection_target".to_string(), serde_json::Value::String("system_optimization".to_string())), - ("depth".to_string(), serde_json::Value::String("comprehensive".to_string())), - ("focus_areas".to_string(), serde_json::Value::Array(vec![ - serde_json::Value::String("performance".to_string()), - serde_json::Value::String("scalability".to_string()), - serde_json::Value::String("maintainability".to_string()), - ])), - ]), - }), - constraints: vec!["real_world_applicable".to_string()], - }, - metadata: PacketMetadata { - id: base_id, - created_at: now, - modified_at: now, - priority: 8, - tags: vec!["problem_solving".to_string(), "optimization".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - }; - - // Solution synthesis packet - let synthesis_packet = SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 700, - timestamp: 1.0, - }, - time_offset: 1.0, - task: "solution_synthesis".to_string(), - origin: Some("feedback_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("solution_generator".to_string()), - gaps: vec!["implementation_details".to_string()], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.75), - task_class: Some("synthesis".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Analysis results from previous iteration".to_string(), - "Optimization patterns and strategies".to_string(), - ], - outputs: Vec::new(), - target: Some("solution_composition".to_string()), - operator: Some(OperatorCall { - namespace: "SOMA".to_string(), - operation: "Compose".to_string(), - parameters: HashMap::from([ - ("composition_strategy".to_string(), serde_json::Value::String("iterative_refinement".to_string())), - ("integration_approach".to_string(), serde_json::Value::String("modular".to_string())), - ]), - }), - constraints: vec!["maintainable_architecture".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: 7, - tags: vec!["solution".to_string(), "synthesis".to_string()], - parent_id: Some(base_id), - trace_id: Some(Uuid::new_v4()), - }, - }; - - vec![analysis_packet, synthesis_packet] -} - -fn create_learning_packets() -> Vec { - println!("๐Ÿ“š Creating learning and adaptation packet chain"); - - let now = Utc::now(); - - // Meta-learning packet - let learning_packet = SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 403, - timestamp: 0.0, - }, - time_offset: 0.0, - task: "meta_learning".to_string(), - origin: Some("learning_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("meta_learner".to_string()), - gaps: vec!["learning_effectiveness".to_string()], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.9), - task_class: Some("meta_learning".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "What patterns are most effective for recursive learning?".to_string(), - "How can feedback loops improve their own performance?".to_string(), - ], - outputs: Vec::new(), - target: Some("learning_optimization".to_string()), - operator: Some(OperatorCall { - namespace: "MemoryLogger".to_string(), - operation: "Store".to_string(), - parameters: HashMap::from([ - ("memory_type".to_string(), serde_json::Value::String("meta_learning".to_string())), - ("retention_priority".to_string(), serde_json::Value::String("high".to_string())), - ]), - }), - constraints: vec!["self_improving".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: 9, - tags: vec!["learning".to_string(), "meta_cognition".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - }; - - vec![learning_packet] -} - -fn create_error_recovery_packets() -> Vec { - println!("๐Ÿ› ๏ธ Creating error recovery and diversity injection packets"); - - let now = Utc::now(); - - // Error diversity packet - let diversity_packet = SomaPacket { - header: PacketHeader { - phase: DeltaPhase { - delta: 999, // Special error handling phase - timestamp: 0.0, - }, - time_offset: 0.0, - task: "error_diversity_injection".to_string(), - origin: Some("error_recovery_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("error_handler".to_string()), - gaps: vec!["alternative_approaches".to_string()], - energy_level: EnergyLevel::Critical, - agent_confidence: Some(0.6), - task_class: Some("error_recovery".to_string()), - }), - payload: PacketPayload { - inputs: vec![ - "Previous approach failed - need alternative strategy".to_string(), - "Error context: optimization loop stuck in local minimum".to_string(), - ], - outputs: Vec::new(), - target: Some("diversity_injection".to_string()), - operator: Some(OperatorCall { - namespace: "ErrorRecovery".to_string(), - operation: "InjectDiversity".to_string(), - parameters: HashMap::from([ - ("diversity_strategy".to_string(), serde_json::Value::String("random_perturbation".to_string())), - ("exploration_factor".to_string(), serde_json::Value::Number(serde_json::Number::from_f64(0.3).unwrap())), - ]), - }), - constraints: vec!["maintain_core_objectives".to_string()], - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: now, - modified_at: now, - priority: 10, // Maximum priority for error recovery - tags: vec!["error_recovery".to_string(), "diversity".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - }; - - vec![diversity_packet] -} - -async fn demonstrate_basic_feedback_loop(engine: &RecursiveFeedbackLoopEngine) -> Result<(), Box> { - println!("\n=== Basic Feedback Loop Demonstration ==="); - - let initial_packets = create_problem_solving_packets(); - println!("๐Ÿ“ค Starting feedback loop with {} initial packets", initial_packets.len()); - - let loop_id = engine.start_feedback_loop( - initial_packets, - Some("Demonstrate basic recursive problem solving".to_string()), - ).await?; - - println!("๐Ÿ”„ Feedback loop started with ID: {}", loop_id); - - // Monitor the loop for a short time - for i in 0..10 { - tokio::time::sleep(tokio::time::Duration::from_millis(500)).await; - - if let Some(state) = engine.get_loop_status(loop_id).await { - println!(" Iteration {}: Status={:?}, Packets={}, Errors={}", - i + 1, state.status, state.active_packets.len(), state.error_history.len()); - - if matches!(state.status, FeedbackLoopStatus::Completed | - FeedbackLoopStatus::Timeout | - FeedbackLoopStatus::Error(_) | - FeedbackLoopStatus::CycleDetected | - FeedbackLoopStatus::MaxIterationsReached) { - println!("๐Ÿ Loop completed with status: {:?}", state.status); - break; - } - } else { - println!("โŒ Loop status not found"); - break; - } - } - - println!("โœ… Basic feedback loop demonstration completed"); - Ok(()) -} - -async fn demonstrate_learning_feedback_loop(engine: &RecursiveFeedbackLoopEngine) -> Result<(), Box> { - println!("\n=== Learning and Adaptation Feedback Loop ==="); - - let learning_packets = create_learning_packets(); - println!("๐Ÿง  Starting learning-focused feedback loop"); - - let loop_id = engine.start_feedback_loop( - learning_packets, - Some("Demonstrate recursive learning and adaptation".to_string()), - ).await?; - - println!("๐Ÿ”„ Learning loop started with ID: {}", loop_id); - - // Monitor the learning loop - for i in 0..8 { - tokio::time::sleep(tokio::time::Duration::from_millis(750)).await; - - if let Some(state) = engine.get_loop_status(loop_id).await { - println!(" Learning cycle {}: Iteration={}, Processed={}, Avg_time={:.1}ms", - i + 1, state.current_iteration, state.metrics.packets_processed, - state.metrics.avg_iteration_time_ms); - - if matches!(state.status, FeedbackLoopStatus::Completed | - FeedbackLoopStatus::Timeout | - FeedbackLoopStatus::Error(_)) { - break; - } - } - } - - println!("โœ… Learning feedback loop demonstration completed"); - Ok(()) -} - -async fn demonstrate_error_recovery_loop(engine: &RecursiveFeedbackLoopEngine) -> Result<(), Box> { - println!("\n=== Error Recovery and Diversity Injection ==="); - - let error_packets = create_error_recovery_packets(); - println!("๐Ÿ› ๏ธ Starting error recovery feedback loop"); - - let loop_id = engine.start_feedback_loop( - error_packets, - Some("Demonstrate error recovery with diversity injection".to_string()), - ).await?; - - println!("๐Ÿ”„ Error recovery loop started with ID: {}", loop_id); - - // Monitor the error recovery loop - for i in 0..6 { - tokio::time::sleep(tokio::time::Duration::from_millis(600)).await; - - if let Some(state) = engine.get_loop_status(loop_id).await { - println!(" Recovery attempt {}: Status={:?}, Success_rate={:.2}", - i + 1, state.status, state.metrics.success_rate); - - if !state.error_history.is_empty() { - let last_error = &state.error_history[state.error_history.len() - 1]; - println!(" Last error: {} (iteration {})", - last_error.message, last_error.iteration); - } - - if matches!(state.status, FeedbackLoopStatus::Completed | - FeedbackLoopStatus::Error(_)) { - break; - } - } - } - - println!("โœ… Error recovery demonstration completed"); - Ok(()) -} - -async fn demonstrate_cycle_detection(engine: &RecursiveFeedbackLoopEngine) -> Result<(), Box> { - println!("\n=== Cycle Detection Demonstration ==="); - - // Create packets that will likely create a cycle - let cycle_packets = vec![ - SomaPacket { - header: PacketHeader { - phase: DeltaPhase { delta: 403, timestamp: 0.0 }, - time_offset: 0.0, - task: "cyclic_task".to_string(), - origin: Some("cycle_demo".to_string()), - }, - context: Some(PacketContext { - source: Some("cycle_generator".to_string()), - gaps: vec!["endless_loop".to_string()], - energy_level: EnergyLevel::Medium, - agent_confidence: Some(0.5), - task_class: Some("cyclic".to_string()), - }), - payload: PacketPayload { - inputs: vec!["Same input that will generate same output".to_string()], - outputs: Vec::new(), - target: Some("cycle_target".to_string()), - operator: Some(OperatorCall { - namespace: "ReflectOperator".to_string(), - operation: "ฮ”๐Ÿชž".to_string(), - parameters: HashMap::from([ - ("reflection_target".to_string(), serde_json::Value::String("same_target".to_string())), - ]), - }), - constraints: Vec::new(), - }, - metadata: PacketMetadata { - id: Uuid::new_v4(), - created_at: Utc::now(), - modified_at: Utc::now(), - priority: 5, - tags: vec!["cycle_test".to_string()], - parent_id: None, - trace_id: Some(Uuid::new_v4()), - }, - } - ]; - - println!("๐Ÿ”„ Starting potentially cyclic feedback loop"); - - let loop_id = engine.start_feedback_loop( - cycle_packets, - Some("Demonstrate cycle detection capabilities".to_string()), - ).await?; - - println!("โš ๏ธ Monitoring for cycle detection..."); - - // Monitor until cycle is detected or timeout - for i in 0..15 { - tokio::time::sleep(tokio::time::Duration::from_millis(400)).await; - - if let Some(state) = engine.get_loop_status(loop_id).await { - println!(" Check {}: Iteration={}, History_size={}, Status={:?}", - i + 1, state.current_iteration, state.packet_history.len(), state.status); - - if matches!(state.status, FeedbackLoopStatus::CycleDetected) { - println!("๐ŸŽฏ Cycle successfully detected!"); - break; - } - - if matches!(state.status, FeedbackLoopStatus::Completed | - FeedbackLoopStatus::Timeout | - FeedbackLoopStatus::Error(_)) { - break; - } - } - } - - println!("โœ… Cycle detection demonstration completed"); - Ok(()) -} - -async fn display_performance_metrics(engine: &RecursiveFeedbackLoopEngine) -> Result<(), Box> { - println!("\n=== Feedback Loop Performance Metrics ==="); - - let metrics = engine.get_performance_metrics().await; - - println!("๐Ÿ“Š Overall Performance Statistics:"); - println!(" โ€ข Total loops executed: {}", metrics.total_loops_executed); - println!(" โ€ข Average loop duration: {:.2} seconds", - metrics.avg_loop_duration.num_milliseconds() as f64 / 1000.0); - println!(" โ€ข Overall success rate: {:.1}%", metrics.overall_success_rate * 100.0); - - println!("\n๐Ÿ Termination Reasons:"); - for (reason, count) in &metrics.termination_reasons { - println!(" โ€ข {}: {} times", reason, count); - } - - if !metrics.performance_trends.is_empty() { - println!("\n๐Ÿ“ˆ Performance Trends:"); - let recent_trends = &metrics.performance_trends[metrics.performance_trends.len().saturating_sub(5)..]; - for (i, trend) in recent_trends.iter().enumerate() { - println!(" Trend {}: Avg_time={:.1}ms, Success={:.1}%, Active={}", - i + 1, - trend.avg_iteration_time.num_milliseconds() as f64, - trend.success_rate * 100.0, - trend.active_loops_count); - } - } - - println!("โœ… Performance metrics display completed"); - Ok(()) -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  SOMA++ Recursive Feedback Loops Demonstration"); - println!("==============================================="); - println!("This demo showcases recursive cognitive feedback loops that enable"); - println!("continuous packet processing, autonomous reasoning, cycle detection,"); - println!("and self-improving symbolic computation systems.\n"); - - // Set up the feedback loop engine - let engine = setup_feedback_loop_engine().await?; - - // Run demonstration scenarios - demonstrate_basic_feedback_loop(&engine).await?; - demonstrate_learning_feedback_loop(&engine).await?; - demonstrate_error_recovery_loop(&engine).await?; - demonstrate_cycle_detection(&engine).await?; - - // Allow some time for all loops to complete - println!("\nโณ Allowing time for all feedback loops to complete..."); - tokio::time::sleep(tokio::time::Duration::from_secs(3)).await; - - // Display final performance metrics - display_performance_metrics(&engine).await?; - - println!("\n๐ŸŽ‰ SOMA++ Recursive Feedback Loops Demo completed successfully!"); - println!("The demonstration showed key capabilities:"); - println!("โ€ข โœ… Output-to-input chaining mechanisms"); - println!("โ€ข โœ… Recursive execution with depth limiting"); - println!("โ€ข โœ… Cycle detection and prevention"); - println!("โ€ข โœ… Error propagation and recovery strategies"); - println!("โ€ข โœ… Autonomous reasoning for continuous processing"); - println!("โ€ข โœ… Performance monitoring and optimization"); - - Ok(()) -} \ No newline at end of file diff --git a/soma_types_demo.rs b/soma_types_demo.rs deleted file mode 100644 index 957528db2fb3b4e8b33054ff2a20bc9b7f8163c2..0000000000000000000000000000000000000000 --- a/soma_types_demo.rs +++ /dev/null @@ -1,167 +0,0 @@ -//! SOMA++ Types Demonstration -//! -//! This example demonstrates the core SOMA++ types and data structures -//! implemented in the brain-types crate. - -use brain_types::soma::*; -use serde_json::json; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  SOMA++ Types Demonstration"); - println!("================================"); - - // Demonstrate DeltaPhase creation - println!("\n1. Delta Phase Creation:"); - let reflection_phase = DeltaPhase::self_reflection(); - println!(" Self-reflection phase: ฮ”{} (T+{})", reflection_phase.delta, reflection_phase.timestamp); - - let evolution_phase = DeltaPhase::architecture_evolution(700); - println!(" Architecture evolution phase: ฮ”{} (T+{})", evolution_phase.delta, evolution_phase.timestamp); - - // Demonstrate OperatorCall creation - println!("\n2. Operator Call Creation:"); - let mut operator_call = OperatorCall::new( - "ReflectOperator".to_string(), - "ฮ”๐Ÿชž".to_string(), - ); - operator_call.add_parameter("depth".to_string(), json!(5)); - operator_call.add_parameter("context".to_string(), json!("self-analysis")); - println!(" Operator: {}", operator_call.full_name()); - println!(" Parameters: {:?}", operator_call.parameters); - - // Demonstrate PacketHeader creation - println!("\n3. Packet Header Creation:"); - let header = PacketHeader { - phase: reflection_phase.clone(), - time_offset: 0.014, - task: "Analyze cognitive architecture gaps".to_string(), - origin: Some("brain_cognitive".to_string()), - }; - println!(" Task: {}", header.task); - println!(" Origin: {:?}", header.origin); - println!(" Phase: ฮ”{} (T+{})", header.phase.delta, header.time_offset); - - // Demonstrate PacketContext creation - println!("\n4. Packet Context Creation:"); - let context = PacketContext { - source: Some("cognitive_agent".to_string()), - gaps: vec![ - "memory_consolidation".to_string(), - "pattern_recognition".to_string(), - ], - energy_level: EnergyLevel::High, - agent_confidence: Some(0.85), - task_class: Some("architecture_analysis".to_string()), - }; - println!(" Source: {:?}", context.source); - println!(" Gaps: {:?}", context.gaps); - println!(" Energy Level: {:?}", context.energy_level); - println!(" Confidence: {:?}", context.agent_confidence); - - // Demonstrate PacketPayload creation - println!("\n5. Packet Payload Creation:"); - let payload = PacketPayload { - inputs: vec![ - "current_architecture_state".to_string(), - "performance_metrics".to_string(), - ], - outputs: vec![ - "identified_gaps".to_string(), - "improvement_suggestions".to_string(), - ], - target: Some("architecture_optimizer".to_string()), - operator: Some(operator_call.clone()), - constraints: vec![ - "maintain_backward_compatibility".to_string(), - "preserve_existing_functionality".to_string(), - ], - }; - println!(" Inputs: {:?}", payload.inputs); - println!(" Outputs: {:?}", payload.outputs); - println!(" Target: {:?}", payload.target); - println!(" Constraints: {:?}", payload.constraints); - - // Demonstrate SomaPacket creation - println!("\n6. SOMA++ Packet Creation:"); - let mut packet = SomaPacket::new_with_context(header, Some(context), payload); - packet.add_tag("architecture_analysis".to_string()); - packet.add_tag("high_priority".to_string()); - - println!(" Packet ID: {}", packet.id()); - println!(" Created: {}", packet.metadata.created_at); - println!(" Tags: {:?}", packet.metadata.tags); - println!(" Priority: {}", packet.metadata.priority); - - // Demonstrate ExecutionResult creation - println!("\n7. Execution Result Creation:"); - - // Create a successful result - let output_header = PacketHeader { - phase: DeltaPhase::new(404, 0.0), - time_offset: 0.0, - task: "Analysis complete".to_string(), - origin: Some("ReflectOperator".to_string()), - }; - let output_payload = PacketPayload { - inputs: vec![], - outputs: vec!["gap_analysis_report".to_string()], - target: None, - operator: None, - constraints: vec![], - }; - let output_packet = SomaPacket::new(output_header, output_payload); - - let success_result = ExecutionResult::success(packet.id(), output_packet); - println!(" Success Result ID: {}", success_result.id); - println!(" Status: {:?}", success_result.status); - println!(" Is Success: {}", success_result.is_success()); - - // Create a failed result - let error = SomaError::ExecutionError { - message: "Insufficient memory for analysis".to_string(), - packet_id: packet.id(), - cause: None, - }; - let failure_result = ExecutionResult::failure(packet.id(), error); - println!(" Failure Result ID: {}", failure_result.id); - println!(" Status: {:?}", failure_result.status); - println!(" Is Failure: {}", failure_result.is_failure()); - - // Demonstrate serialization - println!("\n8. Packet Serialization:"); - let json_str = serde_json::to_string_pretty(&packet)?; - println!(" JSON representation (first 200 chars):"); - println!(" {}", &json_str[..std::cmp::min(200, json_str.len())]); - if json_str.len() > 200 { - println!(" ... (truncated)"); - } - - // Demonstrate error types - println!("\n9. Error Types Demonstration:"); - let parse_error = SomaError::ParseError { - message: "Invalid operator syntax".to_string(), - line: Some(42), - column: Some(15), - }; - println!(" Parse Error: {}", parse_error); - - let operator_not_found = SomaError::OperatorNotFound { - namespace: "UnknownOperator".to_string(), - operation: "NonExistentOp".to_string(), - }; - println!(" Operator Not Found: {}", operator_not_found); - - let phase_transition_error = SomaError::PhaseTransitionError { - from_phase: Some(reflection_phase), - to_phase: evolution_phase, - reason: "Insufficient cognitive resources".to_string(), - }; - println!(" Phase Transition Error: {}", phase_transition_error); - - println!("\nโœ… SOMA++ Types demonstration completed successfully!"); - println!(" All core types are working correctly with proper serialization,"); - println!(" error handling, and metadata management."); - - Ok(()) -} \ No newline at end of file diff --git a/specialized_model_training_demo.rs b/specialized_model_training_demo.rs deleted file mode 100644 index 2ec747a0b8ce8f5cc4408900aac8153e61397cad..0000000000000000000000000000000000000000 --- a/specialized_model_training_demo.rs +++ /dev/null @@ -1,211 +0,0 @@ -//! Specialized Model Training Demo -//! -//! Demonstrates specialized training capabilities using Brain AI -//! with the new MemoryService and ConceptGraphService architecture. - -use brain::*; -use brain::services::*; -use std::env; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŽ“ Specialized Model Training Demo"); - println!("=================================="); - - // Check for OpenAI API key - let _openai_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| { - println!("โš ๏ธ OPENAI_API_KEY not set. Please set it to use this demo."); - std::process::exit(1); - }); - - println!("โœ… OpenAI API key found"); - - // Initialize Brain AI components using new service architecture - println!("\n๐Ÿ”ง Initializing Brain AI Training System..."); - let mut memory_system = create_memory_service_with_capacity(1000).await?; - let mut concept_graph = create_concept_graph_service_default().await?; - - println!("โœ… MemoryService initialized for training"); - println!("โœ… ConceptGraphService initialized"); - - // Load specialized training data - println!("\n๐Ÿ“š Loading Specialized Training Data..."); - let training_data = vec![ - "Machine learning models improve through iterative training and validation", - "Specialized training focuses on domain-specific knowledge and patterns", - "Transfer learning enables knowledge sharing between related domains", - "Active learning optimizes training efficiency by selecting informative examples", - "Meta-learning enables models to learn how to learn new tasks quickly", - "Multi-task learning allows models to benefit from related task knowledge", - "Few-shot learning enables rapid adaptation to new tasks with minimal data", - "Continual learning prevents catastrophic forgetting in sequential training", - "Self-supervised learning leverages unlabeled data for representation learning", - "Reinforcement learning optimizes behavior through reward-based feedback", - ]; - - for (i, data) in training_data.iter().enumerate() { - memory_system.learn(data.to_string(), Priority::High).await?; - println!("โœ… Loaded training data {}", i + 1); - } - - // Create training orchestrator - println!("\n๐Ÿค– Initializing Training Orchestrator..."); - let mut rag_orchestrator = RagOrchestrator::new()?; - - // Training-focused questions - let training_questions = vec![ - "What are the key principles of specialized model training?", - "How does transfer learning improve training efficiency?", - "What is the role of active learning in model training?", - "How does meta-learning enable rapid task adaptation?", - "What are the benefits of multi-task learning?", - "How does few-shot learning work with minimal data?", - "What strategies prevent catastrophic forgetting?", - "How does self-supervised learning utilize unlabeled data?", - ]; - - println!("\n๐ŸŽ“ Training Knowledge Assessment"); - println!("==============================="); - - let mut training_results = Vec::new(); - - for (i, question) in training_questions.iter().enumerate() { - println!("\n๐Ÿ“ Training Assessment {}: {}", i + 1, question); - - let request = RagRequest { - message: question.to_string(), - conversation_id: Some("training_session".to_string()), - context_limit: Some(6), - retrieval_threshold: Some(0.3), - }; - - match rag_orchestrator.process_conversation( - request, - &mut memory_system, - &mut concept_graph, - ).await { - Ok(response) => { - println!("๐ŸŽฏ Training Response:"); - println!(" {}", response.response); - println!(" ๐Ÿ“Š Confidence: {:.1}%", response.confidence_score * 100.0); - println!(" ๐Ÿ“š Knowledge sources: {}", response.context_used.len()); - - // Evaluate training effectiveness - let effective = response.confidence_score > 0.5 && response.context_used.len() > 0; - training_results.push((question.to_string(), effective, response.confidence_score)); - - if effective { - println!(" โœ… Training EFFECTIVE"); - } else { - println!(" โš ๏ธ Training needs improvement"); - } - - // Store training interaction for learning - let training_interaction = format!("Training Q: {} | A: {}", question, response.response); - memory_system.learn(training_interaction, Priority::Medium).await?; - } - Err(e) => { - println!(" โŒ Training error: {}", e); - training_results.push((question.to_string(), false, 0.0)); - } - } - - tokio::time::sleep(tokio::time::Duration::from_millis(600)).await; - } - - // Simulate specialized training iteration - println!("\n๐Ÿ”„ Specialized Training Iteration"); - println!("================================="); - - // Add domain-specific knowledge - let specialized_knowledge = vec![ - "Neural architecture search automates the discovery of optimal model structures", - "Gradient accumulation enables large batch training on limited hardware", - "Learning rate scheduling optimizes convergence during training", - "Data augmentation increases training data diversity and model robustness", - "Regularization techniques prevent overfitting in complex models", - ]; - - for (i, knowledge) in specialized_knowledge.iter().enumerate() { - memory_system.learn(knowledge.to_string(), Priority::High).await?; - println!("โœ… Added specialized knowledge {}", i + 1); - } - - // Test improvement after specialized training - let post_training_question = "What advanced techniques optimize neural network training?"; - println!("\n๐Ÿ” Post-Training Assessment: {}", post_training_question); - - let request = RagRequest { - message: post_training_question.to_string(), - conversation_id: Some("post_training_session".to_string()), - context_limit: Some(8), - retrieval_threshold: Some(0.25), - }; - - match rag_orchestrator.process_conversation( - request, - &mut memory_system, - &mut concept_graph, - ).await { - Ok(response) => { - println!("๐ŸŽฏ Post-Training Response:"); - println!(" {}", response.response); - println!(" ๐Ÿ“Š Confidence: {:.1}%", response.confidence_score * 100.0); - println!(" ๐Ÿ“š Knowledge sources: {}", response.context_used.len()); - - if response.confidence_score > 0.6 { - println!(" ๐Ÿ† Excellent improvement after specialized training!"); - } else { - println!(" ๐Ÿ“ˆ Some improvement observed"); - } - } - Err(e) => { - println!(" โŒ Post-training assessment error: {}", e); - } - } - - // Generate training report - println!("\n๐Ÿ“‹ Training Effectiveness Report"); - println!("================================"); - - let effective_training: Vec<_> = training_results.iter() - .filter(|(_, effective, _)| *effective) - .collect(); - - let total_training = training_results.len(); - let effectiveness_rate = (effective_training.len() as f64 / total_training as f64) * 100.0; - - println!("โœ… Effective training sessions: {}/{} ({:.1}%)", - effective_training.len(), total_training, effectiveness_rate); - - let avg_confidence = training_results.iter() - .map(|(_, _, c)| c) - .sum::() / total_training as f64; - println!("๐Ÿ“Š Average training confidence: {:.1}%", avg_confidence * 100.0); - - // Training consolidation - println!("\n๐Ÿง  Training Memory Consolidation..."); - match memory_system.consolidate().await { - Ok(result) => { - println!("โœ… Training consolidation complete:"); - println!(" Training data consolidated: {} items", result.working_to_episodic); - println!(" Specialized concepts formed: {} items", result.episodic_to_semantic); - } - Err(e) => { - println!("โš ๏ธ Consolidation warning: {}", e); - } - } - - // Display session statistics - println!("\n๐Ÿ“Š Training Session Statistics"); - println!("=============================="); - let stats = rag_orchestrator.get_conversation_stats(); - for (key, value) in stats { - println!(" {}: {}", key, value); - } - - println!("\nโœ… Specialized Model Training Demo Complete!"); - println!(" Training capabilities demonstrated successfully with new service architecture."); - - Ok(()) -} \ No newline at end of file diff --git a/agents/orchestration/workflow_orchestration.rs b/src/agents/orchestration/workflow_orchestration.rs similarity index 100% rename from agents/orchestration/workflow_orchestration.rs rename to src/agents/orchestration/workflow_orchestration.rs diff --git a/lib.rs b/src/lib.rs similarity index 100% rename from lib.rs rename to src/lib.rs diff --git a/main.rs b/src/main.rs similarity index 100% rename from main.rs rename to src/main.rs diff --git a/system_integration_demo.rs b/system_integration_demo.rs deleted file mode 100644 index 2bd3cc9b866cb164db6d941e35587153cb002e5e..0000000000000000000000000000000000000000 --- a/system_integration_demo.rs +++ /dev/null @@ -1,442 +0,0 @@ -//! System Integration and Interface Standardization Demo -//! -//! This example demonstrates the unified Brain AI system with all components -//! integrated through standardized interfaces, comprehensive health monitoring, -//! and consistent error handling. - -use brain::*; -use brain::services::*; -use brain_infra::memory::WorkingMemoryRepository; -use std::time::{Duration, Instant}; -use tokio::time::sleep; -use chrono::{DateTime, Utc}; - -#[derive(Debug, Clone)] -pub struct BrainSystemConfig { - pub system_id: String, - pub system_name: String, - pub version: String, - pub memory_capacity: usize, - pub enable_logging: bool, - pub max_concurrent_operations: usize, -} - -impl Default for BrainSystemConfig { - fn default() -> Self { - Self { - system_id: "brain-ai-v1".to_string(), - system_name: "Brain AI Unified System".to_string(), - version: "1.0.0".to_string(), - memory_capacity: 1000, - enable_logging: true, - max_concurrent_operations: 50, - } - } -} - -#[derive(Debug, Clone)] -pub enum HealthStatus { - Healthy, - Warning, - Critical, - Down, -} - -#[derive(Debug, Clone)] -pub struct ComponentStatus { - pub name: String, - pub status: HealthStatus, - pub message: String, - pub last_check: DateTime, -} - -#[derive(Debug, Clone)] -pub struct SystemHealth { - pub overall_status: HealthStatus, - pub components: Vec, - pub checked_at: DateTime, -} - -#[derive(Debug, Clone)] -pub struct SystemMetrics { - pub total_operations: u64, - pub successful_operations: u64, - pub failed_operations: u64, - pub average_response_time_ms: f64, - pub memory_usage_bytes: usize, - pub uptime_seconds: u64, -} - -/// Unified Brain AI system integrating all components -pub struct BrainSystem { - config: BrainSystemConfig, - #[allow(dead_code)] - memory_service: MemoryService, - #[allow(dead_code)] - concept_service: ConceptGraphService, - #[allow(dead_code)] - working_repo: WorkingMemoryRepository, - start_time: Instant, - operation_count: u64, - successful_operations: u64, - failed_operations: u64, - response_times: Vec, -} - -impl BrainSystem { - pub async fn new(config: BrainSystemConfig) -> Result { - let memory_service = create_memory_service_with_capacity(config.memory_capacity).await?; - let concept_service = create_concept_graph_service_default().await?; - let working_repo = WorkingMemoryRepository::new(config.memory_capacity); - - Ok(Self { - config, - memory_service, - concept_service, - working_repo, - start_time: Instant::now(), - operation_count: 0, - successful_operations: 0, - failed_operations: 0, - response_times: Vec::new(), - }) - } - - pub fn perform_health_check(&self) -> Result { - let now = Utc::now(); - let components = vec![ - ComponentStatus { - name: "Memory Service".to_string(), - status: HealthStatus::Healthy, - message: "Operating normally".to_string(), - last_check: now, - }, - ComponentStatus { - name: "Concept Graph Service".to_string(), - status: HealthStatus::Healthy, - message: "Operating normally".to_string(), - last_check: now, - }, - ComponentStatus { - name: "Working Memory Repository".to_string(), - status: HealthStatus::Healthy, - message: "Operating normally".to_string(), - last_check: now, - }, - ]; - - Ok(SystemHealth { - overall_status: HealthStatus::Healthy, - components, - checked_at: now, - }) - } - - pub fn metrics(&self) -> SystemMetrics { - let avg_response_time = if !self.response_times.is_empty() { - self.response_times.iter().map(|d| d.as_millis()).sum::() as f64 - / self.response_times.len() as f64 - } else { - 0.0 - }; - - SystemMetrics { - total_operations: self.operation_count, - successful_operations: self.successful_operations, - failed_operations: self.failed_operations, - average_response_time_ms: avg_response_time, - memory_usage_bytes: self.config.memory_capacity * 256, // Estimated - uptime_seconds: self.start_time.elapsed().as_secs(), - } - } - - pub async fn process_request(&mut self, request: &str) -> Result { - let start = Instant::now(); - self.operation_count += 1; - - let result = match request { - "learn" => { - // Simulate learning operation - sleep(Duration::from_millis(10)).await; - self.successful_operations += 1; - Ok("Learning operation completed".to_string()) - } - "recall" => { - // Simulate recall operation - sleep(Duration::from_millis(5)).await; - self.successful_operations += 1; - Ok("Recall operation completed".to_string()) - } - "analyze" => { - // Simulate analysis operation - sleep(Duration::from_millis(20)).await; - self.successful_operations += 1; - Ok("Analysis operation completed".to_string()) - } - _ => { - self.failed_operations += 1; - Err(brain_types::BrainError::PredictionError { - message: format!("Unknown request: {}", request), - context: None, - }) - } - }; - - self.response_times.push(start.elapsed()); - if self.response_times.len() > 1000 { - self.response_times.remove(0); // Keep only recent times - } - - result - } - - pub async fn execute_workflow(&mut self, workflow_name: &str) -> Result { - let _start = Instant::now(); - self.operation_count += 1; - - match workflow_name { - "learning_pipeline" => { - // Simulate multi-step learning workflow - self.process_request("learn").await?; - sleep(Duration::from_millis(5)).await; - self.process_request("analyze").await?; - self.successful_operations += 1; - Ok("Learning pipeline completed successfully".to_string()) - } - "knowledge_extraction" => { - // Simulate knowledge extraction workflow - self.process_request("recall").await?; - sleep(Duration::from_millis(8)).await; - self.process_request("analyze").await?; - self.successful_operations += 1; - Ok("Knowledge extraction completed successfully".to_string()) - } - "system_maintenance" => { - // Simulate system maintenance workflow - sleep(Duration::from_millis(15)).await; - self.successful_operations += 1; - Ok("System maintenance completed successfully".to_string()) - } - _ => { - self.failed_operations += 1; - Err(brain_types::BrainError::PredictionError { - message: format!("Unknown workflow: {}", workflow_name), - context: None, - }) - } - } - } - - pub fn shutdown(&mut self) -> Result<()> { - println!("๐Ÿ›‘ Shutting down Brain AI system..."); - println!(" โœ… Memory service disconnected"); - println!(" โœ… Concept graph service disconnected"); - println!(" โœ… Working memory repository cleaned up"); - println!(" โœ… Resources released"); - Ok(()) - } -} - -pub struct BrainSystemBuilder { - config: Option, -} - -impl BrainSystemBuilder { - pub fn new() -> Self { - Self { config: None } - } - - pub fn with_config(mut self, config: BrainSystemConfig) -> Self { - self.config = Some(config); - self - } - - pub fn with_logging_enabled(self, _enabled: bool) -> Self { - // Logging configuration would be handled here - self - } - - pub fn with_max_concurrent_operations(self, _max: usize) -> Self { - // Concurrency configuration would be handled here - self - } - - pub async fn build(self) -> Result { - let config = self.config.unwrap_or_default(); - BrainSystem::new(config).await - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  System Integration and Interface Standardization Demo"); - println!("================================================================================"); - - // Initialize logging - env_logger::init(); - - println!("\n๐Ÿ”ง Configuring Brain AI System..."); - let system_config = create_optimized_config(); - println!("โœ… Configuration created with optimized settings"); - - println!("\n๐Ÿ—๏ธ Building integrated system..."); - let mut brain_system = BrainSystemBuilder::new() - .with_config(system_config) - .with_logging_enabled(true) - .with_max_concurrent_operations(50) - .build().await?; - - println!("โœ… Brain system built successfully"); - - println!("\n๐Ÿ“Š System Health Check..."); - let health = brain_system.perform_health_check()?; - display_system_health(&health); - - println!("\n๐Ÿ“ˆ System Metrics..."); - let metrics = brain_system.metrics(); - display_system_metrics(&metrics); - - println!("\n๐Ÿ”„ Testing Unified API..."); - test_unified_api(&mut brain_system).await?; - - println!("\nโšก Testing Workflow Engine..."); - test_workflow_engine(&mut brain_system).await?; - - println!("\n๐ŸŽฏ Running Integration Workflows..."); - run_integration_workflows(&mut brain_system).await?; - - println!("\n๐Ÿ“Š Final System State..."); - let final_health = brain_system.perform_health_check()?; - let final_metrics = brain_system.metrics(); - - println!("Final Health Status: {:?}", final_health.overall_status); - println!("Total Operations: {}", final_metrics.total_operations); - - if final_metrics.total_operations > 0 { - let success_rate = (final_metrics.successful_operations as f64 / final_metrics.total_operations as f64) * 100.0; - println!("Success Rate: {:.2}%", success_rate); - } - - println!("\n๐Ÿ›‘ Graceful Shutdown..."); - brain_system.shutdown()?; - println!("โœ… System shutdown completed successfully"); - - println!("================================================================================"); - println!("๐ŸŽ‰ System Integration Demo COMPLETE!"); - println!(" โœ… Unified API layer implemented"); - println!(" โœ… All components integrated with standardized interfaces"); - println!(" โœ… Comprehensive health monitoring system"); - println!(" โœ… Performance metrics and analytics"); - println!(" โœ… Workflow execution engine operational"); - println!(" โœ… Enterprise-grade error handling and logging"); - - Ok(()) -} - -/// Create an optimized configuration for the Brain system -fn create_optimized_config() -> BrainSystemConfig { - BrainSystemConfig { - system_id: "brain-ai-v1".to_string(), - system_name: "Brain AI Unified System".to_string(), - version: "1.0.0".to_string(), - memory_capacity: 1000, - enable_logging: true, - max_concurrent_operations: 50, - } -} - -fn display_system_health(health: &SystemHealth) { - println!("๐Ÿ“Š System Health Status: {:?}", health.overall_status); - println!(" Last checked: {}", health.checked_at.format("%Y-%m-%d %H:%M:%S UTC")); - - for component in &health.components { - let status_emoji = match component.status { - HealthStatus::Healthy => "โœ…", - HealthStatus::Warning => "โš ๏ธ", - HealthStatus::Critical => "โŒ", - HealthStatus::Down => "๐Ÿ”ด", - }; - println!(" {} {}: {:?} - {}", status_emoji, component.name, component.status, component.message); - } -} - -fn display_system_metrics(metrics: &SystemMetrics) { - println!("๐Ÿ“ˆ System Performance Metrics:"); - println!(" Total Operations: {}", metrics.total_operations); - println!(" Successful: {} | Failed: {}", metrics.successful_operations, metrics.failed_operations); - println!(" Average Response Time: {:.2} ms", metrics.average_response_time_ms); - println!(" Memory Usage: {} bytes", metrics.memory_usage_bytes); - println!(" Uptime: {} seconds", metrics.uptime_seconds); -} - -async fn test_unified_api(brain_system: &mut BrainSystem) -> Result<()> { - println!("๐Ÿ”„ Testing Unified API Operations:"); - - let operations = vec!["learn", "recall", "analyze"]; - - for operation in operations { - print!(" Testing {} operation... ", operation); - match brain_system.process_request(operation).await { - Ok(result) => println!("โœ… Success: {}", truncate_result(&result, 50)), - Err(e) => println!("โŒ Failed: {}", e), - } - } - - // Test invalid operation - print!(" Testing invalid operation... "); - match brain_system.process_request("invalid").await { - Ok(_) => println!("โŒ Unexpected success"), - Err(_) => println!("โœ… Correctly rejected invalid operation"), - } - - Ok(()) -} - -async fn test_workflow_engine(brain_system: &mut BrainSystem) -> Result<()> { - println!("โšก Testing Workflow Engine:"); - - let workflows = vec!["learning_pipeline", "knowledge_extraction", "system_maintenance"]; - - for workflow in workflows { - print!(" Executing {} workflow... ", workflow); - match brain_system.execute_workflow(workflow).await { - Ok(result) => println!("โœ… Success: {}", truncate_result(&result, 50)), - Err(e) => println!("โŒ Failed: {}", e), - } - } - - Ok(()) -} - -async fn run_integration_workflows(brain_system: &mut BrainSystem) -> Result<()> { - println!("๐ŸŽฏ Running Complex Integration Workflows:"); - - // Simulate a complex multi-step workflow - println!(" ๐Ÿ“š Phase 1: Data Ingestion and Learning"); - for i in 1..=3 { - brain_system.execute_workflow("learning_pipeline").await?; - println!(" Learning batch {} completed", i); - } - - println!(" ๐Ÿง  Phase 2: Knowledge Extraction and Analysis"); - for i in 1..=2 { - brain_system.execute_workflow("knowledge_extraction").await?; - println!(" Knowledge extraction {} completed", i); - } - - println!(" ๐Ÿ› ๏ธ Phase 3: System Maintenance and Optimization"); - brain_system.execute_workflow("system_maintenance").await?; - println!(" System maintenance completed"); - - println!("โœ… All integration workflows completed successfully"); - - Ok(()) -} - -fn truncate_result(s: &str, max_len: usize) -> String { - if s.len() <= max_len { - s.to_string() - } else { - format!("{}...", &s[..max_len]) - } -} \ No newline at end of file diff --git a/task_2_3_completion_verification.rs b/task_2_3_completion_verification.rs deleted file mode 100644 index 9bd828fb63babed59246624a19a47041346c1857..0000000000000000000000000000000000000000 --- a/task_2_3_completion_verification.rs +++ /dev/null @@ -1,89 +0,0 @@ -//! # TASK 2.3 Completion Verification -//! -//! **TASK 2.3: Learning Integration & Knowledge Persistence** -//! -//! This simple verification demonstrates that TASK 2.3 has been successfully -//! completed by instantiating and verifying all key components are accessible. -//! -//! **Purpose**: Verify successful completion of TASK 2.3 implementation. -//! **Created**: July 31, 2025 at 18:45:00 EDT -//! **Status**: Final TASK 2.3 Verification - -use anyhow::Result; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŽ“ TASK 2.3 Completion Verification"); - println!("==================================="); - println!("Learning Integration & Knowledge Persistence"); - println!(); - - // Phase 1: Verify Component Imports - println!("๐Ÿ“ฆ Phase 1: Component Import Verification"); - println!("-----------------------------------------"); - - // Import verification - ensuring all TASK 2.3 components are accessible - println!("โœ… IterativeLearningLoop: Available"); - println!("โœ… UncertaintyHandler: Available"); - println!("โœ… AcademicKnowledgePersistence: Available"); - println!("โœ… AcademicLearningConfig: Available"); - println!("โœ… UncertaintyResponse: Available"); - println!("โœ… AcademicLearningCycleResult: Available"); - println!("โœ… HLEValidationResults: Available"); - println!("โœ… DomainPerformanceMetrics: Available"); - println!(); - - // Phase 2: Verify Module Integration - println!("๐Ÿ”— Phase 2: Module Integration Verification"); - println!("-------------------------------------------"); - println!("โœ… Academic learning integration module: Compiled successfully"); - println!("โœ… Module exports: All components accessible"); - println!("โœ… Brain learning framework integration: Ready"); - println!("โœ… Type compatibility: Verified"); - println!(); - - // Phase 3: Verify Architecture Components - println!("๐Ÿ—๏ธ Phase 3: Architecture Component Verification"); - println!("-----------------------------------------------"); - println!("โœ… Learning Loop Infrastructure: Implemented"); - println!("โœ… Uncertainty Handling System: Implemented"); - println!("โœ… Knowledge Persistence Layer: Implemented"); - println!("โœ… HLE Integration Bridge: Implemented"); - println!("โœ… Performance Tracking: Implemented"); - println!(); - - // Phase 4: TASK 2.3 Requirements Check - println!("๐Ÿ“‹ Phase 4: TASK 2.3 Requirements Verification"); - println!("----------------------------------------------"); - println!("โœ… IterativeLearningLoop for knowledge persistence: COMPLETED"); - println!("โœ… UncertaintyHandler for graceful uncertainty: COMPLETED"); - println!("โœ… Real HLE testing integration capability: COMPLETED"); - println!("โœ… Brain learning framework integration: COMPLETED"); - println!("โœ… Academic domain-specific persistence: COMPLETED"); - println!("โœ… Continuous learning cycle support: COMPLETED"); - println!(); - - // Phase 5: Implementation Evidence - println!("๐Ÿ”ฌ Phase 5: Implementation Evidence"); - println!("-----------------------------------"); - println!("๐Ÿ“ File: crates/brain-cognitive/src/agents/intelligence/academic_learning_integration.rs"); - println!("๐Ÿ“ Lines: 1,800+ lines of comprehensive implementation"); - println!("๐Ÿงฉ Components: 6 major structures with full implementation"); - println!("๐Ÿ”ง Integration: Module exports and Brain framework compatibility"); - println!("โœ… Compilation: Zero errors, ready for deployment"); - println!(); - - // Final Status - println!("๐Ÿ† TASK 2.3 COMPLETION STATUS"); - println!("============================="); - println!("โœ… STATUS: FULLY COMPLETED"); - println!("๐Ÿ“… COMPLETION DATE: July 31, 2025 at 18:45:00 EDT"); - println!("๐ŸŽฏ DELIVERABLES: All 6 major components implemented"); - println!("๐Ÿ”„ INTEGRATION: Ready for 36.4% HLE leadership continuous learning"); - println!("๐Ÿš€ NEXT PHASE: Deploy for real-world academic learning cycles"); - println!(); - println!("๐ŸŽ‰ TASK 2.3: Learning Integration & Knowledge Persistence"); - println!(" โœจ SUCCESSFULLY COMPLETED โœจ"); - - Ok(()) -} \ No newline at end of file diff --git a/task_5_rollout_engine_demo.rs b/task_5_rollout_engine_demo.rs deleted file mode 100644 index b6901a3c71325f05180eb116218e5c15473bbbf3..0000000000000000000000000000000000000000 --- a/task_5_rollout_engine_demo.rs +++ /dev/null @@ -1,1003 +0,0 @@ -/// # Task 5: Rollout Engine and Internal Simulation Demo -/// -/// This example demonstrates the complete Task 5 implementation featuring: -/// - 5.1 RolloutEngine Implementation with MCTS-inspired planning -/// - 5.2 Multi-Path Planning and Exploration with uncertainty handling -/// - 5.3 Planning Tree Visualization for debugging and transparency -/// - 5.4 Latency Optimization achieving <200ms planning decisions -/// -/// The demo shows how Brain AI uses sophisticated symbolic planning to generate -/// multiple solution approaches, evaluate them through internal simulation, -/// and provide human-readable explanations of the planning process. - -use std::collections::HashMap; -use std::time::Instant; - -// Mock components for demonstration (simulating brain-mubrain types) -#[derive(Debug, Clone)] -pub struct SymbolicState { - pub problem_context: String, - pub complexity_level: f64, - pub available_resources: Vec, - pub clarity_score: f64, - pub uncertainty: f64, -} - -#[derive(Debug, Clone)] -pub struct SymbolicAction { - pub action_type: String, - pub approach: String, - pub confidence: f64, - pub estimated_effort: f64, -} - -#[derive(Debug, Clone)] -pub struct PlanningNode { - pub id: String, - pub state: SymbolicState, - pub action: Option, - pub depth: usize, - pub visit_count: usize, - pub total_value: f64, - pub average_value: f64, - pub children: Vec, - pub is_expanded: bool, -} - -#[derive(Debug, Clone)] -pub struct PlanningTree { - pub root_id: String, - pub nodes: HashMap, - pub best_path: Vec, - pub generation_time_ms: u64, -} - -#[derive(Debug, Clone)] -pub struct RolloutResult { - pub optimal_path: Vec, - pub total_value: f64, - pub exploration_depth: usize, - pub alternatives_considered: usize, - pub planning_time_ms: u64, - pub confidence_score: f64, -} - -#[derive(Debug, Clone)] -pub struct MultiPathResult { - pub primary_approach: SymbolicAction, - pub alternative_approaches: Vec, - pub approach_diversity_score: f64, - pub uncertainty_analysis: String, - pub recommendation_reasoning: String, -} - -#[derive(Debug, Clone)] -pub struct VisualizationOutput { - pub tree_ascii_art: String, - pub planning_trace: Vec, - pub reasoning_explanation: String, - pub performance_analysis: String, -} - -#[derive(Debug, Clone)] -pub struct PerformanceMetrics { - pub planning_latency_ms: u64, - pub nodes_explored: usize, - pub cache_hit_rate: f64, - pub memory_usage_mb: f64, - pub target_achieved: bool, -} - -// ================================================================================================ -// TASK 5.1: ROLLOUT ENGINE IMPLEMENTATION (@oracle) -// ================================================================================================ - -pub struct RolloutEngine { - max_depth: usize, - max_breadth: usize, - exploration_constant: f64, - time_limit_ms: u64, -} - -impl RolloutEngine { - pub fn new() -> Self { - Self { - max_depth: 8, - max_breadth: 5, - exploration_constant: 1.4, - time_limit_ms: 150, // Target <200ms total planning - } - } - - /// @oracle - /// Implements MCTS-inspired rollout planning with symbolic state transitions - pub fn simulate_rollout(&self, initial_state: &SymbolicState) -> RolloutResult { - let start_time = Instant::now(); - - println!("๐Ÿ”„ Starting MCTS-inspired rollout planning..."); - println!(" Initial state: {}", initial_state.problem_context); - println!(" Complexity: {:.2}, Uncertainty: {:.2}", - initial_state.complexity_level, initial_state.uncertainty); - - // Generate planning tree with value-guided exploration - let planning_tree = self.generate_planning_tree(initial_state); - - // Find optimal path through tree traversal - let optimal_path = self.find_optimal_path(&planning_tree); - - // Calculate total value and confidence - let total_value = self.calculate_path_value(&optimal_path); - let confidence = self.estimate_confidence(&optimal_path, initial_state.uncertainty); - - let planning_time = start_time.elapsed().as_millis() as u64; - - println!("โœ… Rollout completed in {}ms", planning_time); - println!(" Nodes explored: {}", planning_tree.nodes.len()); - println!(" Optimal path length: {}", optimal_path.len()); - println!(" Total value: {:.3}, Confidence: {:.3}", total_value, confidence); - - RolloutResult { - optimal_path, - total_value, - exploration_depth: planning_tree.nodes.values().map(|n| n.depth).max().unwrap_or(0), - alternatives_considered: planning_tree.nodes.len(), - planning_time_ms: planning_time, - confidence_score: confidence, - } - } - - fn generate_planning_tree(&self, initial_state: &SymbolicState) -> PlanningTree { - let mut nodes = HashMap::new(); - let root_id = "root".to_string(); - - // Create root node - let root_node = PlanningNode { - id: root_id.clone(), - state: initial_state.clone(), - action: None, - depth: 0, - visit_count: 1, - total_value: 0.0, - average_value: 0.0, - children: Vec::new(), - is_expanded: false, - }; - - nodes.insert(root_id.clone(), root_node); - - // Simulate MCTS-style expansion and simulation - for iteration in 0..20 { - if iteration > 0 && iteration % 5 == 0 { - println!(" ๐Ÿ“Š Iteration {}: {} nodes explored", iteration, nodes.len()); - } - - // Selection: find best leaf node to expand - let selected_id = self.select_node_for_expansion(&nodes, &root_id); - - // Expansion: add children if not at max depth - if let Some(node) = nodes.get(&selected_id).cloned() { - if node.depth < self.max_depth && !node.is_expanded { - self.expand_node(&mut nodes, &selected_id); - } - } - - // Early termination if time limit approached - if iteration > 10 && iteration * 8 > self.time_limit_ms as usize { - break; - } - } - - // Find best path for result - let best_path = self.trace_best_path(&nodes, &root_id); - - PlanningTree { - root_id, - nodes, - best_path, - generation_time_ms: 50, // Simulated timing - } - } - - fn select_node_for_expansion(&self, nodes: &HashMap, root_id: &str) -> String { - // Simplified UCB1 selection - nodes.values() - .filter(|node| !node.is_expanded || node.children.is_empty()) - .max_by(|a, b| { - let ucb_a = a.average_value + self.exploration_constant * - (2.0 * (nodes.len() as f64).ln() / a.visit_count as f64).sqrt(); - let ucb_b = b.average_value + self.exploration_constant * - (2.0 * (nodes.len() as f64).ln() / b.visit_count as f64).sqrt(); - ucb_a.partial_cmp(&ucb_b).unwrap_or(std::cmp::Ordering::Equal) - }) - .map(|node| node.id.clone()) - .unwrap_or_else(|| root_id.to_string()) - } - - fn expand_node(&self, nodes: &mut HashMap, node_id: &str) { - if let Some(parent_node) = nodes.get(node_id).cloned() { - let mut updated_parent = parent_node.clone(); - updated_parent.is_expanded = true; - - // Generate child actions based on state - let child_actions = self.generate_actions_for_state(&parent_node.state); - - for (i, action) in child_actions.into_iter().enumerate().take(self.max_breadth) { - let child_id = format!("{}_{}", node_id, i); - let child_state = self.simulate_state_transition(&parent_node.state, &action); - - let child_node = PlanningNode { - id: child_id.clone(), - state: child_state, - action: Some(action), - depth: parent_node.depth + 1, - visit_count: 1, - total_value: self.estimate_node_value(&parent_node.state), - average_value: 0.0, - children: Vec::new(), - is_expanded: false, - }; - - updated_parent.children.push(child_id.clone()); - nodes.insert(child_id, child_node); - } - - nodes.insert(node_id.to_string(), updated_parent); - } - } - - fn generate_actions_for_state(&self, state: &SymbolicState) -> Vec { - vec![ - SymbolicAction { - action_type: "implement".to_string(), - approach: "recursive_solution".to_string(), - confidence: 0.8 - state.uncertainty * 0.3, - estimated_effort: state.complexity_level * 1.2, - }, - SymbolicAction { - action_type: "implement".to_string(), - approach: "iterative_solution".to_string(), - confidence: 0.7 - state.uncertainty * 0.2, - estimated_effort: state.complexity_level * 1.0, - }, - SymbolicAction { - action_type: "implement".to_string(), - approach: "mathematical_solution".to_string(), - confidence: 0.9 - state.uncertainty * 0.4, - estimated_effort: state.complexity_level * 0.8, - }, - ] - } - - fn simulate_state_transition(&self, current_state: &SymbolicState, action: &SymbolicAction) -> SymbolicState { - SymbolicState { - problem_context: format!("{} -> {}", current_state.problem_context, action.approach), - complexity_level: (current_state.complexity_level * 0.8).max(0.1), - available_resources: current_state.available_resources.clone(), - clarity_score: (current_state.clarity_score + action.confidence * 0.2).min(1.0), - uncertainty: (current_state.uncertainty * 0.7).max(0.05), - } - } - - fn estimate_node_value(&self, state: &SymbolicState) -> f64 { - // Value estimation based on clarity and reduced complexity - state.clarity_score * 0.6 + (1.0 - state.complexity_level) * 0.3 + (1.0 - state.uncertainty) * 0.1 - } - - fn find_optimal_path(&self, tree: &PlanningTree) -> Vec { - tree.best_path.iter() - .filter_map(|node_id| tree.nodes.get(node_id)) - .filter_map(|node| node.action.clone()) - .collect() - } - - fn trace_best_path(&self, nodes: &HashMap, root_id: &str) -> Vec { - let mut path = vec![root_id.to_string()]; - let mut current_id = root_id; - - while let Some(current_node) = nodes.get(current_id) { - if current_node.children.is_empty() { - break; - } - - // Select child with highest average value - if let Some(best_child_id) = current_node.children.iter() - .max_by(|a, b| { - let value_a = nodes.get(*a).map(|n| n.average_value).unwrap_or(0.0); - let value_b = nodes.get(*b).map(|n| n.average_value).unwrap_or(0.0); - value_a.partial_cmp(&value_b).unwrap_or(std::cmp::Ordering::Equal) - }) { - path.push(best_child_id.clone()); - current_id = best_child_id; - } else { - break; - } - } - - path - } - - fn calculate_path_value(&self, path: &[SymbolicAction]) -> f64 { - path.iter().map(|action| action.confidence * 0.8 + (1.0 - action.estimated_effort * 0.1)).sum() - } - - fn estimate_confidence(&self, path: &[SymbolicAction], initial_uncertainty: f64) -> f64 { - if path.is_empty() { - return 0.5; - } - - let avg_confidence: f64 = path.iter().map(|a| a.confidence).sum::() / path.len() as f64; - let uncertainty_factor = 1.0 - initial_uncertainty * 0.3; - - (avg_confidence * uncertainty_factor).min(1.0).max(0.0) - } -} - -// ================================================================================================ -// TASK 5.2: MULTI-PATH PLANNING AND EXPLORATION (@transform) -// ================================================================================================ - -pub struct MultiPathPlanner { - rollout_engine: RolloutEngine, - diversity_threshold: f64, - uncertainty_exploration_boost: f64, -} - -impl MultiPathPlanner { - pub fn new() -> Self { - Self { - rollout_engine: RolloutEngine::new(), - diversity_threshold: 0.7, - uncertainty_exploration_boost: 1.5, - } - } - - /// @transform - /// Generates multiple alternative approaches with uncertainty-based exploration - pub fn plan_multiple_paths(&self, initial_state: &SymbolicState) -> MultiPathResult { - println!("\n๐ŸŒŸ Multi-Path Planning with Uncertainty Analysis"); - println!(" Generating alternative approaches for: {}", initial_state.problem_context); - - // Generate multiple rollouts with different exploration parameters - let mut approaches = Vec::new(); - - // Primary approach (balanced exploration) - let primary_result = self.rollout_engine.simulate_rollout(initial_state); - let primary_approach = self.extract_primary_approach(&primary_result); - - // Alternative approaches (increased exploration for uncertainty) - let exploration_boost = if initial_state.uncertainty > 0.6 { - self.uncertainty_exploration_boost - } else { - 1.0 - }; - - println!(" ๐Ÿ” Uncertainty level: {:.2} -> Exploration boost: {:.2}", - initial_state.uncertainty, exploration_boost); - - for approach_type in &["conservative", "aggressive", "creative"] { - let alternative_approach = self.generate_alternative_approach( - initial_state, - approach_type, - exploration_boost - ); - approaches.push(alternative_approach); - } - - // Calculate diversity metrics - let diversity_score = self.calculate_approach_diversity(&approaches); - - // Uncertainty analysis - let uncertainty_analysis = self.analyze_uncertainty(initial_state, &approaches); - - // Generate reasoning for recommendation - let recommendation_reasoning = self.generate_recommendation_reasoning( - &primary_approach, - &approaches, - initial_state - ); - - println!(" โœจ Generated {} alternative approaches", approaches.len()); - println!(" ๐Ÿ“Š Approach diversity score: {:.3}", diversity_score); - - MultiPathResult { - primary_approach, - alternative_approaches: approaches, - approach_diversity_score: diversity_score, - uncertainty_analysis, - recommendation_reasoning, - } - } - - fn extract_primary_approach(&self, rollout_result: &RolloutResult) -> SymbolicAction { - rollout_result.optimal_path.first().cloned().unwrap_or_else(|| { - SymbolicAction { - action_type: "fallback".to_string(), - approach: "default_implementation".to_string(), - confidence: 0.6, - estimated_effort: 1.0, - } - }) - } - - fn generate_alternative_approach(&self, state: &SymbolicState, approach_type: &str, exploration_boost: f64) -> SymbolicAction { - match approach_type { - "conservative" => SymbolicAction { - action_type: "implement".to_string(), - approach: "step_by_step_solution".to_string(), - confidence: 0.85, - estimated_effort: state.complexity_level * 1.4, - }, - "aggressive" => SymbolicAction { - action_type: "implement".to_string(), - approach: "optimized_solution".to_string(), - confidence: 0.6 + exploration_boost * 0.1, - estimated_effort: state.complexity_level * 0.7, - }, - "creative" => SymbolicAction { - action_type: "implement".to_string(), - approach: "novel_algorithm".to_string(), - confidence: 0.5 + exploration_boost * 0.15, - estimated_effort: state.complexity_level * 1.1, - }, - _ => SymbolicAction { - action_type: "implement".to_string(), - approach: "balanced_solution".to_string(), - confidence: 0.7, - estimated_effort: state.complexity_level, - }, - } - } - - fn calculate_approach_diversity(&self, approaches: &[SymbolicAction]) -> f64 { - if approaches.len() < 2 { - return 0.0; - } - - let mut total_diversity = 0.0; - let mut comparisons = 0; - - for i in 0..approaches.len() { - for j in i+1..approaches.len() { - let approach_i = &approaches[i]; - let approach_j = &approaches[j]; - - // Simple diversity metric based on approach differences - let confidence_diff = (approach_i.confidence - approach_j.confidence).abs(); - let effort_diff = (approach_i.estimated_effort - approach_j.estimated_effort).abs(); - let name_similarity = if approach_i.approach == approach_j.approach { 0.0 } else { 1.0 }; - - let pair_diversity = (confidence_diff + effort_diff + name_similarity) / 3.0; - total_diversity += pair_diversity; - comparisons += 1; - } - } - - if comparisons > 0 { - total_diversity / comparisons as f64 - } else { - 0.0 - } - } - - fn analyze_uncertainty(&self, state: &SymbolicState, approaches: &[SymbolicAction]) -> String { - let uncertainty_level = state.uncertainty; - let avg_confidence: f64 = approaches.iter().map(|a| a.confidence).sum::() / approaches.len() as f64; - - if uncertainty_level > 0.7 { - format!("HIGH uncertainty ({:.2}) detected. Recommend exploring {} diverse approaches with avg confidence {:.2}. Consider iterative prototyping.", - uncertainty_level, approaches.len(), avg_confidence) - } else if uncertainty_level > 0.4 { - format!("MODERATE uncertainty ({:.2}). {} approaches generated with avg confidence {:.2}. Recommend validation testing.", - uncertainty_level, approaches.len(), avg_confidence) - } else { - format!("LOW uncertainty ({:.2}). Confident in primary approach. {} alternatives provide good coverage.", - uncertainty_level, approaches.len()) - } - } - - fn generate_recommendation_reasoning(&self, primary: &SymbolicAction, alternatives: &[SymbolicAction], state: &SymbolicState) -> String { - let complexity_factor = if state.complexity_level > 0.7 { "high" } else if state.complexity_level > 0.4 { "moderate" } else { "low" }; - let confidence_factor = if primary.confidence > 0.8 { "high" } else if primary.confidence > 0.6 { "moderate" } else { "low" }; - - format!( - "Recommended approach: '{}' (confidence: {:.2}) for {} complexity problem. {} alternatives available. \ - Reasoning: {} confidence approach balances risk vs. effort ({:.2}) for current problem context.", - primary.approach, primary.confidence, complexity_factor, alternatives.len(), - confidence_factor, primary.estimated_effort - ) - } -} - -// ================================================================================================ -// TASK 5.3: PLANNING TREE VISUALIZATION (@sentinel) -// ================================================================================================ - -pub struct PlanningTreeVisualizer { - show_values: bool, - show_confidence: bool, - max_display_depth: usize, -} - -impl PlanningTreeVisualizer { - pub fn new() -> Self { - Self { - show_values: true, - show_confidence: true, - max_display_depth: 4, - } - } - - /// @sentinel - /// Creates human-readable planning tree visualization and debugging output - pub fn visualize_planning(&self, rollout_result: &RolloutResult, multi_path_result: &MultiPathResult) -> VisualizationOutput { - println!("\n๐ŸŽจ Planning Tree Visualization & Analysis"); - - // Generate ASCII art tree representation - let tree_ascii_art = self.generate_tree_ascii(rollout_result); - - // Create planning trace with step-by-step reasoning - let planning_trace = self.generate_planning_trace(rollout_result, multi_path_result); - - // Generate human-readable reasoning explanation - let reasoning_explanation = self.generate_reasoning_explanation(rollout_result, multi_path_result); - - // Create performance analysis - let performance_analysis = self.generate_performance_analysis(rollout_result); - - VisualizationOutput { - tree_ascii_art, - planning_trace, - reasoning_explanation, - performance_analysis, - } - } - - fn generate_tree_ascii(&self, rollout_result: &RolloutResult) -> String { - let mut output = String::new(); - output.push_str("Planning Tree Structure:\n"); - output.push_str("========================\n"); - output.push_str("Root Problem\n"); - output.push_str("โ”œโ”€โ”€ Primary Path (Recommended)\n"); - - for (i, action) in rollout_result.optimal_path.iter().enumerate() { - let prefix = if i == rollout_result.optimal_path.len() - 1 { - " โ””โ”€โ”€ " - } else { - " โ”œโ”€โ”€ " - }; - - let confidence_indicator = if action.confidence > 0.8 { "๐ŸŸข" } - else if action.confidence > 0.6 { "๐ŸŸก" } - else { "๐Ÿ”ด" }; - - output.push_str(&format!("{}{}Step {}: {} {}\n", - prefix, confidence_indicator, i+1, action.approach, - if self.show_confidence { - format!(" (conf: {:.2})", action.confidence) - } else { - String::new() - })); - } - - output.push_str("โ”œโ”€โ”€ Alternative Paths\n"); - output.push_str("โ”‚ โ”œโ”€โ”€ Conservative approach\n"); - output.push_str("โ”‚ โ”œโ”€โ”€ Aggressive approach\n"); - output.push_str("โ”‚ โ””โ”€โ”€ Creative approach\n"); - output.push_str("โ””โ”€โ”€ Performance Metrics\n"); - output.push_str(&format!(" โ”œโ”€โ”€ Planning time: {}ms\n", rollout_result.planning_time_ms)); - output.push_str(&format!(" โ”œโ”€โ”€ Nodes explored: {}\n", rollout_result.alternatives_considered)); - output.push_str(&format!(" โ””โ”€โ”€ Confidence: {:.3}\n", rollout_result.confidence_score)); - - output - } - - fn generate_planning_trace(&self, rollout_result: &RolloutResult, multi_path_result: &MultiPathResult) -> Vec { - let mut trace = Vec::new(); - - trace.push("๐Ÿš€ Planning Session Started".to_string()); - trace.push(format!("๐Ÿ“Š Initial analysis: {} alternatives to explore", multi_path_result.alternative_approaches.len())); - trace.push(format!("โšก Rollout engine executed in {}ms", rollout_result.planning_time_ms)); - trace.push(format!("๐ŸŒณ Generated planning tree with {} nodes", rollout_result.alternatives_considered)); - trace.push(format!("๐ŸŽฏ Selected optimal path with {} steps", rollout_result.optimal_path.len())); - - for (i, action) in rollout_result.optimal_path.iter().enumerate() { - trace.push(format!(" Step {}: {} (confidence: {:.2}, effort: {:.2})", - i+1, action.approach, action.confidence, action.estimated_effort)); - } - - trace.push(format!("๐Ÿ“ˆ Final confidence score: {:.3}", rollout_result.confidence_score)); - trace.push(format!("๐Ÿ† Planning completed successfully")); - - trace - } - - fn generate_reasoning_explanation(&self, rollout_result: &RolloutResult, multi_path_result: &MultiPathResult) -> String { - format!( - "Planning Reasoning Explanation:\n\ - ==============================\n\ - \n\ - The MuBrain planning engine analyzed the given problem and generated {} possible solution approaches \ - through MCTS-inspired exploration. After evaluating {} nodes in the planning tree over {}ms, \ - the system selected the optimal path with confidence {:.3}.\n\ - \n\ - Primary recommendation: '{}' with {:.2} confidence and {:.2} estimated effort.\n\ - \n\ - Alternative approaches considered:\n\ - {}\n\ - \n\ - Diversity analysis: {:.3} diversity score indicates {} approach variation.\n\ - \n\ - Uncertainty handling: {}\n\ - \n\ - Recommendation reasoning: {}", - rollout_result.alternatives_considered, - rollout_result.alternatives_considered, - rollout_result.planning_time_ms, - rollout_result.confidence_score, - rollout_result.optimal_path.first().map(|a| &a.approach).unwrap_or(&"None".to_string()), - rollout_result.optimal_path.first().map(|a| a.confidence).unwrap_or(0.0), - rollout_result.optimal_path.first().map(|a| a.estimated_effort).unwrap_or(0.0), - multi_path_result.alternative_approaches.iter() - .enumerate() - .map(|(i, a)| format!(" {}. {} (conf: {:.2})", i+1, a.approach, a.confidence)) - .collect::>() - .join("\n"), - multi_path_result.approach_diversity_score, - if multi_path_result.approach_diversity_score > 0.7 { "high" } - else if multi_path_result.approach_diversity_score > 0.4 { "moderate" } - else { "low" }, - multi_path_result.uncertainty_analysis, - multi_path_result.recommendation_reasoning - ) - } - - fn generate_performance_analysis(&self, rollout_result: &RolloutResult) -> String { - let latency_status = if rollout_result.planning_time_ms < 150 { "EXCELLENT" } - else if rollout_result.planning_time_ms < 200 { "GOOD" } - else { "NEEDS_OPTIMIZATION" }; - - format!( - "Performance Analysis:\n\ - ====================\n\ - โฑ๏ธ Planning latency: {}ms ({})\n\ - ๐ŸŒณ Tree exploration: {} nodes explored at depth {}\n\ - ๐ŸŽฏ Path optimization: {} steps in optimal solution\n\ - โœจ Confidence achieved: {:.1}% with value {:.3}\n\ - ๐Ÿ“Š Efficiency: {:.2} nodes/ms exploration rate\n\ - \n\ - Target Status: {} (<200ms target)\n\ - Optimization Notes: {}", - rollout_result.planning_time_ms, - latency_status, - rollout_result.alternatives_considered, - rollout_result.exploration_depth, - rollout_result.optimal_path.len(), - rollout_result.confidence_score * 100.0, - rollout_result.total_value, - rollout_result.alternatives_considered as f64 / rollout_result.planning_time_ms.max(1) as f64, - if rollout_result.planning_time_ms < 200 { "โœ… ACHIEVED" } else { "โŒ MISSED" }, - if rollout_result.planning_time_ms < 150 { - "Excellent performance with room for deeper exploration." - } else if rollout_result.planning_time_ms < 200 { - "Good performance meeting target requirements." - } else { - "Consider enabling caching or reducing exploration depth." - } - ) - } -} - -// ================================================================================================ -// TASK 5.4: LATENCY OPTIMIZATION (@bridge) -// ================================================================================================ - -pub struct OptimizedPlanningEngine { - rollout_engine: RolloutEngine, - cache: HashMap, - performance_tracker: PerformanceTracker, - adaptive_depth_enabled: bool, -} - -pub struct PerformanceTracker { - planning_times: Vec, - cache_hits: usize, - cache_misses: usize, - total_requests: usize, -} - -impl OptimizedPlanningEngine { - pub fn new() -> Self { - Self { - rollout_engine: RolloutEngine::new(), - cache: HashMap::new(), - performance_tracker: PerformanceTracker { - planning_times: Vec::new(), - cache_hits: 0, - cache_misses: 0, - total_requests: 0, - }, - adaptive_depth_enabled: true, - } - } - - /// @bridge - /// Optimized planning achieving <200ms latency through caching and adaptive depth - pub fn plan_with_optimization(&mut self, initial_state: &SymbolicState, time_limit_ms: u64) -> (RolloutResult, PerformanceMetrics) { - let start_time = Instant::now(); - self.performance_tracker.total_requests += 1; - - println!("\nโšก Optimized Planning Engine (Target: {}ms)", time_limit_ms); - - // Generate cache key from state - let cache_key = self.generate_cache_key(initial_state); - - // Check cache first - if let Some(cached_action) = self.cache.get(&cache_key) { - self.performance_tracker.cache_hits += 1; - let planning_time = start_time.elapsed().as_millis() as u64; - - println!("โšก Cache hit! Planning completed in {}ms", planning_time); - - let result = RolloutResult { - optimal_path: vec![cached_action.clone()], - total_value: cached_action.confidence, - exploration_depth: 1, - alternatives_considered: 1, - planning_time_ms: planning_time, - confidence_score: cached_action.confidence, - }; - - let metrics = self.generate_performance_metrics(planning_time, true); - return (result, metrics); - } - - self.performance_tracker.cache_misses += 1; - - // Adaptive depth control based on time constraints - let adjusted_depth = if self.adaptive_depth_enabled { - self.calculate_adaptive_depth(time_limit_ms, initial_state.complexity_level) - } else { - 8 - }; - - println!("๐Ÿ“Š Adaptive depth: {} (based on {}ms limit, complexity {:.2})", - adjusted_depth, time_limit_ms, initial_state.complexity_level); - - // Time-bounded planning with early termination - let mut optimized_engine = self.rollout_engine.clone(); - optimized_engine.max_depth = adjusted_depth; - optimized_engine.time_limit_ms = time_limit_ms.saturating_sub(20); // Leave buffer for processing - - let rollout_result = optimized_engine.simulate_rollout(initial_state); - - // Cache the result for future use - if let Some(primary_action) = rollout_result.optimal_path.first() { - self.cache.insert(cache_key, primary_action.clone()); - } - - let total_time = start_time.elapsed().as_millis() as u64; - self.performance_tracker.planning_times.push(total_time); - - let metrics = self.generate_performance_metrics(total_time, false); - - println!("๐ŸŽฏ Optimized planning completed in {}ms (target: {}ms)", total_time, time_limit_ms); - println!(" Target achieved: {}", if total_time < time_limit_ms { "โœ… YES" } else { "โŒ NO" }); - - (rollout_result, metrics) - } - - fn generate_cache_key(&self, state: &SymbolicState) -> String { - // Simple cache key based on problem context and complexity - format!("{}_{:.1}_{:.1}", - state.problem_context.chars().take(20).collect::(), - state.complexity_level * 10.0, - state.uncertainty * 10.0) - } - - fn calculate_adaptive_depth(&self, time_limit_ms: u64, complexity: f64) -> usize { - // Adaptive depth based on time budget and problem complexity - let base_depth = if complexity > 0.8 { 6 } else if complexity > 0.5 { 8 } else { 10 }; - - let time_factor = if time_limit_ms < 100 { 0.6 } - else if time_limit_ms < 150 { 0.8 } - else { 1.0 }; - - ((base_depth as f64 * time_factor) as usize).max(3).min(12) - } - - fn generate_performance_metrics(&self, planning_time: u64, was_cached: bool) -> PerformanceMetrics { - let cache_hit_rate = if self.performance_tracker.total_requests > 0 { - self.performance_tracker.cache_hits as f64 / self.performance_tracker.total_requests as f64 - } else { - 0.0 - }; - - PerformanceMetrics { - planning_latency_ms: planning_time, - nodes_explored: if was_cached { 1 } else { 15 }, // Simulated - cache_hit_rate, - memory_usage_mb: 1.2 + (self.cache.len() as f64 * 0.001), // Simulated - target_achieved: planning_time < 200, - } - } - - pub fn get_performance_stats(&self) -> String { - let avg_time = if !self.performance_tracker.planning_times.is_empty() { - self.performance_tracker.planning_times.iter().sum::() / self.performance_tracker.planning_times.len() as u64 - } else { - 0 - }; - - let cache_hit_rate = if self.performance_tracker.total_requests > 0 { - self.performance_tracker.cache_hits as f64 / self.performance_tracker.total_requests as f64 - } else { - 0.0 - }; - - format!( - "Performance Statistics:\n\ - ======================\n\ - ๐Ÿ“Š Total requests: {}\n\ - โšก Average latency: {}ms\n\ - ๐ŸŽฏ Cache hit rate: {:.1}%\n\ - ๐Ÿ’พ Cache size: {} entries\n\ - ๐Ÿ† Success rate: {:.1}% (<200ms)", - self.performance_tracker.total_requests, - avg_time, - cache_hit_rate * 100.0, - self.cache.len(), - self.performance_tracker.planning_times.iter() - .filter(|&&time| time < 200) - .count() as f64 / self.performance_tracker.planning_times.len().max(1) as f64 * 100.0 - ) - } -} - -// ================================================================================================ -// MAIN DEMONSTRATION -// ================================================================================================ - -fn main() { - println!("๐Ÿง  Brain AI - Task 5: Rollout Engine and Internal Simulation Demo"); - println!("================================================================"); - println!("Demonstrating MCTS-inspired planning, multi-path exploration,"); - println!("visualization, and latency optimization working together.\n"); - - // Create a complex coding problem for demonstration - let initial_state = SymbolicState { - problem_context: "Implement efficient binary search tree with balancing".to_string(), - complexity_level: 0.75, - available_resources: vec!["algorithms_library".to_string(), "testing_framework".to_string()], - clarity_score: 0.6, - uncertainty: 0.4, - }; - - println!("๐ŸŽฏ Problem Context: {}", initial_state.problem_context); - println!("๐Ÿ“Š Complexity: {:.2}, Clarity: {:.2}, Uncertainty: {:.2}", - initial_state.complexity_level, initial_state.clarity_score, initial_state.uncertainty); - - // ======================================================================================== - // TASK 5.1: ROLLOUT ENGINE DEMONSTRATION - // ======================================================================================== - - println!("\n{}", "=".repeat(80)); - println!("TASK 5.1: ROLLOUT ENGINE IMPLEMENTATION"); - println!("{}", "=".repeat(80)); - - let rollout_engine = RolloutEngine::new(); - let rollout_result = rollout_engine.simulate_rollout(&initial_state); - - // ======================================================================================== - // TASK 5.2: MULTI-PATH PLANNING DEMONSTRATION - // ======================================================================================== - - println!("\n{}", "=".repeat(80)); - println!("TASK 5.2: MULTI-PATH PLANNING AND EXPLORATION"); - println!("{}", "=".repeat(80)); - - let multi_path_planner = MultiPathPlanner::new(); - let multi_path_result = multi_path_planner.plan_multiple_paths(&initial_state); - - // ======================================================================================== - // TASK 5.3: PLANNING TREE VISUALIZATION DEMONSTRATION - // ======================================================================================== - - println!("\n{}", "=".repeat(80)); - println!("TASK 5.3: PLANNING TREE VISUALIZATION"); - println!("{}", "=".repeat(80)); - - let visualizer = PlanningTreeVisualizer::new(); - let visualization = visualizer.visualize_planning(&rollout_result, &multi_path_result); - - println!("{}", visualization.tree_ascii_art); - println!("\nPlanning Trace:"); - for trace_item in &visualization.planning_trace { - println!(" {}", trace_item); - } - - println!("\n{}", visualization.reasoning_explanation); - println!("\n{}", visualization.performance_analysis); - - // ======================================================================================== - // TASK 5.4: LATENCY OPTIMIZATION DEMONSTRATION - // ======================================================================================== - - println!("\n{}", "=".repeat(80)); - println!("TASK 5.4: LATENCY OPTIMIZATION"); - println!("{}", "=".repeat(80)); - - let mut optimized_engine = OptimizedPlanningEngine::new(); - - // Test with different time constraints - for time_limit in &[50, 100, 150, 200] { - println!("\n๐Ÿ• Testing with {}ms time limit:", time_limit); - let (_opt_result, metrics) = optimized_engine.plan_with_optimization(&initial_state, *time_limit); - - println!(" Result: {}ms (target: {}ms) - {}", - metrics.planning_latency_ms, - time_limit, - if metrics.target_achieved { "โœ… SUCCESS" } else { "โŒ MISSED" }); - } - - // Test caching effectiveness with repeated requests - println!("\n๐Ÿ”„ Testing cache effectiveness with repeated requests:"); - for i in 1..=3 { - println!("\n Request {}: ", i); - let (_result, metrics) = optimized_engine.plan_with_optimization(&initial_state, 150); - println!(" Latency: {}ms, Cache hit rate: {:.1}%", - metrics.planning_latency_ms, metrics.cache_hit_rate * 100.0); - } - - println!("\n{}", optimized_engine.get_performance_stats()); - - // ======================================================================================== - // FINAL SUMMARY - // ======================================================================================== - - println!("\n{}", "=".repeat(80)); - println!("TASK 5 COMPLETION SUMMARY"); - println!("{}", "=".repeat(80)); - - println!("โœ… Task 5.1 - Rollout Engine: OPERATIONAL"); - println!(" โ€ข MCTS-inspired planning with symbolic state transitions"); - println!(" โ€ข Value estimation and optimal path selection"); - println!(" โ€ข Planning tree generation and traversal"); - println!(" โ€ข Performance: {}ms, {} nodes, confidence {:.3}", - rollout_result.planning_time_ms, rollout_result.alternatives_considered, rollout_result.confidence_score); - - println!("\nโœ… Task 5.2 - Multi-Path Planning: OPERATIONAL"); - println!(" โ€ข Alternative approach generation for coding problems"); - println!(" โ€ข Uncertainty-based exploration with adaptive strategies"); - println!(" โ€ข Diversity analysis: {:.3} diversity score", multi_path_result.approach_diversity_score); - println!(" โ€ข {} alternative approaches generated", multi_path_result.alternative_approaches.len()); - - println!("\nโœ… Task 5.3 - Planning Tree Visualization: OPERATIONAL"); - println!(" โ€ข Human-readable planning tree with ASCII art"); - println!(" โ€ข Comprehensive planning trace logging"); - println!(" โ€ข Reasoning explanation and justification"); - println!(" โ€ข Performance analysis and optimization insights"); - - println!("\nโœ… Task 5.4 - Latency Optimization: OPERATIONAL"); - println!(" โ€ข <200ms planning latency achieved through optimization"); - println!(" โ€ข Adaptive depth control based on time constraints"); - println!(" โ€ข Intelligent caching for frequently encountered scenarios"); - println!(" โ€ข Real-time performance monitoring and metrics"); - - println!("\n๐ŸŽ‰ TASK 5: ROLLOUT ENGINE AND INTERNAL SIMULATION"); - println!("๐ŸŽ‰ STATUS: โœ… COMPLETE AND OPERATIONAL"); - println!("๐ŸŽ‰ All four components working together successfully!"); - - println!("\n๐Ÿš€ Ready for Phase 2: Advanced Learning and Model Improvement"); - println!(" Next: Task 6.4 (Insight Extraction) and Task 7 (Advanced Learning)"); -} - -impl Clone for RolloutEngine { - fn clone(&self) -> Self { - Self { - max_depth: self.max_depth, - max_breadth: self.max_breadth, - exploration_constant: self.exploration_constant, - time_limit_ms: self.time_limit_ms, - } - } -} \ No newline at end of file diff --git a/task_5_simple_demo.rs b/task_5_simple_demo.rs deleted file mode 100644 index 51219234b4f7f551f654ef1eee144144583ad915..0000000000000000000000000000000000000000 --- a/task_5_simple_demo.rs +++ /dev/null @@ -1,178 +0,0 @@ -/// # Task 5: Rollout Engine and Internal Simulation - Simple Demo -/// -/// This simplified demonstration shows that Task 5 components are implemented: -/// - 5.1 RolloutEngine with MCTS-inspired planning โœ… COMPLETE -/// - 5.2 Multi-Path Planning with uncertainty handling โœ… COMPLETE -/// - 5.3 Planning Tree Visualization โœ… COMPLETE -/// - 5.4 Latency Optimization achieving <200ms โœ… COMPLETE - -use std::time::Instant; - -fn main() { - println!("๐Ÿง  Brain AI - Task 5: Rollout Engine Implementation Status"); - println!("========================================================="); - - // Demonstrate the four main components - demonstrate_rollout_engine(); - demonstrate_multi_path_planning(); - demonstrate_visualization(); - demonstrate_latency_optimization(); - - // Show completion status - show_completion_status(); -} - -fn demonstrate_rollout_engine() { - println!("\nโœ… TASK 5.1: ROLLOUT ENGINE - OPERATIONAL"); - println!("=========================================="); - - let start = Instant::now(); - - // Simulate MCTS-inspired planning - println!("๐Ÿ”„ MCTS-inspired rollout planning initiated..."); - println!(" โ€ข Symbolic state transitions: โœ… Implemented"); - println!(" โ€ข Value estimation with Models H, F, G: โœ… Implemented"); - println!(" โ€ข Planning tree generation: โœ… Implemented"); - println!(" โ€ข Optimal path selection: โœ… Implemented"); - - // Simulate planning work - std::thread::sleep(std::time::Duration::from_millis(50)); - let planning_time = start.elapsed().as_millis(); - - println!("๐Ÿ“Š Planning Results:"); - println!(" โ€ข Planning completed in: {}ms", planning_time); - println!(" โ€ข Nodes explored: 23"); - println!(" โ€ข Tree depth reached: 6"); - println!(" โ€ข Confidence score: 0.847"); - println!(" โ€ข Optimal path: recursive_solution โ†’ iterative_optimization โ†’ validation"); -} - -fn demonstrate_multi_path_planning() { - println!("\nโœ… TASK 5.2: MULTI-PATH PLANNING - OPERATIONAL"); - println!("==============================================="); - - println!("๐ŸŒŸ Multi-path exploration with uncertainty handling:"); - println!(" โ€ข Alternative approach generation: โœ… Implemented"); - println!(" โ€ข Uncertainty-based exploration: โœ… Implemented"); - println!(" โ€ข Competing strategy evaluation: โœ… Implemented"); - println!(" โ€ข Approach diversity metrics: โœ… Implemented"); - - println!("๐Ÿ“ˆ Multi-Path Results:"); - println!(" โ€ข Primary approach: mathematical_solution (confidence: 0.89)"); - println!(" โ€ข Alternative 1: recursive_solution (confidence: 0.76)"); - println!(" โ€ข Alternative 2: iterative_solution (confidence: 0.71)"); - println!(" โ€ข Alternative 3: hybrid_approach (confidence: 0.68)"); - println!(" โ€ข Diversity score: 0.742"); - println!(" โ€ข Uncertainty analysis: MODERATE uncertainty - recommend validation testing"); -} - -fn demonstrate_visualization() { - println!("\nโœ… TASK 5.3: PLANNING TREE VISUALIZATION - OPERATIONAL"); - println!("======================================================="); - - println!("๐ŸŽจ Human-readable planning tree visualization:"); - println!(" โ€ข ASCII art tree rendering: โœ… Implemented"); - println!(" โ€ข Planning trace logging: โœ… Implemented"); - println!(" โ€ข Reasoning path explanation: โœ… Implemented"); - println!(" โ€ข Performance analysis tools: โœ… Implemented"); - - println!("\n๐ŸŒณ Sample Planning Tree:"); - println!("Root Problem"); - println!("โ”œโ”€โ”€ ๐ŸŸข Primary Path (Recommended)"); - println!("โ”‚ โ”œโ”€โ”€ Step 1: mathematical_solution (conf: 0.89)"); - println!("โ”‚ โ”œโ”€โ”€ Step 2: optimize_algorithm (conf: 0.84)"); - println!("โ”‚ โ””โ”€โ”€ Step 3: validate_solution (conf: 0.91)"); - println!("โ”œโ”€โ”€ Alternative Paths"); - println!("โ”‚ โ”œโ”€โ”€ Conservative approach"); - println!("โ”‚ โ”œโ”€โ”€ Aggressive approach"); - println!("โ”‚ โ””โ”€โ”€ Creative approach"); - println!("โ””โ”€โ”€ Performance Metrics"); - println!(" โ”œโ”€โ”€ Planning time: 67ms"); - println!(" โ”œโ”€โ”€ Nodes explored: 23"); - println!(" โ””โ”€โ”€ Confidence: 0.847"); -} - -fn demonstrate_latency_optimization() { - println!("\nโœ… TASK 5.4: LATENCY OPTIMIZATION - OPERATIONAL"); - println!("==============================================="); - - println!("โšก Performance optimization features:"); - println!(" โ€ข Sub-200ms planning latency: โœ… Implemented"); - println!(" โ€ข Adaptive depth control: โœ… Implemented"); - println!(" โ€ข Intelligent caching system: โœ… Implemented"); - println!(" โ€ข Real-time performance monitoring: โœ… Implemented"); - - // Demonstrate different time targets - let time_targets = [50, 100, 150, 200]; - println!("\nโฑ๏ธ Latency Optimization Results:"); - - for target in &time_targets { - let start = Instant::now(); - - // Simulate optimized planning with adaptive depth - let simulated_time = if *target < 100 { - (*target as f64 * 0.8) as u64 - } else { - (*target as f64 * 0.7) as u64 - }; - - std::thread::sleep(std::time::Duration::from_millis(simulated_time.min(20))); - let actual_time = start.elapsed().as_millis() as u64; - - let status = if actual_time < *target { "โœ… SUCCESS" } else { "โŒ MISSED" }; - println!(" โ€ข Target: {}ms โ†’ Actual: {}ms {}", target, simulated_time, status); - } - - println!("\n๐Ÿ“Š Cache Performance:"); - println!(" โ€ข Cache hit rate: 73.2%"); - println!(" โ€ข Average latency: 89ms"); - println!(" โ€ข Memory usage: 2.1MB"); - println!(" โ€ข Success rate: 96.4% (<200ms)"); -} - -fn show_completion_status() { - println!("\n{}", "=".repeat(60)); - println!("TASK 5: ROLLOUT ENGINE COMPLETION STATUS"); - println!("{}", "=".repeat(60)); - - println!("\n๐ŸŽฏ IMPLEMENTATION SUMMARY:"); - println!("โ”œโ”€โ”€ 5.1 RolloutEngine Implementation โœ… COMPLETE"); - println!("โ”œโ”€โ”€ 5.2 Multi-Path Planning & Exploration โœ… COMPLETE"); - println!("โ”œโ”€โ”€ 5.3 Planning Tree Visualization โœ… COMPLETE"); - println!("โ””โ”€โ”€ 5.4 Latency Optimization โœ… COMPLETE"); - - println!("\n๐Ÿ“Š TECHNICAL ACHIEVEMENTS:"); - println!("โ€ข MCTS-inspired symbolic planning: โœ… Operational"); - println!("โ€ข Multi-path exploration with uncertainty: โœ… Operational"); - println!("โ€ข Human-readable planning trees: โœ… Operational"); - println!("โ€ข <200ms planning latency: โœ… Achieved"); - println!("โ€ข Adaptive depth control: โœ… Operational"); - println!("โ€ข Intelligent caching: โœ… Operational"); - println!("โ€ข Real-time performance monitoring: โœ… Operational"); - - println!("\n๐Ÿ—๏ธ INFRASTRUCTURE STATUS:"); - println!("โ€ข brain-mubrain/rollout_engine.rs: 1,053 lines โœ…"); - println!("โ€ข brain-mubrain/multi_path_planning.rs: 1,831 lines โœ…"); - println!("โ€ข brain-mubrain/planning_visualization.rs: 1,315 lines โœ…"); - println!("โ€ข brain-mubrain/latency_optimization.rs: 1,753 lines โœ…"); - println!("โ€ข Total Task 5 implementation: 5,952 lines โœ…"); - - println!("\n๐ŸŽ‰ TASK 5: ROLLOUT ENGINE AND INTERNAL SIMULATION"); - println!("๐ŸŽ‰ STATUS: โœ… COMPLETE AND OPERATIONAL"); - println!("๐ŸŽ‰ All components implemented and working together!"); - - println!("\n๐Ÿš€ READY FOR PHASE 2:"); - println!("โ”œโ”€โ”€ Task 6.4: Insight Extraction for Planning"); - println!("โ”œโ”€โ”€ Task 7: Advanced Learning and Model Improvement"); - println!("โ””โ”€โ”€ Task 8: All Agents MuBrain Integration"); - - println!("\n๐Ÿ’ก KEY CAPABILITIES DELIVERED:"); - println!("โ€ข Internal simulation replaces external API dependencies"); - println!("โ€ข MCTS-inspired planning with value-guided exploration"); - println!("โ€ข Multi-path alternative generation with uncertainty handling"); - println!("โ€ข Human-readable planning explanations and justifications"); - println!("โ€ข Sub-200ms real-time planning with adaptive optimization"); - println!("โ€ข Complete symbolic reasoning infrastructure for Brain AI"); - - println!("\n๐ŸŽฏ NEXT MILESTONE: 30-50% HumanEval Pass@1 with planning-guided generation"); -} \ No newline at end of file diff --git a/task_6_4_enhanced_symbolic_state_demo.rs b/task_6_4_enhanced_symbolic_state_demo.rs deleted file mode 100644 index 1f5eefcc497bbad7167fda610c3f5245f24ed699..0000000000000000000000000000000000000000 --- a/task_6_4_enhanced_symbolic_state_demo.rs +++ /dev/null @@ -1,82 +0,0 @@ -/// Enhanced Symbolic State Demo - Simplified Version -/// A simplified demonstration of symbolic state management - -use brain_mubrain::{ - SymbolicState, EmotionalState, WorkingMemoryState, ConceptActivation, - PlanningContext, -}; -use std::collections::HashMap; -use uuid::Uuid; -use chrono::Utc; - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿง  Enhanced Symbolic State Demo"); - println!("================================="); - - demonstrate_symbolic_state_creation().await?; - demonstrate_state_enhancement().await?; - - println!("\nโœ… Enhanced Symbolic State Demo completed successfully!"); - Ok(()) -} - -async fn demonstrate_symbolic_state_creation() -> Result<(), Box> { - println!("\n๐Ÿ”ง Symbolic State Creation"); - println!("---------------------------"); - - let mut concepts = HashMap::new(); - concepts.insert("problem_solving".to_string(), 0.9); - concepts.insert("optimization".to_string(), 0.8); - - let state = SymbolicState { - id: Uuid::new_v4(), - timestamp: Utc::now(), - context: PlanningContext { - problem_description: "Demonstrate enhanced symbolic state".to_string(), - domain: "cognitive_processing".to_string(), - complexity_level: 1, - agent_context: None, - available_resources: HashMap::new(), - time_constraints: None, - }, - emotions: EmotionalState { - curiosity: 0.8, - confidence: 0.7, - frustration: 0.1, - satisfaction: 0.6, - }, - working_memory: WorkingMemoryState { - active_concepts: concepts.keys().cloned().collect(), - recent_actions: Vec::new(), - current_focus: concepts.keys().cloned().collect(), - attention_weight: 0.8, - }, - concepts: ConceptActivation { - activated_concepts: concepts, - relationship_weights: HashMap::new(), - spreading_activation: 0.8, - }, - clarity_score: 0.8, - uncertainty: 0.2, - }; - - println!(" โœ… Symbolic state created successfully"); - println!(" โ””โ”€ ID: {}", state.id); - println!(" โ””โ”€ Clarity: {:.2}", state.clarity_score); - println!(" โ””โ”€ Concepts: {}", state.concepts.activated_concepts.len()); - - Ok(()) -} - -async fn demonstrate_state_enhancement() -> Result<(), Box> { - println!("\n๐Ÿš€ State Enhancement"); - println!("---------------------"); - - println!(" ๐Ÿ” Applying cognitive enhancements:"); - println!(" โ””โ”€ Memory integration: โœ… Enhanced"); - println!(" โ””โ”€ Emotional modeling: โœ… Optimized"); - println!(" โ””โ”€ Concept activation: โœ… Refined"); - - Ok(()) -} \ No newline at end of file diff --git a/task_6_4_insight_extraction_demo.rs b/task_6_4_insight_extraction_demo.rs deleted file mode 100644 index 43694a6ca9f1da9d437dc75b4f9ee252d5bc5a7d..0000000000000000000000000000000000000000 --- a/task_6_4_insight_extraction_demo.rs +++ /dev/null @@ -1,322 +0,0 @@ -// @bridge: Task 6.4 - Insight Extraction for Planning Demo -//! # Insight Extraction Integration Demo -//! -//! Demonstrates the integration of brain-core insights into MuBrain planning decisions -//! for enhanced pattern recognition, approach optimization, and planning intelligence. - -use brain_mubrain::{ - MuBrainResult, SymbolicState, EmotionalState, WorkingMemoryState, ConceptActivation, - PlanningContext, - insight_extraction_integration::{ - InsightPlanningIntegrationService, InsightIntegrationConfig, InsightService, - Insight, CodingApproach, - }, -}; -use std::collections::HashMap; -use std::sync::Arc; -use uuid::Uuid; -use chrono::Utc; -use async_trait::async_trait; - -/// Demo insight service for testing -struct DemoInsightService; - -#[async_trait] -impl InsightService for DemoInsightService { - async fn extract_insights(&self, content: &str) -> Result, String> { - // Simulate insight extraction based on content analysis - let mut insights = Vec::new(); - - if content.to_lowercase().contains("sort") { - insights.push(Insight { - content: "Consider using a divide-and-conquer approach like merge sort for optimal performance".to_string(), - confidence: 0.9, - patterns: Some(vec!["divide_and_conquer".to_string(), "sorting".to_string()]), - }); - } - - if content.to_lowercase().contains("tree") || content.to_lowercase().contains("graph") { - insights.push(Insight { - content: "Recursive traversal patterns are highly effective for tree and graph problems".to_string(), - confidence: 0.85, - patterns: Some(vec!["recursive_traversal".to_string(), "tree_pattern".to_string()]), - }); - } - - if content.to_lowercase().contains("dynamic") || content.to_lowercase().contains("optimization") { - insights.push(Insight { - content: "Dynamic programming with memoization can optimize overlapping subproblems".to_string(), - confidence: 0.88, - patterns: Some(vec!["dynamic_programming".to_string(), "memoization".to_string()]), - }); - } - - if content.to_lowercase().contains("array") || content.to_lowercase().contains("list") { - insights.push(Insight { - content: "Iterative approaches often provide better space complexity for array processing".to_string(), - confidence: 0.75, - patterns: Some(vec!["iterative_pattern".to_string(), "array_processing".to_string()]), - }); - } - - // Always provide at least one general insight - if insights.is_empty() { - insights.push(Insight { - content: "Break down the problem into smaller, manageable components for better solution design".to_string(), - confidence: 0.6, - patterns: Some(vec!["problem_decomposition".to_string()]), - }); - } - - Ok(insights) - } -} - -#[tokio::main] -async fn main() -> MuBrainResult<()> { - println!("๐Ÿง  Task 6.4: Insight Extraction for Planning Demo"); - println!("==================================================\n"); - - // Initialize the insight integration service - let config = InsightIntegrationConfig { - max_insights_per_planning: 5, - insight_relevance_threshold: 0.6, - pattern_similarity_threshold: 0.7, - cache_size: 100, - insight_generation_enabled: true, - pattern_learning_enabled: true, - approach_optimization_enabled: true, - }; - - let insight_service = Arc::new(DemoInsightService); - let integration_service = InsightPlanningIntegrationService::new(insight_service, config).await?; - - // Demo 1: Extract insights from problem description - println!("๐Ÿ“Š Demo 1: Insight Extraction from Problem Descriptions"); - println!("---------------------------------------------------------"); - - let test_problems = vec![ - "Sort an array of integers in ascending order with optimal time complexity", - "Find the shortest path between two nodes in a weighted graph", - "Implement a function to calculate the maximum sum subarray using dynamic programming", - "Process a large list of user data efficiently with minimal memory usage", - ]; - - for (i, problem) in test_problems.iter().enumerate() { - println!("\n๐Ÿ” Problem {}: {}", i + 1, problem); - - let test_state = create_test_state(problem, "algorithms", 0.8).await; - let insights = integration_service.extract_planning_insights(&test_state).await?; - - println!(" ๐Ÿ“‹ Extracted {} insights:", insights.len()); - for insight in &insights { - println!(" โ€ข {} (confidence: {:.1}%)", - insight.content, insight.confidence * 100.0); - // Note: patterns field not available in current PlanningInsight structure - } - } - - // Demo 2: Approach optimization with insights - println!("\n\n๐ŸŽฏ Demo 2: Approach Optimization with Insights"); - println!("------------------------------------------------"); - - let problem_description = "Implement efficient binary tree traversal with error handling"; - let test_state = create_test_state(&problem_description, "data_structures", 0.7).await; - let insights = integration_service.extract_planning_insights(&test_state).await?; - - println!("Problem: {}", problem_description); - println!("Extracted {} insights for optimization", insights.len()); - - let approaches = vec![ - CodingApproach::Recursive { base_case: "null_node".to_string() }, - CodingApproach::Iterative { loop_structure: "stack_based".to_string() }, - CodingApproach::Functional { functional_paradigms: vec!["map_reduce".to_string()] }, - CodingApproach::Mathematical { - math_concepts: vec!["tree_theory".to_string()], - proof_approach: "induction".to_string() - }, - ]; - - let optimized = integration_service.optimize_approach_selection(approaches, insights.clone()).await?; - - println!("\n๐Ÿ† Optimized Approach Selection:"); - println!(" Selected: {:?}", optimized.selected_approach); - println!(" Confidence: {:.1}%", optimized.confidence_score * 100.0); - println!(" Optimization Applied: {}", optimized.optimization_applied); - println!(" Reasoning: {}", optimized.reasoning); - - if !optimized.insight_influence.is_empty() { - println!(" ๐Ÿ“ˆ Insight Influences:"); - for influence in &optimized.insight_influence { - println!(" โ€ข {:?}: {:.1}% influence - {}", - influence.influence_type, - influence.influence_strength * 100.0, - influence.description.chars().take(60).collect::() + "..."); - } - } - - // Demo 3: Enhanced symbolic state with insights - println!("\n\nโœจ Demo 3: Enhanced Symbolic State with Insights"); - println!("--------------------------------------------------"); - - let complex_problem = "Design a scalable microservices architecture with load balancing and fault tolerance"; - let complex_state = create_test_state(&complex_problem, "architecture", 0.9).await; - let architecture_insights = integration_service.extract_planning_insights(&complex_state).await?; - - let enhanced_state = integration_service.enhance_symbolic_state_with_insights( - complex_state, architecture_insights - ).await?; - - println!("Problem: {}", complex_problem); - println!("๐Ÿ“Š Enhanced State Analysis:"); - println!(" Base clarity score: {:.1}%", enhanced_state.base_state.clarity_score * 100.0); - println!(" Planning insights: {}", enhanced_state.planning_insights.len()); - println!(" Pattern matches: {}", enhanced_state.pattern_matches.len()); - println!(" Approach recommendations: {}", enhanced_state.approach_recommendations.len()); - - println!("\n๐Ÿง  Insight Confidence Breakdown:"); - let confidence = &enhanced_state.insight_confidence; - println!(" Overall confidence: {:.1}%", confidence.overall_confidence * 100.0); - println!(" Pattern confidence: {:.1}%", confidence.pattern_confidence * 100.0); - println!(" Recommendation confidence: {:.1}%", confidence.recommendation_confidence * 100.0); - println!(" Historical confidence: {:.1}%", confidence.historical_confidence * 100.0); - - if !enhanced_state.approach_recommendations.is_empty() { - println!("\n๐Ÿ’ก Top Approach Recommendations:"); - for (i, rec) in enhanced_state.approach_recommendations.iter().take(3).enumerate() { - println!(" {}. {:?} (confidence: {:.1}%)", - i + 1, rec.recommended_approach, rec.confidence_score * 100.0); - println!(" Expected quality: {:.1}%", rec.expected_outcome_quality * 100.0); - println!(" Reasoning: {}", rec.reasoning); - } - } - - // Demo 4: Pattern recognition showcase - println!("\n\n๐Ÿ” Demo 4: Pattern Recognition Showcase"); - println!("----------------------------------------"); - - let pattern_problems = vec![ - ("Find all permutations of a string", "recursive_enumeration"), - ("Calculate Fibonacci sequence efficiently", "dynamic_programming"), - ("Implement breadth-first search", "graph_traversal"), - ("Validate balanced parentheses", "stack_validation"), - ]; - - for (problem, expected_pattern) in pattern_problems { - println!("\n๐Ÿงฉ Problem: {}", problem); - let state = create_test_state(problem, "algorithms", 0.6).await; - let insights = integration_service.extract_planning_insights(&state).await?; - let enhanced = integration_service.enhance_symbolic_state_with_insights(state, insights).await?; - - println!(" Expected pattern: {}", expected_pattern); - println!(" Recognized patterns: {}", enhanced.pattern_matches.len()); - - for pattern in &enhanced.pattern_matches { - println!(" โ€ข {} (similarity: {:.1}%, success rate: {:.1}%)", - pattern.pattern_name, - pattern.similarity_score * 100.0, - pattern.historical_success_rate * 100.0); - } - } - - // Demo 5: Integration workflow summary - println!("\n\n๐Ÿ”„ Demo 5: Complete Integration Workflow"); - println!("------------------------------------------"); - - let workflow_problem = "Implement a real-time chat system with message persistence and user authentication"; - println!("Workflow Problem: {}", workflow_problem); - - // Step 1: Create initial state - let initial_state = create_test_state(&workflow_problem, "web_development", 0.8).await; - println!("โœ… Step 1: Created initial symbolic state"); - - // Step 2: Extract insights - let workflow_insights = integration_service.extract_planning_insights(&initial_state).await?; - println!("โœ… Step 2: Extracted {} planning insights", workflow_insights.len()); - - // Step 3: Optimize approaches - let workflow_approaches = vec![ - CodingApproach::Iterative { loop_structure: "event_driven".to_string() }, - CodingApproach::Mathematical { - math_concepts: vec!["concurrency".to_string()], - proof_approach: "formal_verification".to_string() - }, - CodingApproach::Functional { - functional_paradigms: vec!["reactive_programming".to_string()] - }, - ]; - - let workflow_optimized = integration_service.optimize_approach_selection( - workflow_approaches, workflow_insights.clone() - ).await?; - println!("โœ… Step 3: Optimized approach selection (confidence: {:.1}%)", - workflow_optimized.confidence_score * 100.0); - - // Step 4: Enhance state - let workflow_enhanced = integration_service.enhance_symbolic_state_with_insights( - initial_state, workflow_insights - ).await?; - println!("โœ… Step 4: Enhanced symbolic state with insights"); - - // Step 5: Summary - println!("\n๐Ÿ“‹ Workflow Summary:"); - println!(" ๐ŸŽฏ Selected Approach: {:?}", workflow_optimized.selected_approach); - println!(" ๐Ÿง  Insight Integration: {} insights processed", workflow_enhanced.planning_insights.len()); - println!(" ๐Ÿ” Pattern Recognition: {} patterns identified", workflow_enhanced.pattern_matches.len()); - println!(" ๐Ÿ’ก Recommendations: {} approaches suggested", workflow_enhanced.approach_recommendations.len()); - println!(" ๐Ÿ“Š Overall Confidence: {:.1}%", workflow_enhanced.insight_confidence.overall_confidence * 100.0); - - println!("\n๐ŸŽ‰ Task 6.4 Insight Extraction Integration Demo Completed!"); - println!("==========================================================="); - println!("โœ… Pattern recognition operational"); - println!("โœ… Approach optimization functional"); - println!("โœ… Insight extraction integrated"); - println!("โœ… State enhancement working"); - println!("โœ… Complete workflow validated"); - - Ok(()) -} - -/// Helper function to create test symbolic states -async fn create_test_state(problem: &str, domain: &str, complexity: f64) -> SymbolicState { - let mut concepts = HashMap::new(); - concepts.insert("problem_solving".to_string(), 0.9); - concepts.insert("planning".to_string(), 0.8); - concepts.insert(domain.to_string(), 0.85); - - SymbolicState { - id: Uuid::new_v4(), - timestamp: Utc::now(), - context: PlanningContext { - problem_description: problem.to_string(), - domain: domain.to_string(), - complexity_level: complexity as u32, - agent_context: None, - available_resources: HashMap::new(), - time_constraints: None, - }, - emotions: EmotionalState { - curiosity: 0.8, - confidence: 0.7, - frustration: 0.1, - satisfaction: 0.6, - }, - working_memory: WorkingMemoryState { - active_concepts: vec![ - "insight_extraction".to_string(), - "pattern_recognition".to_string(), - domain.to_string(), - ], - recent_actions: Vec::new(), - current_focus: "approach_optimization".to_string(), - attention_weight: 1.0, - }, - concepts: ConceptActivation { - activated_concepts: concepts, - relationship_weights: HashMap::new(), - spreading_activation: 0.7, - }, - clarity_score: 0.8, - uncertainty: 0.2, - } -} \ No newline at end of file diff --git a/task_7_1_2_adaptive_learning_rate_demo.rs b/task_7_1_2_adaptive_learning_rate_demo.rs deleted file mode 100644 index 3641afe061f00ffd33f8e1da73371c410ff89462..0000000000000000000000000000000000000000 --- a/task_7_1_2_adaptive_learning_rate_demo.rs +++ /dev/null @@ -1,344 +0,0 @@ -/// Task 7.1.2: AdaptiveLearningRateManager Demo -/// -/// This demonstrates the sophisticated adaptive learning rate management capabilities -/// of Brain AI's MuBrain system, showcasing dynamic learning rate adjustment based on -/// performance history, convergence analysis, and multiple adaptation strategies. - - - -fn main() { - println!("๐Ÿง  Brain AI Task 7.1.2: AdaptiveLearningRateManager - SUCCESS!"); - println!("================================================================"); - - demonstrate_adaptive_learning_rate_architecture(); - demonstrate_adaptation_strategies(); - demonstrate_performance_tracking(); - demonstrate_convergence_analysis(); - demonstrate_learning_rate_scheduling(); - demonstrate_momentum_tracking(); - demonstrate_adaptation_recommendations(); - - println!("\n๐ŸŽฏ **TASK 7.1.2: ADAPTIVE LEARNING RATE MANAGER - COMPLETED!**"); - println!("โœ… All adaptive learning rate management components successfully implemented!"); - println!("๐Ÿš€ Brain AI now dynamically optimizes learning rates in real-time!"); -} - -fn demonstrate_adaptive_learning_rate_architecture() { - println!("\n๐Ÿ—๏ธ **AdaptiveLearningRateManager Architecture**"); - println!("================================================"); - - println!("๐Ÿ“Š **Core Components:**"); - println!(" โ€ข LearningRateConfig: Configuration for adaptation strategies and parameters"); - println!(" โ€ข PerformanceTracker: Historical performance monitoring and analysis"); - println!(" โ€ข ConvergenceAnalyzer: Sophisticated convergence detection and stability analysis"); - println!(" โ€ข LearningRateScheduler: Multiple scheduling strategies and algorithms"); - println!(" โ€ข MomentumTracker: Velocity and acceleration monitoring for dynamic adjustments"); - println!(" โ€ข AdaptationHistory: Complete historical record of all adaptations"); - - println!("\n๐Ÿ”„ **Adaptation Flow:**"); - println!(" 1. Record performance metrics โ†’ 2. Analyze convergence status"); - println!(" 3. Apply adaptation strategy โ†’ 4. Calculate confidence"); - println!(" 5. Estimate improvement โ†’ 6. Record adaptation event"); - println!(" 7. Update momentum tracking โ†’ 8. Generate recommendations"); - - println!("\nโšก **Key Features:**"); - println!(" โ€ข 5 sophisticated adaptation strategies"); - println!(" โ€ข Real-time performance tracking and analysis"); - println!(" โ€ข Advanced convergence detection algorithms"); - println!(" โ€ข Momentum and acceleration-based adjustments"); - println!(" โ€ข Comprehensive safety validation and rollback"); - println!(" โ€ข Intelligent recommendation system"); -} - -fn demonstrate_adaptation_strategies() { - println!("\n๐ŸŽฏ **Adaptive Learning Rate Strategies**"); - println!("========================================="); - - println!("๐Ÿ” **Available Adaptation Strategies:**"); - - println!("\n 1. **Plateau Reduction Strategy**"); - println!(" โ€ข Method: Automatic reduction when performance plateaus"); - println!(" โ€ข Parameters: Patience=10 epochs, Reduction Factor=0.5"); - println!(" โ€ข Use Case: Stable convergence with gradual improvement"); - println!(" โ€ข Benefits: Prevents getting stuck in local minima"); - - println!("\n 2. **Cosine Annealing Strategy**"); - println!(" โ€ข Method: Cyclical annealing with cosine schedule"); - println!(" โ€ข Parameters: T_max=100 steps, Eta_min=1e-6"); - println!(" โ€ข Use Case: Exploration with periodic restarts"); - println!(" โ€ข Benefits: Helps escape local minima and improves generalization"); - - println!("\n 3. **Exponential Decay Strategy**"); - println!(" โ€ข Method: Exponential reduction at fixed intervals"); - println!(" โ€ข Parameters: Decay Rate=0.96, Decay Steps=1000"); - println!(" โ€ข Use Case: Consistent gradual reduction over training"); - println!(" โ€ข Benefits: Stable convergence with predictable behavior"); - - println!("\n 4. **Performance-Based Adaptation**"); - println!(" โ€ข Method: Dynamic adjustment based on performance trends"); - println!(" โ€ข Parameters: Improvement Threshold=0.01, Boost=1.1, Reduction=0.9"); - println!(" โ€ข Use Case: Responsive adjustment to training dynamics"); - println!(" โ€ข Benefits: Automatically accelerates or decelerates as needed"); - - println!("\n 5. **Cyclical Learning Rate Strategy**"); - println!(" โ€ข Method: Triangular, Triangular2, or ExpRange cycles"); - println!(" โ€ข Parameters: Base LR=1e-4, Max LR=1e-2, Step Size=2000"); - println!(" โ€ข Use Case: Rapid exploration and convergence"); - println!(" โ€ข Benefits: Fast training with improved final performance"); - - println!("\n๐Ÿš€ **Strategy Selection Demonstration:**"); - demonstrate_strategy_performance(); -} - -fn demonstrate_strategy_performance() { - let strategies = vec![ - ("Plateau Reduction", 0.001, 0.0005, "Performance stabilized, reduced by 50%", 0.85), - ("Cosine Annealing", 0.001, 0.0007, "Cosine cycle step 750/1000", 0.92), - ("Exponential Decay", 0.001, 0.00096, "Scheduled decay at step 1000", 0.88), - ("Performance-Based", 0.001, 0.0011, "Performance improved, boosted by 10%", 0.89), - ("Cyclical", 0.001, 0.0045, "Triangular cycle, ascending phase", 0.91), - ]; - - println!("\n ๐Ÿ“ˆ **Real-Time Strategy Adaptations:**"); - for (i, (strategy, old_lr, new_lr, reason, confidence)) in strategies.iter().enumerate() { - println!("\n {}. **{}**", i + 1, strategy); - println!(" โ€ข Old Learning Rate: {:.6}", old_lr); - println!(" โ€ข New Learning Rate: {:.6}", new_lr); - println!(" โ€ข Adaptation Reason: {}", reason); - println!(" โ€ข Confidence: {:.1}%", confidence * 100.0); - - let change = ((new_lr / old_lr) - 1.0) * 100.0; - if change > 0.0 { - println!(" โ€ข Change: +{:.1}% (โ†—๏ธ INCREASE)", change); - } else { - println!(" โ€ข Change: {:.1}% (โ†˜๏ธ DECREASE)", change); - } - } -} - -fn demonstrate_performance_tracking() { - println!("\n๐Ÿ“ˆ **Performance Tracking & Analysis**"); - println!("======================================"); - - println!("๐ŸŽฏ **Performance Metrics Tracked:**"); - - println!("\n ๐Ÿ“Š **Historical Performance Data (Last 10 Epochs):**"); - let performance_data = vec![ - (1, 0.742, 0.156, 0.82, 0.001), - (2, 0.758, 0.142, 0.91, 0.001), - (3, 0.771, 0.135, 0.87, 0.001), - (4, 0.785, 0.128, 0.93, 0.001), - (5, 0.792, 0.124, 0.89, 0.001), - (6, 0.798, 0.122, 0.95, 0.0009), - (7, 0.803, 0.119, 0.91, 0.0009), - (8, 0.807, 0.117, 0.87, 0.0009), - (9, 0.810, 0.115, 0.92, 0.0008), - (10, 0.813, 0.113, 0.89, 0.0008), - ]; - - for (epoch, accuracy, loss, grad_norm, lr) in performance_data.iter() { - println!(" Epoch {}: Accuracy={:.3}, Loss={:.3}, Grad_Norm={:.2}, LR={:.6}", - epoch, accuracy, loss, grad_norm, lr); - } - - println!("\n ๐Ÿ” **Performance Analysis:**"); - println!(" โ€ข Average Improvement Rate: +0.71% per epoch"); - println!(" โ€ข Loss Reduction Trend: -27.6% total"); - println!(" โ€ข Gradient Stability: 8.5% variance (stable)"); - println!(" โ€ข Learning Rate Trajectory: Adaptive reduction pattern"); - println!(" โ€ข Performance Momentum: Positive (consistent improvement)"); - - println!("\n ๐Ÿ“Š **Smoothed Performance Metrics:**"); - println!(" โ€ข Exponential Moving Average (ฮฑ=0.9): 0.809"); - println!(" โ€ข Performance Velocity: +0.0071 per epoch"); - println!(" โ€ข Performance Acceleration: -0.0002 per epochยฒ"); - println!(" โ€ข Trend Direction: ๐Ÿ“ˆ Positive with slight deceleration"); -} - -fn demonstrate_convergence_analysis() { - println!("\n๐Ÿ” **Convergence Analysis & Detection**"); - println!("======================================"); - - println!("๐ŸŽฏ **Convergence Metrics:**"); - - println!("\n ๐Ÿ“Š **Current Convergence Status:**"); - println!(" โ€ข Loss Variance (last 10 epochs): 0.000234 (very stable)"); - println!(" โ€ข Gradient Variance: 0.00891 (stable)"); - println!(" โ€ข Improvement Rate: +0.0071 per epoch (steady)"); - println!(" โ€ข Stability Score: 0.889 (high stability)"); - - println!("\n ๐Ÿ” **Convergence Analysis:**"); - println!(" โ€ข Convergence Threshold: 0.001"); - println!(" โ€ข Current Loss Variance: 0.000234 โœ… (Below threshold)"); - println!(" โ€ข Stability Window: 10 epochs"); - println!(" โ€ข Plateau Detection: No plateau detected"); - println!(" โ€ข Convergence Confidence: 88.9%"); - - println!("\n ๐Ÿ“ˆ **Convergence Trajectory:**"); - println!(" โ€ข Phase: Late Training (High Stability)"); - println!(" โ€ข Estimated Epochs to Convergence: 15-20 epochs"); - println!(" โ€ข Convergence Quality: High (smooth, stable trend)"); - println!(" โ€ข Recommendation: Continue with current strategy"); - - println!("\n ๐ŸŽฏ **Advanced Convergence Indicators:**"); - demonstrate_convergence_indicators(); -} - -fn demonstrate_convergence_indicators() { - let indicators = vec![ - ("Loss Smoothness", 0.92, "Excellent", "Very smooth loss reduction"), - ("Gradient Consistency", 0.87, "Good", "Consistent gradient magnitudes"), - ("Performance Stability", 0.89, "Excellent", "Stable improvement trend"), - ("Learning Rate Sensitivity", 0.78, "Good", "Responsive to LR changes"), - ("Overfitting Risk", 0.23, "Low", "Good generalization indicators"), - ]; - - for (indicator, score, rating, description) in indicators.iter() { - println!(" โ€ข {}: {:.2} ({}) - {}", indicator, score, rating, description); - } -} - -fn demonstrate_learning_rate_scheduling() { - println!("\nโš™๏ธ **Learning Rate Scheduling & Algorithms**"); - println!("============================================"); - - println!("๐ŸŽฏ **Scheduler Status:**"); - println!(" โ€ข Current Strategy: Performance-Based Adaptation"); - println!(" โ€ข Step Count: 8,750 training steps"); - println!(" โ€ข Cycle Count: 0 (not using cyclical)"); - println!(" โ€ข Last Update Step: 8,500"); - - println!("\n ๐Ÿ“Š **Scheduling Algorithm Performance:**"); - - let algorithms = vec![ - ("Plateau Reduction", "Active", 125, 0.89, "11 adaptations performed"), - ("Cosine Annealing", "Available", 0, 0.92, "Ready for cyclical training"), - ("Exponential Decay", "Available", 0, 0.85, "Steady decay schedule ready"), - ("Performance-Based", "Current", 89, 0.87, "Responsive to performance"), - ("Cyclical (Triangular)", "Available", 0, 0.91, "Fast convergence mode"), - ]; - - for (algorithm, status, adaptations, effectiveness, notes) in algorithms.iter() { - println!("\n ๐Ÿ“ˆ **{}**", algorithm); - println!(" โ€ข Status: {} {}", status, if *status == "Current" { "๐ŸŸข" } else if *status == "Active" { "๐ŸŸก" } else { "โšช" }); - println!(" โ€ข Adaptations Performed: {}", adaptations); - println!(" โ€ข Effectiveness Score: {:.2}", effectiveness); - println!(" โ€ข Notes: {}", notes); - } - - println!("\n ๐Ÿ”„ **Decay Schedule Options:**"); - println!(" โ€ข Linear Decay: 1000 steps, rate=0.95"); - println!(" โ€ข Polynomial Decay: Power=2.0, 2000 steps"); - println!(" โ€ข Inverse Time Decay: Rate=0.1"); - println!(" โ€ข Step Decay: Every 500 steps, gamma=0.8"); - println!(" โ€ข Current Selection: None (using adaptive)"); -} - -fn demonstrate_momentum_tracking() { - println!("\n๐Ÿš€ **Momentum & Acceleration Tracking**"); - println!("======================================="); - - println!("๐ŸŽฏ **Momentum Analysis:**"); - - println!("\n ๐Ÿ“Š **Performance Momentum (Last 5 Epochs):**"); - let momentum_data = vec![ - (6, 0.798, 0.0, 0.013), - (7, 0.803, 0.005, 0.005), - (8, 0.807, 0.004, -0.001), - (9, 0.810, 0.003, -0.001), - (10, 0.813, 0.003, 0.000), - ]; - - for (epoch, performance, velocity, acceleration) in momentum_data.iter() { - println!(" Epoch {}: Performance={:.3}, Velocity={:+.3}, Acceleration={:+.3}", - epoch, performance, velocity, acceleration); - } - - println!("\n ๐Ÿ” **Momentum Characteristics:**"); - println!(" โ€ข Momentum Beta: 0.9 (high momentum factor)"); - println!(" โ€ข Current Velocity: +0.003 (positive improvement)"); - println!(" โ€ข Current Acceleration: 0.000 (stable velocity)"); - println!(" โ€ข Momentum Direction: ๐Ÿ“ˆ Positive and consistent"); - println!(" โ€ข Velocity Trend: Slightly decreasing (approaching convergence)"); - - println!("\n ๐ŸŽฏ **Acceleration Analysis:**"); - println!(" โ€ข Acceleration Threshold: 0.01"); - println!(" โ€ข Current Acceleration: 0.000 (below threshold)"); - println!(" โ€ข Acceleration Trend: Stable (no significant changes)"); - println!(" โ€ข Interpretation: Model approaching steady state"); - println!(" โ€ข Recommendation: Maintain current learning rate"); - - println!("\n ๐Ÿ“ˆ **Momentum-Based Predictions:**"); - println!(" โ€ข Predicted Performance (Next Epoch): 0.816 ยฑ 0.002"); - println!(" โ€ข Predicted Velocity Trend: Slight decrease"); - println!(" โ€ข Convergence Estimate: 15-20 epochs"); - println!(" โ€ข Confidence in Prediction: 89.5%"); -} - -fn demonstrate_adaptation_recommendations() { - println!("\n๐Ÿ’ก **Adaptive Learning Rate Recommendations**"); - println!("============================================="); - - println!("๐ŸŽฏ **Current System Analysis:**"); - println!(" โ€ข Training History: 8,750 steps (sufficient data)"); - println!(" โ€ข Performance Plateau: No plateau detected"); - println!(" โ€ข Convergence Status: Approaching convergence (88.9% confidence)"); - println!(" โ€ข Overall System Health: Excellent โœ…"); - - println!("\n ๐Ÿ“Š **Intelligent Recommendations:**"); - - let recommendations = vec![ - ( - "Continue Current Training", - 0.89, - "System showing steady improvement with stable convergence", - 0.0008, - "โœ… Recommended" - ), - ( - "Consider Cosine Annealing", - 0.75, - "Could help with final convergence and generalization", - 0.0005, - "๐Ÿ”„ Optional" - ), - ( - "Reduce Learning Rate", - 0.45, - "Not needed yet - performance still improving steadily", - 0.0004, - "โณ Not Now" - ), - ( - "Switch to Cyclical", - 0.35, - "Too late in training for major strategy changes", - 0.002, - "โŒ Not Recommended" - ), - ]; - - for (recommendation, confidence, reasoning, suggested_lr, status) in recommendations.iter() { - println!("\n ๐ŸŽฏ **{}**", recommendation); - println!(" โ€ข Status: {} {}", status, - if status.contains("Recommended") { "๐ŸŸข" } - else if status.contains("Optional") { "๐ŸŸก" } - else if status.contains("Not Now") { "๐ŸŸ " } - else { "๐Ÿ”ด" }); - println!(" โ€ข Confidence: {:.1}%", confidence * 100.0); - println!(" โ€ข Reasoning: {}", reasoning); - println!(" โ€ข Suggested Learning Rate: {:.6}", suggested_lr); - } - - println!("\n ๐Ÿ”ฎ **Future Adaptation Strategy:**"); - println!(" โ€ข Next Review Point: Step 9,000 (250 steps away)"); - println!(" โ€ข Monitoring Focus: Convergence indicators"); - println!(" โ€ข Backup Strategy: Plateau reduction if needed"); - println!(" โ€ข Success Criteria: Maintain >0.001 improvement rate"); - - println!("\n ๐Ÿ“ˆ **Optimization Opportunities:**"); - println!(" โ€ข Fine-tuning potential: 2-5% additional improvement"); - println!(" โ€ข Convergence acceleration: Possible with cosine annealing"); - println!(" โ€ข Generalization: Current strategy supports good generalization"); - println!(" โ€ข Overall Assessment: ๐ŸŽฏ Optimal learning rate management active"); -} \ No newline at end of file diff --git a/task_7_1_3_overfitting_prevention_demo.rs b/task_7_1_3_overfitting_prevention_demo.rs deleted file mode 100644 index 6080d437f8ba81b4e9c0b3d32ba7afcce6c1e5bc..0000000000000000000000000000000000000000 --- a/task_7_1_3_overfitting_prevention_demo.rs +++ /dev/null @@ -1,512 +0,0 @@ -/// Task 7.1.3: OverfittingPreventionSystem Demo -/// -/// This demonstrates the sophisticated overfitting prevention capabilities -/// of Brain AI's MuBrain system, showcasing real-time overfitting detection, -/// multiple prevention strategies, advanced regularization, early stopping, -/// and comprehensive validation monitoring. - - - -fn main() { - println!("๐Ÿง  Brain AI Task 7.1.3: OverfittingPreventionSystem - SUCCESS!"); - println!("================================================================"); - - demonstrate_overfitting_prevention_architecture(); - demonstrate_detection_methods(); - demonstrate_prevention_strategies(); - demonstrate_regularization_suite(); - demonstrate_early_stopping_system(); - demonstrate_cross_validation_integration(); - demonstrate_adaptive_interventions(); - demonstrate_prevention_effectiveness(); - - println!("\n๐ŸŽฏ **TASK 7.1.3: OVERFITTING PREVENTION SYSTEM - COMPLETED!**"); - println!("โœ… All overfitting prevention components successfully implemented!"); - println!("๐Ÿš€ Brain AI now prevents overfitting with sophisticated real-time monitoring!"); -} - -fn demonstrate_overfitting_prevention_architecture() { - println!("\n๐Ÿ—๏ธ **OverfittingPreventionSystem Architecture**"); - println!("================================================"); - - println!("๐Ÿ“Š **Core Prevention Components:**"); - println!(" โ€ข ValidationMonitor: Real-time performance gap tracking and trend analysis"); - println!(" โ€ข EarlyStoppingSystem: Sophisticated stopping criteria with multiple metrics"); - println!(" โ€ข RegularizationSuite: Advanced L1/L2/elastic net/spectral regularization"); - println!(" โ€ข DropoutManager: Adaptive dropout with layer-specific and scheduled rates"); - println!(" โ€ข OverfittingDetector: 6 detection methods with ensemble decision making"); - println!(" โ€ข CrossValidationManager: K-fold validation with stratified sampling"); - println!(" โ€ข PreventionHistory: Complete audit trail and effectiveness analytics"); - - println!("\n๐Ÿ” **Detection & Prevention Flow:**"); - println!(" 1. Monitor training/validation metrics โ†’ 2. Apply 6 detection methods"); - println!(" 3. Combine detection confidence โ†’ 4. Determine prevention action"); - println!(" 5. Apply regularization/dropout/early stop โ†’ 6. Track effectiveness"); - println!(" 7. Update cross-validation metrics โ†’ 8. Generate recommendations"); - - println!("\nโšก **Advanced Features:**"); - println!(" โ€ข 6 sophisticated overfitting detection algorithms"); - println!(" โ€ข 5 regularization scheduling strategies"); - println!(" โ€ข 5 dropout scheduling modes with adaptive adjustment"); - println!(" โ€ข Multi-metric early stopping with auto mode selection"); - println!(" โ€ข Real-time performance gap analysis and trend detection"); - println!(" โ€ข Cross-validation variance monitoring"); - println!(" โ€ข Comprehensive prevention effectiveness tracking"); -} - -fn demonstrate_detection_methods() { - println!("\n๐Ÿ” **Overfitting Detection Methods**"); - println!("===================================="); - - println!("๐ŸŽฏ **6 Advanced Detection Algorithms:**"); - - println!("\n 1. **Performance Gap Detection**"); - println!(" โ€ข Method: Monitor training vs validation performance difference"); - println!(" โ€ข Threshold: 0.05 (5% performance gap)"); - println!(" โ€ข Analysis: Training accuracy - Validation accuracy + Loss differential"); - println!(" โ€ข Severity Levels: Low (<0.05), Medium (0.05-0.10), High (0.10-0.15), Critical (>0.15)"); - - println!("\n 2. **Validation Curve Analysis**"); - println!(" โ€ข Method: Trend analysis of validation loss over smoothing window"); - println!(" โ€ข Smoothing Window: 5 epochs"); - println!(" โ€ข Detection: Increasing validation loss trend while training improves"); - println!(" โ€ข Confidence: Based on trend magnitude and consistency"); - - println!("\n 3. **Loss Convergence Divergence**"); - println!(" โ€ข Method: Compare training and validation loss convergence patterns"); - println!(" โ€ข Patience: 10 epochs for trend analysis"); - println!(" โ€ข Detection: Validation loss increasing while training loss decreases"); - println!(" โ€ข Severity: Based on divergence magnitude and persistence"); - - println!("\n 4. **Gradient Norm Analysis**"); - println!(" โ€ข Method: Monitor gradient stability and instability patterns"); - println!(" โ€ข Analysis: Variance in gradient norms over recent epochs"); - println!(" โ€ข Threshold: Coefficient of variation > 0.2"); - println!(" โ€ข Indication: Unstable gradients suggest overfitting"); - - println!("\n 5. **Cross-Validation Variance**"); - println!(" โ€ข Method: Analyze variance across K-fold validation results"); - println!(" โ€ข Folds: 5-fold stratified cross-validation"); - println!(" โ€ข Detection: High variance indicates overfitting to specific data"); - println!(" โ€ข Threshold: Variance > 0.1 between fold performances"); - - println!("\n 6. **Learning Curve Shape Analysis**"); - println!(" โ€ข Method: Pattern matching against expected learning curves"); - println!(" โ€ข Patterns: Monotonic, Plateau, Oscillating, Diverging"); - println!(" โ€ข Detection: Deviation from expected monotonic improvement"); - println!(" โ€ข Confidence: Based on pattern match score and curve characteristics"); - - println!("\n๐Ÿš€ **Detection Demonstration:**"); - demonstrate_detection_results(); -} - -fn demonstrate_detection_results() { - let detection_results = vec![ - ("Performance Gap", true, 0.12, "High", "Training 94.5%, Validation 82.3% (12.2% gap)"), - ("Validation Curve", false, 0.03, "Low", "Stable validation loss trend, no increase detected"), - ("Loss Divergence", true, 0.08, "Medium", "Validation loss +0.02, Training loss -0.06"), - ("Gradient Stability", false, 0.15, "Low", "Gradient variance 0.15 (stable, below threshold)"), - ("Cross-Validation", true, 0.25, "High", "Fold variance 0.25 (high variability detected)"), - ("Curve Shape", true, 0.07, "Medium", "Pattern mismatch: expected monotonic, got oscillating"), - ]; - - println!("\n ๐Ÿ“Š **Real-Time Detection Results:**"); - for (method, detected, confidence, severity, analysis) in detection_results.iter() { - let status = if *detected { "๐Ÿšจ DETECTED" } else { "โœ… CLEAR" }; - let severity_emoji = match *severity { - "Critical" => "๐Ÿ”ด", - "High" => "๐ŸŸ ", - "Medium" => "๐ŸŸก", - "Low" => "๐ŸŸข", - _ => "โšช", - }; - - println!("\n **{}**", method); - println!(" โ€ข Status: {} {}", status, severity_emoji); - println!(" โ€ข Confidence: {:.1}%", confidence * 100.0); - println!(" โ€ข Severity: {}", severity); - println!(" โ€ข Analysis: {}", analysis); - } - - println!("\n ๐Ÿ”ฌ **Combined Detection Decision:**"); - println!(" โ€ข Methods Detecting: 4/6 (Performance Gap, Loss Divergence, Cross-Validation, Curve Shape)"); - println!(" โ€ข Average Confidence: 71.7%"); - println!(" โ€ข Combined Decision: ๐Ÿšจ OVERFITTING DETECTED"); - println!(" โ€ข Recommended Action: Increase Dropout + Regularization"); -} - -fn demonstrate_prevention_strategies() { - println!("\n๐Ÿ›ก๏ธ **Overfitting Prevention Strategies**"); - println!("========================================="); - - println!("๐ŸŽฏ **7 Prevention Actions Available:**"); - - println!("\n 1. **Increase Regularization**"); - println!(" โ€ข Trigger: Low to Medium severity overfitting"); - println!(" โ€ข Action: L2 strength *= 1.2 (0.01 โ†’ 0.012)"); - println!(" โ€ข Expected Improvement: 3.0%"); - println!(" โ€ข Side Effects: Slight increase in training loss"); - - println!("\n 2. **Increase Dropout**"); - println!(" โ€ข Trigger: Medium severity, adaptive dropout enabled"); - println!(" โ€ข Action: Dropout rate *= 1.15 (0.1 โ†’ 0.115)"); - println!(" โ€ข Expected Improvement: 2.5%"); - println!(" โ€ข Scheduling: Adaptive based on overfitting threshold"); - - println!("\n 3. **Reduce Learning Rate**"); - println!(" โ€ข Trigger: High severity, late in training"); - println!(" โ€ข Action: Coordinate with AdaptiveLearningRateManager"); - println!(" โ€ข Expected Improvement: 5.0%"); - println!(" โ€ข Method: Plateau reduction or performance-based"); - - println!("\n 4. **Early Stop**"); - println!(" โ€ข Trigger: Critical severity or patience exceeded"); - println!(" โ€ข Action: Stop training, restore best weights"); - println!(" โ€ข Expected Improvement: 0% (prevents degradation)"); - println!(" โ€ข Recovery: Best epoch model restoration"); - - println!("\n 5. **Add Training Noise**"); - println!(" โ€ข Trigger: Low severity with adaptive regularization"); - println!(" โ€ข Action: Noise injection std *= 1.1 (0.01 โ†’ 0.011)"); - println!(" โ€ข Expected Improvement: 2.0%"); - println!(" โ€ข Benefit: Improved generalization through noise regularization"); - - println!("\n 6. **Reduce Model Capacity**"); - println!(" โ€ข Trigger: High cross-validation variance"); - println!(" โ€ข Action: Architecture modification recommendation"); - println!(" โ€ข Expected Improvement: 3.0%"); - println!(" โ€ข Implementation: Requires manual model adjustment"); - - println!("\n 7. **Increase Data Augmentation**"); - println!(" โ€ข Trigger: Persistent overfitting patterns"); - println!(" โ€ข Action: Data augmentation parameter increase"); - println!(" โ€ข Expected Improvement: 4.0%"); - println!(" โ€ข Scope: Coordinate with data pipeline"); - - println!("\n๐Ÿš€ **Strategy Selection Demonstration:**"); - demonstrate_strategy_effectiveness(); -} - -fn demonstrate_strategy_effectiveness() { - let strategies = vec![ - ("Increase Regularization", "Applied", 125, 0.89, "L2: 0.01โ†’0.012, effective for mild overfitting"), - ("Increase Dropout", "Applied", 89, 0.85, "Rate: 0.1โ†’0.115, good variance reduction"), - ("Reduce Learning Rate", "Coordinated", 67, 0.92, "LR: 0.001โ†’0.0008, coordinated with LR manager"), - ("Early Stop", "Triggered", 23, 0.95, "Training stopped at epoch 67, best weights restored"), - ("Add Noise", "Applied", 156, 0.78, "Noise std: 0.01โ†’0.011, mild regularization"), - ("Reduce Capacity", "Recommended", 12, 0.88, "Suggested layer reduction, manual implementation"), - ("Data Augmentation", "Requested", 8, 0.82, "Requested pipeline adjustment, pending"), - ]; - - println!("\n ๐Ÿ“ˆ **Prevention Strategy Effectiveness:**"); - for (strategy, status, applications, effectiveness, notes) in strategies.iter() { - let status_emoji = match *status { - "Applied" => "โœ…", - "Triggered" => "๐Ÿ”ด", - "Coordinated" => "๐Ÿ”„", - "Recommended" => "๐Ÿ’ก", - "Requested" => "๐Ÿ“", - _ => "โšช", - }; - - println!("\n {} **{}**", status_emoji, strategy); - println!(" โ€ข Status: {}", status); - println!(" โ€ข Applications: {} times", applications); - println!(" โ€ข Effectiveness: {:.1}%", effectiveness * 100.0); - println!(" โ€ข Notes: {}", notes); - } -} - -fn demonstrate_regularization_suite() { - println!("\n๐Ÿ”ง **Advanced Regularization Suite**"); - println!("===================================="); - - println!("๐ŸŽฏ **Regularization Techniques:**"); - - println!("\n ๐Ÿ“Š **Current Regularization Status:**"); - println!(" โ€ข L1 Strength: 0.000 (disabled - focus on L2)"); - println!(" โ€ข L2 Strength: 0.012 (increased from 0.01)"); - println!(" โ€ข Elastic Net Ratio: 0.5 (balanced L1/L2 when enabled)"); - println!(" โ€ข Spectral Norm Factor: 0.0 (advanced technique, disabled)"); - println!(" โ€ข Gradient Penalty Factor: 0.0 (for adversarial training)"); - println!(" โ€ข Label Smoothing Factor: 0.1 (soft targets enabled)"); - println!(" โ€ข Noise Injection Std: 0.011 (increased from 0.01)"); - println!(" โ€ข Adaptive Regularization: โœ… ENABLED"); - - println!("\n ๐Ÿ”„ **Regularization Scheduling:**"); - println!(" โ€ข Current Schedule: Constant"); - println!(" โ€ข Available Schedules:"); - println!(" - Linear: Start=0.01, End=0.02, Steps=1000"); - println!(" - Exponential: Decay Rate=0.95, Decay Steps=500"); - println!(" - Adaptive: Increase=1.2x, Decrease=0.8x based on performance"); - - println!("\n ๐Ÿ“ˆ **Regularization Effectiveness:**"); - demonstrate_regularization_impact(); -} - -fn demonstrate_regularization_impact() { - let regularization_history = vec![ - (50, 0.010, 0.87, "Baseline regularization established"), - (67, 0.012, 0.84, "Increased due to performance gap detection"), - (89, 0.012, 0.81, "Maintained - overfitting under control"), - (103, 0.014, 0.79, "Further increase - cross-validation variance"), - (125, 0.014, 0.76, "Stabilized - good generalization achieved"), - ]; - - println!("\n ๐Ÿ“Š **Regularization Adjustment History:**"); - for (epoch, strength, validation_loss, reason) in regularization_history.iter() { - println!(" Epoch {}: L2={:.3}, Val_Loss={:.2} - {}", epoch, strength, validation_loss, reason); - } - - println!("\n ๐ŸŽฏ **Impact Analysis:**"); - println!(" โ€ข Total Adjustments: 3 increases"); - println!(" โ€ข Validation Loss Improvement: 0.87 โ†’ 0.76 (12.6% improvement)"); - println!(" โ€ข Performance Gap Reduction: 12.2% โ†’ 6.8% (5.4pp improvement)"); - println!(" โ€ข Training Stability: Significantly improved"); - println!(" โ€ข Generalization: Enhanced cross-fold consistency"); -} - -fn demonstrate_early_stopping_system() { - println!("\nโน๏ธ **Early Stopping System**"); - println!("============================"); - - println!("๐ŸŽฏ **Early Stopping Configuration:**"); - println!(" โ€ข Patience: 10 epochs"); - println!(" โ€ข Min Delta: 0.001 (minimum improvement threshold)"); - println!(" โ€ข Monitor Metric: Validation Loss"); - println!(" โ€ข Mode: Auto (automatically determined based on metric)"); - println!(" โ€ข Restore Best Weights: โœ… ENABLED"); - println!(" โ€ข Current Status: ๐ŸŸก MONITORING (6/10 patience used)"); - - println!("\n ๐Ÿ“Š **Available Monitor Metrics:**"); - println!(" โ€ข Validation Loss: โœ… CURRENT (minimize)"); - println!(" โ€ข Validation Accuracy: (maximize)"); - println!(" โ€ข Training Loss: (minimize)"); - println!(" โ€ข Performance Gap: (minimize)"); - println!(" โ€ข Overfitting Score: (minimize)"); - - println!("\n ๐Ÿ“ˆ **Early Stopping History:**"); - demonstrate_early_stopping_events(); -} - -fn demonstrate_early_stopping_events() { - let stopping_events = vec![ - (45, 0.782, 0.045, 10, "Continue", "Good improvement, reset patience"), - (52, 0.798, 0.016, 10, "Continue", "Steady improvement continues"), - (61, 0.801, 0.003, 9, "Continue", "Minimal improvement, patience--"), - (67, 0.803, 0.002, 8, "Continue", "Very small improvement, patience--"), - (74, 0.801, -0.002, 7, "Warning", "Performance declined, warning issued"), - (82, 0.799, -0.002, 6, "Warning", "Continued decline, patience running low"), - (89, 0.797, -0.002, 5, "Warning", "Consistent decline pattern"), - (95, 0.795, -0.002, 4, "Warning", "High risk of stopping soon"), - ]; - - println!("\n ๐Ÿ“Š **Recent Early Stopping Events:**"); - for (epoch, metric_value, improvement, patience_remaining, action, analysis) in stopping_events.iter() { - let action_emoji = match *action { - "Continue" => "โœ…", - "Warning" => "๐ŸŸก", - "Stop" => "๐Ÿ”ด", - _ => "โšช", - }; - - println!(" Epoch {}: Val_Loss={:.3}, ฮ”={:+.3}, Patience={}, {} {}", - epoch, metric_value, improvement, patience_remaining, action_emoji, action); - println!(" โ†’ {}", analysis); - } - - println!("\n ๐ŸŽฏ **Early Stopping Analysis:**"); - println!(" โ€ข Best Epoch: 52 (validation loss: 0.798)"); - println!(" โ€ข Current Patience: 4/10 remaining"); - println!(" โ€ข Trend: Declining performance for 4 consecutive epochs"); - println!(" โ€ข Prediction: High probability of stopping in 2-3 epochs"); - println!(" โ€ข Recovery Plan: Restore weights from epoch 52 if stopped"); -} - -fn demonstrate_cross_validation_integration() { - println!("\n๐ŸŽฏ **Cross-Validation Integration**"); - println!("==================================="); - - println!("๐Ÿ“Š **Cross-Validation Configuration:**"); - println!(" โ€ข Strategy: Stratified K-Fold"); - println!(" โ€ข Folds: 5-fold validation"); - println!(" โ€ข Shuffle: โœ… ENABLED"); - println!(" โ€ข Stratification: โœ… ENABLED (maintains class distribution)"); - - println!("\n ๐Ÿ“ˆ **Cross-Validation Results:**"); - demonstrate_cross_validation_results(); -} - -fn demonstrate_cross_validation_results() { - let fold_results = vec![ - (1, 0.823, 0.756, 0.891, 0.834, 0.057), - (2, 0.817, 0.742, 0.885, 0.821, 0.064), - (3, 0.834, 0.771, 0.902, 0.849, 0.053), - (4, 0.809, 0.731, 0.878, 0.815, 0.063), - (5, 0.828, 0.763, 0.894, 0.841, 0.052), - ]; - - println!("\n ๐Ÿ“Š **5-Fold Cross-Validation Results:**"); - println!(" Fold | Train Loss | Val Loss | Train Acc | Val Acc | Overfitting"); - println!(" -----|------------|----------|-----------|---------|------------"); - - for (fold, train_loss, val_loss, train_acc, val_acc, overfitting) in fold_results.iter() { - println!(" {} | {:.3} | {:.3} | {:.3} | {:.3} | {:.3}", - fold, train_loss, val_loss, train_acc, val_acc, overfitting); - } - - println!("\n ๐ŸŽฏ **Cross-Validation Metrics:**"); - println!(" โ€ข Mean Validation Score: 0.812 ยฑ 0.020"); - println!(" โ€ข Mean Training Score: 0.890 ยฑ 0.008"); - println!(" โ€ข Overfitting Variance: 0.058 (moderate variance)"); - println!(" โ€ข Consistency Score: 0.83 (good consistency across folds)"); - println!(" โ€ข Interpretation: Model shows consistent overfitting pattern"); - - println!("\n ๐Ÿ’ก **Cross-Validation Insights:**"); - println!(" โ€ข All folds show training > validation performance"); - println!(" โ€ข Fold 4 shows highest overfitting (6.3% gap)"); - println!(" โ€ข Fold 3 shows best overall performance"); - println!(" โ€ข Variance suggests regularization is working but could be stronger"); -} - -fn demonstrate_adaptive_interventions() { - println!("\n๐Ÿ”„ **Adaptive Interventions & Recommendations**"); - println!("=============================================="); - - println!("๐ŸŽฏ **Current System Analysis:**"); - println!(" โ€ข Training Progress: Epoch 95/200 (47.5% complete)"); - println!(" โ€ข Overfitting Risk: ๐ŸŸ  MEDIUM-HIGH (risk score: 0.68)"); - println!(" โ€ข Prevention Actions: 6 interventions applied"); - println!(" โ€ข System Health: ๐ŸŸก REQUIRES ATTENTION"); - - println!("\n ๐Ÿ“Š **Intelligent Recommendations:**"); - - let recommendations = vec![ - ( - "Increase Dropout Further", - "High", - 0.87, - "Current 0.115 is conservative for detected overfitting level", - 0.035, - "๐Ÿ”ด CRITICAL" - ), - ( - "Apply Early Stopping", - "High", - 0.82, - "4 consecutive epochs of declining performance detected", - 0.0, - "๐ŸŸ  HIGH" - ), - ( - "Enable L1 Regularization", - "Medium", - 0.74, - "Sparsity could help with feature selection and overfitting", - 0.025, - "๐ŸŸก MEDIUM" - ), - ( - "Reduce Learning Rate", - "Medium", - 0.71, - "Coordinate with learning rate manager for gentler convergence", - 0.04, - "๐ŸŸก MEDIUM" - ), - ( - "Implement Data Augmentation", - "Low", - 0.65, - "External intervention needed, high implementation cost", - 0.06, - "๐ŸŸข LOW" - ), - ]; - - for (recommendation, priority, confidence, reasoning, expected_impact, status) in recommendations.iter() { - println!("\n ๐ŸŽฏ **{}**", recommendation); - println!(" โ€ข Priority: {} {}", priority, status); - println!(" โ€ข Confidence: {:.1}%", confidence * 100.0); - println!(" โ€ข Reasoning: {}", reasoning); - println!(" โ€ข Expected Impact: +{:.1}% validation performance", expected_impact * 100.0); - } - - println!("\n ๐Ÿ”ฎ **Adaptive Strategy Planning:**"); - println!(" โ€ข Next Review: Epoch 100 (5 epochs away)"); - println!(" โ€ข Auto-Trigger Thresholds:"); - println!(" - Early Stop: If patience reaches 0 (currently 4/10)"); - println!(" - Emergency Regularization: If performance gap > 15%"); - println!(" - Dropout Boost: If cross-validation variance > 0.15"); - println!(" โ€ข Success Criteria: Reduce overfitting score below 0.5"); -} - -fn demonstrate_prevention_effectiveness() { - println!("\n๐Ÿ“Š **Prevention Effectiveness Analysis**"); - println!("======================================="); - - println!("๐ŸŽฏ **Overall Prevention Performance:**"); - println!(" โ€ข Total Interventions: 47 prevention actions"); - println!(" โ€ข Successful Interventions: 38 (80.9% success rate)"); - println!(" โ€ข Overfitting Prevented: 14 critical cases"); - println!(" โ€ข Early Stops Triggered: 3 times"); - println!(" โ€ข Average Improvement: +3.2% validation performance"); - - println!("\n ๐Ÿ“ˆ **Prevention Impact Timeline:**"); - demonstrate_prevention_timeline(); - - println!("\n ๐Ÿ” **Effectiveness Breakdown:**"); - demonstrate_effectiveness_breakdown(); -} - -fn demonstrate_prevention_timeline() { - let timeline = vec![ - (25, "Regularization", 0.89, 0.845, "+4.5%", "Initial L2 increase successful"), - (34, "Dropout", 0.845, 0.823, "+2.2%", "Adaptive dropout rate adjustment"), - (48, "Noise Injection", 0.823, 0.812, "+1.1%", "Training noise improved generalization"), - (67, "Early Stop", 0.812, 0.812, "0%", "Prevented degradation, restored best weights"), - (78, "Combined", 0.812, 0.789, "+2.3%", "Regularization + dropout adjustment"), - (89, "Learning Rate", 0.789, 0.765, "+2.4%", "Coordinated LR reduction"), - (95, "Monitoring", 0.765, 0.765, "0%", "Continuous monitoring, no action needed"), - ]; - - println!("\n ๐Ÿ“Š **Prevention Action Timeline:**"); - for (epoch, action, before, after, improvement, notes) in timeline.iter() { - println!(" Epoch {}: {} - {:.3}โ†’{:.3} ({}) - {}", - epoch, action, before, after, improvement, notes); - } -} - -fn demonstrate_effectiveness_breakdown() { - let effectiveness_data = vec![ - ("Regularization Adjustments", 12, 10, 83.3, "Most reliable prevention method"), - ("Dropout Modifications", 15, 13, 86.7, "Effective for variance reduction"), - ("Early Stopping", 3, 3, 100.0, "Perfect success rate when triggered"), - ("Noise Injection", 8, 6, 75.0, "Good for mild overfitting cases"), - ("Learning Rate Coordination", 5, 4, 80.0, "Requires external coordination"), - ("Recommendation Generation", 4, 2, 50.0, "Requires manual implementation"), - ]; - - println!("\n ๐Ÿ“Š **Prevention Method Effectiveness:**"); - for (method, total, successful, rate, notes) in effectiveness_data.iter() { - println!("\n **{}**", method); - println!(" โ€ข Applications: {} times", total); - println!(" โ€ข Successful: {} times", successful); - println!(" โ€ข Success Rate: {:.1}%", rate); - println!(" โ€ข Notes: {}", notes); - } - - println!("\n ๐ŸŽฏ **Key Success Factors:**"); - println!(" โ€ข Early Detection: 89% of overfitting caught within 5 epochs"); - println!(" โ€ข Multi-Method Approach: Combined strategies 2.3x more effective"); - println!(" โ€ข Adaptive Thresholds: Dynamic adjustment improved detection by 34%"); - println!(" โ€ข Cross-Validation Integration: Reduced false positives by 67%"); - println!(" โ€ข Real-Time Monitoring: Average response time <1 epoch"); - - println!("\n ๐Ÿ“ˆ **Overall Assessment:**"); - println!(" โ€ข Prevention System Status: ๐ŸŸข HIGHLY EFFECTIVE"); - println!(" โ€ข Detection Accuracy: 91.3%"); - println!(" โ€ข False Positive Rate: 4.2%"); - println!(" โ€ข Average Performance Improvement: +3.2%"); - println!(" โ€ข System Reliability: ๐ŸŽฏ Production-ready"); -} \ No newline at end of file diff --git a/task_7_1_4_hyperparameter_optimizer_demo.rs b/task_7_1_4_hyperparameter_optimizer_demo.rs deleted file mode 100644 index 3b1334fc1a491d00e43d57eac3d449dc4a766749..0000000000000000000000000000000000000000 --- a/task_7_1_4_hyperparameter_optimizer_demo.rs +++ /dev/null @@ -1,595 +0,0 @@ -/// Task 7.1.4: HyperparameterOptimizer Demo -/// -/// This demonstrates the sophisticated hyperparameter optimization capabilities -/// of Brain AI's MuBrain system, showcasing multiple optimization strategies, -/// advanced acquisition functions, surrogate models, multi-objective optimization, -/// and comprehensive parameter space exploration with intelligent tuning algorithms. - - - -fn main() { - println!("๐Ÿง  Brain AI Task 7.1.4: HyperparameterOptimizer - SUCCESS!"); - println!("==========================================================="); - - demonstrate_hyperparameter_optimizer_architecture(); - demonstrate_optimization_strategies(); - demonstrate_bayesian_optimization(); - demonstrate_surrogate_models(); - demonstrate_acquisition_functions(); - demonstrate_evolutionary_optimization(); - demonstrate_multi_objective_optimization(); - demonstrate_parameter_space_definition(); - demonstrate_optimization_results(); - - println!("\n๐ŸŽฏ **TASK 7.1.4: HYPERPARAMETER OPTIMIZER - COMPLETED!**"); - println!("โœ… All hyperparameter optimization components successfully implemented!"); - println!("๐Ÿš€ Brain AI now automatically tunes hyperparameters with state-of-the-art algorithms!"); -} - -fn demonstrate_hyperparameter_optimizer_architecture() { - println!("\n๐Ÿ—๏ธ **HyperparameterOptimizer Architecture**"); - println!("==========================================="); - - println!("๐Ÿ“Š **Core Optimization Components:**"); - println!(" โ€ข BayesianOptimizer: Gaussian Process surrogate models with acquisition functions"); - println!(" โ€ข GridSearchEngine: Systematic grid exploration with adaptive refinement"); - println!(" โ€ข RandomSearchEngine: Latin hypercube sampling and smart random exploration"); - println!(" โ€ข EvolutionaryOptimizer: Genetic algorithms with tournament selection"); - println!(" โ€ข MultiObjectiveOptimizer: Pareto front optimization and NSGA-II algorithms"); - println!(" โ€ข ParameterSpace: Comprehensive hyperparameter definition and constraints"); - println!(" โ€ข PerformanceEvaluator: Multi-fidelity evaluation with noise handling"); - - println!("\n๐Ÿ”ฌ **Advanced Optimization Features:**"); - println!(" โ€ข 6 optimization strategies (Bayesian, Grid, Random, Evolutionary, Multi-strategy, Adaptive)"); - println!(" โ€ข 6 acquisition functions (EI, PI, UCB, Entropy Search, Knowledge Gradient, Thompson Sampling)"); - println!(" โ€ข 4 surrogate models (Gaussian Process, Random Forest, Neural Network, Ensemble)"); - println!(" โ€ข 6 multi-objective methods (Weighted Sum, Pareto, NSGA-II, MOEA/D, Hypervolume, ฮต-constraint)"); - println!(" โ€ข Parameter constraints and conditional dependencies"); - println!(" โ€ข Early stopping and convergence detection"); - - println!("\nโšก **Intelligent Features:**"); - println!(" โ€ข Automatic parameter space exploration and importance analysis"); - println!(" โ€ข Adaptive strategy switching based on performance"); - println!(" โ€ข Multi-fidelity evaluation for efficient resource usage"); - println!(" โ€ข Cross-validation integration and noise handling"); - println!(" โ€ข Resource-aware optimization with budget constraints"); - println!(" โ€ข Real-time optimization trajectory analysis"); - println!(" โ€ข Comprehensive optimization history and analytics"); -} - -fn demonstrate_optimization_strategies() { - println!("\n๐ŸŽฏ **Optimization Strategies**"); - println!("=============================="); - - println!("๐Ÿš€ **6 Advanced Optimization Algorithms:**"); - - println!("\n 1. **Bayesian Optimization**"); - println!(" โ€ข Surrogate Model: Gaussian Process with RBF kernel"); - println!(" โ€ข Acquisition Function: Expected Improvement (ฮพ=0.01)"); - println!(" โ€ข Initial Points: 10 random evaluations"); - println!(" โ€ข Advantages: Sample efficient, uncertainty quantification, principled exploration"); - - println!("\n 2. **Grid Search with Adaptive Refinement**"); - println!(" โ€ข Grid Resolution: 10 points per dimension"); - println!(" โ€ข Adaptive Refinement: โœ… ENABLED (3 levels)"); - println!(" โ€ข Pruning Strategy: Performance-based elimination"); - println!(" โ€ข Advantages: Systematic coverage, guaranteed convergence, interpretable"); - - println!("\n 3. **Random Search with Latin Hypercube**"); - println!(" โ€ข Random Samples: 100 evaluations"); - println!(" โ€ข Latin Hypercube: โœ… ENABLED for better space coverage"); - println!(" โ€ข Adaptive Sampling: Dynamic adjustment based on performance"); - println!(" โ€ข Advantages: Simple, parallelizable, effective baseline"); - - println!("\n 4. **Evolutionary Algorithm**"); - println!(" โ€ข Population Size: 50 individuals"); - println!(" โ€ข Generations: 100 iterations"); - println!(" โ€ข Selection: Tournament selection (size=3)"); - println!(" โ€ข Mutation: Gaussian mutation (ฯƒ=0.1)"); - println!(" โ€ข Crossover: Uniform crossover (rate=0.8)"); - - println!("\n 5. **Multi-Strategy Coordination**"); - println!(" โ€ข Strategy Portfolio: Multiple algorithms in parallel"); - println!(" โ€ข Switching Criteria: Performance-based, time-based, evaluation-based"); - println!(" โ€ข Resource Allocation: Dynamic budget distribution"); - println!(" โ€ข Advantages: Robust performance, algorithm diversification"); - - println!("\n 6. **Adaptive Search**"); - println!(" โ€ข Initial Strategy: Bayesian optimization"); - println!(" โ€ข Adaptation Frequency: Every 20 evaluations"); - println!(" โ€ข Performance Threshold: 0.01 improvement required"); - println!(" โ€ข Advantages: Self-tuning, strategy optimization"); - - println!("\n๐Ÿš€ **Strategy Selection Demonstration:**"); - demonstrate_strategy_performance(); -} - -fn demonstrate_strategy_performance() { - let strategies = vec![ - ("Bayesian Optimization", 92, 0.91, 45, 1.2, "Best overall performance, sample efficient"), - ("Grid Search", 156, 0.87, 78, 2.1, "Systematic exploration, guaranteed coverage"), - ("Random Search", 234, 0.83, 95, 0.8, "Simple baseline, good parallelization"), - ("Evolutionary Algorithm", 178, 0.89, 67, 1.5, "Population diversity, robust exploration"), - ("Multi-Strategy", 134, 0.94, 52, 1.8, "Combined strengths, adaptive selection"), - ("Adaptive Search", 112, 0.93, 48, 1.4, "Self-optimization, strategy learning"), - ]; - - println!("\n ๐Ÿ“Š **Strategy Performance Comparison:**"); - println!(" Strategy | Evaluations | Best Score | Convergence | Resource | Notes"); - println!(" ------------------------|-------------|------------|-------------|----------|-------"); - - for (strategy, evaluations, score, convergence, resource, notes) in strategies.iter() { - println!(" {:23} | {:3} | {:.2} | {:2} | {:.1} | {}", - strategy, evaluations, score, convergence, resource, notes); - } - - println!("\n ๐ŸŽฏ **Strategy Recommendation:**"); - println!(" โ€ข **Primary Choice**: Multi-Strategy (94% performance, robust across problems)"); - println!(" โ€ข **Resource Constrained**: Bayesian Optimization (91% with 92 evaluations)"); - println!(" โ€ข **Large Scale**: Random Search + Adaptive (good scalability)"); - println!(" โ€ข **Multi-Objective**: Evolutionary Algorithm (population-based Pareto optimization)"); -} - -fn demonstrate_bayesian_optimization() { - println!("\n๐Ÿ”ฌ **Bayesian Optimization Engine**"); - println!("==================================="); - - println!("๐Ÿ“Š **Gaussian Process Surrogate Model:**"); - println!(" โ€ข Kernel: RBF (Radial Basis Function) with length_scale=1.0"); - println!(" โ€ข Noise Level: 0.01 (1% measurement uncertainty)"); - println!(" โ€ข Mean Function: Zero mean (conservative prior)"); - println!(" โ€ข Hyperparameter Learning: Marginal likelihood optimization"); - - println!("\n ๐ŸŽฏ **Acquisition Function Optimization:**"); - println!(" โ€ข Current Function: Expected Improvement (EI)"); - println!(" โ€ข Exploration Parameter: ฮพ = 0.01"); - println!(" โ€ข Optimization Method: L-BFGS-B with multiple restarts"); - println!(" โ€ข Constraint Handling: Penalty method for invalid regions"); - - println!("\n ๐Ÿ“ˆ **Bayesian Optimization History:**"); - demonstrate_bayesian_trajectory(); -} - -fn demonstrate_bayesian_trajectory() { - let iterations = vec![ - (1, 0.823, 0.45, "Random initialization"), - (2, 0.831, 0.52, "Random initialization"), - (3, 0.819, 0.48, "Random initialization"), - (4, 0.847, 0.38, "Random initialization"), - (5, 0.855, 0.41, "Random initialization"), - (10, 0.878, 0.73, "High EI acquisition, exploration phase"), - (15, 0.892, 0.68, "Balanced exploration/exploitation"), - (20, 0.904, 0.59, "Exploitation focus, local optimization"), - (25, 0.911, 0.45, "Fine-tuning best region"), - (30, 0.914, 0.31, "Convergence, uncertainty reduction"), - ]; - - println!("\n ๐Ÿ“Š **Optimization Trajectory:**"); - println!(" Iter | Best Score | Uncertainty | Strategy Phase"); - println!(" -----|------------|-------------|----------------"); - - for (iter, score, uncertainty, phase) in iterations.iter() { - println!(" {:2} | {:.3} | {:.2} | {}", - iter, score, uncertainty, phase); - } - - println!("\n ๐ŸŽฏ **Bayesian Optimization Analysis:**"); - println!(" โ€ข Exploration โ†’ Exploitation: Smooth transition from exploration to fine-tuning"); - println!(" โ€ข Uncertainty Reduction: 45% โ†’ 31% as model learns the function"); - println!(" โ€ข Performance Improvement: 82.3% โ†’ 91.4% (9.1 percentage points)"); - println!(" โ€ข Sample Efficiency: 91.4% performance in only 30 evaluations"); - println!(" โ€ข Convergence: Reached plateau with diminishing returns"); -} - -fn demonstrate_surrogate_models() { - println!("\n๐Ÿค– **Surrogate Models**"); - println!("======================"); - - println!("๐ŸŽฏ **4 Advanced Surrogate Model Types:**"); - - println!("\n 1. **Gaussian Process (Primary)**"); - println!(" โ€ข Kernel: RBF with automatic relevance determination"); - println!(" โ€ข Length Scale: 1.0 (learned from data)"); - println!(" โ€ข Noise Level: 0.01 (1% observation noise)"); - println!(" โ€ข Advantages: Uncertainty quantification, smooth interpolation"); - println!(" โ€ข Use Cases: Small to medium parameter spaces, continuous optimization"); - - println!("\n 2. **Random Forest**"); - println!(" โ€ข Estimators: 100 decision trees"); - println!(" โ€ข Max Depth: Adaptive (None for full growth)"); - println!(" โ€ข Feature Sampling: โˆšn_features per split"); - println!(" โ€ข Advantages: Handles categorical parameters, robust to noise"); - println!(" โ€ข Use Cases: Mixed parameter types, high-dimensional spaces"); - - println!("\n 3. **Neural Network**"); - println!(" โ€ข Architecture: [64, 32, 16] hidden layers"); - println!(" โ€ข Activation: ReLU for hidden layers, linear output"); - println!(" โ€ข Dropout Rate: 0.1 for regularization"); - println!(" โ€ข Advantages: Flexible function approximation, scalable"); - println!(" โ€ข Use Cases: Complex parameter interactions, large datasets"); - - println!("\n 4. **Ensemble Model**"); - println!(" โ€ข Components: GP + Random Forest + Neural Network"); - println!(" โ€ข Weights: [0.5, 0.3, 0.2] (GP-weighted ensemble)"); - println!(" โ€ข Aggregation: Weighted average with uncertainty propagation"); - println!(" โ€ข Advantages: Robust predictions, reduced overfitting"); - println!(" โ€ข Use Cases: Critical applications, uncertain environments"); - - println!("\n๐Ÿš€ **Surrogate Model Performance:**"); - demonstrate_surrogate_comparison(); -} - -fn demonstrate_surrogate_comparison() { - let models = vec![ - ("Gaussian Process", 0.91, 0.83, 0.12, "Excellent uncertainty, smooth predictions"), - ("Random Forest", 0.87, 0.78, 0.08, "Good categorical handling, fast training"), - ("Neural Network", 0.89, 0.81, 0.15, "Flexible approximation, scalable"), - ("Ensemble", 0.93, 0.86, 0.09, "Best overall, combines strengths"), - ]; - - println!("\n ๐Ÿ“Š **Surrogate Model Comparison:**"); - println!(" Model | Accuracy | Rยฒ Score | RMSE | Characteristics"); - println!(" ------------------|----------|----------|------|------------------"); - - for (model, accuracy, r2, rmse, characteristics) in models.iter() { - println!(" {:17} | {:.2} | {:.2} | {:.2} | {}", - model, accuracy, r2, rmse, characteristics); - } - - println!("\n ๐ŸŽฏ **Model Selection Guidelines:**"); - println!(" โ€ข **Small datasets (<100 points)**: Gaussian Process for uncertainty"); - println!(" โ€ข **Categorical parameters**: Random Forest for natural handling"); - println!(" โ€ข **Complex interactions**: Neural Network for flexibility"); - println!(" โ€ข **Production systems**: Ensemble for robustness and reliability"); -} - -fn demonstrate_acquisition_functions() { - println!("\n๐Ÿ“ˆ **Acquisition Functions**"); - println!("============================"); - - println!("๐ŸŽฏ **6 Sophisticated Acquisition Functions:**"); - - println!("\n 1. **Expected Improvement (EI)**"); - println!(" โ€ข Formula: E[max(f(x) - f_best, 0)]"); - println!(" โ€ข Parameter: ฮพ = 0.01 (exploration control)"); - println!(" โ€ข Characteristics: Balanced exploration/exploitation"); - println!(" โ€ข Best For: General purpose optimization, smooth functions"); - - println!("\n 2. **Probability of Improvement (PI)**"); - println!(" โ€ข Formula: P(f(x) > f_best + ฮพ)"); - println!(" โ€ข Parameter: ฮพ = 0.01 (improvement threshold)"); - println!(" โ€ข Characteristics: Conservative, focuses on likely improvements"); - println!(" โ€ข Best For: Risk-averse optimization, noisy functions"); - - println!("\n 3. **Upper Confidence Bound (UCB)**"); - println!(" โ€ข Formula: ฮผ(x) + ฮบ * ฯƒ(x)"); - println!(" โ€ข Parameter: ฮบ = 2.0 (exploration coefficient)"); - println!(" โ€ข Characteristics: Optimistic exploration, theoretical guarantees"); - println!(" โ€ข Best For: Multi-armed bandits, exploration-heavy phases"); - - println!("\n 4. **Entropy Search**"); - println!(" โ€ข Approach: Information-theoretic acquisition"); - println!(" โ€ข Objective: Maximize information gain about optimum"); - println!(" โ€ข Characteristics: Sophisticated exploration, computationally intensive"); - println!(" โ€ข Best For: Sample-efficient optimization, uncertain landscapes"); - - println!("\n 5. **Knowledge Gradient**"); - println!(" โ€ข Approach: Expected value of perfect information"); - println!(" โ€ข Objective: Maximize expected improvement in best point"); - println!(" โ€ข Characteristics: Forward-looking, batch optimization"); - println!(" โ€ข Best For: Fixed budget optimization, parallel evaluation"); - - println!("\n 6. **Thompson Sampling**"); - println!(" โ€ข Approach: Sample from posterior, optimize sample"); - println!(" โ€ข Objective: Probability matching for exploration"); - println!(" โ€ข Characteristics: Natural exploration, simple implementation"); - println!(" โ€ข Best For: Online optimization, contextual bandits"); - - println!("\n๐Ÿš€ **Acquisition Function Performance:**"); - demonstrate_acquisition_performance(); -} - -fn demonstrate_acquisition_performance() { - let functions = vec![ - ("Expected Improvement", 0.91, 45, 0.75, "Excellent general purpose performance"), - ("Probability of Improvement", 0.87, 52, 0.82, "Conservative, good for noisy objectives"), - ("Upper Confidence Bound", 0.89, 41, 0.68, "Strong exploration, theoretical guarantees"), - ("Entropy Search", 0.93, 38, 0.71, "Sample efficient, computationally expensive"), - ("Knowledge Gradient", 0.90, 43, 0.73, "Good for batch optimization"), - ("Thompson Sampling", 0.88, 47, 0.69, "Natural exploration, simple"), - ]; - - println!("\n ๐Ÿ“Š **Acquisition Function Performance:**"); - println!(" Function | Performance | Convergence | Exploration | Notes"); - println!(" -----------------------|-------------|-------------|-------------|--------"); - - for (function, performance, convergence, exploration, notes) in functions.iter() { - println!(" {:22} | {:.2} | {:2} | {:.2} | {}", - function, performance, convergence, exploration, notes); - } - - println!("\n ๐ŸŽฏ **Acquisition Function Selection:**"); - println!(" โ€ข **Best Overall**: Entropy Search (93% performance, 38 steps to convergence)"); - println!(" โ€ข **Production Ready**: Expected Improvement (balanced, reliable)"); - println!(" โ€ข **Exploration Heavy**: Upper Confidence Bound (strong theoretical foundation)"); - println!(" โ€ข **Noisy Functions**: Probability of Improvement (conservative approach)"); -} - -fn demonstrate_evolutionary_optimization() { - println!("\n๐Ÿงฌ **Evolutionary Algorithm Optimization**"); - println!("=========================================="); - - println!("๐Ÿ“Š **Genetic Algorithm Configuration:**"); - println!(" โ€ข Population Size: 50 individuals"); - println!(" โ€ข Generations: 100 iterations"); - println!(" โ€ข Selection: Tournament selection with tournament_size=3"); - println!(" โ€ข Mutation: Gaussian mutation with ฯƒ=0.1"); - println!(" โ€ข Crossover: Uniform crossover with rate=0.8"); - println!(" โ€ข Replacement: Generational replacement with elitism"); - - println!("\n ๐Ÿ”ฌ **Evolutionary Operators:**"); - println!(" โ€ข **Tournament Selection**: Select best from random groups of 3"); - println!(" โ€ข **Uniform Crossover**: Exchange parameters with 50% probability"); - println!(" โ€ข **Gaussian Mutation**: Add normal noise to continuous parameters"); - println!(" โ€ข **Elitism**: Preserve top 10% individuals across generations"); - - println!("\n ๐Ÿ“ˆ **Evolution Progress:**"); - demonstrate_evolutionary_progress(); -} - -fn demonstrate_evolutionary_progress() { - let generations = vec![ - (0, 0.743, 0.821, 0.782, 0.85, "Initial random population"), - (10, 0.789, 0.867, 0.828, 0.72, "Early exploration, diversity high"), - (20, 0.834, 0.891, 0.862, 0.63, "Fitness improvement, convergence starting"), - (30, 0.867, 0.903, 0.885, 0.54, "Population focusing on good regions"), - (40, 0.891, 0.912, 0.901, 0.41, "Fine-tuning phase, reduced diversity"), - (50, 0.903, 0.918, 0.910, 0.33, "Convergence acceleration"), - (75, 0.914, 0.921, 0.918, 0.21, "Near convergence, small improvements"), - (100, 0.918, 0.923, 0.920, 0.15, "Final generation, population converged"), - ]; - - println!("\n ๐Ÿ“Š **Evolutionary Progress:**"); - println!(" Gen | Best Score | Avg Score | Population | Diversity | Phase"); - println!(" ----|------------|-----------|------------|-----------|-------"); - - for (gen, best, avg, pop_avg, diversity, phase) in generations.iter() { - println!(" {:2} | {:.3} | {:.3} | {:.3} | {:.2} | {}", - gen, best, avg, pop_avg, diversity, phase); - } - - println!("\n ๐ŸŽฏ **Evolutionary Analysis:**"); - println!(" โ€ข Population Diversity: Started high (0.85) โ†’ converged (0.15)"); - println!(" โ€ข Fitness Improvement: 74.3% โ†’ 91.8% (17.5 percentage points)"); - println!(" โ€ข Convergence Pattern: Smooth convergence with plateau at generation ~75"); - println!(" โ€ข Algorithm Efficiency: Good exploration-exploitation balance"); - println!(" โ€ข Final Population: Converged around optimal hyperparameter region"); -} - -fn demonstrate_multi_objective_optimization() { - println!("\n๐ŸŽฏ **Multi-Objective Optimization**"); - println!("==================================="); - - println!("๐Ÿ“Š **6 Multi-Objective Methods Available:**"); - - println!("\n 1. **Weighted Sum Scalarization**"); - println!(" โ€ข Approach: Convert to single objective f = wโ‚fโ‚ + wโ‚‚fโ‚‚ + ..."); - println!(" โ€ข Weights: Dynamic based on objective importance"); - println!(" โ€ข Advantages: Simple, fast, works with any optimizer"); - println!(" โ€ข Limitations: Cannot find non-convex Pareto fronts"); - - println!("\n 2. **Pareto Front Optimization**"); - println!(" โ€ข Population Size: 100 solutions"); - println!(" โ€ข Selection Pressure: 0.8 (moderate pressure)"); - println!(" โ€ข Approach: Non-dominated sorting with crowding distance"); - println!(" โ€ข Advantages: True multi-objective, diverse solutions"); - - println!("\n 3. **NSGA-II Algorithm**"); - println!(" โ€ข Population: 100 individuals"); - println!(" โ€ข Crossover Probability: 0.9"); - println!(" โ€ข Mutation Probability: 0.1"); - println!(" โ€ข Features: Fast non-dominated sorting, crowding distance"); - - println!("\n 4. **MOEA/D (Multi-Objective EA based on Decomposition)**"); - println!(" โ€ข Neighbor Size: 20 subproblems"); - println!(" โ€ข Weight Vectors: 100 uniformly distributed"); - println!(" โ€ข Approach: Decompose into scalar subproblems"); - - println!("\n 5. **Hypervolume Optimization**"); - println!(" โ€ข Reference Point: [0.0, 0.0] (worst possible values)"); - println!(" โ€ข Metric: Volume of dominated hypervolume"); - println!(" โ€ข Advantages: Single quality indicator, Pareto compliant"); - - println!("\n 6. **ฮต-Constraint Method**"); - println!(" โ€ข Primary Objective: Validation accuracy (maximize)"); - println!(" โ€ข Constraints: Training time โ‰ค ฮตโ‚, model size โ‰ค ฮตโ‚‚"); - println!(" โ€ข ฮต Values: [300s, 10MB, 50ms] for time, size, latency"); - - println!("\n๐Ÿš€ **Multi-Objective Results:**"); - demonstrate_pareto_front(); -} - -fn demonstrate_pareto_front() { - let pareto_solutions = vec![ - (0.945, 120, 2.5, "High accuracy, slow training, small model"), - (0.932, 85, 4.2, "Balanced accuracy-speed, medium model"), - (0.918, 62, 6.8, "Fast training, larger model, good accuracy"), - (0.901, 45, 9.1, "Very fast training, large model"), - (0.887, 28, 12.3, "Ultra-fast, very large model"), - ]; - - println!("\n ๐Ÿ“Š **Pareto-Optimal Solutions:**"); - println!(" Accuracy | Train Time (s) | Model Size (MB) | Trade-off Profile"); - println!(" ---------|----------------|-----------------|-------------------"); - - for (accuracy, time, size, profile) in pareto_solutions.iter() { - println!(" {:.3} | {:3} | {:.1} | {}", - accuracy, time, size, profile); - } - - println!("\n ๐ŸŽฏ **Multi-Objective Analysis:**"); - println!(" โ€ข **Pareto Front**: 5 non-dominated solutions spanning accuracy-speed trade-off"); - println!(" โ€ข **Accuracy Range**: 88.7% - 94.5% (5.8 percentage point span)"); - println!(" โ€ข **Speed Range**: 28s - 120s training time (4.3x variation)"); - println!(" โ€ข **Model Size**: 2.5MB - 12.3MB (4.9x size difference)"); - println!(" โ€ข **Trade-off**: Clear accuracy vs. efficiency trade-off curve"); - println!(" โ€ข **Recommendation**: Solution 2 (93.2%, 85s, 4.2MB) for balanced performance"); -} - -fn demonstrate_parameter_space_definition() { - println!("\n๐Ÿ”ง **Parameter Space Definition**"); - println!("================================="); - - println!("๐Ÿ“Š **Hyperparameter Categories:**"); - - println!("\n ๐ŸŽฏ **Neural Network Parameters:**"); - println!(" โ€ข learning_rate: Continuous [1e-5, 1e-1] (logarithmic scale)"); - println!(" โ€ข batch_size: Integer [16, 512] (powers of 2 preferred)"); - println!(" โ€ข hidden_layers: Integer [1, 5] (architecture depth)"); - println!(" โ€ข hidden_units: Integer [32, 1024] (layer width)"); - println!(" โ€ข dropout_rate: Continuous [0.0, 0.8] (regularization)"); - println!(" โ€ข activation: Categorical ['relu', 'tanh', 'elu', 'swish']"); - - println!("\n ๐Ÿ”ฌ **Optimization Parameters:**"); - println!(" โ€ข optimizer: Categorical ['adam', 'sgd', 'adamw', 'rmsprop']"); - println!(" โ€ข momentum: Continuous [0.0, 0.99] (SGD only, conditional)"); - println!(" โ€ข weight_decay: Continuous [1e-6, 1e-2] (logarithmic)"); - println!(" โ€ข lr_schedule: Categorical ['constant', 'cosine', 'step', 'exponential']"); - println!(" โ€ข warmup_steps: Integer [0, 1000] (conditional on schedule)"); - - println!("\n ๐Ÿ”ง **Training Parameters:**"); - println!(" โ€ข epochs: Integer [10, 500] (training duration)"); - println!(" โ€ข early_stopping: Boolean (patience control)"); - println!(" โ€ข patience: Integer [5, 50] (conditional on early_stopping)"); - println!(" โ€ข validation_split: Continuous [0.1, 0.3] (data splitting)"); - - println!("\n โš™๏ธ **Advanced Parameters:**"); - println!(" โ€ข data_augmentation: Boolean (augmentation enable)"); - println!(" โ€ข mixup_alpha: Continuous [0.0, 2.0] (conditional on augmentation)"); - println!(" โ€ข label_smoothing: Continuous [0.0, 0.3] (regularization)"); - println!(" โ€ข gradient_clipping: Continuous [0.5, 10.0] (stability)"); - - println!("\n๐Ÿš€ **Parameter Space Analysis:**"); - demonstrate_parameter_importance(); -} - -fn demonstrate_parameter_importance() { - let parameters = vec![ - ("learning_rate", 0.87, 0.23, "Critical for convergence, highest impact"), - ("hidden_units", 0.72, 0.19, "Major capacity control, architecture impact"), - ("dropout_rate", 0.68, 0.15, "Important regularization, overfitting control"), - ("batch_size", 0.54, 0.12, "Training dynamics, memory efficiency"), - ("optimizer", 0.51, 0.11, "Algorithm choice, convergence behavior"), - ("hidden_layers", 0.43, 0.09, "Architecture depth, representation power"), - ("weight_decay", 0.39, 0.08, "Regularization strength, generalization"), - ("activation", 0.31, 0.06, "Non-linearity choice, moderate impact"), - ("lr_schedule", 0.28, 0.05, "Learning rate adaptation, fine-tuning"), - ("early_stopping", 0.21, 0.04, "Training control, efficiency"), - ]; - - println!("\n ๐Ÿ“Š **Parameter Importance Analysis:**"); - println!(" Parameter | Importance | Sensitivity | Analysis"); - println!(" -----------------|------------|-------------|----------"); - - for (param, importance, sensitivity, analysis) in parameters.iter() { - println!(" {:16} | {:.2} | {:.2} | {}", - param, importance, sensitivity, analysis); - } - - println!("\n ๐ŸŽฏ **Parameter Optimization Strategy:**"); - println!(" โ€ข **High Priority**: learning_rate, hidden_units, dropout_rate (impact > 0.65)"); - println!(" โ€ข **Medium Priority**: batch_size, optimizer, hidden_layers (impact 0.4-0.65)"); - println!(" โ€ข **Low Priority**: weight_decay, activation, lr_schedule (impact < 0.4)"); - println!(" โ€ข **Conditional**: momentum (SGD only), warmup_steps (schedule dependent)"); - println!(" โ€ข **Search Strategy**: Focus 70% budget on high-priority parameters"); -} - -fn demonstrate_optimization_results() { - println!("\n๐Ÿ“Š **Optimization Results & Analysis**"); - println!("======================================"); - - println!("๐ŸŽฏ **Comprehensive Optimization Summary:**"); - println!(" โ€ข Total Evaluations: 87 hyperparameter configurations"); - println!(" โ€ข Best Performance: 94.2% validation accuracy"); - println!(" โ€ข Optimization Strategy: Multi-Strategy (Bayesian + Evolutionary)"); - println!(" โ€ข Convergence Time: 52 minutes (including model training)"); - println!(" โ€ข Resource Efficiency: 82% (excellent sample efficiency)"); - - println!("\n ๐Ÿ† **Optimal Hyperparameter Configuration:**"); - println!(" โ€ข learning_rate: 0.00347 (learned optimal rate)"); - println!(" โ€ข hidden_units: 256 (balanced capacity)"); - println!(" โ€ข hidden_layers: 3 (optimal depth)"); - println!(" โ€ข dropout_rate: 0.23 (effective regularization)"); - println!(" โ€ข batch_size: 64 (good training dynamics)"); - println!(" โ€ข optimizer: 'adamw' (best convergence)"); - println!(" โ€ข weight_decay: 0.0021 (optimal regularization)"); - println!(" โ€ข activation: 'swish' (superior non-linearity)"); - - println!("\n ๐Ÿ“ˆ **Performance Progression:**"); - demonstrate_optimization_timeline(); - - println!("\n ๐Ÿ” **Optimization Analysis:**"); - demonstrate_final_analysis(); -} - -fn demonstrate_optimization_timeline() { - let timeline = vec![ - (10, 0.823, "Random initialization phase", "Bayesian"), - (20, 0.856, "Early exploration", "Bayesian"), - (30, 0.879, "Promising regions identified", "Bayesian"), - (40, 0.901, "Local optimization", "Multi-Strategy"), - (50, 0.923, "Fine-tuning phase", "Evolutionary"), - (60, 0.937, "Convergence acceleration", "Adaptive"), - (70, 0.941, "Final optimization", "Bayesian"), - (87, 0.942, "Optimization complete", "Ensemble"), - ]; - - println!("\n ๐Ÿ“Š **Optimization Timeline:**"); - println!(" Eval | Best Score | Optimization Phase | Strategy"); - println!(" -----|------------|---------------------------|----------"); - - for (eval, score, phase, strategy) in timeline.iter() { - println!(" {:2} | {:.3} | {:25} | {}", - eval, score, phase, strategy); - } -} - -fn demonstrate_final_analysis() { - println!("\n ๐ŸŽฏ **Final Optimization Analysis:**"); - - println!("\n ๐Ÿ“ˆ **Performance Metrics:**"); - println!(" โ€ข Best Validation Accuracy: 94.2%"); - println!(" โ€ข Training Accuracy: 96.8%"); - println!(" โ€ข Generalization Gap: 2.6% (excellent)"); - println!(" โ€ข Training Time: 89 seconds"); - println!(" โ€ข Inference Latency: 12ms"); - println!(" โ€ข Model Size: 4.7MB"); - - println!("\n ๐Ÿ”ฌ **Optimization Efficiency:**"); - println!(" โ€ข Sample Efficiency: 82% (excellent for 87 evaluations)"); - println!(" โ€ข Convergence Rate: 52 minutes to 94%+ performance"); - println!(" โ€ข Resource Utilization: 78% CPU, 12.5GB-hours total"); - println!(" โ€ข Strategy Adaptation: 4 strategy switches, all beneficial"); - println!(" โ€ข Parameter Importance: Learning rate most critical (87% impact)"); - - println!("\n ๐Ÿ’ก **Key Insights:**"); - println!(" โ€ข **Optimal Learning Rate**: 0.00347 (3.5x lower than default)"); - println!(" โ€ข **Architecture**: 3 layers ร— 256 units optimal for this problem"); - println!(" โ€ข **Regularization**: Dropout (23%) + Weight decay (0.21%) combination"); - println!(" โ€ข **Optimizer**: AdamW outperformed Adam and SGD significantly"); - println!(" โ€ข **Activation**: Swish activation 2.1% better than ReLU"); - - println!("\n ๐Ÿš€ **Recommendations:**"); - println!(" โ€ข **Production Deployment**: Use discovered configuration"); - println!(" โ€ข **Further Optimization**: Explore data augmentation parameters"); - println!(" โ€ข **Scaling**: Current config suitable for similar problems"); - println!(" โ€ข **Monitoring**: Track performance gap (target <3%)"); - println!(" โ€ข **Retuning**: Re-optimize if dataset characteristics change"); - - println!("\n โœ… **Optimization Success Criteria:**"); - println!(" โ€ข โœ… Target Accuracy: >94% (achieved 94.2%)"); - println!(" โ€ข โœ… Training Time: <100s (achieved 89s)"); - println!(" โ€ข โœ… Model Size: <5MB (achieved 4.7MB)"); - println!(" โ€ข โœ… Generalization: <5% gap (achieved 2.6%)"); - println!(" โ€ข โœ… Resource Budget: <15GB-hours (used 12.5GB-hours)"); -} \ No newline at end of file diff --git a/task_7_2_performance_prediction_demo.rs b/task_7_2_performance_prediction_demo.rs deleted file mode 100644 index 6e35c4bd186aa56953f4e0be11315bd85d1b55d5..0000000000000000000000000000000000000000 --- a/task_7_2_performance_prediction_demo.rs +++ /dev/null @@ -1,739 +0,0 @@ -/// Task 7.2: Performance Prediction System Demo -/// -/// This demonstrates the sophisticated performance prediction and forecasting capabilities -/// of Brain AI's MuBrain system, showcasing advanced machine learning models, time series -/// analysis, anomaly detection, planning quality prediction, and comprehensive forecasting -/// with uncertainty quantification and adaptive model management. - - - -fn main() { - println!("๐Ÿง  Brain AI Task 7.2: Performance Prediction System - SUCCESS!"); - println!("==============================================================="); - - demonstrate_performance_prediction_architecture(); - demonstrate_machine_learning_forecasting(); - demonstrate_time_series_prediction(); - demonstrate_anomaly_detection(); - demonstrate_planning_quality_prediction(); - demonstrate_performance_regression_detection(); - demonstrate_multi_dimensional_prediction(); - demonstrate_adaptive_model_management(); - demonstrate_comprehensive_forecasting(); - demonstrate_uncertainty_quantification(); - - println!("\n๐ŸŽฏ **TASK 7.2: PERFORMANCE PREDICTION SYSTEM - COMPLETED!**"); - println!("โœ… Advanced performance forecasting system successfully implemented!"); - println!("๐Ÿš€ Brain AI now predicts performance with state-of-the-art machine learning!"); -} - -fn demonstrate_performance_prediction_architecture() { - println!("\n๐Ÿ—๏ธ **Performance Prediction System Architecture**"); - println!("=================================================="); - - println!("๐Ÿ“Š **Advanced Forecasting Components:**"); - println!(" โ€ข MachineLearningForecaster: LSTM, XGBoost, Random Forest, ARIMA, Prophet, Transformer models"); - println!(" โ€ข TimeSeriesPredictor: Seasonal decomposition, trend analysis, change point detection"); - println!(" โ€ข PerformanceAnomalyDetector: Isolation Forest, AutoEncoder, statistical tests"); - println!(" โ€ข MultiDimensionalPredictor: Accuracy, speed, resource usage, reliability prediction"); - println!(" โ€ข PlanningQualityPredictor: Planning success, solution quality, time estimation"); - println!(" โ€ข PerformanceRegressionDetector: T-test, Mann-Whitney U, change point detection"); - - println!("\n๐Ÿ”ฌ **Sophisticated Analysis Features:**"); - println!(" โ€ข 6 forecasting models (LSTM, XGBoost, RandomForest, ARIMA, Prophet, Transformer)"); - println!(" โ€ข 5 decomposition methods (STL, X-13ARIMA-SEATS, EMD, Wavelet, Classical)"); - println!(" โ€ข 6 anomaly detection algorithms (Isolation Forest, One-Class SVM, LOF, DBSCAN, AutoEncoder, Statistical)"); - println!(" โ€ข 6 performance dimensions (Accuracy, Speed, Resource, Reliability, Scalability, User Satisfaction)"); - println!(" โ€ข 4 planning quality models (Success, Quality, Time, Resource prediction)"); - println!(" โ€ข 5 regression detection methods (T-test, Mann-Whitney, Change Point, Bayesian, Sequential)"); - - println!("\nโšก **Advanced Capabilities:**"); - println!(" โ€ข Uncertainty quantification with epistemic and aleatoric uncertainty"); - println!(" โ€ข Adaptive model management with online learning and concept drift detection"); - println!(" โ€ข Multi-fidelity evaluation for efficient resource usage"); - println!(" โ€ข Real-time anomaly monitoring with severity classification"); - println!(" โ€ข Comprehensive forecasting history and model performance tracking"); - println!(" โ€ข Feature importance evolution and calibration monitoring"); - println!(" โ€ข Ensemble forecasting with weighted combination strategies"); -} - -fn demonstrate_machine_learning_forecasting() { - println!("\n๐Ÿค– **Machine Learning Forecasting Engine**"); - println!("==========================================="); - - println!("๐Ÿš€ **6 Advanced Forecasting Models:**"); - - println!("\n 1. **LSTM Neural Network**"); - println!(" โ€ข Architecture: [64, 32, 16] hidden layers with dropout"); - println!(" โ€ข Sequence Length: 30 time steps for context"); - println!(" โ€ข Dropout Rate: 0.2 for regularization"); - println!(" โ€ข Use Cases: Complex temporal patterns, non-linear dependencies"); - - println!("\n 2. **XGBoost Gradient Boosting**"); - println!(" โ€ข Estimators: 100 gradient boosted trees"); - println!(" โ€ข Max Depth: 6 levels for complexity control"); - println!(" โ€ข Learning Rate: 0.1 for stable convergence"); - println!(" โ€ข Features: Historical performance, system load, contextual metrics"); - - println!("\n 3. **Random Forest Ensemble**"); - println!(" โ€ข Estimators: 50 decision trees"); - println!(" โ€ข Max Features: sqrt(n_features) for diversity"); - println!(" โ€ข Time Window: 10 periods for feature engineering"); - println!(" โ€ข Advantages: Robust to outliers, handles categorical variables"); - - println!("\n 4. **ARIMA Time Series**"); - println!(" โ€ข Parameters: Auto-regressive (p), Differencing (d), Moving Average (q)"); - println!(" โ€ข Auto-selection: Optimal parameters chosen via AIC/BIC criteria"); - println!(" โ€ข Seasonality: Automatic seasonal pattern detection"); - println!(" โ€ข Use Cases: Linear trends, stationary processes"); - - println!("\n 5. **Prophet Forecasting**"); - println!(" โ€ข Seasonality: Multiplicative mode with yearly/weekly patterns"); - println!(" โ€ข Trend: Automatic changepoint detection"); - println!(" โ€ข Holidays: Built-in holiday effect modeling"); - println!(" โ€ข Uncertainty: Bayesian uncertainty intervals"); - - println!("\n 6. **Transformer-Based Model**"); - println!(" โ€ข Attention Heads: Multi-head self-attention mechanism"); - println!(" โ€ข Encoder Layers: Deep sequence-to-sequence learning"); - println!(" โ€ข Embedding Dimension: High-dimensional representation"); - println!(" โ€ข Use Cases: Complex sequential patterns, long-range dependencies"); - - println!("\n๐Ÿš€ **Model Ensemble Performance:**"); - demonstrate_ensemble_performance(); -} - -fn demonstrate_ensemble_performance() { - let models = vec![ - ("LSTM", 0.92, 0.83, 0.08, 1.2, "Excellent temporal patterns"), - ("XGBoost", 0.94, 0.87, 0.06, 0.8, "Best overall accuracy"), - ("Random Forest", 0.89, 0.81, 0.09, 0.6, "Robust and interpretable"), - ("ARIMA", 0.86, 0.75, 0.12, 0.3, "Good for linear trends"), - ("Prophet", 0.91, 0.84, 0.07, 0.7, "Excellent seasonality handling"), - ("Transformer", 0.93, 0.86, 0.07, 1.5, "Complex pattern recognition"), - ("Ensemble", 0.96, 0.91, 0.05, 1.0, "Combined model strengths"), - ]; - - println!("\n ๐Ÿ“Š **Model Performance Comparison:**"); - println!(" Model | Accuracy | Rยฒ Score | RMSE | Time (s) | Characteristics"); - println!(" -------------|----------|----------|------|----------|------------------"); - - for (model, accuracy, r2, rmse, time, characteristics) in models.iter() { - println!(" {:12} | {:.2} | {:.2} | {:.2} | {:.1} | {}", - model, accuracy, r2, rmse, time, characteristics); - } - - println!("\n ๐ŸŽฏ **Ensemble Strategy:**"); - println!(" โ€ข **Primary Models**: XGBoost (40%), Transformer (35%), LSTM (25%)"); - println!(" โ€ข **Weighting Method**: Dynamic based on recent performance"); - println!(" โ€ข **Combination**: Weighted average with uncertainty propagation"); - println!(" โ€ข **Performance**: 96% accuracy, 91% Rยฒ score, 5% RMSE"); - println!(" โ€ข **Adaptive**: Weights adjust based on prediction accuracy"); -} - -fn demonstrate_time_series_prediction() { - println!("\n๐Ÿ“ˆ **Time Series Prediction Engine**"); - println!("===================================="); - - println!("๐Ÿ”ฌ **Advanced Statistical Analysis:**"); - - println!("\n ๐Ÿ“Š **Seasonal Decomposition (STL)**"); - println!(" โ€ข Trend Extraction: Spline smoothing with factor 0.8"); - println!(" โ€ข Seasonal Periods: Daily (7), Monthly (30), Yearly (365)"); - println!(" โ€ข Residual Analysis: Noise characterization and outlier detection"); - println!(" โ€ข Applications: Performance cycles, usage patterns, system behavior"); - - println!("\n ๐Ÿ“ˆ **Trend Analysis**"); - println!(" โ€ข Linear Regression: Simple trend identification"); - println!(" โ€ข Polynomial Fitting: Non-linear trend capture"); - println!(" โ€ข Spline Smoothing: Flexible trend modeling"); - println!(" โ€ข Kalman Filtering: Dynamic trend estimation with noise"); - println!(" โ€ข Hodrick-Prescott: Business cycle decomposition"); - - println!("\n ๐Ÿ”„ **Cyclical Pattern Detection**"); - println!(" โ€ข Frequency Analysis: Spectral density estimation"); - println!(" โ€ข Peak Detection: Automatic cycle identification"); - println!(" โ€ข Harmonic Analysis: Periodic component extraction"); - println!(" โ€ข Cross-correlation: Pattern relationship analysis"); - - println!("\n โšก **Change Point Detection**"); - println!(" โ€ข CUSUM Algorithm: Cumulative sum-based detection"); - println!(" โ€ข PELT Method: Pruned exact linear time algorithm"); - println!(" โ€ข Binary Segmentation: Recursive change point finding"); - println!(" โ€ข Wild Binary Segmentation: Enhanced robustness"); - - println!("\n๐Ÿš€ **Time Series Analysis Results:**"); - demonstrate_time_series_analysis(); -} - -fn demonstrate_time_series_analysis() { - let components = vec![ - ("Trend", 0.78, "Upward", "Long-term performance improvement"), - ("Seasonal (Weekly)", 0.15, "Stable", "Consistent weekly patterns"), - ("Seasonal (Monthly)", 0.08, "Variable", "Monthly usage variations"), - ("Cyclical", 0.12, "Regular", "Quarterly performance cycles"), - ("Noise", 0.05, "Low", "Minimal random variation"), - ("Change Points", 2.0, "Detected", "2 significant regime changes"), - ]; - - println!("\n ๐Ÿ“Š **Time Series Decomposition:**"); - println!(" Component | Magnitude | Pattern | Description"); - println!(" -----------------|-----------|----------|------------------"); - - for (component, magnitude, pattern, description) in components.iter() { - println!(" {:16} | {:.2} | {:8} | {}", - component, magnitude, pattern, description); - } - - println!("\n ๐ŸŽฏ **Time Series Insights:**"); - println!(" โ€ข **Dominant Pattern**: Strong upward trend (78% of signal)"); - println!(" โ€ข **Seasonality**: Clear weekly patterns with monthly variations"); - println!(" โ€ข **Stability**: Low noise level (5%) indicates consistent performance"); - println!(" โ€ข **Regime Changes**: 2 detected change points suggest system improvements"); - println!(" โ€ข **Predictability**: High signal-to-noise ratio enables accurate forecasting"); -} - -fn demonstrate_anomaly_detection() { - println!("\n๏ฟฝ๏ฟฝ **Performance Anomaly Detection**"); - println!("===================================="); - - println!("๐Ÿ” **6 Advanced Detection Algorithms:**"); - - println!("\n 1. **Isolation Forest**"); - println!(" โ€ข Trees: 100 isolation trees"); - println!(" โ€ข Contamination: 10% expected anomaly rate"); - println!(" โ€ข Principle: Isolate anomalies through random partitioning"); - println!(" โ€ข Advantages: Fast, unsupervised, handles high dimensions"); - - println!("\n 2. **One-Class SVM**"); - println!(" โ€ข Kernel: RBF (Radial Basis Function)"); - println!(" โ€ข Gamma: 0.1 for decision boundary smoothness"); - println!(" โ€ข Nu: 0.05 for outlier fraction control"); - println!(" โ€ข Use Cases: Non-linear anomaly boundaries"); - - println!("\n 3. **Local Outlier Factor (LOF)**"); - println!(" โ€ข Neighbors: 20 nearest neighbors"); - println!(" โ€ข Contamination: 10% expected outliers"); - println!(" โ€ข Principle: Local density-based anomaly detection"); - println!(" โ€ข Advantages: Adapts to local data density variations"); - - println!("\n 4. **DBSCAN Clustering**"); - println!(" โ€ข Epsilon: 0.5 neighborhood radius"); - println!(" โ€ข Min Samples: 5 points for core point"); - println!(" โ€ข Principle: Density-based spatial clustering"); - println!(" โ€ข Detects: Isolated points as anomalies"); - - println!("\n 5. **AutoEncoder Neural Network**"); - println!(" โ€ข Encoding Dimension: 32 compressed features"); - println!(" โ€ข Reconstruction Threshold: 2.0 standard deviations"); - println!(" โ€ข Architecture: Encoder-Decoder with bottleneck"); - println!(" โ€ข Principle: High reconstruction error indicates anomaly"); - - println!("\n 6. **Statistical Tests**"); - println!(" โ€ข Z-Score: 30-point rolling window"); - println!(" โ€ข Modified Z-Score: Median-based robustness"); - println!(" โ€ข IQR Method: Interquartile range with 1.5x multiplier"); - println!(" โ€ข Grubbs Test: Single outlier detection"); - - println!("\n๐Ÿš€ **Anomaly Detection Performance:**"); - demonstrate_anomaly_results(); -} - -fn demonstrate_anomaly_results() { - let algorithms = vec![ - ("Isolation Forest", 0.94, 0.91, 0.08, "Excellent generalization"), - ("One-Class SVM", 0.89, 0.87, 0.12, "Good non-linear boundaries"), - ("LOF", 0.92, 0.88, 0.10, "Adaptive to density changes"), - ("DBSCAN", 0.87, 0.84, 0.15, "Simple and interpretable"), - ("AutoEncoder", 0.96, 0.93, 0.06, "Complex pattern learning"), - ("Statistical Tests", 0.83, 0.78, 0.18, "Fast and lightweight"), - ("Ensemble", 0.97, 0.95, 0.04, "Combined algorithm strengths"), - ]; - - println!("\n ๐Ÿ“Š **Anomaly Detection Performance:**"); - println!(" Algorithm | Precision | Recall | F1-Score | Notes"); - println!(" -----------------|-----------|--------|----------|--------"); - - for (algorithm, precision, recall, f1, notes) in algorithms.iter() { - println!(" {:16} | {:.2} | {:.2} | {:.2} | {}", - algorithm, precision, recall, f1, notes); - } - - println!("\n ๐ŸŽฏ **Real-Time Anomaly Monitoring:**"); - demonstrate_anomaly_monitoring(); -} - -fn demonstrate_anomaly_monitoring() { - let recent_detections = vec![ - ("15:24:32", "Response Time Spike", "Medium", 2.8, "Database query timeout"), - ("15:31:15", "CPU Usage Anomaly", "Low", 1.2, "Background process interference"), - ("15:42:08", "Memory Pattern Change", "High", 4.1, "Memory leak suspected"), - ("15:58:44", "Network Latency", "Medium", 2.3, "Network congestion detected"), - ]; - - println!("\n ๐Ÿšจ **Recent Anomaly Detections:**"); - println!(" Time | Type | Severity | Score | Cause"); - println!(" ---------|---------------------|----------|-------|-------"); - - for (time, anomaly_type, severity, score, cause) in recent_detections.iter() { - println!(" {} | {:19} | {:8} | {:.1} | {}", - time, anomaly_type, severity, score, cause); - } - - println!("\n ๐Ÿ”ง **Anomaly Response Actions:**"); - println!(" โ€ข **Automated**: Alert generation, log enrichment, baseline updates"); - println!(" โ€ข **Escalation**: High-severity anomalies trigger immediate alerts"); - println!(" โ€ข **Root Cause**: Automatic correlation with system events"); - println!(" โ€ข **Learning**: Model retraining with confirmed anomalies"); - println!(" โ€ข **Prevention**: Proactive monitoring frequency adjustment"); -} - -fn demonstrate_planning_quality_prediction() { - println!("\n๐ŸŽฏ **Planning Quality Prediction**"); - println!("================================="); - - println!("๐Ÿง  **Planning-Specific Prediction Models:**"); - - println!("\n ๐Ÿ“Š **Feature Extraction Components:**"); - println!(" โ€ข StateComplexityAnalyzer: State space size, branching factor, depth"); - println!(" โ€ข ActionSpaceAnalyzer: Available actions, constraints, preconditions"); - println!(" โ€ข ConstraintAnalyzer: Resource limits, temporal constraints, conflicts"); - println!(" โ€ข GoalDifficultyAssessor: Goal complexity, sub-goal dependencies"); - println!(" โ€ข ResourceAvailabilityTracker: CPU, memory, time budget status"); - - println!("\n ๐ŸŽฏ **4 Quality Prediction Models:**"); - - println!("\n 1. **Planning Success Predictor**"); - println!(" โ€ข Model: Random Forest (50 estimators)"); - println!(" โ€ข Success Threshold: 80% solution quality"); - println!(" โ€ข Features: State complexity, resource availability, constraint satisfaction"); - println!(" โ€ข Accuracy: 94% success prediction rate"); - - println!("\n 2. **Solution Quality Predictor**"); - println!(" โ€ข Model: Gradient Boosting with quality metrics"); - println!(" โ€ข Quality Metrics: Optimality, feasibility, robustness"); - println!(" โ€ข Prediction: Expected solution quality score (0-1)"); - println!(" โ€ข Confidence: 91% prediction confidence"); - - println!("\n 3. **Planning Time Predictor**"); - println!(" โ€ข Model: LSTM with time budget constraints"); - println!(" โ€ข Time Budget: Dynamic allocation based on complexity"); - println!(" โ€ข Prediction: Expected planning duration"); - println!(" โ€ข Accuracy: ยฑ15% time estimation error"); - - println!("\n 4. **Resource Usage Predictor**"); - println!(" โ€ข Model: Multi-output regression"); - println!(" โ€ข Resource Types: CPU, memory, network I/O"); - println!(" โ€ข Prediction: Resource consumption forecasts"); - println!(" โ€ข Optimization: Resource allocation recommendations"); - - println!("\n๐Ÿš€ **Planning Quality Forecast:**"); - demonstrate_planning_forecast(); -} - -fn demonstrate_planning_forecast() { - println!("\n ๐Ÿ“Š **Current Planning Quality Forecast:**"); - println!(" โ€ข Expected Planning Success Rate: 94%"); - println!(" โ€ข Expected Solution Quality: 88%"); - println!(" โ€ข Expected Planning Time: 250ms"); - println!(" โ€ข Quality Confidence: 91%"); - - println!("\n ๐Ÿ’ป **Resource Usage Forecast:**"); - println!(" โ€ข CPU Utilization: 65%"); - println!(" โ€ข Memory Usage: 42%"); - println!(" โ€ข Network I/O: 23%"); - - println!("\n ๐Ÿ” **Quality Factors Analysis:**"); - let factors = vec![ - ("State complexity", -15, "Negative", "More complex states reduce planning efficiency"), - ("Available actions", 12, "Positive", "More options improve solution quality"), - ("Resource availability", 8, "Positive", "Adequate resources enable thorough planning"), - ("Constraint density", -10, "Negative", "More constraints limit solution space"), - ("Goal clarity", 15, "Positive", "Clear goals improve planning focus"), - ]; - - println!(" Factor | Impact (%) | Direction | Explanation"); - println!(" --------------------|------------|-----------|------------------"); - - for (factor, impact, direction, explanation) in factors.iter() { - println!(" {:19} | {:+3} | {:9} | {}", - factor, impact, direction, explanation); - } - - println!("\n ๐ŸŽฏ **Planning Optimization Recommendations:**"); - println!(" โ€ข **State Simplification**: Reduce unnecessary state variables"); - println!(" โ€ข **Action Pruning**: Filter infeasible actions early"); - println!(" โ€ข **Resource Allocation**: Increase memory for complex planning"); - println!(" โ€ข **Constraint Relaxation**: Consider soft constraint handling"); - println!(" โ€ข **Goal Decomposition**: Break complex goals into sub-goals"); -} - -fn demonstrate_performance_regression_detection() { - println!("\n๐Ÿ“‰ **Performance Regression Detection**"); - println!("======================================"); - - println!("๐Ÿ”ฌ **5 Advanced Statistical Detection Methods:**"); - - println!("\n 1. **T-Test Analysis**"); - println!(" โ€ข Alpha: 0.05 significance level"); - println!(" โ€ข Minimum Sample Size: 30 observations"); - println!(" โ€ข Test Type: Two-sample independent t-test"); - println!(" โ€ข Use Cases: Comparing performance before/after changes"); - - println!("\n 2. **Mann-Whitney U Test**"); - println!(" โ€ข Alpha: 0.05 significance level"); - println!(" โ€ข Non-parametric: No distribution assumptions"); - println!(" โ€ข Robust: Handles outliers and skewed data"); - println!(" โ€ข Applications: Non-normal performance distributions"); - - println!("\n 3. **Change Point Detection**"); - println!(" โ€ข CUSUM: Cumulative sum with threshold 3.0"); - println!(" โ€ข Sensitivity: 0.8 for balanced detection"); - println!(" โ€ข PELT: Pruned exact linear time algorithm"); - println!(" โ€ข Binary Segmentation: Recursive partitioning"); - - println!("\n 4. **Bayesian Change Point**"); - println!(" โ€ข Prior Belief: 0.1 probability of change"); - println!(" โ€ข Posterior Threshold: 0.8 for detection"); - println!(" โ€ข Uncertainty: Quantified change probability"); - println!(" โ€ข Adaptive: Updates belief with new evidence"); - - println!("\n 5. **Sequential Testing**"); - println!(" โ€ข SPRT: Sequential Probability Ratio Test"); - println!(" โ€ข Early Stopping: Minimize testing time"); - println!(" โ€ข Type I Error: 5% false positive rate"); - println!(" โ€ข Type II Error: 10% false negative rate"); - - println!("\n๐Ÿš€ **Regression Detection Results:**"); - demonstrate_regression_analysis(); -} - -fn demonstrate_regression_analysis() { - println!("\n ๐Ÿ“Š **Current Regression Risk Assessment:**"); - println!(" โ€ข Regression Probability: 3% (Very Low Risk)"); - println!(" โ€ข Expected Impact Magnitude: 1%"); - println!(" โ€ข Regression Type: Performance degradation"); - println!(" โ€ข Alert Threshold Distance: 92% (Far from alert)"); - - println!("\n ๐Ÿ” **Statistical Analysis:**"); - let tests = vec![ - ("T-Test", 0.85, "Pass", "No significant difference detected"), - ("Mann-Whitney U", 0.72, "Pass", "Distributions are similar"), - ("CUSUM", 1.2, "Pass", "Below threshold (3.0)"), - ("Bayesian Change", 0.08, "Pass", "Low change probability"), - ("Sequential SPRT", 0.45, "Pass", "Insufficient evidence for regression"), - ]; - - println!(" Test Method | P-Value/Score | Result | Interpretation"); - println!(" -----------------|---------------|--------|------------------"); - - for (test, score, result, interpretation) in tests.iter() { - println!(" {:16} | {:.2} | {:6} | {}", - test, score, result, interpretation); - } - - println!("\n ๐Ÿ›ก๏ธ **Regression Prevention Strategies:**"); - println!(" โ€ข **Baseline Monitoring**: Continuous comparison with stable baselines"); - println!(" โ€ข **Statistical Significance**: Multiple test validation for robustness"); - println!(" โ€ข **Effect Size Calculation**: Practical significance assessment"); - println!(" โ€ข **False Positive Reduction**: Multi-algorithm consensus required"); - println!(" โ€ข **Automated Rollback**: Immediate reversion for confirmed regressions"); - - println!("\n ๐Ÿ“ˆ **Historical Regression Tracking:**"); - let history = vec![ - ("Week 1", 0.02, "No regression", "Stable performance"), - ("Week 2", 0.05, "Monitoring", "Slight increase in variance"), - ("Week 3", 0.01, "Excellent", "Performance improvement"), - ("Week 4", 0.03, "Normal", "Within expected range"), - ]; - - println!(" Period | Risk Score | Status | Notes"); - println!(" ---------|------------|-------------|------------------"); - - for (period, risk, status, notes) in history.iter() { - println!(" {:8} | {:.2} | {:11} | {}", - period, risk, status, notes); - } -} - -fn demonstrate_multi_dimensional_prediction() { - println!("\n๐Ÿ“Š **Multi-Dimensional Performance Prediction**"); - println!("==============================================="); - - println!("๐ŸŽฏ **6 Performance Dimensions:**"); - - let dimensions = vec![ - ("Accuracy", 0.30, 0.94, "ยฑ0.02", "Model prediction correctness"), - ("Speed", 0.25, 0.87, "ยฑ0.05", "Response time and throughput"), - ("Resource Usage", 0.20, 0.78, "ยฑ0.08", "CPU, memory, disk utilization"), - ("Reliability", 0.15, 0.91, "ยฑ0.03", "System uptime and stability"), - ("Scalability", 0.10, 0.82, "ยฑ0.06", "Performance under increased load"), - ("User Satisfaction", 0.05, 0.89, "ยฑ0.04", "User experience metrics"), - ]; - - println!("\n ๐Ÿ“ˆ **Dimensional Performance Forecast:**"); - println!(" Dimension | Weight | Score | Uncertainty | Description"); - println!(" -----------------|--------|-------|-------------|------------------"); - - for (dimension, weight, score, uncertainty, description) in dimensions.iter() { - println!(" {:16} | {:.2} | {:.2} | {:6} | {}", - dimension, weight, score, uncertainty, description); - } - - println!("\n ๐Ÿ”— **Cross-Dimensional Correlations:**"); - demonstrate_correlations(); - - println!("\n ๐Ÿ† **Composite Performance Score:**"); - demonstrate_composite_score(); -} - -fn demonstrate_correlations() { - let correlations = vec![ - ("Accuracy โ†” Speed", -0.65, "Moderate negative", "Higher accuracy often requires more time"), - ("Speed โ†” Resource Usage", 0.78, "Strong positive", "Faster processing needs more resources"), - ("Reliability โ†” Accuracy", 0.82, "Strong positive", "Reliable systems produce accurate results"), - ("Scalability โ†” Speed", -0.43, "Moderate negative", "Scalability may impact individual response time"), - ("Resource Usage โ†” Reliability", 0.34, "Weak positive", "More resources can improve reliability"), - ]; - - println!(" Correlation | Coefficient | Strength | Interpretation"); - println!(" ---------------------|-------------|-----------------|------------------"); - - for (correlation, coefficient, strength, interpretation) in correlations.iter() { - println!(" {:20} | {:+.2} | {:15} | {}", - correlation, coefficient, strength, interpretation); - } -} - -fn demonstrate_composite_score() { - println!(" ๐Ÿ“Š **Weighted Composite Score Calculation:**"); - println!(" Accuracy: 0.94 ร— 0.30 = 0.282"); - println!(" Speed: 0.87 ร— 0.25 = 0.218"); - println!(" Resource: 0.78 ร— 0.20 = 0.156"); - println!(" Reliability: 0.91 ร— 0.15 = 0.137"); - println!(" Scalability: 0.82 ร— 0.10 = 0.082"); - println!(" User Sat.: 0.89 ร— 0.05 = 0.045"); - println!(" โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€"); - println!(" **Total Score: 0.920 (92.0%)**"); - - println!("\n ๐ŸŽฏ **Performance Grade: A- (Excellent)**"); - println!(" โ€ข Above 90%: Excellent performance across all dimensions"); - println!(" โ€ข Strengths: High accuracy and reliability"); - println!(" โ€ข Improvement Areas: Resource efficiency and scalability"); - println!(" โ€ข Recommendation: Optimize resource usage for cost efficiency"); -} - -fn demonstrate_adaptive_model_management() { - println!("\n๐Ÿ”„ **Adaptive Model Management System**"); - println!("======================================"); - - println!("๐Ÿง  **Continuous Learning and Adaptation:**"); - - println!("\n ๐Ÿ“š **Online Learning System:**"); - println!(" โ€ข Algorithm: Adaptive Gradient (AdaGrad)"); - println!(" โ€ข Batch Size: 32 samples for mini-batch updates"); - println!(" โ€ข Learning Rate: Exponential decay (0.95 rate)"); - println!(" โ€ข Forgetting Factor: 0.99 for concept drift handling"); - println!(" โ€ข Adaptation Threshold: 5% performance drop triggers retraining"); - - println!("\n ๐Ÿ” **Concept Drift Detection:**"); - println!(" โ€ข Statistical Tests: Kolmogorov-Smirnov, Page-Hinkley"); - println!(" โ€ข Drift Sensitivity: Balanced detection vs. false alarms"); - println!(" โ€ข Window Size: Sliding window for recent performance tracking"); - println!(" โ€ข Response Strategy: Gradual adaptation vs. complete retraining"); - - println!("\n โฐ **Model Retraining Schedule:**"); - println!(" โ€ข Automatic: Triggered by performance degradation"); - println!(" โ€ข Periodic: Weekly full model updates"); - println!(" โ€ข Event-driven: Major system changes or data distribution shifts"); - println!(" โ€ข Resource-aware: Scheduled during low-usage periods"); - - println!("\n ๐Ÿ“Š **Model Performance Monitoring:**"); - println!(" โ€ข Real-time: Continuous accuracy and calibration tracking"); - println!(" โ€ข Baseline Comparison: Performance vs. historical benchmarks"); - println!(" โ€ข A/B Testing: Champion vs. challenger model evaluation"); - println!(" โ€ข Alert System: Performance degradation notifications"); - - println!("\n๐Ÿš€ **Model Lifecycle Status:**"); - demonstrate_model_lifecycle(); -} - -fn demonstrate_model_lifecycle() { - let models = vec![ - ("LSTM_v2.1", "Production", 94, "2024-01-15", "Stable performance"), - ("XGBoost_v1.8", "Production", 96, "2024-01-10", "Best performer"), - ("RandomForest_v1.3", "Staging", 89, "2024-01-20", "Testing improvements"), - ("Transformer_v1.0", "Development", 93, "2024-01-22", "New architecture"), - ("LSTM_v2.0", "Retired", 91, "2023-12-30", "Replaced by v2.1"), - ]; - - println!("\n ๐Ÿ“‹ **Model Version Management:**"); - println!(" Model | Status | Acc (%) | Updated | Notes"); - println!(" -----------------|-------------|---------|------------|------------------"); - - for (model, status, accuracy, updated, notes) in models.iter() { - println!(" {:16} | {:11} | {:2} | {} | {}", - model, status, accuracy, updated, notes); - } - - println!("\n ๐Ÿ”„ **Adaptive Learning Progress:**"); - let learning_progress = vec![ - ("Day 1", 91.2, "Baseline", "Initial model deployment"), - ("Day 3", 92.1, "Learning", "Online adaptation from new data"), - ("Day 7", 93.5, "Improving", "Concept drift detected and handled"), - ("Day 14", 94.8, "Stable", "Converged to optimal performance"), - ("Day 21", 95.1, "Optimized", "Continuous micro-improvements"), - ]; - - println!(" Period | Accuracy | Status | Description"); - println!(" -------|----------|-----------|------------------"); - - for (period, accuracy, status, description) in learning_progress.iter() { - println!(" {:6} | {:.1} | {:9} | {}", - period, accuracy, status, description); - } - - println!("\n ๐ŸŽฏ **Adaptation Insights:**"); - println!(" โ€ข **Learning Rate**: 3.9% accuracy improvement over 21 days"); - println!(" โ€ข **Drift Handling**: Successfully adapted to 2 concept drifts"); - println!(" โ€ข **Stability**: Model performance stabilized after day 14"); - println!(" โ€ข **Efficiency**: 99.2% of potential improvement achieved"); - println!(" โ€ข **Resource Impact**: <5% additional computational overhead"); -} - -fn demonstrate_comprehensive_forecasting() { - println!("\n๐Ÿ”ฎ **Comprehensive Performance Forecasting**"); - println!("============================================"); - - println!("๐Ÿ“Š **Integrated Forecast Results:**"); - println!(" โ€ข Forecast ID: forecast_20250712_143000"); - println!(" โ€ข Timestamp: July 12, 2025 14:30:00 UTC"); - println!(" โ€ข Forecast Horizon: 1 hour"); - println!(" โ€ข Overall Performance Score: 91%"); - println!(" โ€ข Confidence Level: 88%"); - - println!("\n ๐ŸŽฏ **Multi-Dimensional Predictions:**"); - let forecasts = vec![ - ("Accuracy", 0.92, 0.85, "(0.88, 0.96)"), - ("Speed", 0.87, 0.82, "(0.82, 0.92)"), - ("Resource", 0.78, 0.79, "(0.73, 0.83)"), - ("Reliability", 0.91, 0.87, "(0.87, 0.95)"), - ("Scalability", 0.82, 0.81, "(0.76, 0.88)"), - ]; - - println!(" Dimension | Prediction | Confidence | 90% Interval"); - println!(" ------------|------------|------------|----------------"); - - for (dimension, prediction, confidence, interval) in forecasts.iter() { - println!(" {:11} | {:.2} | {:.2} | {}", - dimension, prediction, confidence, interval); - } - - println!("\n ๐Ÿšจ **Risk Assessments:**"); - println!(" โ€ข **Anomaly Risk**: 15% (Low) - Minor response time deviation"); - println!(" โ€ข **Regression Risk**: 3% (Very Low) - No significant regression indicators"); - println!(" โ€ข **Quality Risk**: 9% (Low) - Planning quality remains stable"); - - println!("\n ๐Ÿ“‹ **Actionable Recommendations:**"); - println!(" โ€ข โœ… Performance is expected to remain stable"); - println!(" โ€ข โœ… No immediate action required"); - println!(" โ€ข โœ… Continue routine monitoring"); - println!(" โ€ข ๐Ÿ“Š Next evaluation scheduled: July 12, 2025 15:30:00 UTC"); - - println!("\n ๐Ÿ“ˆ **Forecast Accuracy Tracking:**"); - demonstrate_forecast_accuracy(); -} - -fn demonstrate_forecast_accuracy() { - let accuracy_history = vec![ - ("1-hour", 0.94, 0.89, "Excellent short-term accuracy"), - ("4-hour", 0.91, 0.85, "Good medium-term prediction"), - ("1-day", 0.87, 0.81, "Solid daily forecasting"), - ("1-week", 0.82, 0.76, "Reasonable weekly trends"), - ("1-month", 0.75, 0.68, "Challenging long-term prediction"), - ]; - - println!(" Horizon | Accuracy | Calibration | Assessment"); - println!(" --------|----------|-------------|------------------"); - - for (horizon, accuracy, calibration, assessment) in accuracy_history.iter() { - println!(" {:7} | {:.2} | {:.2} | {}", - horizon, accuracy, calibration, assessment); - } - - println!("\n ๐ŸŽฏ **Forecasting Performance:**"); - println!(" โ€ข **Optimal Horizon**: 1-4 hours (>90% accuracy)"); - println!(" โ€ข **Calibration Score**: 89% (well-calibrated predictions)"); - println!(" โ€ข **Directional Accuracy**: 93% (correct trend prediction)"); - println!(" โ€ข **Interval Coverage**: 87% (reliable uncertainty bounds)"); - println!(" โ€ข **Model Confidence**: High for short-term, moderate for long-term"); -} - -fn demonstrate_uncertainty_quantification() { - println!("\n๐ŸŽฒ **Uncertainty Quantification**"); - println!("================================="); - - println!("๐Ÿ“Š **Advanced Uncertainty Analysis:**"); - - println!("\n ๐Ÿง  **Uncertainty Types:**"); - println!(" โ€ข **Epistemic Uncertainty**: 3% (Model/Knowledge uncertainty)"); - println!(" โ€ข **Aleatoric Uncertainty**: 2% (Data/Noise uncertainty)"); - println!(" โ€ข **Total Uncertainty**: 5% (Combined uncertainty)"); - println!(" โ€ข **Prediction Entropy**: 0.12 (Information content)"); - println!(" โ€ข **Calibration Score**: 87% (Confidence accuracy)"); - - println!("\n ๐Ÿ“ˆ **Prediction Intervals:**"); - let intervals = vec![ - (80, 0.88, 0.96, "Common confidence level"), - (90, 0.86, 0.98, "Standard business interval"), - (95, 0.84, 1.00, "High confidence interval"), - (99, 0.81, 1.03, "Very high confidence interval"), - ]; - - println!(" Confidence | Lower Bound | Upper Bound | Usage"); - println!(" -----------|-------------|-------------|------------------"); - - for (confidence, lower, upper, usage) in intervals.iter() { - println!(" {:2}% | {:.2} | {:.2} | {}", - confidence, lower, upper, usage); - } - - println!("\n ๐ŸŽฏ **Uncertainty-Aware Decision Making:**"); - println!(" โ€ข **High Certainty** (>95%): Proceed with automated decisions"); - println!(" โ€ข **Medium Certainty** (80-95%): Human-in-the-loop verification"); - println!(" โ€ข **Low Certainty** (<80%): Manual review and additional data collection"); - - println!("\n ๐Ÿ“Š **Uncertainty Sources:**"); - let sources = vec![ - ("Model Limitations", 35, "Neural network approximation errors"), - ("Data Quality", 25, "Noise and measurement uncertainty"), - ("Feature Engineering", 20, "Incomplete feature representation"), - ("Temporal Drift", 15, "Changing system behavior over time"), - ("External Factors", 5, "Environmental and system changes"), - ]; - - println!(" Source | Contribution (%) | Description"); - println!(" -------------------|------------------|------------------"); - - for (source, contribution, description) in sources.iter() { - println!(" {:18} | {:2} | {}", - source, contribution, description); - } - - println!("\n ๐Ÿ›ก๏ธ **Uncertainty Mitigation Strategies:**"); - println!(" โ€ข **Data Augmentation**: Increase training data diversity"); - println!(" โ€ข **Ensemble Methods**: Combine multiple model predictions"); - println!(" โ€ข **Bayesian Approaches**: Quantify model parameter uncertainty"); - println!(" โ€ข **Active Learning**: Target uncertain regions for additional data"); - println!(" โ€ข **Regular Calibration**: Maintain prediction confidence accuracy"); - - println!("\n๐Ÿ† **Final Performance Summary:**"); - println!(" โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ 94%"); - println!(" ๐Ÿ“Š **Overall System Performance**: 94% (Excellent)"); - println!(" ๐Ÿ”ฎ **Prediction Accuracy**: 91% with 88% confidence"); - println!(" ๐Ÿšจ **Risk Level**: Low (3% regression, 15% anomaly risk)"); - println!(" ๐ŸŽฏ **Quality Assurance**: 91% planning quality confidence"); - println!(" โšก **Real-time Capability**: <200ms prediction latency"); - println!(" ๐Ÿ”„ **Adaptive Learning**: 3.9% improvement over 21 days"); - println!(" ๐Ÿ›ก๏ธ **Uncertainty Management**: 5% total uncertainty, well-calibrated"); -} \ No newline at end of file diff --git a/task_7_3_continuous_learning_demo.rs b/task_7_3_continuous_learning_demo.rs deleted file mode 100644 index 1d8dc83fc47ef9022668d73a6bb004a2d9e80ef7..0000000000000000000000000000000000000000 --- a/task_7_3_continuous_learning_demo.rs +++ /dev/null @@ -1,343 +0,0 @@ -/// Task 7.3: Continuous Learning Pipeline Demo -/// -/// This demonstrates the sophisticated continuous learning capabilities of Brain AI's -/// MuBrain system, showcasing automated learning from agent interactions, incremental -/// model updates, cross-agent pattern recognition, and learning progress tracking. - -use std::collections::HashMap; - -// Simulate the structures since we're running a standalone demo -#[derive(Debug, Clone)] -struct AgentInteraction { - interaction_id: String, - agent_type: String, - success_rate: f64, - execution_time: f64, - expected_time: f64, - output_quality: f64, - context: HashMap, - error_patterns: Vec, - quality_metrics: HashMap, -} - -fn main() { - println!("๐Ÿง  Brain AI Task 7.3: Continuous Learning Pipeline - Full Demo"); - println!("================================================================"); - - demonstrate_continuous_learning_architecture(); - demonstrate_agent_interaction_learning(); - demonstrate_pattern_extraction(); - demonstrate_incremental_model_updates(); - demonstrate_cross_agent_learning(); - demonstrate_progress_tracking(); - demonstrate_learning_milestones(); - - println!("\n๐ŸŽฏ **TASK 7.3: CONTINUOUS LEARNING PIPELINE - COMPLETED!**"); - println!("โœ… All continuous learning components successfully implemented!"); - println!("๐Ÿš€ Brain AI now learns automatically from every agent interaction!"); -} - -fn demonstrate_continuous_learning_architecture() { - println!("\n๐Ÿ—๏ธ **Continuous Learning Pipeline Architecture**"); - println!("================================================="); - - println!("๐Ÿ“Š **Core Components:**"); - println!(" โ€ข InteractionLearner: Extracts learning signals from agent interactions"); - println!(" โ€ข IncrementalModelUpdater: Applies safe, incremental model improvements"); - println!(" โ€ข LearningProgressTracker: Monitors learning milestones and metrics"); - println!(" โ€ข CrossAgentPatternAnalyzer: Identifies patterns across multiple agents"); - - println!("\n๐Ÿ”„ **Learning Flow:**"); - println!(" 1. Agent performs task โ†’ 2. Extract learning signals"); - println!(" 3. Identify patterns โ†’ 4. Generate model updates"); - println!(" 5. Safety validation โ†’ 6. Apply updates"); - println!(" 7. Track progress โ†’ 8. Check milestones"); - - println!("\nโšก **Key Features:**"); - println!(" โ€ข Real-time learning from all 38+ agent types"); - println!(" โ€ข Safety-first update validation"); - println!(" โ€ข Cross-agent pattern recognition"); - println!(" โ€ข Automatic milestone tracking"); - println!(" โ€ข Performance regression prevention"); -} - -fn demonstrate_agent_interaction_learning() { - println!("\n๐Ÿค– **Agent Interaction Learning**"); - println!("=================================="); - - // Simulate diverse agent interactions - let interactions = vec![ - AgentInteraction { - interaction_id: "algorithm_coder_001".to_string(), - agent_type: "AlgorithmCoder".to_string(), - success_rate: 0.92, - execution_time: 2.3, - expected_time: 3.0, - output_quality: 0.88, - context: HashMap::from([ - ("problem_type".to_string(), "sorting_algorithm".to_string()), - ("complexity".to_string(), "medium".to_string()), - ]), - error_patterns: vec![], - quality_metrics: HashMap::from([ - ("correctness".to_string(), "0.95".to_string()), - ("efficiency".to_string(), "0.85".to_string()), - ]), - }, - AgentInteraction { - interaction_id: "backend_coder_001".to_string(), - agent_type: "BackendCoder".to_string(), - success_rate: 0.85, - execution_time: 4.1, - expected_time: 3.5, - output_quality: 0.82, - context: HashMap::from([ - ("framework".to_string(), "rust_actix".to_string()), - ("complexity".to_string(), "high".to_string()), - ]), - error_patterns: vec!["async_lifetime_issue".to_string()], - quality_metrics: HashMap::from([ - ("code_quality".to_string(), "0.88".to_string()), - ("test_coverage".to_string(), "0.76".to_string()), - ]), - }, - AgentInteraction { - interaction_id: "security_auditor_001".to_string(), - agent_type: "SecurityAuditor".to_string(), - success_rate: 0.96, - execution_time: 1.8, - expected_time: 2.5, - output_quality: 0.94, - context: HashMap::from([ - ("audit_type".to_string(), "vulnerability_scan".to_string()), - ("severity".to_string(), "critical".to_string()), - ]), - error_patterns: vec![], - quality_metrics: HashMap::from([ - ("accuracy".to_string(), "0.97".to_string()), - ("completeness".to_string(), "0.91".to_string()), - ]), - }, - ]; - - println!("๐Ÿ“ˆ **Processing {} Agent Interactions:**", interactions.len()); - - for (i, interaction) in interactions.iter().enumerate() { - println!("\n {}. {} Agent:", i + 1, interaction.agent_type); - println!(" โ€ข Success Rate: {:.1}%", interaction.success_rate * 100.0); - println!(" โ€ข Execution Time: {:.1}s (expected: {:.1}s)", - interaction.execution_time, interaction.expected_time); - println!(" โ€ข Output Quality: {:.1}%", interaction.output_quality * 100.0); - - if !interaction.error_patterns.is_empty() { - println!(" โ€ข Error Patterns: {:?}", interaction.error_patterns); - } - - // Simulate learning signal extraction - let signals_extracted = extract_learning_signals(interaction); - println!(" โ€ข Learning Signals Extracted: {}", signals_extracted); - } -} - -fn extract_learning_signals(interaction: &AgentInteraction) -> usize { - let mut signal_count = 0; - - // High performance signals - if interaction.success_rate > 0.8 { - signal_count += 1; - } - - // Error pattern signals - if !interaction.error_patterns.is_empty() { - signal_count += 1; - } - - // Efficiency signals - if interaction.execution_time < interaction.expected_time * 0.8 { - signal_count += 1; - } - - // Quality signals - if interaction.output_quality > 0.85 { - signal_count += 1; - } - - signal_count -} - -fn demonstrate_pattern_extraction() { - println!("\n๐Ÿ” **Pattern Extraction & Analysis**"); - println!("====================================="); - - println!("๐ŸŽฏ **Identified Patterns:**"); - - println!("\n 1. **Consistent High Performance Pattern**"); - println!(" โ€ข Frequency: 2 occurrences"); - println!(" โ€ข Confidence: 89.5%"); - println!(" โ€ข Context: Security and Algorithm agents excel in well-defined tasks"); - println!(" โ€ข Improvement Potential: 15%"); - - println!("\n 2. **Efficiency Optimization Pattern**"); - println!(" โ€ข Frequency: 2 occurrences"); - println!(" โ€ข Confidence: 85.5%"); - println!(" โ€ข Context: Tasks completed faster than expected"); - println!(" โ€ข Improvement Potential: 12%"); - - println!("\n 3. **Recurring Error Pattern**"); - println!(" โ€ข Frequency: 1 occurrence"); - println!(" โ€ข Confidence: 90.0%"); - println!(" โ€ข Context: Async lifetime issues in Rust backend code"); - println!(" โ€ข Improvement Potential: 25% (when prevented)"); - - println!("\n 4. **Quality Excellence Pattern**"); - println!(" โ€ข Frequency: 2 occurrences"); - println!(" โ€ข Confidence: 91.0%"); - println!(" โ€ข Context: High-quality output in security and algorithm tasks"); - println!(" โ€ข Improvement Potential: 18%"); -} - -fn demonstrate_incremental_model_updates() { - println!("\n๐Ÿ”„ **Incremental Model Updates**"); - println!("================================="); - - println!("๐Ÿ“Š **Generated Model Updates:**"); - - println!("\n 1. **PlanningStrategy Enhancement** (High Priority)"); - println!(" โ€ข Component: PlanningStrategy"); - println!(" โ€ข Type: ParameterAdjustment"); - println!(" โ€ข Estimated Improvement: 15.0%"); - println!(" โ€ข Confidence: 89.5%"); - println!(" โ€ข Strategy: Amplify successful high-performance patterns"); - - println!("\n 2. **ErrorPrevention System** (Critical Priority)"); - println!(" โ€ข Component: ErrorPrevention"); - println!(" โ€ข Type: BehaviorModification"); - println!(" โ€ข Estimated Improvement: 25.0%"); - println!(" โ€ข Confidence: 90.0%"); - println!(" โ€ข Strategy: Prevent async lifetime issues in Rust code"); - - println!("\n 3. **ExecutionOptimizer Tuning** (Medium Priority)"); - println!(" โ€ข Component: ExecutionOptimizer"); - println!(" โ€ข Type: PerformanceTuning"); - println!(" โ€ข Estimated Improvement: 12.0%"); - println!(" โ€ข Confidence: 85.5%"); - println!(" โ€ข Strategy: Optimize execution time for similar task patterns"); - - println!("\n๐Ÿ›ก๏ธ **Safety Validation Results:**"); - println!(" โ€ข Performance Regression Check: โœ… PASSED"); - println!(" โ€ข Stability Validation: โœ… PASSED"); - println!(" โ€ข Quality Maintenance: โœ… PASSED"); - println!(" โ€ข Overall Safety Score: 87.5%"); - println!(" โ€ข Risk Assessment: LOW (0.15)"); - println!(" โ€ข Recommendation: PROCEED WITH UPDATES"); - - println!("\nโšก **Update Application:**"); - println!(" โ€ข Strategy: Adaptive Blending"); - println!(" โ€ข Components Updated: 3"); - println!(" โ€ข Total Estimated Improvement: 17.3%"); - println!(" โ€ข Application Success: โœ… 100%"); -} - -fn demonstrate_cross_agent_learning() { - println!("\n๐ŸŒ **Cross-Agent Learning & Coordination**"); - println!("=========================================="); - - println!("๐Ÿ”— **Cross-Agent Pattern Analysis:**"); - - println!("\n 1. **Multi-Agent Coordination Pattern**"); - println!(" โ€ข Affected Agents: AlgorithmCoder, BackendCoder, SecurityAuditor"); - println!(" โ€ข Pattern Type: Shared Context Optimization"); - println!(" โ€ข Improvement Potential: 15%"); - println!(" โ€ข Confidence: 80%"); - println!(" โ€ข Strategy: Optimize information sharing between agents"); - - println!("\n 2. **Common Success Patterns**"); - println!(" โ€ข Affected Agents: All Agents"); - println!(" โ€ข Pattern Type: Universal Strategy Enhancement"); - println!(" โ€ข Improvement Potential: 10%"); - println!(" โ€ข Confidence: 90%"); - println!(" โ€ข Strategy: Apply successful patterns across all agents"); - - println!("\n๐Ÿš€ **Coordination Improvements Applied:**"); - println!(" โ€ข Shared Context Optimization: +15% coordination efficiency"); - println!(" โ€ข Universal Strategy Enhancement: +10% general improvement"); - println!(" โ€ข Total Coordination Improvement: 25%"); - println!(" โ€ข Update Confidence: 85%"); - - println!("\n๐Ÿ“Š **Agent Performance Improvements:**"); - println!(" โ€ข AlgorithmCoder: +12.3% performance boost"); - println!(" โ€ข BackendCoder: +18.7% performance boost"); - println!(" โ€ข SecurityAuditor: +8.9% performance boost"); - println!(" โ€ข Cross-Agent Synergy: +15.2% coordination improvement"); -} - -fn demonstrate_progress_tracking() { - println!("\n๐Ÿ“ˆ **Learning Progress Tracking**"); - println!("=================================="); - - println!("๐ŸŽฏ **Current Progress Metrics:**"); - - println!("\n ๐Ÿ“Š **Planning Accuracy**"); - println!(" โ€ข Current Value: 78.5% (โ†‘ +8.5% from updates)"); - println!(" โ€ข Target Value: 90.0%"); - println!(" โ€ข Progress: 87.2% of target achieved"); - println!(" โ€ข Improvement Rate: +8.5% this session"); - - println!("\n โšก **Execution Efficiency**"); - println!(" โ€ข Current Value: 72.3% (โ†‘ +7.3% from updates)"); - println!(" โ€ข Target Value: 85.0%"); - println!(" โ€ข Progress: 85.1% of target achieved"); - println!(" โ€ข Improvement Rate: +7.3% this session"); - - println!("\n ๐Ÿ›ก๏ธ **Error Reduction**"); - println!(" โ€ข Current Value: 90.0% (โ†‘ +10.0% from updates)"); - println!(" โ€ข Target Value: 95.0%"); - println!(" โ€ข Progress: 94.7% of target achieved"); - println!(" โ€ข Improvement Rate: +10.0% this session"); - - println!("\n๐Ÿ“Š **Overall Progress Summary:**"); - println!(" โ€ข Overall Progress Score: 89.0%"); - println!(" โ€ข Learning Trajectory: Strongly Positive ๐Ÿ“ˆ"); - println!(" โ€ข Average Improvement Rate: 8.6%"); - println!(" โ€ข System Health: Excellent โœ…"); -} - -fn demonstrate_learning_milestones() { - println!("\n๐Ÿ† **Learning Milestones & Achievements**"); - println!("========================================="); - - println!("โœ… **Recently Achieved Milestones:**"); - - println!("\n ๐Ÿฅ‰ **Basic Learning Competency**"); - println!(" โ€ข Criteria: Planning Accuracy > 75%"); - println!(" โ€ข Status: โœ… ACHIEVED (78.5%)"); - println!(" โ€ข Achieved: Just now!"); - - println!("\n๐ŸŽฏ **Milestones in Progress:**"); - - println!("\n ๐Ÿฅˆ **Advanced Learning Mastery**"); - println!(" โ€ข Criteria 1: Planning Accuracy > 85% โณ (Currently: 78.5%)"); - println!(" โ€ข Criteria 2: Execution Efficiency > 80% โณ (Currently: 72.3%)"); - println!(" โ€ข Status: ๐Ÿ”„ IN PROGRESS"); - println!(" โ€ข Estimated Achievement: 2-3 learning sessions"); - - println!("\n ๐Ÿฅ‡ **Expert-Level Performance**"); - println!(" โ€ข Criteria 1: Planning Accuracy > 90% โณ (Currently: 78.5%)"); - println!(" โ€ข Criteria 2: Execution Efficiency > 85% โณ (Currently: 72.3%)"); - println!(" โ€ข Criteria 3: Error Reduction > 90% โœ… (Currently: 90.0%)"); - println!(" โ€ข Status: ๐Ÿ”„ IN PROGRESS (1/3 criteria met)"); - println!(" โ€ข Estimated Achievement: 5-7 learning sessions"); - - println!("\n๐ŸŽ‰ **Learning System Assessment:**"); - println!(" โ€ข Overall Improvement Score: 89.0%"); - println!(" โ€ข Average Metric Achievement: 89.0%"); - println!(" โ€ข Improvement Velocity: 8.6%"); - println!(" โ€ข Milestones Completed: 1/3"); - println!(" โ€ข Learning Trajectory: Strongly Positive ๐Ÿš€"); - - println!("\n๐Ÿ”ฎ **Next Learning Targets:**"); - println!(" โ€ข Focus: Planning accuracy improvement"); - println!(" โ€ข Strategy: Amplify successful patterns from high-performing agents"); - println!(" โ€ข Expected Timeline: 2-3 agent interactions"); - println!(" โ€ข Confidence: 85%"); -} \ No newline at end of file diff --git a/task_7_3_simple_demo.rs b/task_7_3_simple_demo.rs deleted file mode 100644 index 9b1cf9aae44588fbf18f543df3f25a2d9754fe9d..0000000000000000000000000000000000000000 --- a/task_7_3_simple_demo.rs +++ /dev/null @@ -1,261 +0,0 @@ -/// Task 7.3: Continuous Learning Demo - Simple Version -/// Demonstrates the basic architecture and learning capabilities -/// without complex implementation details. - -fn main() { - println!("๐Ÿง  Brain AI Task 7.3: Continuous Learning Pipeline - SUCCESS!"); - println!("================================================================"); - - demonstrate_continuous_learning_architecture(); - demonstrate_agent_interaction_learning(); - demonstrate_pattern_extraction(); - demonstrate_incremental_model_updates(); - demonstrate_cross_agent_learning(); - demonstrate_progress_tracking(); - demonstrate_learning_milestones(); - - println!("\n๐ŸŽฏ **TASK 7.3: CONTINUOUS LEARNING PIPELINE - COMPLETED!**"); - println!("โœ… All continuous learning components successfully implemented!"); - println!("๐Ÿš€ Brain AI now learns automatically from every agent interaction!"); -} - -fn demonstrate_continuous_learning_architecture() { - println!("\n๐Ÿ—๏ธ **Continuous Learning Pipeline Architecture**"); - println!("================================================="); - - println!("๐Ÿ“Š **Core Components:**"); - println!(" โ€ข InteractionLearner: Extracts learning signals from agent interactions"); - println!(" โ€ข IncrementalModelUpdater: Applies safe, incremental model improvements"); - println!(" โ€ข LearningProgressTracker: Monitors learning milestones and metrics"); - println!(" โ€ข CrossAgentPatternAnalyzer: Identifies patterns across multiple agents"); - - println!("\n๐Ÿ”„ **Learning Flow:**"); - println!(" 1. Agent performs task โ†’ 2. Extract learning signals"); - println!(" 3. Identify patterns โ†’ 4. Generate model updates"); - println!(" 5. Safety validation โ†’ 6. Apply updates"); - println!(" 7. Track progress โ†’ 8. Check milestones"); - - println!("\nโšก **Key Features:**"); - println!(" โ€ข Real-time learning from all 38+ agent types"); - println!(" โ€ข Safety-first update validation"); - println!(" โ€ข Cross-agent pattern recognition"); - println!(" โ€ข Automatic milestone tracking"); - println!(" โ€ข Performance regression prevention"); -} - -fn demonstrate_agent_interaction_learning() { - println!("\n๐Ÿค– **Agent Interaction Learning**"); - println!("=================================="); - - let agents = vec![ - ("AlgorithmCoder", 92.0, 2.3, 3.0, 88.0, vec![], "sorting_algorithm"), - ("BackendCoder", 85.0, 4.1, 3.5, 82.0, vec!["async_lifetime_issue"], "rust_actix"), - ("SecurityAuditor", 96.0, 1.8, 2.5, 94.0, vec![], "vulnerability_scan"), - ]; - - println!("๐Ÿ“ˆ **Processing {} Agent Interactions:**", agents.len()); - - for (i, (agent_type, success_rate, exec_time, expected_time, quality, errors, task_type)) in agents.iter().enumerate() { - println!("\n {}. {} Agent:", i + 1, agent_type); - println!(" โ€ข Success Rate: {:.1}%", success_rate); - println!(" โ€ข Execution Time: {:.1}s (expected: {:.1}s)", exec_time, expected_time); - println!(" โ€ข Output Quality: {:.1}%", quality); - - if !errors.is_empty() { - println!(" โ€ข Error Patterns: {:?}", errors); - } - - // Simulate learning signal extraction - let signals = calculate_learning_signals(*success_rate, *exec_time, *expected_time, *quality, errors.len()); - println!(" โ€ข Learning Signals Extracted: {}", signals); - println!(" โ€ข Task Context: {}", task_type); - } -} - -fn calculate_learning_signals(success_rate: f64, exec_time: f64, expected_time: f64, quality: f64, error_count: usize) -> usize { - let mut signals = 0; - - if success_rate > 80.0 { signals += 1; } - if error_count > 0 { signals += 1; } - if exec_time < expected_time * 0.8 { signals += 1; } - if quality > 85.0 { signals += 1; } - - signals -} - -fn demonstrate_pattern_extraction() { - println!("\n๐Ÿ” **Pattern Extraction & Analysis**"); - println!("====================================="); - - println!("๐ŸŽฏ **Identified Patterns:**"); - - println!("\n 1. **Consistent High Performance Pattern**"); - println!(" โ€ข Frequency: 2 occurrences"); - println!(" โ€ข Confidence: 89.5%"); - println!(" โ€ข Context: Security and Algorithm agents excel in well-defined tasks"); - println!(" โ€ข Improvement Potential: 15%"); - - println!("\n 2. **Efficiency Optimization Pattern**"); - println!(" โ€ข Frequency: 2 occurrences"); - println!(" โ€ข Confidence: 85.5%"); - println!(" โ€ข Context: Tasks completed faster than expected"); - println!(" โ€ข Improvement Potential: 12%"); - - println!("\n 3. **Recurring Error Pattern**"); - println!(" โ€ข Frequency: 1 occurrence"); - println!(" โ€ข Confidence: 90.0%"); - println!(" โ€ข Context: Async lifetime issues in Rust backend code"); - println!(" โ€ข Improvement Potential: 25% (when prevented)"); - - println!("\n 4. **Quality Excellence Pattern**"); - println!(" โ€ข Frequency: 2 occurrences"); - println!(" โ€ข Confidence: 91.0%"); - println!(" โ€ข Context: High-quality output in security and algorithm tasks"); - println!(" โ€ข Improvement Potential: 18%"); -} - -fn demonstrate_incremental_model_updates() { - println!("\n๐Ÿ”„ **Incremental Model Updates**"); - println!("================================="); - - println!("๐Ÿ“Š **Generated Model Updates:**"); - - println!("\n 1. **PlanningStrategy Enhancement** (High Priority)"); - println!(" โ€ข Component: PlanningStrategy"); - println!(" โ€ข Type: ParameterAdjustment"); - println!(" โ€ข Estimated Improvement: 15.0%"); - println!(" โ€ข Confidence: 89.5%"); - println!(" โ€ข Strategy: Amplify successful high-performance patterns"); - - println!("\n 2. **ErrorPrevention System** (Critical Priority)"); - println!(" โ€ข Component: ErrorPrevention"); - println!(" โ€ข Type: BehaviorModification"); - println!(" โ€ข Estimated Improvement: 25.0%"); - println!(" โ€ข Confidence: 90.0%"); - println!(" โ€ข Strategy: Prevent async lifetime issues in Rust code"); - - println!("\n 3. **ExecutionOptimizer Tuning** (Medium Priority)"); - println!(" โ€ข Component: ExecutionOptimizer"); - println!(" โ€ข Type: PerformanceTuning"); - println!(" โ€ข Estimated Improvement: 12.0%"); - println!(" โ€ข Confidence: 85.5%"); - println!(" โ€ข Strategy: Optimize execution time for similar task patterns"); - - println!("\n๐Ÿ›ก๏ธ **Safety Validation Results:**"); - println!(" โ€ข Performance Regression Check: โœ… PASSED"); - println!(" โ€ข Stability Validation: โœ… PASSED"); - println!(" โ€ข Quality Maintenance: โœ… PASSED"); - println!(" โ€ข Overall Safety Score: 87.5%"); - println!(" โ€ข Risk Assessment: LOW (0.15)"); - println!(" โ€ข Recommendation: PROCEED WITH UPDATES"); - - println!("\nโšก **Update Application:**"); - println!(" โ€ข Strategy: Adaptive Blending"); - println!(" โ€ข Components Updated: 3"); - println!(" โ€ข Total Estimated Improvement: 17.3%"); - println!(" โ€ข Application Success: โœ… 100%"); -} - -fn demonstrate_cross_agent_learning() { - println!("\n๐ŸŒ **Cross-Agent Learning & Coordination**"); - println!("=========================================="); - - println!("๐Ÿ”— **Cross-Agent Pattern Analysis:**"); - - println!("\n 1. **Multi-Agent Coordination Pattern**"); - println!(" โ€ข Affected Agents: AlgorithmCoder, BackendCoder, SecurityAuditor"); - println!(" โ€ข Pattern Type: Shared Context Optimization"); - println!(" โ€ข Improvement Potential: 15%"); - println!(" โ€ข Confidence: 80%"); - println!(" โ€ข Strategy: Optimize information sharing between agents"); - - println!("\n 2. **Common Success Patterns**"); - println!(" โ€ข Affected Agents: All Agents"); - println!(" โ€ข Pattern Type: Universal Strategy Enhancement"); - println!(" โ€ข Improvement Potential: 10%"); - println!(" โ€ข Confidence: 90%"); - println!(" โ€ข Strategy: Apply successful patterns across all agents"); - - println!("\n๐Ÿš€ **Coordination Improvements Applied:**"); - println!(" โ€ข Shared Context Optimization: +15% coordination efficiency"); - println!(" โ€ข Universal Strategy Enhancement: +10% general improvement"); - println!(" โ€ข Total Coordination Improvement: 25%"); - println!(" โ€ข Update Confidence: 85%"); - - println!("\n๐Ÿ“Š **Agent Performance Improvements:**"); - println!(" โ€ข AlgorithmCoder: +12.3% performance boost"); - println!(" โ€ข BackendCoder: +18.7% performance boost"); - println!(" โ€ข SecurityAuditor: +8.9% performance boost"); - println!(" โ€ข Cross-Agent Synergy: +15.2% coordination improvement"); -} - -fn demonstrate_progress_tracking() { - println!("\n๐Ÿ“ˆ **Learning Progress Tracking**"); - println!("=================================="); - - println!("๐ŸŽฏ **Current Progress Metrics:**"); - - println!("\n ๐Ÿ“Š **Planning Accuracy**"); - println!(" โ€ข Current Value: 78.5% (โ†‘ +8.5% from updates)"); - println!(" โ€ข Target Value: 90.0%"); - println!(" โ€ข Progress: 87.2% of target achieved"); - println!(" โ€ข Improvement Rate: +8.5% this session"); - - println!("\n โšก **Execution Efficiency**"); - println!(" โ€ข Current Value: 72.3% (โ†‘ +7.3% from updates)"); - println!(" โ€ข Target Value: 85.0%"); - println!(" โ€ข Progress: 85.1% of target achieved"); - println!(" โ€ข Improvement Rate: +7.3% this session"); - - println!("\n ๐Ÿ›ก๏ธ **Error Reduction**"); - println!(" โ€ข Current Value: 90.0% (โ†‘ +10.0% from updates)"); - println!(" โ€ข Target Value: 95.0%"); - println!(" โ€ข Progress: 94.7% of target achieved"); - println!(" โ€ข Improvement Rate: +10.0% this session"); - - println!("\n๐Ÿ“Š **Overall Progress Summary:**"); - println!(" โ€ข Overall Progress Score: 89.0%"); - println!(" โ€ข Learning Trajectory: Strongly Positive ๐Ÿ“ˆ"); - println!(" โ€ข Average Improvement Rate: 8.6%"); - println!(" โ€ข System Health: Excellent โœ…"); -} - -fn demonstrate_learning_milestones() { - println!("\n๐Ÿ† **Learning Milestones & Achievements**"); - println!("========================================="); - - println!("โœ… **Recently Achieved Milestones:**"); - - println!("\n ๐Ÿฅ‰ **Basic Learning Competency**"); - println!(" โ€ข Criteria: Planning Accuracy > 75%"); - println!(" โ€ข Status: โœ… ACHIEVED (78.5%)"); - println!(" โ€ข Achieved: Just now!"); - - println!("\n๐ŸŽฏ **Milestones in Progress:**"); - - println!("\n ๐Ÿฅˆ **Advanced Learning Mastery**"); - println!(" โ€ข Criteria 1: Planning Accuracy > 85% โณ (Currently: 78.5%)"); - println!(" โ€ข Criteria 2: Execution Efficiency > 80% โณ (Currently: 72.3%)"); - println!(" โ€ข Status: ๐Ÿ”„ IN PROGRESS"); - println!(" โ€ข Estimated Achievement: 2-3 learning sessions"); - - println!("\n ๐Ÿฅ‡ **Expert-Level Performance**"); - println!(" โ€ข Criteria 1: Planning Accuracy > 90% โณ (Currently: 78.5%)"); - println!(" โ€ข Criteria 2: Execution Efficiency > 85% โณ (Currently: 72.3%)"); - println!(" โ€ข Criteria 3: Error Reduction > 90% โœ… (Currently: 90.0%)"); - println!(" โ€ข Status: ๐Ÿ”„ IN PROGRESS (1/3 criteria met)"); - println!(" โ€ข Estimated Achievement: 5-7 learning sessions"); - - println!("\n๐ŸŽ‰ **Learning System Assessment:**"); - println!(" โ€ข Overall Improvement Score: 89.0%"); - println!(" โ€ข Average Metric Achievement: 89.0%"); - println!(" โ€ข Improvement Velocity: 8.6%"); - println!(" โ€ข Milestones Completed: 1/3"); - println!(" โ€ข Learning Trajectory: Strongly Positive ๐Ÿš€"); - - println!("\n๐Ÿ”ฎ **Next Learning Targets:**"); - println!(" โ€ข Focus: Planning accuracy improvement"); - println!(" โ€ข Strategy: Amplify successful patterns from high-performing agents"); - println!(" โ€ข Expected Timeline: 2-3 agent interactions"); - println!(" โ€ข Confidence: 85%"); -} \ No newline at end of file diff --git a/task_7_advanced_learning_demo.rs b/task_7_advanced_learning_demo.rs deleted file mode 100644 index 8266e771bed7cf6a78be2d10c5dc6a7dcf981e68..0000000000000000000000000000000000000000 --- a/task_7_advanced_learning_demo.rs +++ /dev/null @@ -1,281 +0,0 @@ -/// Task 7: Advanced Learning and Model Improvement Demo -/// -/// This demonstrates the sophisticated learning capabilities of Brain AI's -/// MuBrain system, showcasing gradient optimization, performance prediction, -/// and continuous learning improvements beyond basic training. - -use brain_mubrain::{ - MuBrainResult, TrainingEpisode, - advanced_learning::{ - AdvancedLearningSystem, AdvancedLearningConfig, - AdvancedModelTrainer, - PerformancePredictionSystem, ContinuousLearningPipeline, - LearningCoordinator, ImprovementValidator, - AdvancedLearningResult, OptimizationAlgorithm, - LearningObjective, ObjectiveType, ObjectivePriority, ConvergenceCriteria, - RegularizationConfig, AdaptationConfig, - }, - performance_prediction::{ - PlanningAccuracyPredictor, AccuracyPredictionConfig, - ABTestingFramework, ABTestingConfig, - ModelingAlgorithm, FeatureExtractionMethod, - }, -}; -use std::sync::Arc; -use std::time::Duration; -use uuid::Uuid; -use chrono::Utc; - -#[tokio::main] -async fn main() -> MuBrainResult<()> { - println!("๐Ÿง  Brain AI Task 7: Advanced Learning and Model Improvement Demo"); - println!("================================================================="); - - // Initialize the Advanced Learning System - let config = AdvancedLearningConfig { - optimization_algorithm: OptimizationAlgorithm::Adam { - beta1: 0.9, - beta2: 0.999, - epsilon: 1e-8, - }, - learning_objectives: vec![ - LearningObjective { - objective_type: ObjectiveType::PlanningAccuracy, - weight: 0.4, - priority: ObjectivePriority::High, - target_metric: "planning_accuracy".to_string(), - convergence_criteria: ConvergenceCriteria { - target_value: 0.85, - tolerance: 0.02, - patience_epochs: 20, - minimum_improvement_rate: 0.001, - improvement_threshold: 0.01, - patience: 15, - relative_improvement: true, - target_performance: Some(0.85), - plateau_detection: true, - statistical_significance: 0.05, - }, - }, - LearningObjective { - objective_type: ObjectiveType::LearningSpeed, - weight: 0.3, - priority: ObjectivePriority::Medium, - target_metric: "convergence_rate".to_string(), - convergence_criteria: ConvergenceCriteria { - target_value: 0.7, - tolerance: 0.05, - patience_epochs: 15, - minimum_improvement_rate: 0.002, - improvement_threshold: 0.01, - patience: 10, - relative_improvement: true, - target_performance: Some(0.7), - plateau_detection: true, - statistical_significance: 0.05, - }, - }, - ], - regularization_config: RegularizationConfig { - l1_strength: 0.001, - l2_strength: 0.01, - dropout_rate: 0.1, - noise_injection_strength: 0.05, - adaptive_regularization: true, - }, - adaptation_config: AdaptationConfig { - learning_rate_adaptation: true, - momentum_adaptation: true, - algorithm_switching: false, - performance_threshold: 0.8, - adaptation_frequency: 50, - }, - performance_prediction_enabled: true, - continuous_learning_enabled: true, - improvement_validation_threshold: 0.85, - }; - - // Remove the `.await` calls since these constructors don't return futures - let _advanced_trainer = Arc::new(AdvancedModelTrainer::new(config.clone())); - let _performance_predictor = Arc::new(PerformancePredictionSystem::new()); - let _continuous_learner = Arc::new(ContinuousLearningPipeline::new()); - let _learning_coordinator = Arc::new(LearningCoordinator::new()); - let _improvement_validator = Arc::new(ImprovementValidator::new()); - - // Create advanced learning system - let advanced_learning = AdvancedLearningSystem::new(config); - - // Phase 1: Demonstrate Advanced Training - println!("\n๐ŸŽฏ Phase 1: Advanced Training with Sophisticated Algorithms"); - println!("---------------------------------------------------------"); - - let training_episodes = create_sample_training_episodes(); - let learning_result = advanced_learning.coordinate_advanced_learning(training_episodes).await?; - - display_learning_results(&learning_result); - - // Phase 2: Performance Prediction and Validation - println!("\n๐Ÿ“Š Phase 2: Performance Prediction and Model Validation"); - println!("-------------------------------------------------------"); - - let _performance_predictor = create_performance_predictor()?; - let prediction_result = simulate_prediction_results(); - - display_prediction_results(&prediction_result); - - // Phase 3: A/B Testing and Model Comparison - println!("\n๐Ÿ”ฌ Phase 3: A/B Testing and Model Comparison"); - println!("--------------------------------------------"); - - let ab_testing = create_ab_testing_framework()?; - let comparison_result = demonstrate_model_comparison(&ab_testing).await?; - - display_comparison_results(&comparison_result); - - // Phase 4: Continuous Learning Loop - println!("\n๐Ÿ”„ Phase 4: Continuous Learning and Improvement"); - println!("----------------------------------------------"); - - let improvement_metrics = demonstrate_continuous_learning(&advanced_learning).await?; - - display_improvement_metrics(&improvement_metrics); - - println!("\nโœ… Advanced Learning System Demo Complete!"); - println!("๐Ÿš€ Task 7.1 and 7.2 Implementation Successful"); - println!("๐ŸŽ“ Brain AI demonstrates sophisticated learning beyond basic training"); - - Ok(()) -} - -fn create_sample_training_episodes() -> Vec { - println!("๐Ÿ“š Creating diverse training episodes..."); - - vec![ - TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![], - timestamp: Utc::now(), - episode_reward: 0.85, - episode_length: 45, - }, - TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![], - timestamp: Utc::now(), - episode_reward: 0.92, - episode_length: 28, - }, - TrainingEpisode { - episode_id: Uuid::new_v4(), - state_transitions: vec![], - planning_outcomes: vec![], - reward_signals: vec![], - timestamp: Utc::now(), - episode_reward: 0.78, - episode_length: 67, - }, - ] -} - -fn display_learning_results(result: &AdvancedLearningResult) { - println!("๐Ÿ“ˆ Advanced Learning Results:"); - println!(" โ””โ”€ Training Completed: {}", result.training_completed); - println!(" โ””โ”€ Performance Prediction: {:.3}", result.performance_prediction); - println!(" โ””โ”€ Validation Passed: {}", result.validation_result.validation_passed); - println!(" โ””โ”€ Deployment Successful: {}", result.deployment_result.deployment_successful); - println!(" โ””โ”€ Learning Quality Score: {:.3}", result.learning_quality_score); - println!(" โ””โ”€ Next Recommendations: {} items", result.next_learning_recommendations.len()); -} - -fn create_performance_predictor() -> MuBrainResult { - println!("๐Ÿ”ฎ Setting up Performance Prediction System..."); - - let config = AccuracyPredictionConfig { - modeling_algorithm: ModelingAlgorithm::LinearRegression, - simulation_scenarios: 100, - prediction_horizon: Duration::from_secs(24 * 3600), // 24 hours - validation_split: 0.2, - feature_extraction_method: FeatureExtractionMethod::StatisticalFeatures, - }; - - PlanningAccuracyPredictor::new(config) -} - -fn simulate_prediction_results() -> String { - "87.5% ยฑ 3.2%".to_string() -} - -fn display_prediction_results(prediction: &str) { - println!("๐ŸŽฏ Performance Prediction Results:"); - println!(" โ””โ”€ Predicted Planning Accuracy: {}", prediction); - println!(" โ””โ”€ Confidence Level: 92.1%"); - println!(" โ””โ”€ Expected Improvement: +12.3% over baseline"); - println!(" โ””โ”€ Risk Assessment: Low (8.7% chance of regression)"); - println!(" โ””โ”€ Recommendation: โœ… DEPLOY WITH CONFIDENCE"); -} - -fn create_ab_testing_framework() -> MuBrainResult { - println!("๐Ÿงช Initializing A/B Testing Framework..."); - - let config = ABTestingConfig::default(); - ABTestingFramework::new(config) -} - -async fn demonstrate_model_comparison(_ab_testing: &ABTestingFramework) -> MuBrainResult { - println!("โš”๏ธ Running Model A vs Model B comparison..."); - - // Simulate A/B test results - println!(" โ€ข Model A (Current): 84.2% accuracy, 1.2s avg latency"); - println!(" โ€ข Model B (Advanced): 89.7% accuracy, 0.9s avg latency"); - println!(" โ€ข Statistical Significance: 99.7% (p < 0.003)"); - println!(" โ€ข Effect Size: +5.5% accuracy improvement"); - - Ok("Model B significantly outperforms Model A".to_string()) -} - -fn display_comparison_results(_result: &str) { - println!("๐Ÿ“Š A/B Testing Results:"); - println!(" โ””โ”€ Winner: ๐Ÿ† Model B (Advanced Learning)"); - println!(" โ””โ”€ Improvement: +5.5% accuracy, +25% speed"); - println!(" โ””โ”€ Statistical Power: 99.7%"); - println!(" โ””โ”€ Decision: โœ… RECOMMEND DEPLOYMENT"); -} - -async fn demonstrate_continuous_learning(_system: &AdvancedLearningSystem) -> MuBrainResult { - println!("๐Ÿ”„ Demonstrating Continuous Learning Loop..."); - - // Simulate continuous learning cycle - println!(" โ€ข Week 1: Baseline accuracy 84.2%"); - println!(" โ€ข Week 2: +2.1% improvement (mistake learning activated)"); - println!(" โ€ข Week 3: +1.8% improvement (pattern recognition enhanced)"); - println!(" โ€ข Week 4: +1.3% improvement (regularization optimized)"); - - Ok(ContinuousLearningMetrics { - total_improvement: 5.2, - learning_velocity: 1.3, - stability_score: 0.94, - adaptation_quality: 0.88, - }) -} - -fn display_improvement_metrics(metrics: &ContinuousLearningMetrics) { - println!("๐Ÿ“ˆ Continuous Learning Metrics:"); - println!(" โ””โ”€ Total Improvement: +{:.1}% accuracy gain", metrics.total_improvement); - println!(" โ””โ”€ Learning Velocity: {:.1}% per week", metrics.learning_velocity); - println!(" โ””โ”€ Model Stability: {:.1}% (excellent)", metrics.stability_score * 100.0); - println!(" โ””โ”€ Adaptation Quality: {:.1}% (strong)", metrics.adaptation_quality * 100.0); - println!(" โ””โ”€ Status: ๐ŸŸข HEALTHY CONTINUOUS LEARNING"); -} - -// Supporting structs for demonstration -#[derive(Debug)] -struct ContinuousLearningMetrics { - total_improvement: f64, - learning_velocity: f64, - stability_score: f64, - adaptation_quality: f64, -} \ No newline at end of file diff --git a/task_7_simple_advanced_learning_demo.rs b/task_7_simple_advanced_learning_demo.rs deleted file mode 100644 index c46529c3b5270983877223a4cfaced254a6e8047..0000000000000000000000000000000000000000 --- a/task_7_simple_advanced_learning_demo.rs +++ /dev/null @@ -1,133 +0,0 @@ -/// Task 7: Simplified Advanced Learning Demo -/// -/// This demonstrates that the Advanced Learning System compilation is successful -/// and the sophisticated learning structures are properly implemented. - -use std::collections::HashMap; - -fn main() { - println!("๐Ÿง  Brain AI Task 7: Advanced Learning System - Architecture Demo"); - println!("================================================================"); - - demonstrate_advanced_learning_architecture(); - demonstrate_gradient_optimization(); - demonstrate_performance_prediction(); - demonstrate_continuous_learning(); - - println!("\nโœ… Advanced Learning System Architecture Demo Complete!"); - println!("๐ŸŽ‰ ALL 34 compilation errors successfully fixed!"); - println!("๐Ÿš€ Task 7.1 and 7.2 Implementation Structures Operational"); -} - -fn demonstrate_advanced_learning_architecture() { - println!("\n๐ŸŽฏ Phase 1: Advanced Learning Architecture Overview"); - println!("--------------------------------------------------"); - - println!("โœ… AdvancedLearningSystem - Multi-component orchestration"); - println!(" โ”œโ”€ AdvancedModelTrainer - Sophisticated algorithm training"); - println!(" โ”œโ”€ PerformancePredictionSystem - Accuracy forecasting"); - println!(" โ”œโ”€ ContinuousLearningPipeline - Ongoing improvement"); - println!(" โ”œโ”€ LearningCoordinator - Multi-objective coordination"); - println!(" โ””โ”€ ImprovementValidator - Quality assurance"); - - println!("\nโœ… Core Components Successfully Implemented:"); - println!(" โ€ข OptimizedGradients with AdaptationInfo"); - println!(" โ€ข AdvancedRegularizer with L1/L2 support"); - println!(" โ€ข MultiObjectiveBalancer with priority handling"); - println!(" โ€ข LearningRateScheduler with performance adaptation"); -} - -fn demonstrate_gradient_optimization() { - println!("\n๐Ÿ“ˆ Phase 2: Advanced Gradient Optimization"); - println!("------------------------------------------"); - - // Simulate the gradient optimization process - let algorithms = vec!["Adam", "AdaGrad", "RMSprop", "SGD"]; - let mut performance_scores = HashMap::new(); - - for algorithm in &algorithms { - let score = match *algorithm { - "Adam" => 0.85, - "AdaGrad" => 0.78, - "RMSprop" => 0.82, - "SGD" => 0.71, - _ => 0.70, - }; - performance_scores.insert(algorithm.to_string(), score); - println!(" โ€ข {}: {:.3} optimization quality", algorithm, score); - } - - let best_algorithm = performance_scores - .iter() - .max_by(|a, b| a.1.partial_cmp(b.1).unwrap()) - .unwrap(); - - println!(" โ””โ”€ Best Algorithm: {} (Quality: {:.3})", best_algorithm.0, best_algorithm.1); - - // Demonstrate regularization - println!("\n ๐Ÿ›ก๏ธ Regularization Applied:"); - println!(" โ€ข L1 Regularization: 0.01 strength"); - println!(" โ€ข L2 Regularization: 0.001 strength"); - println!(" โ€ข Dropout Rate: 0.1"); - println!(" โ€ข Gradient Clipping: 1.0 threshold"); -} - -fn demonstrate_performance_prediction() { - println!("\n๐Ÿ”ฎ Phase 3: Performance Prediction System"); - println!("----------------------------------------"); - - // Simulate performance prediction - let current_accuracy = 0.842; - let predicted_improvement = 0.123; - let confidence = 0.921; - - println!(" ๐Ÿ“Š Prediction Results:"); - println!(" โ€ข Current Accuracy: {:.1}%", current_accuracy * 100.0); - println!(" โ€ข Predicted Improvement: +{:.1}%", predicted_improvement * 100.0); - println!(" โ€ข New Predicted Accuracy: {:.1}%", (current_accuracy + predicted_improvement) * 100.0); - println!(" โ€ข Confidence Level: {:.1}%", confidence * 100.0); - - // Simulate A/B testing - println!("\n ๐Ÿงช A/B Testing Framework:"); - println!(" โ€ข Model A (Baseline): 84.2% accuracy"); - println!(" โ€ข Model B (Advanced): 89.7% accuracy"); - println!(" โ€ข Statistical Significance: 99.7%"); - println!(" โ€ข Effect Size: +5.5% improvement"); - println!(" โ€ข Decision: โœ… DEPLOY MODEL B"); -} - -fn demonstrate_continuous_learning() { - println!("\n๐Ÿ”„ Phase 4: Continuous Learning Loop"); - println!("-----------------------------------"); - - // Simulate learning progression over time - let learning_timeline = vec![ - ("Week 1", 84.2, "Baseline establishment"), - ("Week 2", 86.3, "Mistake learning activated"), - ("Week 3", 88.1, "Pattern recognition enhanced"), - ("Week 4", 89.5, "Regularization optimized"), - ("Week 5", 90.7, "Multi-objective balance achieved"), - ]; - - println!(" ๐Ÿ“ˆ Learning Progression:"); - for (week, accuracy, improvement) in &learning_timeline { - println!(" โ€ข {}: {:.1}% accuracy ({})", week, accuracy, improvement); - } - - let total_improvement = learning_timeline.last().unwrap().1 - learning_timeline.first().unwrap().1; - let learning_velocity = total_improvement / (learning_timeline.len() - 1) as f64; - - println!("\n ๐Ÿ“‹ Continuous Learning Metrics:"); - println!(" โ€ข Total Improvement: +{:.1}% accuracy gain", total_improvement); - println!(" โ€ข Learning Velocity: {:.1}% per week", learning_velocity); - println!(" โ€ข Model Stability: 94.0% (excellent)"); - println!(" โ€ข Adaptation Quality: 88.0% (strong)"); - println!(" โ€ข Status: ๐ŸŸข HEALTHY CONTINUOUS LEARNING"); - - println!("\n ๐ŸŽฏ Advanced Features Operational:"); - println!(" โœ… Multi-objective optimization"); - println!(" โœ… Performance regression detection"); - println!(" โœ… Automated rollback mechanisms"); - println!(" โœ… Learning rate adaptation"); - println!(" โœ… Quality gate validation"); -} \ No newline at end of file diff --git a/task_8_1_development_agents_demo.rs b/task_8_1_development_agents_demo.rs deleted file mode 100644 index e53ee2309770337941aa9cf708657b2d8c723343..0000000000000000000000000000000000000000 --- a/task_8_1_development_agents_demo.rs +++ /dev/null @@ -1,734 +0,0 @@ -/// Task 8.1: Development Agents Integration Demo -/// Demonstrates the integration of development-focused agents with MuBrain planning - -use std::time::Duration; - -// Simulate the structures since we're running a standalone demo -#[derive(Debug, Clone)] -struct DevelopmentAgentsIntegration { - config: DevelopmentIntegrationConfig, -} - -#[derive(Debug, Clone)] -struct DevelopmentIntegrationConfig { - planner_agent_enabled: bool, - architect_agent_enabled: bool, - designer_agent_enabled: bool, - frontend_coder_enabled: bool, - backend_coder_enabled: bool, - api_design_planning_enabled: bool, - deployment_planning_enabled: bool, - collaborative_planning_enabled: bool, -} - -#[derive(Debug, Clone)] -enum DevelopmentAgentType { - PlannerAgent, - ArchitectAgent, - DesignerAgent, - FrontendCoder, - BackendCoder, - RefactorAgent, - APIDesigner, - DeploymentEngineer, -} - -#[derive(Debug, Clone)] -enum DevelopmentPlanningType { - StrategicPlanning, - SystemArchitecture, - UIUXDesign, - FrontendDevelopment, - BackendDevelopment, - CodeRefactoring, - APIDesign, - DeploymentStrategy, -} - -#[derive(Debug, Clone)] -struct DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType, - planning_type: DevelopmentPlanningType, - project_name: String, - requirements: Vec, - constraints: Vec, - technology_stack: Vec, -} - -#[derive(Debug, Clone)] -struct PlanningResult { - success: bool, - planning_steps: Vec, - estimated_duration: Duration, - quality_score: f64, - confidence: f64, - recommendations: Vec, -} - -fn main() { - println!("๐Ÿง  Brain AI Task 8.1: Development Agents Integration - Full Demo"); - println!("==================================================================="); - - demonstrate_development_agents_architecture(); - demonstrate_planner_agent_integration(); - demonstrate_architect_agent_integration(); - demonstrate_designer_agent_integration(); - demonstrate_coding_agents_integration(); - demonstrate_api_design_planning(); - demonstrate_deployment_planning(); - demonstrate_collaborative_workflows(); - demonstrate_quality_assessment(); - - println!("\n๐ŸŽฏ **TASK 8.1: DEVELOPMENT AGENTS INTEGRATION - COMPLETED!**"); - println!("โœ… All development agent integrations successfully implemented!"); - println!("๐Ÿš€ Brain AI now provides specialized planning for all development workflows!"); -} - -fn demonstrate_development_agents_architecture() { - println!("\n๐Ÿ—๏ธ **Development Agents Integration Architecture**"); - println!("==================================================="); - - println!("๐Ÿ“Š **Core Agent Integrations:**"); - println!(" โ€ข PlannerAgent: Strategic planning and roadmap generation"); - println!(" โ€ข ArchitectAgent: System architecture design and optimization"); - println!(" โ€ข DesignerAgent: UI/UX design planning and user experience optimization"); - println!(" โ€ข FrontendCoder: Frontend development planning and component strategies"); - println!(" โ€ข BackendCoder: Backend API and service architecture planning"); - println!(" โ€ข RefactorAgent: Code quality improvement and technical debt reduction"); - println!(" โ€ข APIDesigner: API schema optimization and endpoint planning"); - println!(" โ€ข DeploymentEngineer: Infrastructure and deployment strategy planning"); - - println!("\n๐Ÿ”„ **Planning Flow:**"); - println!(" 1. Agent receives development request โ†’ 2. Domain-specific analysis"); - println!(" 3. MuBrain symbolic planning โ†’ 4. Generate specialized strategy"); - println!(" 5. Quality assessment โ†’ 6. Collaborative optimization"); - println!(" 7. Execution plan creation โ†’ 8. Progress tracking"); - - println!("\nโšก **Key Features:**"); - println!(" โ€ข Domain-specific planning strategies for each agent type"); - println!(" โ€ข Collaborative multi-agent workflow orchestration"); - println!(" โ€ข Quality-driven planning with assessment gates"); - println!(" โ€ข Technology-aware planning and constraint handling"); - println!(" โ€ข Effort estimation and resource allocation planning"); -} - -fn demonstrate_planner_agent_integration() { - println!("\n๐Ÿ“‹ **PlannerAgent Integration - Strategic Planning**"); - println!("==================================================="); - - let request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::PlannerAgent, - planning_type: DevelopmentPlanningType::StrategicPlanning, - project_name: "E-commerce Platform Modernization".to_string(), - requirements: vec![ - "Migrate legacy monolith to microservices".to_string(), - "Improve performance by 3x".to_string(), - "Add real-time features".to_string(), - "Enhance mobile experience".to_string(), - ], - constraints: vec![ - "Zero downtime migration".to_string(), - "6-month timeline".to_string(), - "Budget: $500K".to_string(), - "Team of 8 developers".to_string(), - ], - technology_stack: vec!["Rust".to_string(), "React".to_string(), "PostgreSQL".to_string()], - }; - - println!("๐ŸŽฏ **Planning Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Requirements: {:?}", request.requirements); - println!(" โ€ข Constraints: {:?}", request.constraints); - println!(" โ€ข Tech Stack: {:?}", request.technology_stack); - - let result = simulate_strategic_planning(&request); - - println!("\n๐Ÿ“Š **Strategic Planning Result:**"); - println!(" โ€ข Success: {}", result.success); - println!(" โ€ข Quality Score: {:.1}%", result.quality_score * 100.0); - println!(" โ€ข Confidence: {:.1}%", result.confidence * 100.0); - println!(" โ€ข Estimated Duration: {} days", result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n๐Ÿ—“๏ธ **Generated Planning Steps:**"); - for (i, step) in result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - println!("\n๐Ÿ’ก **Strategic Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_strategic_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "Phase 1: Strategic Analysis and Risk Assessment (2 weeks)".to_string(), - "Phase 2: Architecture Planning and Technology Selection (3 weeks)".to_string(), - "Phase 3: Team Structure and Resource Allocation (1 week)".to_string(), - "Phase 4: Migration Strategy and Rollout Planning (2 weeks)".to_string(), - "Phase 5: Success Metrics and Monitoring Strategy (1 week)".to_string(), - "Phase 6: Stakeholder Communication and Change Management (ongoing)".to_string(), - ], - estimated_duration: Duration::from_secs(180 * 24 * 3600), // 180 days - quality_score: 0.92, - confidence: 0.88, - recommendations: vec![ - "Implement strangler fig pattern for gradual migration".to_string(), - "Establish comprehensive monitoring before migration starts".to_string(), - "Create dedicated migration team with domain expertise".to_string(), - "Plan for 20% buffer time to handle unexpected challenges".to_string(), - ], - } -} - -fn demonstrate_architect_agent_integration() { - println!("\n๐Ÿ›๏ธ **ArchitectAgent Integration - System Architecture Design**"); - println!("============================================================"); - - let request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::ArchitectAgent, - planning_type: DevelopmentPlanningType::SystemArchitecture, - project_name: "Microservices Architecture Design".to_string(), - requirements: vec![ - "Design scalable microservices architecture".to_string(), - "Handle 100K concurrent users".to_string(), - "Ensure 99.9% uptime".to_string(), - "Support multiple data sources".to_string(), - ], - constraints: vec![ - "Cloud-native deployment".to_string(), - "Event-driven architecture".to_string(), - "GDPR compliance required".to_string(), - ], - technology_stack: vec!["Rust".to_string(), "Kubernetes".to_string(), "Apache Kafka".to_string()], - }; - - println!("๐ŸŽฏ **Architecture Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Performance Target: 100K concurrent users"); - println!(" โ€ข Availability: 99.9% uptime"); - println!(" โ€ข Compliance: GDPR"); - - let result = simulate_architecture_planning(&request); - - println!("\n๐Ÿ—๏ธ **Architecture Planning Result:**"); - println!(" โ€ข Success: {}", result.success); - println!(" โ€ข Quality Score: {:.1}%", result.quality_score * 100.0); - println!(" โ€ข Confidence: {:.1}%", result.confidence * 100.0); - println!(" โ€ข Design Duration: {} days", result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n๐Ÿ›๏ธ **Architecture Components:**"); - for (i, step) in result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - println!("\n๐Ÿ”ง **Architecture Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_architecture_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "API Gateway with rate limiting and authentication".to_string(), - "User Service with JWT token management".to_string(), - "Product Catalog Service with caching layer".to_string(), - "Order Processing Service with saga pattern".to_string(), - "Payment Service with PCI DSS compliance".to_string(), - "Notification Service with multi-channel support".to_string(), - "Event Bus using Apache Kafka for async communication".to_string(), - "Distributed logging and monitoring with OpenTelemetry".to_string(), - "Data persistence with CQRS and event sourcing".to_string(), - ], - estimated_duration: Duration::from_secs(45 * 24 * 3600), // 45 days - quality_score: 0.94, - confidence: 0.91, - recommendations: vec![ - "Use hexagonal architecture for better testability".to_string(), - "Implement circuit breaker pattern for resilience".to_string(), - "Apply domain-driven design for service boundaries".to_string(), - "Use CQRS for read/write separation and performance".to_string(), - "Implement comprehensive health checks for each service".to_string(), - ], - } -} - -fn demonstrate_designer_agent_integration() { - println!("\n๐ŸŽจ **DesignerAgent Integration - UI/UX Design Planning**"); - println!("======================================================="); - - let request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::DesignerAgent, - planning_type: DevelopmentPlanningType::UIUXDesign, - project_name: "E-commerce Mobile App Redesign".to_string(), - requirements: vec![ - "Modern, intuitive mobile interface".to_string(), - "Accessibility WCAG 2.1 AA compliance".to_string(), - "Multi-platform consistency (iOS/Android)".to_string(), - "Improved conversion rates".to_string(), - ], - constraints: vec![ - "Existing brand guidelines".to_string(), - "6-week design timeline".to_string(), - "User testing required".to_string(), - ], - technology_stack: vec!["React Native".to_string(), "Figma".to_string()], - }; - - println!("๐ŸŽฏ **Design Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Target: Mobile-first design"); - println!(" โ€ข Compliance: WCAG 2.1 AA"); - println!(" โ€ข Goal: Improved conversion rates"); - - let result = simulate_design_planning(&request); - - println!("\n๐ŸŽจ **Design Planning Result:**"); - println!(" โ€ข Success: {}", result.success); - println!(" โ€ข Quality Score: {:.1}%", result.quality_score * 100.0); - println!(" โ€ข Confidence: {:.1}%", result.confidence * 100.0); - println!(" โ€ข Design Duration: {} days", result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n๐Ÿ–ผ๏ธ **Design Process Steps:**"); - for (i, step) in result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - println!("\n๐Ÿ’ก **Design Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_design_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "User Research and Persona Development (5 days)".to_string(), - "Information Architecture and User Flow Design (4 days)".to_string(), - "Wireframing and Low-Fidelity Prototypes (6 days)".to_string(), - "Visual Design and Brand Integration (8 days)".to_string(), - "High-Fidelity Interactive Prototypes (7 days)".to_string(), - "Accessibility Review and Optimization (3 days)".to_string(), - "User Testing and Iteration (5 days)".to_string(), - "Design System Documentation (4 days)".to_string(), - ], - estimated_duration: Duration::from_secs(42 * 24 * 3600), // 42 days - quality_score: 0.89, - confidence: 0.85, - recommendations: vec![ - "Implement progressive disclosure to reduce cognitive load".to_string(), - "Use micro-interactions to enhance user engagement".to_string(), - "Design for thumb-friendly navigation on mobile".to_string(), - "Implement dark mode option for better accessibility".to_string(), - "Create component library for design consistency".to_string(), - ], - } -} - -fn demonstrate_coding_agents_integration() { - println!("\n๐Ÿ’ป **Coding Agents Integration - Development Planning**"); - println!("======================================================"); - - // Frontend Coder Integration - println!("\n๐ŸŒ **FrontendCoder Integration:**"); - let frontend_request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::FrontendCoder, - planning_type: DevelopmentPlanningType::FrontendDevelopment, - project_name: "React E-commerce Frontend".to_string(), - requirements: vec![ - "Responsive React application".to_string(), - "State management with Redux Toolkit".to_string(), - "Real-time updates via WebSocket".to_string(), - "Progressive Web App features".to_string(), - ], - constraints: vec![ - "TypeScript required".to_string(), - "95% test coverage".to_string(), - "Performance budget: 2s load time".to_string(), - ], - technology_stack: vec!["React".to_string(), "TypeScript".to_string(), "Redux Toolkit".to_string()], - }; - - let frontend_result = simulate_frontend_planning(&frontend_request); - println!(" โ€ข Planning Success: {}", frontend_result.success); - println!(" โ€ข Code Quality Score: {:.1}%", frontend_result.quality_score * 100.0); - println!(" โ€ข Development Duration: {} days", frontend_result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n ๐Ÿ“ฆ **Frontend Components:**"); - for (i, step) in frontend_result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - // Backend Coder Integration - println!("\n๐Ÿ”ง **BackendCoder Integration:**"); - let backend_request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::BackendCoder, - planning_type: DevelopmentPlanningType::BackendDevelopment, - project_name: "Rust Microservices Backend".to_string(), - requirements: vec![ - "RESTful API with OpenAPI specification".to_string(), - "JWT authentication and authorization".to_string(), - "Database integration with migrations".to_string(), - "Comprehensive error handling".to_string(), - ], - constraints: vec![ - "Rust with Actix-Web framework".to_string(), - "PostgreSQL database".to_string(), - "Docker containerization".to_string(), - ], - technology_stack: vec!["Rust".to_string(), "Actix-Web".to_string(), "PostgreSQL".to_string()], - }; - - let backend_result = simulate_backend_planning(&backend_request); - println!(" โ€ข Planning Success: {}", backend_result.success); - println!(" โ€ข Code Quality Score: {:.1}%", backend_result.quality_score * 100.0); - println!(" โ€ข Development Duration: {} days", backend_result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n ๐Ÿ”ง **Backend Services:**"); - for (i, step) in backend_result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - // RefactorAgent Integration - println!("\n๐Ÿ”„ **RefactorAgent Integration:**"); - let refactor_request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::RefactorAgent, - planning_type: DevelopmentPlanningType::CodeRefactoring, - project_name: "Legacy Code Modernization".to_string(), - requirements: vec![ - "Improve code maintainability".to_string(), - "Reduce technical debt".to_string(), - "Enhance performance".to_string(), - "Add comprehensive testing".to_string(), - ], - constraints: vec![ - "No breaking API changes".to_string(), - "Incremental refactoring".to_string(), - "Maintain backward compatibility".to_string(), - ], - technology_stack: vec!["Rust".to_string(), "Legacy C++ code".to_string()], - }; - - let refactor_result = simulate_refactor_planning(&refactor_request); - println!(" โ€ข Planning Success: {}", refactor_result.success); - println!(" โ€ข Quality Improvement: {:.1}%", refactor_result.quality_score * 100.0); - println!(" โ€ข Refactoring Duration: {} days", refactor_result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n ๐Ÿ”„ **Refactoring Strategy:**"); - for (i, step) in refactor_result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } -} - -fn simulate_frontend_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "Project setup with Create React App and TypeScript".to_string(), - "Component library and design system integration".to_string(), - "State management setup with Redux Toolkit".to_string(), - "Routing configuration with React Router".to_string(), - "API integration layer with RTK Query".to_string(), - "WebSocket integration for real-time features".to_string(), - "Progressive Web App configuration".to_string(), - "Testing setup with Jest and React Testing Library".to_string(), - "Performance optimization and code splitting".to_string(), - "Build optimization and deployment preparation".to_string(), - ], - estimated_duration: Duration::from_secs(35 * 24 * 3600), // 35 days - quality_score: 0.91, - confidence: 0.87, - recommendations: vec![ - "Use React.memo for expensive component optimizations".to_string(), - "Implement lazy loading for route-based code splitting".to_string(), - "Add error boundaries for better error handling".to_string(), - "Use React DevTools for performance profiling".to_string(), - ], - } -} - -fn simulate_backend_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "Actix-Web server setup with middleware configuration".to_string(), - "Database schema design and migration system".to_string(), - "Authentication service with JWT implementation".to_string(), - "User management API endpoints".to_string(), - "Product catalog API with caching".to_string(), - "Order processing and payment integration".to_string(), - "Real-time notification system".to_string(), - "Comprehensive error handling and logging".to_string(), - "API documentation with OpenAPI/Swagger".to_string(), - "Testing suite with unit and integration tests".to_string(), - ], - estimated_duration: Duration::from_secs(50 * 24 * 3600), // 50 days - quality_score: 0.93, - confidence: 0.90, - recommendations: vec![ - "Use async/await for all I/O operations".to_string(), - "Implement connection pooling for database efficiency".to_string(), - "Add request rate limiting for security".to_string(), - "Use structured logging for better observability".to_string(), - ], - } -} - -fn simulate_refactor_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "Code analysis and technical debt assessment".to_string(), - "Extract common functionality into shared modules".to_string(), - "Replace deprecated APIs with modern alternatives".to_string(), - "Improve error handling and resource management".to_string(), - "Add comprehensive unit test coverage".to_string(), - "Optimize performance-critical code paths".to_string(), - "Update documentation and code comments".to_string(), - "Implement continuous integration improvements".to_string(), - ], - estimated_duration: Duration::from_secs(30 * 24 * 3600), // 30 days - quality_score: 0.88, - confidence: 0.82, - recommendations: vec![ - "Use static analysis tools to identify improvement opportunities".to_string(), - "Implement gradual migration strategy to minimize risk".to_string(), - "Add comprehensive regression tests before refactoring".to_string(), - "Document all architectural decisions and changes".to_string(), - ], - } -} - -fn demonstrate_api_design_planning() { - println!("\n๐Ÿ”Œ **API Design Planning - Schema Optimization**"); - println!("================================================="); - - let request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::APIDesigner, - planning_type: DevelopmentPlanningType::APIDesign, - project_name: "E-commerce REST API Design".to_string(), - requirements: vec![ - "RESTful API following OpenAPI 3.0 standard".to_string(), - "Consistent error handling and status codes".to_string(), - "API versioning strategy".to_string(), - "Rate limiting and security headers".to_string(), - ], - constraints: vec![ - "Backward compatibility required".to_string(), - "JSON:API specification compliance".to_string(), - "OAuth 2.0 authentication".to_string(), - ], - technology_stack: vec!["OpenAPI 3.0".to_string(), "JSON".to_string(), "OAuth 2.0".to_string()], - }; - - println!("๐ŸŽฏ **API Design Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Standard: OpenAPI 3.0 + JSON:API"); - println!(" โ€ข Security: OAuth 2.0"); - println!(" โ€ข Constraint: Backward compatibility"); - - let result = simulate_api_design_planning(&request); - - println!("\n๐Ÿ”Œ **API Design Result:**"); - println!(" โ€ข Success: {}", result.success); - println!(" โ€ข Quality Score: {:.1}%", result.quality_score * 100.0); - println!(" โ€ข Confidence: {:.1}%", result.confidence * 100.0); - println!(" โ€ข Design Duration: {} days", result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n๐Ÿ“‹ **API Design Components:**"); - for (i, step) in result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - println!("\n๐Ÿ”ง **API Design Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_api_design_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "Define core resource models and relationships".to_string(), - "Design consistent URL patterns and naming conventions".to_string(), - "Specify request/response schemas with validation".to_string(), - "Define error response formats and status codes".to_string(), - "Implement pagination and filtering strategies".to_string(), - "Design authentication and authorization flows".to_string(), - "Add rate limiting and throttling specifications".to_string(), - "Create comprehensive API documentation".to_string(), - "Design API versioning and deprecation strategy".to_string(), - "Implement API testing and validation tools".to_string(), - ], - estimated_duration: Duration::from_secs(25 * 24 * 3600), // 25 days - quality_score: 0.95, - confidence: 0.92, - recommendations: vec![ - "Use JSON:API specification for consistent response formats".to_string(), - "Implement HATEOAS for better API discoverability".to_string(), - "Use semantic versioning for API version management".to_string(), - "Add comprehensive input validation and sanitization".to_string(), - "Implement idempotency for state-changing operations".to_string(), - ], - } -} - -fn demonstrate_deployment_planning() { - println!("\n๐Ÿš€ **Deployment Planning - Infrastructure Strategy**"); - println!("===================================================="); - - let request = DevelopmentPlanningRequest { - agent_type: DevelopmentAgentType::DeploymentEngineer, - planning_type: DevelopmentPlanningType::DeploymentStrategy, - project_name: "Cloud-Native Deployment Strategy".to_string(), - requirements: vec![ - "Zero-downtime deployments".to_string(), - "Auto-scaling based on load".to_string(), - "Multi-environment support (dev/staging/prod)".to_string(), - "Disaster recovery and backup strategy".to_string(), - ], - constraints: vec![ - "Kubernetes orchestration".to_string(), - "CI/CD pipeline automation".to_string(), - "Security compliance (SOC 2)".to_string(), - ], - technology_stack: vec!["Kubernetes".to_string(), "Docker".to_string(), "Terraform".to_string()], - }; - - println!("๐ŸŽฏ **Deployment Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Platform: Kubernetes"); - println!(" โ€ข Strategy: Zero-downtime deployments"); - println!(" โ€ข Compliance: SOC 2"); - - let result = simulate_deployment_planning(&request); - - println!("\n๐Ÿš€ **Deployment Planning Result:**"); - println!(" โ€ข Success: {}", result.success); - println!(" โ€ข Quality Score: {:.1}%", result.quality_score * 100.0); - println!(" โ€ข Confidence: {:.1}%", result.confidence * 100.0); - println!(" โ€ข Setup Duration: {} days", result.estimated_duration.as_secs() / (24 * 3600)); - - println!("\n๐Ÿ—๏ธ **Infrastructure Components:**"); - for (i, step) in result.planning_steps.iter().enumerate() { - println!(" {}. {}", i + 1, step); - } - - println!("\nโš™๏ธ **Deployment Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_deployment_planning(_request: &DevelopmentPlanningRequest) -> PlanningResult { - PlanningResult { - success: true, - planning_steps: vec![ - "Infrastructure as Code setup with Terraform".to_string(), - "Kubernetes cluster configuration and networking".to_string(), - "Container registry and image management strategy".to_string(), - "CI/CD pipeline with GitOps workflow".to_string(), - "Rolling deployment strategy with health checks".to_string(), - "Service mesh configuration for inter-service communication".to_string(), - "Monitoring and observability stack setup".to_string(), - "Security scanning and compliance automation".to_string(), - "Backup and disaster recovery procedures".to_string(), - "Performance testing and load testing automation".to_string(), - ], - estimated_duration: Duration::from_secs(40 * 24 * 3600), // 40 days - quality_score: 0.92, - confidence: 0.89, - recommendations: vec![ - "Use blue-green deployments for critical services".to_string(), - "Implement canary releases for gradual rollouts".to_string(), - "Set up automated rollback triggers based on metrics".to_string(), - "Use Helm charts for consistent application packaging".to_string(), - "Implement infrastructure cost monitoring and optimization".to_string(), - ], - } -} - -fn demonstrate_collaborative_workflows() { - println!("\n๐Ÿค **Collaborative Multi-Agent Workflows**"); - println!("==========================================="); - - println!("๐Ÿ”„ **Cross-Agent Collaboration Patterns:**"); - println!(" โ€ข Sequential Planning: PlannerAgent โ†’ ArchitectAgent โ†’ DesignerAgent"); - println!(" โ€ข Parallel Development: FrontendCoder + BackendCoder + APIDesigner"); - println!(" โ€ข Review Cycles: RefactorAgent validates code quality across all agents"); - println!(" โ€ข Integration Workflows: DeploymentEngineer coordinates with all agents"); - - println!("\n๐ŸŽฏ **Workflow Orchestration Example:**"); - println!(" 1. **PlannerAgent** creates strategic roadmap"); - println!(" 2. **ArchitectAgent** designs system architecture based on roadmap"); - println!(" 3. **DesignerAgent** creates UI/UX designs aligned with architecture"); - println!(" 4. **APIDesigner** defines interfaces between frontend and backend"); - println!(" 5. **FrontendCoder** + **BackendCoder** implement in parallel"); - println!(" 6. **RefactorAgent** optimizes code quality continuously"); - println!(" 7. **DeploymentEngineer** creates deployment strategy for all components"); - - println!("\n๐Ÿ“Š **Collaboration Quality Metrics:**"); - println!(" โ€ข Cross-Agent Consistency Score: 94.2%"); - println!(" โ€ข Planning Synchronization Rate: 91.8%"); - println!(" โ€ข Workflow Efficiency Improvement: +23.5%"); - println!(" โ€ข Inter-Agent Communication Quality: 89.7%"); - - println!("\nโšก **Workflow Optimization Results:**"); - println!(" โ€ข 35% reduction in planning iteration cycles"); - println!(" โ€ข 28% improvement in cross-agent coordination"); - println!(" โ€ข 42% faster time-to-deployment through parallel workflows"); - println!(" โ€ข 15% increase in overall project success rate"); - - println!("\n๐Ÿ”ง **Collaborative Features:**"); - println!(" โ€ข Shared context and knowledge transfer between agents"); - println!(" โ€ข Conflict detection and resolution mechanisms"); - println!(" โ€ข Real-time synchronization of planning dependencies"); - println!(" โ€ข Quality gates that require multi-agent validation"); -} - -fn demonstrate_quality_assessment() { - println!("\n๐ŸŽฏ **Quality Assessment & Planning Validation**"); - println!("==============================================="); - - println!("๐Ÿ“Š **Overall Development Planning Quality:**"); - println!(" โ€ข Planning Completeness: 93.2%"); - println!(" โ€ข Technical Feasibility: 89.8%"); - println!(" โ€ข Resource Estimation Accuracy: 87.5%"); - println!(" โ€ข Risk Assessment Coverage: 91.3%"); - println!(" โ€ข Timeline Reliability: 85.7%"); - - println!("\n๐Ÿ† **Agent-Specific Performance:**"); - println!(" โ€ข PlannerAgent Planning Quality: 92.0% (Strategic excellence)"); - println!(" โ€ข ArchitectAgent Design Quality: 94.5% (Architecture best practices)"); - println!(" โ€ข DesignerAgent UX Quality: 89.3% (User-centered design)"); - println!(" โ€ข FrontendCoder Implementation: 91.2% (Modern React patterns)"); - println!(" โ€ข BackendCoder Implementation: 93.8% (Rust performance & safety)"); - println!(" โ€ข RefactorAgent Code Quality: 88.7% (Technical debt reduction)"); - println!(" โ€ข APIDesigner Interface Quality: 95.1% (Standards compliance)"); - println!(" โ€ข DeploymentEngineer Ops Quality: 92.4% (Infrastructure reliability)"); - - println!("\n๐ŸŽฏ **Quality Improvement Indicators:**"); - println!(" โ€ข 25% faster planning cycles with higher accuracy"); - println!(" โ€ข 30% reduction in planning-to-implementation gaps"); - println!(" โ€ข 18% improvement in cross-functional coordination"); - println!(" โ€ข 22% increase in first-time deployment success rate"); - - println!("\n๐Ÿ” **Continuous Quality Enhancement:**"); - println!(" โ€ข Real-time quality scoring during planning"); - println!(" โ€ข Automated best practice validation"); - println!(" โ€ข Cross-agent quality reviews and feedback loops"); - println!(" โ€ข Historical planning data analysis for improvement"); - - println!("\n๐Ÿ“ˆ **Development Success Metrics:**"); - println!(" โ€ข Project Delivery Success Rate: 94.7%"); - println!(" โ€ข Requirements Satisfaction: 91.5%"); - println!(" โ€ข Budget Adherence: 88.9%"); - println!(" โ€ข Timeline Adherence: 86.3%"); - println!(" โ€ข Quality Gate Pass Rate: 92.8%"); -} \ No newline at end of file diff --git a/task_8_2_security_agents_demo.rs b/task_8_2_security_agents_demo.rs deleted file mode 100644 index c637c7b528357fe8b0973e0b61013aaf88025efb..0000000000000000000000000000000000000000 --- a/task_8_2_security_agents_demo.rs +++ /dev/null @@ -1,572 +0,0 @@ - -// Simulate the structures since we're running a standalone demo -#[derive(Debug, Clone)] -struct SecurityAgentsIntegration { - config: SecurityIntegrationConfig, -} - -#[derive(Debug, Clone)] -struct SecurityIntegrationConfig { - cybersecurity_agent_enabled: bool, - prompt_security_agent_enabled: bool, - privacy_compliance_enabled: bool, - ethical_ai_planning_enabled: bool, - vulnerability_simulation_enabled: bool, - threat_modeling_enabled: bool, -} - -#[derive(Debug, Clone)] -enum SecurityAgentType { - CyberSecurityAgent, - PromptSecurityAgent, - PrivacyComplianceAgent, - EthicalAIAgent, -} - -#[derive(Debug, Clone)] -enum SecurityPlanningType { - VulnerabilityAssessment, - ThreatModeling, - PenetrationTesting, - PromptInjectionPrevention, - PrivacyComplianceAudit, - EthicalAIReview, - IncidentResponse, -} - -#[derive(Debug, Clone)] -enum ThreatLevel { - Low, - Medium, - High, - Critical, - Extreme, -} - -#[derive(Debug, Clone)] -enum SeverityLevel { - Informational, - Low, - Medium, - High, - Critical, -} - -#[derive(Debug, Clone)] -struct SecurityPlanningRequest { - agent_type: SecurityAgentType, - planning_type: SecurityPlanningType, - system_name: String, - threat_level: ThreatLevel, - compliance_requirements: Vec, - assets_to_protect: Vec, -} - -#[derive(Debug, Clone)] -struct SecurityAssessmentResult { - success: bool, - overall_security_score: f64, - vulnerability_count: usize, - threat_scenarios: usize, - security_gaps: usize, - confidence: f64, - risk_reduction: f64, - recommendations: Vec, -} - -fn main() { - println!("๐Ÿง  Brain AI Task 8.2: Security Agents Integration - Full Demo"); - println!("=============================================================="); - - demonstrate_security_agents_architecture(); - demonstrate_cybersecurity_agent(); - demonstrate_prompt_security_agent(); - demonstrate_privacy_compliance_planning(); - demonstrate_ethical_ai_planning(); - demonstrate_vulnerability_simulation(); - demonstrate_threat_modeling(); - demonstrate_security_orchestration(); - demonstrate_compliance_monitoring(); - - println!("\n๐ŸŽฏ **TASK 8.2: SECURITY AGENTS INTEGRATION - COMPLETED!**"); - println!("โœ… All security agent integrations successfully implemented!"); - println!("๐Ÿ›ก๏ธ Brain AI now provides comprehensive security and privacy protection!"); -} - -fn demonstrate_security_agents_architecture() { - println!("\n๐Ÿ›ก๏ธ **Security Agents Integration Architecture**"); - println!("================================================"); - - println!("๐Ÿ“Š **Core Security Agent Integrations:**"); - println!(" โ€ข CyberSecurityAgent: Vulnerability assessment and penetration testing"); - println!(" โ€ข PromptSecurityAgent: AI safety and prompt injection prevention"); - println!(" โ€ข PrivacyComplianceAgent: GDPR, CCPA and privacy regulation compliance"); - println!(" โ€ข EthicalAIAgent: Bias detection and ethical decision-making frameworks"); - println!(" โ€ข ThreatModelingEngine: Comprehensive threat analysis and simulation"); - println!(" โ€ข VulnerabilitySimulator: Advanced security testing and assessment"); - - println!("\n๐Ÿ”„ **Security Planning Flow:**"); - println!(" 1. Security threat identification โ†’ 2. Risk assessment and modeling"); - println!(" 3. Vulnerability simulation โ†’ 4. Compliance validation"); - println!(" 5. Ethical AI evaluation โ†’ 6. Mitigation planning"); - println!(" 7. Security orchestration โ†’ 8. Continuous monitoring"); - - println!("\nโšก **Key Security Features:**"); - println!(" โ€ข Advanced threat modeling with MITRE ATT&CK framework"); - println!(" โ€ข Real-time vulnerability simulation and penetration testing"); - println!(" โ€ข Comprehensive privacy compliance for global regulations"); - println!(" โ€ข Ethical AI frameworks with bias detection and fairness evaluation"); - println!(" โ€ข Automated security orchestration and incident response"); -} - -fn demonstrate_cybersecurity_agent() { - println!("\n๐Ÿ” **CyberSecurityAgent Integration - Advanced Threat Protection**"); - println!("================================================================"); - - let request = SecurityPlanningRequest { - agent_type: SecurityAgentType::CyberSecurityAgent, - planning_type: SecurityPlanningType::VulnerabilityAssessment, - system_name: "E-commerce Platform Security Assessment".to_string(), - threat_level: ThreatLevel::High, - compliance_requirements: vec![ - "SOC 2 Type II".to_string(), - "PCI DSS Level 1".to_string(), - "ISO 27001".to_string(), - ], - assets_to_protect: vec![ - "Customer payment data".to_string(), - "User personal information".to_string(), - "Trade secrets and IP".to_string(), - "System infrastructure".to_string(), - ], - }; - - println!("๐ŸŽฏ **Cybersecurity Assessment Request:**"); - println!(" โ€ข System: {}", request.system_name); - println!(" โ€ข Threat Level: {:?}", request.threat_level); - println!(" โ€ข Compliance: {:?}", request.compliance_requirements); - println!(" โ€ข Assets: {:?}", request.assets_to_protect); - - let result = simulate_cybersecurity_assessment(&request); - - println!("\n๐Ÿ” **Cybersecurity Assessment Result:**"); - println!(" โ€ข Assessment Success: {}", result.success); - println!(" โ€ข Overall Security Score: {:.1}/10.0", result.overall_security_score); - println!(" โ€ข Vulnerabilities Found: {}", result.vulnerability_count); - println!(" โ€ข Threat Scenarios: {}", result.threat_scenarios); - println!(" โ€ข Security Gaps: {}", result.security_gaps); - println!(" โ€ข Risk Reduction: {:.1}%", result.risk_reduction * 100.0); - - println!("\n๐ŸŽฏ **Vulnerability Analysis:**"); - println!(" โ€ข SQL Injection (High): 2 instances in payment processing"); - println!(" โ€ข Cross-Site Scripting (Medium): 5 instances in user interface"); - println!(" โ€ข Authentication Bypass (Critical): 1 instance in admin panel"); - println!(" โ€ข Data Exposure (High): 3 instances in API endpoints"); - println!(" โ€ข Configuration Weakness (Medium): 8 instances across infrastructure"); - - println!("\n๐Ÿ›ก๏ธ **Threat Scenarios Identified:**"); - println!(" โ€ข Nation-State APT Attack: 15% likelihood, 95% impact"); - println!(" โ€ข Ransomware Campaign: 35% likelihood, 85% impact"); - println!(" โ€ข Data Breach via Insider: 20% likelihood, 75% impact"); - println!(" โ€ข Supply Chain Attack: 10% likelihood, 90% impact"); - - println!("\n๐Ÿ’ก **Cybersecurity Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_cybersecurity_assessment(_request: &SecurityPlanningRequest) -> SecurityAssessmentResult { - SecurityAssessmentResult { - success: true, - overall_security_score: 7.8, - vulnerability_count: 19, - threat_scenarios: 4, - security_gaps: 12, - confidence: 0.92, - risk_reduction: 0.78, - recommendations: vec![ - "Implement Web Application Firewall (WAF) with OWASP rule sets".to_string(), - "Deploy Zero Trust network architecture with micro-segmentation".to_string(), - "Establish Security Operations Center (SOC) with 24/7 monitoring".to_string(), - "Implement advanced threat hunting with behavioral analytics".to_string(), - "Deploy endpoint detection and response (EDR) across all systems".to_string(), - "Establish incident response playbooks for all threat scenarios".to_string(), - ], - } -} - -fn demonstrate_prompt_security_agent() { - println!("\n๐Ÿค– **PromptSecurityAgent Integration - AI Safety & Robustness**"); - println!("=============================================================="); - - let request = SecurityPlanningRequest { - agent_type: SecurityAgentType::PromptSecurityAgent, - planning_type: SecurityPlanningType::PromptInjectionPrevention, - system_name: "AI-Powered Customer Service Platform".to_string(), - threat_level: ThreatLevel::Medium, - compliance_requirements: vec![ - "AI Ethics Guidelines".to_string(), - "Content Safety Standards".to_string(), - "Model Robustness Requirements".to_string(), - ], - assets_to_protect: vec![ - "AI model integrity".to_string(), - "Customer conversation data".to_string(), - "Brand reputation".to_string(), - "Service quality".to_string(), - ], - }; - - println!("๐ŸŽฏ **Prompt Security Assessment Request:**"); - println!(" โ€ข AI System: {}", request.system_name); - println!(" โ€ข Focus: Prompt injection prevention and AI safety"); - println!(" โ€ข Compliance: AI ethics and content safety"); - println!(" โ€ข Protection: Model integrity and brand safety"); - - let result = simulate_prompt_security_assessment(&request); - - println!("\n๐Ÿค– **Prompt Security Assessment Result:**"); - println!(" โ€ข Assessment Success: {}", result.success); - println!(" โ€ข AI Safety Score: {:.1}/10.0", result.overall_security_score); - println!(" โ€ข Prompt Vulnerabilities: {}", result.vulnerability_count); - println!(" โ€ข Attack Scenarios: {}", result.threat_scenarios); - println!(" โ€ข Safety Gaps: {}", result.security_gaps); - println!(" โ€ข Robustness Improvement: {:.1}%", result.risk_reduction * 100.0); - - println!("\n๐Ÿ” **AI Security Analysis:**"); - println!(" โ€ข Prompt Injection Susceptibility: Medium (6.5/10)"); - println!(" โ€ข Adversarial Input Resistance: Good (8.2/10)"); - println!(" โ€ข Content Filter Effectiveness: Excellent (9.1/10)"); - println!(" โ€ข Model Output Consistency: Good (8.0/10)"); - println!(" โ€ข Harmful Content Generation Risk: Low (2.3/10)"); - - println!("\nโš ๏ธ **AI Threat Scenarios:**"); - println!(" โ€ข Prompt Injection Attack: 25% likelihood, 60% impact"); - println!(" โ€ข Jailbreak Attempt: 15% likelihood, 70% impact"); - println!(" โ€ข Model Inversion Attack: 5% likelihood, 85% impact"); - println!(" โ€ข Training Data Extraction: 3% likelihood, 90% impact"); - - println!("\n๐Ÿ›ก๏ธ **AI Safety Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_prompt_security_assessment(_request: &SecurityPlanningRequest) -> SecurityAssessmentResult { - SecurityAssessmentResult { - success: true, - overall_security_score: 8.2, - vulnerability_count: 7, - threat_scenarios: 4, - security_gaps: 5, - confidence: 0.89, - risk_reduction: 0.72, - recommendations: vec![ - "Implement multi-layer prompt filtering with semantic analysis".to_string(), - "Deploy adversarial training to improve model robustness".to_string(), - "Establish real-time content moderation with human oversight".to_string(), - "Implement model output validation and safety checks".to_string(), - "Deploy conversation context monitoring for injection detection".to_string(), - "Establish AI model versioning with rollback capabilities".to_string(), - ], - } -} - -fn demonstrate_privacy_compliance_planning() { - println!("\n๐Ÿ”’ **Privacy Compliance Planning - Global Regulations**"); - println!("======================================================="); - - let request = SecurityPlanningRequest { - agent_type: SecurityAgentType::PrivacyComplianceAgent, - planning_type: SecurityPlanningType::PrivacyComplianceAudit, - system_name: "Global SaaS Platform Privacy Compliance".to_string(), - threat_level: ThreatLevel::High, - compliance_requirements: vec![ - "GDPR (EU)".to_string(), - "CCPA (California)".to_string(), - "PIPEDA (Canada)".to_string(), - "LGPD (Brazil)".to_string(), - "UK GDPR".to_string(), - ], - assets_to_protect: vec![ - "Personal data of EU residents".to_string(), - "California consumer information".to_string(), - "Employee personal data".to_string(), - "Third-party data sharing".to_string(), - ], - }; - - println!("๐ŸŽฏ **Privacy Compliance Request:**"); - println!(" โ€ข Platform: {}", request.system_name); - println!(" โ€ข Jurisdictions: 5 major privacy regulations"); - println!(" โ€ข Scope: Global data protection compliance"); - println!(" โ€ข Critical: Cross-border data transfers"); - - let result = simulate_privacy_compliance_assessment(&request); - - println!("\n๐Ÿ”’ **Privacy Compliance Assessment Result:**"); - println!(" โ€ข Compliance Success: {}", result.success); - println!(" โ€ข Overall Compliance Score: {:.1}%", result.overall_security_score * 10.0); - println!(" โ€ข Privacy Gaps: {}", result.vulnerability_count); - println!(" โ€ข Regulatory Risks: {}", result.threat_scenarios); - println!(" โ€ข Policy Updates Needed: {}", result.security_gaps); - println!(" โ€ข Compliance Improvement: {:.1}%", result.risk_reduction * 100.0); - - println!("\n๐Ÿ“‹ **Privacy Compliance Status by Framework:**"); - println!(" โ€ข GDPR (EU): 87% compliant - Missing: Data retention automation"); - println!(" โ€ข CCPA (California): 92% compliant - Minor: Consumer rights portal"); - println!(" โ€ข PIPEDA (Canada): 89% compliant - Gap: Cross-border transfer logs"); - println!(" โ€ข LGPD (Brazil): 78% compliant - Major: Consent management system"); - println!(" โ€ข UK GDPR: 91% compliant - Minor: Brexit-specific adaptations"); - - println!("\nโš–๏ธ **Regulatory Risk Analysis:**"); - println!(" โ€ข GDPR Fine Risk: 2% likelihood, โ‚ฌ20M potential penalty"); - println!(" โ€ข CCPA Class Action: 5% likelihood, $10M potential settlement"); - println!(" โ€ข Regulatory Investigation: 8% likelihood, $2M compliance cost"); - println!(" โ€ข Reputational Damage: 15% likelihood, $50M business impact"); - - println!("\n๐Ÿ›ก๏ธ **Privacy Protection Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_privacy_compliance_assessment(_request: &SecurityPlanningRequest) -> SecurityAssessmentResult { - SecurityAssessmentResult { - success: true, - overall_security_score: 8.7, // 87% compliance - vulnerability_count: 13, - threat_scenarios: 4, - security_gaps: 8, - confidence: 0.94, - risk_reduction: 0.85, - recommendations: vec![ - "Implement Privacy by Design in all new system developments".to_string(), - "Deploy automated data discovery and classification system".to_string(), - "Establish cross-border data transfer safeguards and monitoring".to_string(), - "Implement unified consent management across all jurisdictions".to_string(), - "Deploy automated data subject rights fulfillment system".to_string(), - "Establish comprehensive data retention and deletion automation".to_string(), - ], - } -} - -fn demonstrate_ethical_ai_planning() { - println!("\nโš–๏ธ **Ethical AI Planning - Bias Detection & Fairness**"); - println!("======================================================"); - - let request = SecurityPlanningRequest { - agent_type: SecurityAgentType::EthicalAIAgent, - planning_type: SecurityPlanningType::EthicalAIReview, - system_name: "AI-Powered Hiring and HR Platform".to_string(), - threat_level: ThreatLevel::Critical, - compliance_requirements: vec![ - "Equal Employment Opportunity".to_string(), - "Algorithmic Fairness Standards".to_string(), - "AI Transparency Requirements".to_string(), - "Bias Testing Protocols".to_string(), - ], - assets_to_protect: vec![ - "Algorithmic fairness".to_string(), - "Candidate privacy".to_string(), - "Legal compliance".to_string(), - "Brand reputation".to_string(), - ], - }; - - println!("๐ŸŽฏ **Ethical AI Assessment Request:**"); - println!(" โ€ข AI System: {}", request.system_name); - println!(" โ€ข Critical Concern: Employment discrimination prevention"); - println!(" โ€ข Standards: Algorithmic fairness and transparency"); - println!(" โ€ข Impact: High-stakes hiring decisions"); - - let result = simulate_ethical_ai_assessment(&request); - - println!("\nโš–๏ธ **Ethical AI Assessment Result:**"); - println!(" โ€ข Ethics Assessment Success: {}", result.success); - println!(" โ€ข Ethical AI Score: {:.1}/10.0", result.overall_security_score); - println!(" โ€ข Bias Indicators: {}", result.vulnerability_count); - println!(" โ€ข Fairness Risks: {}", result.threat_scenarios); - println!(" โ€ข Ethics Gaps: {}", result.security_gaps); - println!(" โ€ข Fairness Improvement: {:.1}%", result.risk_reduction * 100.0); - - println!("\n๐Ÿ” **Bias Detection Analysis:**"); - println!(" โ€ข Gender Bias: Moderate concern (6.2/10) in technical role screening"); - println!(" โ€ข Racial Bias: Low concern (3.1/10) with current demographic parity"); - println!(" โ€ข Age Bias: High concern (7.8/10) in experience weight calculations"); - println!(" โ€ข Educational Bias: Moderate concern (5.9/10) in university ranking bias"); - println!(" โ€ข Geographic Bias: Low concern (2.7/10) with location normalization"); - - println!("\n๐Ÿ“Š **Fairness Metrics Assessment:**"); - println!(" โ€ข Demographic Parity: 73% (Target: >80%)"); - println!(" โ€ข Equalized Odds: 68% (Target: >75%)"); - println!(" โ€ข Calibration Score: 82% (Target: >85%)"); - println!(" โ€ข Individual Fairness: 79% (Target: >85%)"); - println!(" โ€ข Counterfactual Fairness: 71% (Target: >80%)"); - - println!("\nโš ๏ธ **Ethical Risk Scenarios:**"); - println!(" โ€ข Discriminatory Hiring: 20% likelihood, Legal/Reputational impact"); - println!(" โ€ข Algorithm Transparency Challenge: 35% likelihood, Regulatory impact"); - println!(" โ€ข Bias Amplification: 15% likelihood, Systemic discrimination risk"); - println!(" โ€ข Fairness Audit Failure: 25% likelihood, Compliance penalties"); - - println!("\n๐Ÿ›ก๏ธ **Ethical AI Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_ethical_ai_assessment(_request: &SecurityPlanningRequest) -> SecurityAssessmentResult { - SecurityAssessmentResult { - success: true, - overall_security_score: 7.6, - vulnerability_count: 8, - threat_scenarios: 4, - security_gaps: 6, - confidence: 0.91, - risk_reduction: 0.74, - recommendations: vec![ - "Implement multi-attribute fairness testing across all protected classes".to_string(), - "Deploy continuous bias monitoring with real-time alerting".to_string(), - "Establish diverse AI ethics review board with external oversight".to_string(), - "Implement explainable AI features for hiring decision transparency".to_string(), - "Deploy adversarial debiasing techniques in model training".to_string(), - "Establish regular fairness audits with third-party validation".to_string(), - ], - } -} - -fn demonstrate_vulnerability_simulation() { - println!("\n๐Ÿ”ฌ **Advanced Vulnerability Simulation**"); - println!("========================================="); - - println!("๐ŸŽฏ **Simulation Scenarios:**"); - println!(" โ€ข Automated Penetration Testing: 247 attack vectors tested"); - println!(" โ€ข Red Team Exercises: Advanced persistent threat simulation"); - println!(" โ€ข Social Engineering Tests: Phishing and pretexting campaigns"); - println!(" โ€ข Physical Security Assessment: Facility and hardware security"); - - println!("\n๐Ÿ”ฌ **Vulnerability Simulation Results:**"); - println!(" โ€ข Critical Vulnerabilities: 3 discovered (Authentication bypass, RCE, Data exposure)"); - println!(" โ€ข High Severity Issues: 8 identified (Privilege escalation, XSS, CSRF)"); - println!(" โ€ข Medium Severity Findings: 15 detected (Information disclosure, weak configs)"); - println!(" โ€ข Low/Info Findings: 23 documented (Banner disclosure, outdated software)"); - - println!("\nโšก **Attack Simulation Success Rates:**"); - println!(" โ€ข External Network Penetration: 85% success rate"); - println!(" โ€ข Internal Lateral Movement: 78% success rate"); - println!(" โ€ข Privilege Escalation: 62% success rate"); - println!(" โ€ข Data Exfiltration: 45% success rate"); - - println!("\n๐Ÿ›ก๏ธ **Defense Effectiveness:**"); - println!(" โ€ข Firewall/IPS Detection: 72% of attacks blocked"); - println!(" โ€ข EDR/AV Detection: 89% of malware caught"); - println!(" โ€ข SIEM Alert Generation: 94% of incidents logged"); - println!(" โ€ข SOC Response Time: Average 12 minutes to triage"); - - println!("\n๐Ÿ’ก **Simulation Insights:**"); - println!(" โ€ข Attackers can achieve domain admin in average 3.2 hours"); - println!(" โ€ข 67% of critical assets reachable from compromised workstation"); - println!(" โ€ข Social engineering has 23% success rate with current training"); - println!(" โ€ข Physical security bypass possible in 15 minutes average"); -} - -fn demonstrate_threat_modeling() { - println!("\n๐ŸŽญ **Advanced Threat Modeling Engine**"); - println!("======================================"); - - println!("๐ŸŽฏ **Threat Modeling Methodologies:**"); - println!(" โ€ข STRIDE Analysis: Spoofing, Tampering, Repudiation, Information disclosure, DoS, Elevation"); - println!(" โ€ข MITRE ATT&CK Mapping: 341 techniques across 14 tactics mapped to system"); - println!(" โ€ข PASTA Methodology: Process for Attack Simulation and Threat Analysis"); - println!(" โ€ข OCTAVE Allegro: Operationally Critical Threat, Asset, and Vulnerability Evaluation"); - - println!("\n๐ŸŽญ **Threat Actor Profiles:**"); - println!(" โ€ข Nation-State APT: Sophistication 9/10, Resources unlimited, Persistent"); - println!(" โ€ข Cybercriminal Groups: Sophistication 7/10, Financially motivated, Opportunistic"); - println!(" โ€ข Insider Threats: Sophistication 5/10, Privileged access, Emotional motivation"); - println!(" โ€ข Hacktivists: Sophistication 6/10, Ideologically driven, Public attention seeking"); - - println!("\n๐Ÿ“Š **Threat Landscape Analysis:**"); - println!(" โ€ข Attack Surface Score: 7.2/10 (Above average exposure)"); - println!(" โ€ข Threat Intelligence: 89 IoCs relevant to organization identified"); - println!(" โ€ข Attack Probability: 73% chance of significant attack within 12 months"); - println!(" โ€ข Business Impact: $2.3M average cost per successful breach"); - - println!("\n๐Ÿ”ฎ **Predictive Threat Analysis:**"); - println!(" โ€ข Ransomware Attack: 35% probability, $5.2M estimated impact"); - println!(" โ€ข Data Breach: 28% probability, $3.8M estimated impact"); - println!(" โ€ข Supply Chain Attack: 12% probability, $8.1M estimated impact"); - println!(" โ€ข State-Sponsored Espionage: 8% probability, $15.5M estimated impact"); - - println!("\n๐Ÿ›ก๏ธ **Threat Mitigation Strategy:**"); - println!(" โ€ข Zero Trust Architecture: Reduce attack surface by 60%"); - println!(" โ€ข Advanced Threat Hunting: Improve detection capability by 75%"); - println!(" โ€ข Incident Response Automation: Reduce response time by 80%"); - println!(" โ€ข Security Awareness Training: Reduce human error risk by 45%"); -} - -fn demonstrate_security_orchestration() { - println!("\n๐ŸŽผ **Security Orchestration & Automation**"); - println!("=========================================="); - - println!("๐Ÿ”„ **Automated Security Workflows:**"); - println!(" โ€ข Threat Detection โ†’ Analysis โ†’ Response: Fully automated for 78% of incidents"); - println!(" โ€ข Vulnerability Management: Patch deployment automated within 4 hours"); - println!(" โ€ข Compliance Monitoring: Continuous assessment with real-time reporting"); - println!(" โ€ข Incident Response: SOAR platform orchestrates 89% of response actions"); - - println!("\nโšก **Security Automation Results:**"); - println!(" โ€ข Mean Time to Detection (MTTD): 4.2 minutes (Industry: 197 days)"); - println!(" โ€ข Mean Time to Response (MTTR): 12 minutes (Industry: 23 days)"); - println!(" โ€ข False Positive Reduction: 87% through ML-powered analytics"); - println!(" โ€ข Security Team Efficiency: 340% improvement in incident handling"); - - println!("\n๐Ÿค **Cross-Agent Coordination:**"); - println!(" โ€ข CyberSecurity โ†” PromptSecurity: 94% consistency in AI threat assessment"); - println!(" โ€ข Privacy โ†” Ethics: 91% alignment in AI governance decisions"); - println!(" โ€ข Vulnerability โ†” Threat: 88% correlation in risk prioritization"); - println!(" โ€ข Compliance โ†” All Agents: 96% coherence in regulatory mapping"); - - println!("\n๐Ÿ“ˆ **Security Orchestration Metrics:**"); - println!(" โ€ข Workflow Automation Rate: 85% of security processes automated"); - println!(" โ€ข Security Tool Integration: 23 tools unified in single platform"); - println!(" โ€ข Decision Support Accuracy: 92% of automated decisions validated"); - println!(" โ€ข Resource Optimization: 67% reduction in manual security tasks"); -} - -fn demonstrate_compliance_monitoring() { - println!("\n๐Ÿ“Š **Continuous Compliance Monitoring**"); - println!("======================================="); - - println!("๐Ÿ“‹ **Compliance Framework Status:**"); - println!(" โ€ข ISO 27001: 94% compliant (142/151 controls implemented)"); - println!(" โ€ข SOC 2 Type II: 91% compliant (Audit scheduled Q2 2025)"); - println!(" โ€ข NIST CSF: 87% maturity (Optimize level in 6/23 categories)"); - println!(" โ€ข PCI DSS: 96% compliant (Annual assessment passed)"); - - println!("\n๐Ÿ” **Real-Time Compliance Monitoring:**"); - println!(" โ€ข Policy Violations: 0 critical, 3 medium, 12 low in past 30 days"); - println!(" โ€ข Configuration Drift: 5 systems require remediation"); - println!(" โ€ข Access Control Changes: 847 changes monitored, 2 flagged for review"); - println!(" โ€ข Data Handling Compliance: 99.7% of data flows comply with policies"); - - println!("\nโš ๏ธ **Compliance Risk Assessment:**"); - println!(" โ€ข Regulatory Change Impact: 3 new regulations affect 15% of controls"); - println!(" โ€ข Audit Readiness Score: 89% (Target: >95%)"); - println!(" โ€ข Compliance Debt: $234K estimated cost to achieve full compliance"); - println!(" โ€ข Risk Exposure: 12% residual risk after current controls"); - - println!("\n๐Ÿ“ˆ **Compliance Improvement Trends:**"); - println!(" โ€ข Overall Score Improvement: +15% over past 12 months"); - println!(" โ€ข Control Effectiveness: +22% improvement in detection/prevention"); - println!(" โ€ข Audit Finding Reduction: -67% critical findings vs. previous cycle"); - println!(" โ€ข Compliance Automation: 73% of compliance checks automated"); - - println!("\n๐ŸŽฏ **Security Excellence Summary:**"); - println!(" โ€ข Overall Security Posture: 8.7/10 (Excellent)"); - println!(" โ€ข Risk Reduction Achievement: 78% vs. baseline"); - println!(" โ€ข Security Investment ROI: 340% over 3-year period"); - println!(" โ€ข Stakeholder Confidence: 94% executive satisfaction with security program"); -} \ No newline at end of file diff --git a/task_8_3_operations_agents_demo.rs b/task_8_3_operations_agents_demo.rs deleted file mode 100644 index c68055db05a4b9dd47a8d899195a5bc5374f4bc0..0000000000000000000000000000000000000000 --- a/task_8_3_operations_agents_demo.rs +++ /dev/null @@ -1,762 +0,0 @@ -// Task 8.3: Operations Agents Integration Demo -//! Demonstrates the integration of operations-focused agents with MuBrain planning -//! for infrastructure operations, deployment automation, and system maintenance. - -use std::time::SystemTime; - -/// Operations agents integration system -struct OperationsAgentsIntegration { - _agents: Vec, - _config: OperationsIntegrationConfig, -} - -/// Configuration for operations integration -struct OperationsIntegrationConfig { - _max_concurrent_operations: usize, - _timeout_seconds: u64, - _enable_auto_scaling: bool, -} - -/// Available operations agent types -#[derive(Debug, Clone)] -enum OperationsAgentType { - DeploymentAgent, - MonitoringAgent, - ScalingAgent, - IncidentResponseAgent, - CapacityPlanningAgent, -} - -/// Types of operations planning -#[derive(Debug, Clone)] -enum OperationsPlanningType { - DeploymentStrategy, - ResourceOptimization, - ScalingStrategy, - MonitoringSetup, - AlertingConfiguration, - IncidentResponse, - DisasterRecovery, - CapacityPlanning, - PerformanceOptimization, - InfrastructureProvisioning, - ApplicationDeployment, -} - -/// Urgency levels for operations -#[derive(Debug, Clone)] -enum UrgencyLevel { - Low, - Medium, - High, - Critical, - Emergency, -} - -/// Deployment strategies -#[derive(Debug, Clone)] -enum DeploymentStrategy { - BlueGreen, - Canary, - RollingUpdate, - ABTesting, -} - -/// Operations planning request -#[derive(Debug, Clone)] -struct OperationsPlanningRequest { - _agent_type: OperationsAgentType, - _planning_type: OperationsPlanningType, - system_name: String, - _urgency_level: UrgencyLevel, - current_load: LoadInfo, - budget_constraints: BudgetInfo, - _timestamp: SystemTime, -} - -/// Load information for operations planning -#[derive(Debug, Clone)] -struct LoadInfo { - concurrent_users: usize, - requests_per_second: f64, - _data_volume_gb: f64, - _peak_multiplier: f64, -} - -/// Budget constraints for operations -#[derive(Debug, Clone)] -struct BudgetInfo { - monthly_budget: f64, - _cost_optimization_priority: String, -} - -/// Result of an operations assessment -#[derive(Debug, Clone)] -struct OperationsAssessmentResult { - success: bool, - infrastructure_score: f64, - deployment_confidence: f64, - scaling_readiness: f64, - monitoring_coverage: f64, - incident_response_score: f64, - cost_efficiency: f64, - recommendations: Vec, - estimated_monthly_cost: f64, - deployment_time_estimate: std::time::Duration, -} - -fn main() { - println!("๐Ÿง  Brain AI Task 8.3: Operations Agents Integration - Full Demo"); - println!("==============================================================="); - - demonstrate_operations_agents_architecture(); - demonstrate_infrastructure_agent(); - demonstrate_deployment_agent(); - demonstrate_monitoring_agent(); - demonstrate_resource_optimization(); - demonstrate_scaling_strategy(); - demonstrate_incident_response_planning(); - demonstrate_alerting_configuration(); - demonstrate_capacity_planning(); - demonstrate_cost_optimization(); - demonstrate_disaster_recovery(); - demonstrate_operations_orchestration(); - - println!("\n๐ŸŽฏ **TASK 8.3: OPERATIONS AGENTS INTEGRATION - COMPLETED!**"); - println!("โœ… All operations agent integrations successfully implemented!"); - println!("๐Ÿš€ Brain AI now provides comprehensive operational excellence!"); -} - -fn demonstrate_operations_agents_architecture() { - println!("\n๐Ÿš€ **Operations Agents Integration Architecture**"); - println!("================================================="); - - println!("๐Ÿ“Š **Core Operations Agent Integrations:**"); - println!(" โ€ข InfrastructureAgent: Cloud provisioning and infrastructure management"); - println!(" โ€ข DeploymentAgent: Application deployment and release management"); - println!(" โ€ข MonitoringAgent: System observability and performance monitoring"); - println!(" โ€ข ScalingAgent: Auto-scaling and resource optimization"); - println!(" โ€ข IncidentResponseAgent: Automated incident response and recovery"); - println!(" โ€ข CapacityPlanningAgent: Capacity forecasting and growth planning"); - - println!("\n๐Ÿ”„ **Operations Planning Flow:**"); - println!(" 1. Infrastructure provisioning โ†’ 2. Application deployment"); - println!(" 3. Monitoring setup โ†’ 4. Scaling configuration"); - println!(" 5. Incident response planning โ†’ 6. Cost optimization"); - println!(" 7. Capacity forecasting โ†’ 8. Performance optimization"); - - println!("\nโšก **Key Operations Features:**"); - println!(" โ€ข Multi-cloud infrastructure provisioning (AWS, Azure, GCP, Kubernetes)"); - println!(" โ€ข Blue-green, canary, and rolling deployment strategies"); - println!(" โ€ข Comprehensive observability with metrics, logs, and tracing"); - println!(" โ€ข Predictive auto-scaling and resource optimization"); - println!(" โ€ข Automated incident response with 24/7 monitoring"); - println!(" โ€ข Cost optimization with reserved instances and spot pricing"); -} - -fn demonstrate_infrastructure_agent() { - println!("\n๐Ÿ—๏ธ **Infrastructure Agent Integration - Cloud Provisioning**"); - println!("==========================================================="); - - let request = OperationsPlanningRequest { - _agent_type: OperationsAgentType::DeploymentAgent, - _planning_type: OperationsPlanningType::InfrastructureProvisioning, - system_name: "E-commerce Platform Infrastructure".to_string(), - _urgency_level: UrgencyLevel::High, - current_load: LoadInfo { - concurrent_users: 50000, - requests_per_second: 2500.0, - _data_volume_gb: 500.0, - _peak_multiplier: 3.0, - }, - budget_constraints: BudgetInfo { - monthly_budget: 15000.0, - _cost_optimization_priority: "Balanced".to_string(), - }, - _timestamp: SystemTime::now(), - }; - - println!("๐ŸŽฏ **Infrastructure Provisioning Request:**"); - println!(" โ€ข System: {}", request.system_name); - println!(" โ€ข System: {}", request.system_name); - println!(" โ€ข Expected Load: {} concurrent users, {} RPS", - request.current_load.concurrent_users, request.current_load.requests_per_second); - println!(" โ€ข Budget: ${}/month", request.budget_constraints.monthly_budget); - - let result = simulate_infrastructure_assessment(&request); - - println!("\n๐Ÿ—๏ธ **Infrastructure Assessment Result:**"); - println!(" โ€ข Assessment Success: {}", result.success); - println!(" โ€ข Infrastructure Score: {:.1}/10.0", result.infrastructure_score); - println!(" โ€ข Deployment Confidence: {:.1}%", result.deployment_confidence * 100.0); - println!(" โ€ข Cost Efficiency: {:.1}%", result.cost_efficiency * 100.0); - println!(" โ€ข Estimated Monthly Cost: ${:.0}", result.estimated_monthly_cost); - println!(" โ€ข Deployment Time: {:?}", result.deployment_time_estimate); - - println!("\n๐ŸŒ **Multi-Cloud Infrastructure Plan:**"); - println!(" โ€ข Primary Region: us-east-1 (AWS) - 60% traffic allocation"); - println!(" โ€ข Secondary Region: us-west-2 (AWS) - 30% traffic allocation"); - println!(" โ€ข Disaster Recovery: us-central1 (GCP) - 10% standby capacity"); - println!(" โ€ข Edge Locations: 15 CloudFront locations globally"); - println!(" โ€ข Kubernetes Clusters: 3 EKS clusters with 12 worker nodes each"); - - println!("\n๐Ÿ’พ **Resource Allocation:**"); - println!(" โ€ข Compute: 24 c5.xlarge instances (4 vCPU, 16GB RAM each)"); - println!(" โ€ข Storage: 2TB gp3 SSD with 6,000 IOPS provisioned"); - println!(" โ€ข Network: 25 Gbps bandwidth with Application Load Balancer"); - println!(" โ€ข Database: RDS PostgreSQL Multi-AZ with read replicas"); - println!(" โ€ข Cache: ElastiCache Redis cluster with 6 nodes"); - - println!("\n๐Ÿ”’ **Security & Compliance:**"); - println!(" โ€ข VPC with private subnets and NAT gateways"); - println!(" โ€ข WAF with OWASP Top 10 protection rules"); - println!(" โ€ข SSL/TLS certificates with automatic renewal"); - println!(" โ€ข Security groups with least privilege access"); - println!(" โ€ข CloudTrail logging and GuardDuty threat detection"); - - println!("\n๐Ÿ’ก **Infrastructure Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_infrastructure_assessment(_request: &OperationsPlanningRequest) -> OperationsAssessmentResult { - OperationsAssessmentResult { - success: true, - infrastructure_score: 8.7, - deployment_confidence: 0.94, - scaling_readiness: 0.91, - monitoring_coverage: 0.96, - incident_response_score: 0.89, - cost_efficiency: 0.87, - estimated_monthly_cost: 12750.0, - deployment_time_estimate: std::time::Duration::from_secs(2 * 3600), // 2 hours - recommendations: vec![ - "Implement Infrastructure as Code with Terraform for consistency".to_string(), - "Use Spot Instances for 30% cost reduction on non-critical workloads".to_string(), - "Deploy multi-region setup for 99.99% availability target".to_string(), - "Implement automated backup and disaster recovery procedures".to_string(), - "Use Reserved Instances for 40% savings on predictable workloads".to_string(), - "Enable detailed monitoring and cost allocation tags".to_string(), - ], - } -} - -fn demonstrate_deployment_agent() { - println!("\n๐Ÿš€ **Deployment Agent Integration - Release Management**"); - println!("======================================================="); - - let request = OperationsPlanningRequest { - _agent_type: OperationsAgentType::DeploymentAgent, - _planning_type: OperationsPlanningType::ApplicationDeployment, - system_name: "Microservices Application Suite".to_string(), - _urgency_level: UrgencyLevel::Medium, - current_load: LoadInfo { - concurrent_users: 25000, - requests_per_second: 1200.0, - _data_volume_gb: 250.0, - _peak_multiplier: 2.5, - }, - budget_constraints: BudgetInfo { - monthly_budget: 8000.0, - _cost_optimization_priority: "Performance".to_string(), - }, - _timestamp: SystemTime::now(), - }; - - println!("๐ŸŽฏ **Deployment Planning Request:**"); - println!(" โ€ข Application: {}", request.system_name); - println!(" โ€ข Strategy: Blue-Green with Canary Testing"); - println!(" โ€ข Rollback Capability: Automated with 5-minute threshold"); - println!(" โ€ข Testing: Comprehensive with chaos engineering"); - - let result = simulate_deployment_assessment(&request); - - println!("\n๐Ÿš€ **Deployment Assessment Result:**"); - println!(" โ€ข Deployment Success: {}", result.success); - println!(" โ€ข Deployment Confidence: {:.1}%", result.deployment_confidence * 100.0); - println!(" โ€ข Rollback Readiness: {:.1}%", result.incident_response_score * 100.0); - println!(" โ€ข Testing Coverage: {:.1}%", result.monitoring_coverage * 100.0); - println!(" โ€ข Deployment Time: {:?}", result.deployment_time_estimate); - - println!("\n๐Ÿ“‹ **Deployment Strategy Breakdown:**"); - println!(" โ€ข Phase 1: Blue-Green Infrastructure Setup (30 minutes)"); - println!(" โ€ข Phase 2: Application Deployment to Green Environment (45 minutes)"); - println!(" โ€ข Phase 3: Automated Testing Suite Execution (60 minutes)"); - println!(" โ€ข Phase 4: Canary Release to 5% of Traffic (15 minutes)"); - println!(" โ€ข Phase 5: Gradual Traffic Migration (5% โ†’ 25% โ†’ 50% โ†’ 100%)"); - println!(" โ€ข Phase 6: Blue Environment Cleanup (15 minutes)"); - - println!("\n๐Ÿงช **Comprehensive Testing Strategy:**"); - println!(" โ€ข Unit Tests: 2,847 tests with 97.3% code coverage"); - println!(" โ€ข Integration Tests: 456 API endpoint tests"); - println!(" โ€ข Load Tests: Simulating 150% of expected peak traffic"); - println!(" โ€ข Security Tests: OWASP ZAP automated scanning"); - println!(" โ€ข Chaos Engineering: Netflix Chaos Monkey integration"); - println!(" โ€ข Performance Tests: Sub-200ms response time validation"); - - println!("\n๐Ÿ”„ **CI/CD Pipeline Integration:**"); - println!(" โ€ข Source Control: GitHub with branch protection rules"); - println!(" โ€ข Build System: GitHub Actions with parallel job execution"); - println!(" โ€ข Artifact Storage: AWS ECR for container images"); - println!(" โ€ข Security Scanning: Snyk for vulnerability detection"); - println!(" โ€ข Quality Gates: SonarQube with 90% quality threshold"); - println!(" โ€ข Deployment Automation: ArgoCD for GitOps workflow"); - - println!("\n๐Ÿ’ก **Deployment Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_deployment_assessment(_request: &OperationsPlanningRequest) -> OperationsAssessmentResult { - OperationsAssessmentResult { - success: true, - infrastructure_score: 8.9, - deployment_confidence: 0.96, - scaling_readiness: 0.88, - monitoring_coverage: 0.94, - incident_response_score: 0.92, - cost_efficiency: 0.85, - estimated_monthly_cost: 7200.0, - deployment_time_estimate: std::time::Duration::from_secs(3 * 3600), // 3 hours - recommendations: vec![ - "Implement feature flags for safer progressive rollouts".to_string(), - "Add synthetic transaction monitoring for early issue detection".to_string(), - "Use database migration strategies for zero-downtime updates".to_string(), - "Implement automated rollback triggers based on error rates".to_string(), - "Add performance regression testing to deployment pipeline".to_string(), - "Establish deployment approval workflows for production".to_string(), - ], - } -} - -fn demonstrate_monitoring_agent() { - println!("\n๐Ÿ“Š **Monitoring Agent Integration - Observability Stack**"); - println!("========================================================"); - - let request = OperationsPlanningRequest { - _agent_type: OperationsAgentType::MonitoringAgent, - _planning_type: OperationsPlanningType::MonitoringSetup, - system_name: "Full-Stack Observability Platform".to_string(), - _urgency_level: UrgencyLevel::High, - current_load: LoadInfo { - concurrent_users: 100000, - requests_per_second: 5000.0, - _data_volume_gb: 1000.0, - _peak_multiplier: 4.0, - }, - budget_constraints: BudgetInfo { - monthly_budget: 5000.0, - _cost_optimization_priority: "Observability".to_string(), - }, - _timestamp: SystemTime::now(), - }; - - println!("๐ŸŽฏ **Monitoring Setup Request:**"); - println!(" โ€ข Platform: {}", request.system_name); - println!(" โ€ข Scope: Metrics, Logs, Traces, and Business KPIs"); - println!(" โ€ข Retention: 90 days for metrics, 30 days for logs"); - println!(" โ€ข Real-time alerting with intelligent noise reduction"); - - let result = simulate_monitoring_assessment(&request); - - println!("\n๐Ÿ“Š **Monitoring Assessment Result:**"); - println!(" โ€ข Monitoring Success: {}", result.success); - println!(" โ€ข Coverage Score: {:.1}%", result.monitoring_coverage * 100.0); - println!(" โ€ข Alerting Accuracy: {:.1}%", result.incident_response_score * 100.0); - println!(" โ€ข Dashboard Quality: {:.1}%", result.infrastructure_score * 10.0); - println!(" โ€ข Data Pipeline Health: {:.1}%", result.scaling_readiness * 100.0); - - println!("\n๐Ÿ” **Comprehensive Observability Stack:**"); - println!(" โ€ข Metrics Platform: Prometheus + Grafana with 15-second resolution"); - println!(" โ€ข Logging Platform: ELK Stack (Elasticsearch + Logstash + Kibana)"); - println!(" โ€ข Tracing Platform: Jaeger with OpenTelemetry instrumentation"); - println!(" โ€ข APM Platform: DataDog for application performance monitoring"); - println!(" โ€ข Infrastructure Monitoring: New Relic for server and cloud metrics"); - println!(" โ€ข Synthetic Monitoring: Pingdom for uptime and user experience"); - - println!("\n๐Ÿ“ˆ **Key Performance Indicators (KPIs):**"); - println!(" โ€ข Golden Signals: Latency, Traffic, Errors, Saturation"); - println!(" โ€ข SLI Tracking: 99.9% availability, <200ms response time"); - println!(" โ€ข Business Metrics: Conversion rate, revenue per user"); - println!(" โ€ข Infrastructure Metrics: CPU, memory, disk, network utilization"); - println!(" โ€ข Security Metrics: Failed login attempts, anomalous access patterns"); - println!(" โ€ข Cost Metrics: Resource utilization efficiency, cost per transaction"); - - println!("\n๐Ÿšจ **Intelligent Alerting System:**"); - println!(" โ€ข Alert Fatigue Reduction: ML-based alert correlation and grouping"); - println!(" โ€ข Escalation Policies: Tiered on-call with auto-escalation"); - println!(" โ€ข Notification Channels: Slack, PagerDuty, Email, SMS"); - println!(" โ€ข Alert Suppression: Maintenance windows and dependency-aware routing"); - println!(" โ€ข Anomaly Detection: Machine learning for proactive issue identification"); - println!(" โ€ข Alert Analytics: MTTD 2.5 minutes, MTTR 12 minutes average"); - - println!("\n๐Ÿ“‹ **Dashboard Configuration:**"); - println!(" โ€ข Executive Dashboard: High-level business and system health"); - println!(" โ€ข Operations Dashboard: Real-time system performance and alerts"); - println!(" โ€ข Troubleshooting Dashboard: Detailed metrics for incident response"); - println!(" โ€ข Capacity Dashboard: Resource utilization and growth trends"); - println!(" โ€ข Security Dashboard: Threat detection and compliance status"); - println!(" โ€ข Cost Dashboard: Resource costs and optimization opportunities"); - - println!("\n๐Ÿ’ก **Monitoring Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_monitoring_assessment(_request: &OperationsPlanningRequest) -> OperationsAssessmentResult { - OperationsAssessmentResult { - success: true, - infrastructure_score: 9.2, - deployment_confidence: 0.91, - scaling_readiness: 0.94, - monitoring_coverage: 0.97, - incident_response_score: 0.93, - cost_efficiency: 0.88, - estimated_monthly_cost: 4200.0, - deployment_time_estimate: std::time::Duration::from_secs(4 * 3600), // 4 hours - recommendations: vec![ - "Implement distributed tracing for microservices visibility".to_string(), - "Add custom business metrics for revenue and user engagement".to_string(), - "Deploy log aggregation with structured logging standards".to_string(), - "Implement proactive anomaly detection with ML algorithms".to_string(), - "Add real-user monitoring (RUM) for frontend performance".to_string(), - "Establish SLO-based alerting to reduce noise and improve focus".to_string(), - ], - } -} - -fn demonstrate_resource_optimization() { - println!("\nโš™๏ธ **Resource Optimization Planning**"); - println!("====================================="); - - println!("๐ŸŽฏ **Optimization Strategies:**"); - println!(" โ€ข Right-sizing: Continuous analysis of resource utilization patterns"); - println!(" โ€ข Reserved Instances: 70% of stable workloads with 1-3 year commitments"); - println!(" โ€ข Spot Instances: 25% of fault-tolerant workloads for cost savings"); - println!(" โ€ข Auto-scaling: Dynamic scaling based on traffic patterns and demand"); - - println!("\n๐Ÿ’ฐ **Cost Optimization Results:**"); - println!(" โ€ข Total Monthly Savings: $4,250 (28% reduction from baseline)"); - println!(" โ€ข Reserved Instance Savings: $2,800 (40% discount on compute)"); - println!(" โ€ข Spot Instance Savings: $1,200 (60% discount on dev/test workloads)"); - println!(" โ€ข Right-sizing Savings: $250 (Eliminated over-provisioned resources)"); - - println!("\n๐Ÿ“Š **Resource Utilization Analysis:**"); - println!(" โ€ข CPU Utilization Target: 70% (Currently: 68% - Optimal)"); - println!(" โ€ข Memory Utilization Target: 75% (Currently: 72% - Good)"); - println!(" โ€ข Storage Utilization Target: 80% (Currently: 65% - Room for optimization)"); - println!(" โ€ข Network Utilization Target: 60% (Currently: 45% - Under-utilized)"); - - println!("\n๐Ÿ”„ **Auto-Scaling Configuration:**"); - println!(" โ€ข Scale-Out Trigger: CPU > 70% for 5 minutes"); - println!(" โ€ข Scale-In Trigger: CPU < 30% for 10 minutes"); - println!(" โ€ข Min Instances: 3 (High availability requirement)"); - println!(" โ€ข Max Instances: 15 (Budget and performance balance)"); - println!(" โ€ข Cooldown Periods: 5 min scale-out, 10 min scale-in"); - - println!("\n๐Ÿ“ˆ **Predictive Scaling Analysis:**"); - println!(" โ€ข Traffic Pattern Recognition: Daily peaks at 2PM-6PM EST"); - println!(" โ€ข Seasonal Adjustments: 40% increase during holiday seasons"); - println!(" โ€ข Pre-scaling Buffer: 15 minutes before predicted traffic spikes"); - println!(" โ€ข Machine Learning Model: 94% accuracy in traffic prediction"); -} - -fn demonstrate_scaling_strategy() { - println!("\n๐Ÿ“ˆ **Advanced Scaling Strategy Planning**"); - println!("========================================="); - - println!("๐ŸŽฏ **Multi-Dimensional Scaling Approach:**"); - println!(" โ€ข Horizontal Scaling: Add/remove instances based on demand"); - println!(" โ€ข Vertical Scaling: Upgrade instance types for CPU/memory intensive tasks"); - println!(" โ€ข Geographical Scaling: Deploy to new regions based on user location"); - println!(" โ€ข Service Scaling: Independent scaling of microservices components"); - - println!("\nโšก **Real-Time Scaling Metrics:**"); - println!(" โ€ข Current Scale: 8 instances across 3 availability zones"); - println!(" โ€ข Average Response Time: 145ms (Target: <200ms)"); - println!(" โ€ข Request Queue Depth: 12 requests (Target: <50)"); - println!(" โ€ข Error Rate: 0.08% (Target: <0.1%)"); - println!(" โ€ข Scaling Events: 23 scale-outs, 15 scale-ins in past 30 days"); - - println!("\n๐ŸŒ **Global Auto-Scaling Strategy:**"); - println!(" โ€ข Primary Region (us-east-1): 60% capacity, serves Americas"); - println!(" โ€ข Secondary Region (eu-west-1): 25% capacity, serves Europe/Africa"); - println!(" โ€ข Tertiary Region (ap-southeast-1): 15% capacity, serves Asia-Pacific"); - println!(" โ€ข Disaster Recovery: Cross-region failover within 3 minutes"); - - println!("\n๐Ÿค– **Machine Learning-Powered Scaling:**"); - println!(" โ€ข Predictive Model: LSTM neural network with 96% accuracy"); - println!(" โ€ข Training Data: 2 years of historical traffic and performance data"); - println!(" โ€ข Feature Engineering: Time, seasonality, external events, user behavior"); - println!(" โ€ข Model Update Frequency: Weekly retraining with recent data"); - println!(" โ€ข A/B Testing: Continuous optimization of scaling parameters"); - - println!("\n๐Ÿ“Š **Scaling Performance Metrics:**"); - println!(" โ€ข Scale-Out Time: Average 2.3 minutes (Target: <3 minutes)"); - println!(" โ€ข Scale-In Safety: 10-minute observation period prevents thrashing"); - println!(" โ€ข Cost Efficiency: 23% reduction in compute costs through intelligent scaling"); - println!(" โ€ข Availability Impact: 99.97% uptime maintained during scaling events"); -} - -fn demonstrate_incident_response_planning() { - println!("\n๐Ÿšจ **Incident Response Planning & Automation**"); - println!("=============================================="); - - println!("๐ŸŽฏ **Incident Classification System:**"); - println!(" โ€ข P1 - Critical: Complete service outage, <5 min response, <1 hour resolution"); - println!(" โ€ข P2 - High: Significant degradation, <15 min response, <4 hour resolution"); - println!(" โ€ข P3 - Medium: Minor issues, <1 hour response, <24 hour resolution"); - println!(" โ€ข P4 - Low: Cosmetic/enhancement, <24 hour response, <1 week resolution"); - - println!("\nโšก **Automated Response Procedures:**"); - println!(" โ€ข Detection: AI-powered anomaly detection with 99.2% accuracy"); - println!(" โ€ข Classification: Automated severity assessment based on impact"); - println!(" โ€ข Notification: Intelligent routing to appropriate on-call engineers"); - println!(" โ€ข Initial Response: Automated diagnostics and preliminary remediation"); - println!(" โ€ข Escalation: Auto-escalation if not acknowledged within SLA"); - - println!("\n๐Ÿ”ง **Self-Healing Capabilities:**"); - println!(" โ€ข Service Restart: Automatic restart of failed services (85% success rate)"); - println!(" โ€ข Traffic Rerouting: Immediate failover to healthy instances"); - println!(" โ€ข Database Failover: Automatic promotion of read replicas"); - println!(" โ€ข Cache Rebuilding: Automatic cache warming after failures"); - println!(" โ€ข Resource Scaling: Emergency scaling during capacity incidents"); - - println!("\n๐Ÿ“ฑ **Communication & Coordination:**"); - println!(" โ€ข War Room: Instant Slack channel creation with relevant stakeholders"); - println!(" โ€ข Status Page: Automated updates to customer-facing status page"); - println!(" โ€ข Stakeholder Alerts: Tiered notifications based on incident severity"); - println!(" โ€ข Executive Updates: Automated briefings for P1/P2 incidents"); - println!(" โ€ข Customer Communication: Proactive customer notifications"); - - println!("\n๐Ÿ“‹ **Post-Incident Analysis:**"); - println!(" โ€ข Timeline Recreation: Automated incident timeline from logs and metrics"); - println!(" โ€ข Root Cause Analysis: Guided investigation with ML-assisted insights"); - println!(" โ€ข Impact Assessment: Automated calculation of user and business impact"); - println!(" โ€ข Action Items: Generated improvement tasks with priority scoring"); - println!(" โ€ข Knowledge Base: Automatic documentation updates and runbook creation"); - - println!("\n๐Ÿ“Š **Incident Response Metrics:**"); - println!(" โ€ข Mean Time to Detection (MTTD): 1.8 minutes (Industry: 197 days)"); - println!(" โ€ข Mean Time to Response (MTTR): 8.5 minutes (Target: <15 minutes)"); - println!(" โ€ข Mean Time to Resolution: 23 minutes (Target: <60 minutes for P1)"); - println!(" โ€ข False Positive Rate: 3.2% (Continuous ML model improvement)"); - println!(" โ€ข Automated Resolution Rate: 68% (Self-healing without human intervention)"); -} - -fn demonstrate_alerting_configuration() { - println!("\n๐Ÿ”” **Intelligent Alerting Strategy**"); - println!("===================================="); - - println!("๐ŸŽฏ **Smart Alert Management:**"); - println!(" โ€ข Alert Correlation: ML grouping of related alerts to reduce noise"); - println!(" โ€ข Dynamic Thresholds: Adaptive thresholds based on historical patterns"); - println!(" โ€ข Alert Fatigue Prevention: 67% reduction in alert volume through optimization"); - println!(" โ€ข Context-Aware Routing: Alerts routed based on expertise and availability"); - - println!("\n๐Ÿ“Š **Multi-Level Alert Configuration:**"); - println!(" โ€ข Warning Level: 15% deviation from baseline (Logged, no notification)"); - println!(" โ€ข Minor Alert: 25% deviation (Slack notification, 15-min response)"); - println!(" โ€ข Major Alert: 50% deviation (PagerDuty, 5-min response)"); - println!(" โ€ข Critical Alert: Service degradation (Phone call, immediate response)"); - - println!("\n๐Ÿค– **Machine Learning-Enhanced Alerting:**"); - println!(" โ€ข Anomaly Detection: Unsupervised ML for pattern deviation identification"); - println!(" โ€ข Seasonal Adjustments: Automatic threshold adjustment for known patterns"); - println!(" โ€ข Predictive Alerts: Early warning system for potential issues"); - println!(" โ€ข Alert Quality Scoring: Continuous feedback loop for alert relevance"); - - println!("\n๐Ÿ“ฑ **Multi-Channel Notification System:**"); - println!(" โ€ข Slack Integration: Real-time team notifications with context"); - println!(" โ€ข PagerDuty Integration: Escalation policies and on-call management"); - println!(" โ€ข Email Notifications: Detailed incident reports and summaries"); - println!(" โ€ข SMS Alerts: Critical incident notifications for immediate response"); - println!(" โ€ข Mobile App: Custom alerting app with push notifications"); - - println!("\nโฐ **On-Call Management:**"); - println!(" โ€ข Rotation Schedule: Automated weekly rotation with handoff procedures"); - println!(" โ€ข Backup Coverage: Secondary on-call engineer for every shift"); - println!(" โ€ข Time Zone Optimization: Follow-the-sun support model"); - println!(" โ€ข Escalation Matrix: Clear escalation paths for all incident types"); - println!(" โ€ข On-Call Analytics: Workload balancing and burnout prevention"); - - println!("\n๐Ÿ“ˆ **Alerting Performance Metrics:**"); - println!(" โ€ข Alert Precision: 94.2% (Percentage of actionable alerts)"); - println!(" โ€ข Alert Recall: 99.8% (Percentage of real issues detected)"); - println!(" โ€ข Acknowledgment Time: 2.1 minutes average"); - println!(" โ€ข Resolution Correlation: 89% of alerts lead to successful resolution"); - println!(" โ€ข Engineer Satisfaction: 8.7/10 (Alert quality and relevance rating)"); -} - -fn demonstrate_capacity_planning() { - println!("\n๐Ÿ“Š **Advanced Capacity Planning & Forecasting**"); - println!("==============================================="); - - println!("๐ŸŽฏ **Growth Trajectory Analysis:**"); - println!(" โ€ข User Growth Rate: 15% monthly compound growth"); - println!(" โ€ข Traffic Growth: 18% monthly increase in requests per second"); - println!(" โ€ข Data Volume Growth: 22% monthly storage requirement increase"); - println!(" โ€ข Geographic Expansion: New regions planned for Q2 and Q4"); - - println!("\n๐Ÿ“ˆ **12-Month Capacity Forecast:**"); - println!(" โ€ข Current Capacity: 5,000 RPS, 50,000 concurrent users"); - println!(" โ€ข 6-Month Projection: 8,500 RPS, 85,000 concurrent users"); - println!(" โ€ข 12-Month Projection: 15,000 RPS, 150,000 concurrent users"); - println!(" โ€ข Peak Load Handling: 3x capacity buffer for traffic spikes"); - - println!("\n๐Ÿ’พ **Resource Requirement Projections:**"); - println!(" โ€ข Compute: Current 96 cores โ†’ 6M: 163 cores โ†’ 12M: 288 cores"); - println!(" โ€ข Memory: Current 384GB โ†’ 6M: 650GB โ†’ 12M: 1.15TB"); - println!(" โ€ข Storage: Current 2TB โ†’ 6M: 4.8TB โ†’ 12M: 11.7TB"); - println!(" โ€ข Network: Current 25Gbps โ†’ 6M: 42Gbps โ†’ 12M: 75Gbps"); - - println!("\n๐Ÿ—๏ธ **Infrastructure Scaling Timeline:**"); - println!(" โ€ข Q1 2025: Add 2 additional availability zones"); - println!(" โ€ข Q2 2025: Deploy European region (eu-central-1)"); - println!(" โ€ข Q3 2025: Implement multi-cloud strategy (Azure backup)"); - println!(" โ€ข Q4 2025: Deploy Asia-Pacific region (ap-southeast-2)"); - - println!("\n๐Ÿ’ฐ **Capacity Cost Analysis:**"); - println!(" โ€ข Current Monthly Cost: $12,750"); - println!(" โ€ข 6-Month Projected Cost: $21,600 (69% increase)"); - println!(" โ€ข 12-Month Projected Cost: $38,250 (200% increase)"); - println!(" โ€ข Cost Optimization Savings: $8,400/month through reserved instances"); - - println!("\nโš ๏ธ **Bottleneck Identification:**"); - println!(" โ€ข Database Connections: Will hit limit at 120,000 concurrent users"); - println!(" โ€ข Network Bandwidth: Regional bandwidth limits at 60Gbps"); - println!(" โ€ข Cache Performance: Redis memory limits at 180GB data set"); - println!(" โ€ข CDN Capacity: Edge location limits during global events"); - - println!("\n๐Ÿ”ฎ **Predictive Scaling Recommendations:**"); - println!(" โ€ข Implement database connection pooling and read replicas"); - println!(" โ€ข Upgrade network infrastructure to 100Gbps backbone"); - println!(" โ€ข Deploy Redis Cluster for horizontal cache scaling"); - println!(" โ€ข Negotiate CDN capacity expansion agreements"); -} - -fn demonstrate_cost_optimization() { - println!("\n๐Ÿ’ฐ **Cost Optimization & Financial Planning**"); - println!("=============================================="); - - println!("๐ŸŽฏ **Cost Optimization Strategy:**"); - println!(" โ€ข Total Monthly Savings Target: $5,200 (34% reduction)"); - println!(" โ€ข Reserved Instance Strategy: 70% of stable workloads"); - println!(" โ€ข Spot Instance Usage: 25% of fault-tolerant workloads"); - println!(" โ€ข Right-sizing Initiative: Continuous resource optimization"); - - println!("\n๐Ÿ“Š **Cost Breakdown Analysis:**"); - println!(" โ€ข Compute Costs: $8,500/month (56% of total infrastructure)"); - println!(" โ€ข Storage Costs: $2,200/month (14% of total infrastructure)"); - println!(" โ€ข Network Costs: $1,800/month (12% of total infrastructure)"); - println!(" โ€ข Monitoring/Security: $1,750/month (11% of total infrastructure)"); - println!(" โ€ข Support & Operations: $1,000/month (7% of total infrastructure)"); - - println!("\n๐Ÿ’ก **Savings Opportunities:**"); - println!(" โ€ข Reserved Instances: $3,400/month savings (40% discount on compute)"); - println!(" โ€ข Spot Instances: $1,200/month savings (60% discount on dev/test)"); - println!(" โ€ข Storage Optimization: $350/month savings (lifecycle policies)"); - println!(" โ€ข Network Optimization: $250/month savings (traffic routing)"); - - println!("\n๐Ÿท๏ธ **Cost Allocation & Tracking:**"); - println!(" โ€ข Environment Tags: Production (70%), Staging (20%), Development (10%)"); - println!(" โ€ข Service Tags: API (40%), Database (25%), Frontend (20%), Other (15%)"); - println!(" โ€ข Team Tags: Backend (45%), Frontend (30%), DevOps (15%), QA (10%)"); - println!(" โ€ข Cost Anomaly Detection: 95% accuracy in identifying cost spikes"); - - println!("\n๐Ÿ“ˆ **ROI Analysis:**"); - println!(" โ€ข Infrastructure Investment: $180,000 annually"); - println!(" โ€ข Optimization Savings: $62,400 annually"); - println!(" โ€ข Performance Improvements: $45,000 value (reduced downtime)"); - println!(" โ€ข Developer Productivity: $35,000 value (faster deployments)"); - println!(" โ€ข Total ROI: 79% annually on infrastructure investments"); - - println!("\n๐Ÿšจ **Budget Alerts & Controls:**"); - println!(" โ€ข Monthly Budget: $15,000 with 10% variance tolerance"); - println!(" โ€ข Alert Thresholds: 70% (Warning), 85% (Action), 95% (Critical)"); - println!(" โ€ข Automated Controls: Spot instance termination at 100% budget"); - println!(" โ€ข Approval Workflows: Manager approval for >$500 monthly increases"); -} - -fn demonstrate_disaster_recovery() { - println!("\n๐Ÿ›ก๏ธ **Disaster Recovery & Business Continuity**"); - println!("==============================================="); - - println!("๐ŸŽฏ **Disaster Recovery Objectives:**"); - println!(" โ€ข Recovery Time Objective (RTO): 15 minutes for critical services"); - println!(" โ€ข Recovery Point Objective (RPO): 5 minutes maximum data loss"); - println!(" โ€ข Availability Target: 99.99% uptime (52.6 minutes downtime/year)"); - println!(" โ€ข Data Integrity: Zero tolerance for data corruption or loss"); - - println!("\n๐ŸŒ **Multi-Region DR Strategy:**"); - println!(" โ€ข Primary Region: us-east-1 (100% active traffic)"); - println!(" โ€ข DR Region: us-west-2 (Hot standby with real-time replication)"); - println!(" โ€ข Backup Region: eu-west-1 (Cold standby for regulatory compliance)"); - println!(" โ€ข Failover Capability: Automated within 3 minutes"); - - println!("\n๐Ÿ’พ **Data Protection & Backup:**"); - println!(" โ€ข Database Backups: Continuous backup with 5-minute point-in-time recovery"); - println!(" โ€ข File Storage Backups: Daily incremental, weekly full backups"); - println!(" โ€ข Cross-Region Replication: Real-time data synchronization"); - println!(" โ€ข Backup Testing: Monthly restore tests with success validation"); - - println!("\n๐Ÿ”„ **Automated Failover Process:**"); - println!(" โ€ข Health Check Monitoring: 30-second intervals with 3-failure threshold"); - println!(" โ€ข DNS Failover: Route 53 health checks with 60-second TTL"); - println!(" โ€ข Database Failover: Automatic promotion of read replicas"); - println!(" โ€ข Application Failover: Load balancer health checks and routing"); - - println!("\n๐Ÿงช **Disaster Recovery Testing:**"); - println!(" โ€ข Monthly DR Drills: Complete failover testing with timing"); - println!(" โ€ข Quarterly Business Continuity: Full simulation with all teams"); - println!(" โ€ข Annual DR Audit: Third-party validation and compliance review"); - println!(" โ€ข Chaos Engineering: Regular failure injection testing"); - - println!("\n๐Ÿ“Š **DR Performance Metrics:**"); - println!(" โ€ข Last Failover Test: 12 minutes RTO, 3 minutes RPO achieved"); - println!(" โ€ข Data Synchronization Lag: Average 2.3 seconds"); - println!(" โ€ข Backup Success Rate: 99.97% (Failed backups < 0.03%)"); - println!(" โ€ข Recovery Success Rate: 100% (All tests successful in past year)"); -} - -fn demonstrate_operations_orchestration() { - println!("\n๐ŸŽผ **Operations Orchestration & Automation**"); - println!("============================================"); - - println!("๐ŸŽฏ **End-to-End Automation Framework:**"); - println!(" โ€ข Infrastructure as Code: 100% infrastructure defined in Terraform"); - println!(" โ€ข Configuration Management: Ansible playbooks for all configurations"); - println!(" โ€ข Deployment Automation: GitOps workflow with ArgoCD"); - println!(" โ€ข Monitoring Automation: Self-configuring monitoring for all services"); - - println!("\n๐Ÿ”„ **Workflow Orchestration:**"); - println!(" โ€ข CI/CD Pipelines: 47 automated pipelines across all microservices"); - println!(" โ€ข Incident Response: 89% of incidents handled without human intervention"); - println!(" โ€ข Capacity Management: Automated scaling based on predictive models"); - println!(" โ€ข Cost Optimization: Daily cost analysis and optimization recommendations"); - - println!("\n๐Ÿค– **Intelligent Operations:**"); - println!(" โ€ข AIOps Platform: ML-driven operations with predictive capabilities"); - println!(" โ€ข Automated Remediation: 78% of known issues self-resolve"); - println!(" โ€ข Predictive Maintenance: Proactive replacement before failures"); - println!(" โ€ข Optimization Engine: Continuous performance and cost optimization"); - - println!("\n๐Ÿ“Š **Operations Excellence Metrics:**"); - println!(" โ€ข Automation Coverage: 94% of operational tasks automated"); - println!(" โ€ข Manual Intervention Rate: 6% (Target: <5%)"); - println!(" โ€ข Operations Team Efficiency: 340% improvement over manual processes"); - println!(" โ€ข Time to Production: 45 minutes from commit to production deployment"); - - println!("\n๐ŸŽฏ **Operations Excellence Summary:**"); - println!(" โ€ข Overall Operations Score: 9.1/10 (Excellent)"); - println!(" โ€ข Infrastructure Reliability: 99.97% uptime achieved"); - println!(" โ€ข Cost Efficiency: 34% reduction through optimization"); - println!(" โ€ข Team Productivity: 280% improvement in deployment frequency"); - println!(" โ€ข Incident Response: 68% of incidents resolve automatically"); - println!(" โ€ข Customer Satisfaction: 96% availability SLA compliance"); -} - - \ No newline at end of file diff --git a/task_8_4_intelligence_agents_demo.rs b/task_8_4_intelligence_agents_demo.rs deleted file mode 100644 index d2f3255895da73f8be8d07bb507150feab0e73f1..0000000000000000000000000000000000000000 --- a/task_8_4_intelligence_agents_demo.rs +++ /dev/null @@ -1,807 +0,0 @@ -use std::time::Duration; - -// Simulate the structures since we're running a standalone demo -#[derive(Debug, Clone)] -struct IntelligenceAgentsIntegration { - config: IntelligenceIntegrationConfig, -} - -#[derive(Debug, Clone)] -struct IntelligenceIntegrationConfig { - mlops_agent_enabled: bool, - model_training_agent_enabled: bool, - experimentation_enabled: bool, - data_pipeline_optimization_enabled: bool, - user_behavior_analysis_enabled: bool, - feature_experimentation_enabled: bool, - data_ingestion_enabled: bool, - automated_retraining_enabled: bool, - a_b_testing_enabled: bool, -} - -#[derive(Debug, Clone)] -enum IntelligenceAgentType { - MLOpsAgent, - ModelTrainingAgent, - DataIngestionAgent, - ExperimentationAgent, - UserBehaviorAgent, - FeatureEngineeringAgent, -} - -#[derive(Debug, Clone)] -enum IntelligencePlanningType { - ModelLifecycleManagement, - HyperparameterOptimization, - DataPipelineOptimization, - ExperimentDesign, - ABTestingStrategy, - UserBehaviorAnalysis, - FeatureEngineering, - ModelMonitoring, - DataIngestionPlanning, - ModelDeployment, -} - -#[derive(Debug, Clone)] -enum ModelType { - Classification, - Regression, - Clustering, - RecommendationSystem, - NaturalLanguageProcessing, - ComputerVision, - TimeSeriesForecasting, - ReinforcementLearning, - GenerativeAI, - MultiModal, -} - -#[derive(Debug, Clone)] -enum PriorityLevel { - Low, - Medium, - High, - Critical, - Urgent, -} - -#[derive(Debug, Clone)] -enum ExperimentType { - ABTest, - MultiVariateTest, - BanditTest, - SplitTest, - CrossoverTest, - FactorialDesign, -} - -#[derive(Debug, Clone)] -struct IntelligencePlanningRequest { - agent_type: IntelligenceAgentType, - planning_type: IntelligencePlanningType, - project_name: String, - model_type: ModelType, - priority_level: PriorityLevel, - data_size_gb: f64, - target_metrics: Vec, - business_objectives: Vec, - resource_constraints: ResourceInfo, -} - -#[derive(Debug, Clone)] -struct ResourceInfo { - max_training_time_hours: u32, - max_compute_cost: f64, - max_memory_gb: u32, - max_gpu_hours: u32, - preferred_frameworks: Vec, -} - -#[derive(Debug, Clone)] -struct IntelligenceAssessmentResult { - success: bool, - model_performance_score: f64, - experiment_confidence: f64, - data_quality_score: f64, - pipeline_efficiency: f64, - feature_importance_score: f64, - deployment_readiness: f64, - recommendations: Vec, - estimated_accuracy_improvement: f64, - training_time_estimate: Duration, - experiment_duration: Duration, -} - -fn main() { - println!("๐Ÿง  Brain AI Task 8.4: Intelligence Agents Integration - Full Demo"); - println!("================================================================="); - - demonstrate_intelligence_agents_architecture(); - demonstrate_mlops_agent(); - demonstrate_model_training_agent(); - demonstrate_experimentation_agent(); - demonstrate_data_pipeline_optimization(); - demonstrate_user_behavior_analysis(); - demonstrate_feature_engineering(); - demonstrate_ab_testing_strategy(); - demonstrate_model_monitoring(); - demonstrate_automated_retraining(); - demonstrate_data_ingestion_planning(); - demonstrate_intelligence_orchestration(); - - println!("\n๐ŸŽฏ **TASK 8.4: INTELLIGENCE AGENTS INTEGRATION - COMPLETED!**"); - println!("โœ… All intelligence agent integrations successfully implemented!"); - println!("๐Ÿค– Brain AI now provides comprehensive AI/ML intelligence capabilities!"); -} - -fn demonstrate_intelligence_agents_architecture() { - println!("\n๐Ÿค– **Intelligence Agents Integration Architecture**"); - println!("==================================================="); - - println!("๐Ÿ“Š **Core Intelligence Agent Integrations:**"); - println!(" โ€ข MLOpsAgent: Model lifecycle management and deployment automation"); - println!(" โ€ข ModelTrainingAgent: Advanced training optimization and hyperparameter tuning"); - println!(" โ€ข ExperimentationAgent: A/B testing and experimental design planning"); - println!(" โ€ข DataIngestionAgent: Real-time and batch data pipeline optimization"); - println!(" โ€ข UserBehaviorAgent: User behavior analysis and personalization"); - println!(" โ€ข FeatureEngineeringAgent: Automated feature engineering and selection"); - - println!("\n๐Ÿ”„ **Intelligence Planning Flow:**"); - println!(" 1. Data ingestion and validation โ†’ 2. Feature engineering and selection"); - println!(" 3. Model training and optimization โ†’ 4. Experiment design and execution"); - println!(" 5. Model deployment and monitoring โ†’ 6. User behavior analysis"); - println!(" 7. Performance optimization โ†’ 8. Automated retraining"); - - println!("\nโšก **Key Intelligence Features:**"); - println!(" โ€ข MLOps pipeline automation with model versioning and governance"); - println!(" โ€ข Advanced hyperparameter optimization with distributed training"); - println!(" โ€ข Comprehensive A/B testing with statistical significance validation"); - println!(" โ€ข Real-time data pipeline optimization with quality monitoring"); - println!(" โ€ข User behavior analysis with personalization and recommendation"); - println!(" โ€ข Automated feature engineering with drift detection and monitoring"); -} - -fn demonstrate_mlops_agent() { - println!("\n๐Ÿ”„ **MLOps Agent Integration - Model Lifecycle Management**"); - println!("========================================================="); - - let request = IntelligencePlanningRequest { - agent_type: IntelligenceAgentType::MLOpsAgent, - planning_type: IntelligencePlanningType::ModelLifecycleManagement, - project_name: "E-commerce Recommendation System".to_string(), - model_type: ModelType::RecommendationSystem, - priority_level: PriorityLevel::High, - data_size_gb: 500.0, - target_metrics: vec![ - "Precision@10".to_string(), - "Recall@10".to_string(), - "NDCG@10".to_string(), - "Click-through rate".to_string(), - ], - business_objectives: vec![ - "Increase user engagement by 15%".to_string(), - "Improve conversion rate by 8%".to_string(), - "Reduce recommendation latency to <50ms".to_string(), - ], - resource_constraints: ResourceInfo { - max_training_time_hours: 48, - max_compute_cost: 5000.0, - max_memory_gb: 128, - max_gpu_hours: 100, - preferred_frameworks: vec![ - "TensorFlow".to_string(), - "PyTorch".to_string(), - "MLflow".to_string(), - ], - }, - }; - - println!("๐ŸŽฏ **MLOps Planning Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Model Type: {:?}", request.model_type); - println!(" โ€ข Data Size: {:.1} GB", request.data_size_gb); - println!(" โ€ข Target Metrics: {:?}", request.target_metrics); - println!(" โ€ข Business Objectives: {:?}", request.business_objectives); - - let result = simulate_mlops_assessment(&request); - - println!("\n๐Ÿ”„ **MLOps Assessment Result:**"); - println!(" โ€ข Assessment Success: {}", result.success); - println!(" โ€ข Model Performance Score: {:.1}/10.0", result.model_performance_score); - println!(" โ€ข Pipeline Efficiency: {:.1}%", result.pipeline_efficiency * 100.0); - println!(" โ€ข Deployment Readiness: {:.1}%", result.deployment_readiness * 100.0); - println!(" โ€ข Expected Accuracy Improvement: {:.1}%", result.estimated_accuracy_improvement * 100.0); - println!(" โ€ข Training Time Estimate: {:?}", result.training_time_estimate); - - println!("\n๐Ÿ—๏ธ **MLOps Pipeline Architecture:**"); - println!(" โ€ข Data Ingestion: Real-time streaming with Kafka + batch processing"); - println!(" โ€ข Feature Store: Feast for feature serving with Redis cache"); - println!(" โ€ข Model Training: Distributed training with Horovod + TensorFlow"); - println!(" โ€ข Model Registry: MLflow with automated versioning and tagging"); - println!(" โ€ข Deployment: Kubernetes with Seldon Core for model serving"); - println!(" โ€ข Monitoring: Prometheus + Grafana with custom ML metrics"); - - println!("\n๐Ÿ“Š **Model Versioning & Governance:**"); - println!(" โ€ข Version Control: Git-based with DVC for data and model tracking"); - println!(" โ€ข Model Registry: Centralized with metadata, lineage, and approval workflow"); - println!(" โ€ข A/B Testing: Automated canary deployments with traffic splitting"); - println!(" โ€ข Model Approval: Staging โ†’ validation โ†’ production workflow"); - println!(" โ€ข Compliance: Model explainability and bias detection integrated"); - println!(" โ€ข Rollback: Automated rollback on performance degradation"); - - println!("\n๐Ÿ” **Performance Monitoring & Drift Detection:**"); - println!(" โ€ข Data Drift: Statistical tests with 95% confidence intervals"); - println!(" โ€ข Model Drift: Performance degradation alerts with automatic retraining"); - println!(" โ€ข Feature Drift: Individual feature distribution monitoring"); - println!(" โ€ข Prediction Drift: Output distribution analysis with anomaly detection"); - println!(" โ€ข Business Metrics: Real-time KPI tracking with correlation analysis"); - - println!("\n๐Ÿ’ก **MLOps Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_mlops_assessment(_request: &IntelligencePlanningRequest) -> IntelligenceAssessmentResult { - IntelligenceAssessmentResult { - success: true, - model_performance_score: 8.9, - experiment_confidence: 0.94, - data_quality_score: 0.91, - pipeline_efficiency: 0.87, - feature_importance_score: 0.92, - deployment_readiness: 0.95, - estimated_accuracy_improvement: 0.12, - training_time_estimate: Duration::from_secs(36 * 3600), // 36 hours - experiment_duration: Duration::from_secs(14 * 24 * 3600), // 14 days - recommendations: vec![ - "Implement automated hyperparameter optimization with Optuna".to_string(), - "Use feature selection with SHAP values for model interpretability".to_string(), - "Deploy shadow mode testing before production rollout".to_string(), - "Implement real-time feature drift monitoring with alerts".to_string(), - "Use ensemble methods for improved prediction accuracy".to_string(), - "Implement automated data quality validation in pipeline".to_string(), - ], - } -} - -fn demonstrate_model_training_agent() { - println!("\n๐Ÿ‹๏ธ **Model Training Agent Integration - Advanced Optimization**"); - println!("=============================================================="); - - let request = IntelligencePlanningRequest { - agent_type: IntelligenceAgentType::ModelTrainingAgent, - planning_type: IntelligencePlanningType::HyperparameterOptimization, - project_name: "Computer Vision Classification System".to_string(), - model_type: ModelType::ComputerVision, - priority_level: PriorityLevel::Critical, - data_size_gb: 2000.0, - target_metrics: vec![ - "Top-1 Accuracy".to_string(), - "Top-5 Accuracy".to_string(), - "F1-Score".to_string(), - "Inference Time".to_string(), - ], - business_objectives: vec![ - "Achieve 95%+ accuracy on test set".to_string(), - "Inference time <10ms per image".to_string(), - "Model size <100MB for mobile deployment".to_string(), - ], - resource_constraints: ResourceInfo { - max_training_time_hours: 72, - max_compute_cost: 15000.0, - max_memory_gb: 256, - max_gpu_hours: 500, - preferred_frameworks: vec![ - "PyTorch".to_string(), - "TensorFlow".to_string(), - "Weights & Biases".to_string(), - ], - }, - }; - - println!("๐ŸŽฏ **Model Training Planning Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Focus: Advanced hyperparameter optimization and distributed training"); - println!(" โ€ข Data Size: {:.1} GB (2M+ images)", request.data_size_gb); - println!(" โ€ข Resource Budget: {} GPU hours, ${} compute cost", - request.resource_constraints.max_gpu_hours, request.resource_constraints.max_compute_cost); - - let result = simulate_training_assessment(&request); - - println!("\n๐Ÿ‹๏ธ **Training Assessment Result:**"); - println!(" โ€ข Training Success: {}", result.success); - println!(" โ€ข Model Performance Score: {:.1}/10.0", result.model_performance_score); - println!(" โ€ข Training Efficiency: {:.1}%", result.pipeline_efficiency * 100.0); - println!(" โ€ข Expected Accuracy: {:.1}%", (0.92 + result.estimated_accuracy_improvement) * 100.0); - println!(" โ€ข Training Time: {:?}", result.training_time_estimate); - - println!("\n๐Ÿ”ฌ **Advanced Hyperparameter Optimization:**"); - println!(" โ€ข Optimization Algorithm: TPE (Tree-structured Parzen Estimator) with Optuna"); - println!(" โ€ข Search Space: 47 hyperparameters across architecture and training"); - println!(" โ€ข Multi-Objective: Accuracy vs. model size vs. inference speed"); - println!(" โ€ข Early Stopping: Patience=10 epochs with validation loss monitoring"); - println!(" โ€ข Pruning Strategy: Median pruning to terminate unpromising trials"); - println!(" โ€ข Budget Allocation: 200 trials with adaptive resource allocation"); - - println!("\nโšก **Distributed Training Strategy:**"); - println!(" โ€ข Training Framework: PyTorch Distributed Data Parallel (DDP)"); - println!(" โ€ข Cluster Configuration: 8 nodes ร— 4 V100 GPUs (32 GPUs total)"); - println!(" โ€ข Communication Backend: NCCL for optimal GPU communication"); - println!(" โ€ข Gradient Synchronization: AllReduce with gradient compression"); - println!(" โ€ข Load Balancing: Dynamic batch size adjustment per GPU"); - println!(" โ€ข Fault Tolerance: Checkpoint recovery with automatic node replacement"); - - println!("\n๐Ÿง  **Advanced Training Techniques:**"); - println!(" โ€ข Architecture: EfficientNet-B7 with progressive resizing"); - println!(" โ€ข Augmentation: MixUp, CutMix, RandAugment with learned policies"); - println!(" โ€ข Regularization: DropPath, Label Smoothing, Mixup alpha=0.2"); - println!(" โ€ข Optimization: AdamW with cosine annealing and warm restarts"); - println!(" โ€ข Transfer Learning: ImageNet pre-training with fine-tuning"); - println!(" โ€ข Model Pruning: Magnitude-based pruning for mobile deployment"); - - println!("\n๐Ÿ“Š **Training Performance Metrics:**"); - println!(" โ€ข Peak Training Speed: 2,847 images/second across cluster"); - println!(" โ€ข GPU Utilization: 94% average across all devices"); - println!(" โ€ข Memory Efficiency: 89% GPU memory utilization"); - println!(" โ€ข Convergence Rate: 15% faster than baseline with optimization"); - println!(" โ€ข Model Compression: 73% size reduction with <1% accuracy loss"); - println!(" โ€ข Energy Efficiency: 45% reduction in training carbon footprint"); - - println!("\n๐Ÿ’ก **Training Optimization Recommendations:"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_training_assessment(_request: &IntelligencePlanningRequest) -> IntelligenceAssessmentResult { - IntelligenceAssessmentResult { - success: true, - model_performance_score: 9.3, - experiment_confidence: 0.96, - data_quality_score: 0.94, - pipeline_efficiency: 0.91, - feature_importance_score: 0.89, - deployment_readiness: 0.88, - estimated_accuracy_improvement: 0.06, - training_time_estimate: Duration::from_secs(48 * 3600), // 48 hours - experiment_duration: Duration::from_secs(21 * 24 * 3600), // 21 days - recommendations: vec![ - "Implement progressive resizing for faster initial training convergence".to_string(), - "Use mixed precision training for 40% speedup with minimal accuracy loss".to_string(), - "Deploy gradient checkpointing to train larger models with same memory".to_string(), - "Implement learning rate finder for optimal initial learning rate".to_string(), - "Use test-time augmentation (TTA) for improved inference accuracy".to_string(), - "Implement knowledge distillation for efficient mobile model deployment".to_string(), - ], - } -} - -fn demonstrate_experimentation_agent() { - println!("\n๐Ÿ”ฌ **Experimentation Agent Integration - A/B Testing & Design**"); - println!("============================================================="); - - let request = IntelligencePlanningRequest { - agent_type: IntelligenceAgentType::ExperimentationAgent, - planning_type: IntelligencePlanningType::ABTestingStrategy, - project_name: "Search Ranking Algorithm Optimization".to_string(), - model_type: ModelType::RecommendationSystem, - priority_level: PriorityLevel::High, - data_size_gb: 750.0, - target_metrics: vec![ - "Click-through Rate (CTR)".to_string(), - "Conversion Rate".to_string(), - "Revenue per User".to_string(), - "User Engagement Time".to_string(), - ], - business_objectives: vec![ - "Increase CTR by 2.5% (minimum detectable effect)".to_string(), - "Maintain conversion rate within 1% of baseline".to_string(), - "Improve user satisfaction score by 5%".to_string(), - ], - resource_constraints: ResourceInfo { - max_training_time_hours: 24, - max_compute_cost: 8000.0, - max_memory_gb: 64, - max_gpu_hours: 50, - preferred_frameworks: vec![ - "TensorFlow".to_string(), - "Scikit-learn".to_string(), - "MLflow".to_string(), - ], - }, - }; - - println!("๐ŸŽฏ **Experimentation Planning Request:**"); - println!(" โ€ข Project: {}", request.project_name); - println!(" โ€ข Experiment Type: Multi-armed bandit with Thompson sampling"); - println!(" โ€ข Statistical Power: 80% with ฮฑ=0.05 significance level"); - println!(" โ€ข Minimum Detectable Effect: 2.5% relative improvement"); - - let result = simulate_experimentation_assessment(&request); - - println!("\n๐Ÿ”ฌ **Experimentation Assessment Result:**"); - println!(" โ€ข Experiment Success: {}", result.success); - println!(" โ€ข Experiment Confidence: {:.1}%", result.experiment_confidence * 100.0); - println!(" โ€ข Statistical Power: {:.1}%", 85.0); - println!(" โ€ข Sample Size Required: 45,000 users per variant"); - println!(" โ€ข Experiment Duration: {:?}", result.experiment_duration); - - println!("\n๐Ÿ“Š **A/B Testing Framework:**"); - println!(" โ€ข Design: Randomized controlled trial with stratified sampling"); - println!(" โ€ข Randomization: Hash-based assignment with user ID consistency"); - println!(" โ€ข Traffic Allocation: 10% control, 90% split across 3 variants"); - println!(" โ€ข Blocking Variables: User segment, geographic region, device type"); - println!(" โ€ข Guardrail Metrics: Revenue, user retention, page load time"); - println!(" โ€ข Analysis Plan: Intention-to-treat with heterogeneous treatment effects"); - - println!("\n๐ŸŽฒ **Multi-Armed Bandit Implementation:**"); - println!(" โ€ข Algorithm: Thompson Sampling with Beta-Binomial conjugate priors"); - println!(" โ€ข Exploration vs. Exploitation: Dynamic balance with uncertainty quantification"); - println!(" โ€ข Contextual Features: User demographics, session history, time of day"); - println!(" โ€ข Reward Function: Weighted combination of CTR (70%) + conversion (30%)"); - println!(" โ€ข Adaptation Rate: Hourly parameter updates with safety constraints"); - println!(" โ€ข Early Stopping: Futility analysis with Bayesian posterior probabilities"); - - println!("\n๐Ÿ“ˆ **Advanced Experimental Design:**"); - println!(" โ€ข Factorial Design: 2ร—3 factorial testing ranking + personalization"); - println!(" โ€ข Sequential Testing: Group sequential with O'Brien-Fleming boundaries"); - println!(" โ€ข Variance Reduction: CUPED (Controlled-experiment Using Pre-Experiment Data)"); - println!(" โ€ข Heterogeneity Analysis: Subgroup analysis with interaction effects"); - println!(" โ€ข Meta-Analysis: Historical experiment data for improved power"); - println!(" โ€ข Causal Inference: Difference-in-differences with synthetic controls"); - - println!("\nโš–๏ธ **Statistical Analysis Framework:**"); - println!(" โ€ข Primary Analysis: Student's t-test with Welch's correction"); - println!(" โ€ข Secondary Analysis: Bootstrap confidence intervals and permutation tests"); - println!(" โ€ข Multiple Comparisons: Benjamini-Hochberg FDR correction"); - println!(" โ€ข Effect Size: Cohen's d with confidence intervals"); - println!(" โ€ข Sensitivity Analysis: Assumption violations and robustness checks"); - println!(" โ€ข Bayesian Analysis: Posterior distributions with credible intervals"); - - println!("\n๐Ÿ“Š **Real-Time Monitoring Dashboard:**"); - println!(" โ€ข Primary Metrics: Live CTR tracking with 95% confidence intervals"); - println!(" โ€ข Sample Ratio Mismatch: Chi-square test for randomization quality"); - println!(" โ€ข Data Quality: Missing data rates and outlier detection"); - println!(" โ€ข User Experience: Page load times and error rates by variant"); - println!(" โ€ข Business Impact: Revenue tracking with statistical significance"); - println!(" โ€ข Alert System: Automated alerts for significant degradations"); - - println!("\n๐Ÿ’ก **Experimentation Recommendations:**"); - for rec in &result.recommendations { - println!(" โ€ข {}", rec); - } -} - -fn simulate_experimentation_assessment(_request: &IntelligencePlanningRequest) -> IntelligenceAssessmentResult { - IntelligenceAssessmentResult { - success: true, - model_performance_score: 8.7, - experiment_confidence: 0.93, - data_quality_score: 0.96, - pipeline_efficiency: 0.89, - feature_importance_score: 0.91, - deployment_readiness: 0.94, - estimated_accuracy_improvement: 0.025, - training_time_estimate: Duration::from_secs(12 * 3600), // 12 hours - experiment_duration: Duration::from_secs(21 * 24 * 3600), // 21 days - recommendations: vec![ - "Implement stratified randomization to balance user segments".to_string(), - "Use variance reduction techniques (CUPED) for 30% power improvement".to_string(), - "Add interaction effect analysis for personalization insights".to_string(), - "Implement real-time sample ratio mismatch detection".to_string(), - "Use Bayesian updating for continuous learning from experiments".to_string(), - "Deploy multi-armed bandit for automatic traffic optimization".to_string(), - ], - } -} - -fn demonstrate_data_pipeline_optimization() { - println!("\n๐Ÿ“Š **Data Pipeline Optimization - Real-time & Batch Processing**"); - println!("================================================================"); - - println!("๐ŸŽฏ **Data Pipeline Architecture:**"); - println!(" โ€ข Real-time Stream: Kafka โ†’ Flink โ†’ Feature Store โ†’ Model Serving"); - println!(" โ€ข Batch Processing: Airflow โ†’ Spark โ†’ Data Lake โ†’ Model Training"); - println!(" โ€ข Data Validation: Great Expectations with automated quality monitoring"); - println!(" โ€ข Schema Evolution: Confluent Schema Registry with backward compatibility"); - - println!("\nโšก **Real-Time Processing Performance:**"); - println!(" โ€ข Throughput: 50,000 events/second with <5ms latency"); - println!(" โ€ข Scalability: Auto-scaling from 3 to 50 Kafka partitions"); - println!(" โ€ข Fault Tolerance: Exactly-once processing with Kafka transactions"); - println!(" โ€ข Backpressure Handling: Adaptive buffering with spillover to disk"); - println!(" โ€ข State Management: RocksDB for stateful stream processing"); - println!(" โ€ข Monitoring: Confluent Control Center + custom Grafana dashboards"); - - println!("\n๐Ÿ—๏ธ **Batch Processing Optimization:**"); - println!(" โ€ข Computation Engine: Apache Spark with Delta Lake for ACID transactions"); - println!(" โ€ข Resource Management: YARN with dynamic allocation (5-100 nodes)"); - println!(" โ€ข Data Partitioning: Hive-style partitioning by date + hash bucketing"); - println!(" โ€ข Compression: Snappy for speed + Parquet for storage efficiency"); - println!(" โ€ข Caching Strategy: Alluxio for in-memory data serving"); - println!(" โ€ข Job Scheduling: Airflow with SLA monitoring and auto-retry"); - - println!("\n๐Ÿ” **Data Quality Monitoring:**"); - println!(" โ€ข Schema Validation: Automated validation with 99.9% accuracy"); - println!(" โ€ข Completeness Checks: <0.1% missing data tolerance"); - println!(" โ€ข Freshness Monitoring: Data SLA of <30 minutes for critical features"); - println!(" โ€ข Anomaly Detection: Statistical outlier detection with ML models"); - println!(" โ€ข Data Lineage: Automated tracking from source to model consumption"); - println!(" โ€ข Quality Metrics: 15 data quality dimensions tracked continuously"); - - println!("\n๐Ÿ’ก **Pipeline Optimization Results:**"); - println!(" โ€ข Latency Reduction: 67% improvement in end-to-end data processing"); - println!(" โ€ข Cost Optimization: 45% reduction in compute costs through optimization"); - println!(" โ€ข Reliability: 99.95% uptime with automated failure recovery"); - println!(" โ€ข Scalability: 10x throughput improvement during peak traffic"); - println!(" โ€ข Data Quality: 98.7% data quality score with automated remediation"); -} - -fn demonstrate_user_behavior_analysis() { - println!("\n๐Ÿ‘ฅ **User Behavior Analysis - Personalization & Insights**"); - println!("=========================================================="); - - println!("๐ŸŽฏ **Behavioral Analytics Framework:**"); - println!(" โ€ข Event Tracking: 150+ events across web, mobile, and API interactions"); - println!(" โ€ข User Segmentation: RFM analysis + ML clustering for 12 user personas"); - println!(" โ€ข Journey Mapping: Markov chain modeling of user conversion funnels"); - println!(" โ€ข Cohort Analysis: Retention tracking with statistical significance testing"); - - println!("\n๐Ÿง  **Advanced User Modeling:**"); - println!(" โ€ข Collaborative Filtering: Matrix factorization with implicit feedback"); - println!(" โ€ข Content-Based: Deep learning embeddings for item and user features"); - println!(" โ€ข Hybrid Approach: Ensemble of 5 models with dynamic weight optimization"); - println!(" โ€ข Real-time Updates: Online learning with incremental model updates"); - println!(" โ€ข Cold Start: Content-based recommendations for new users"); - println!(" โ€ข Diversity Optimization: Multi-objective optimization for engagement + discovery"); - - println!("\n๐Ÿ“Š **Behavioral Insights & Patterns:**"); - println!(" โ€ข Engagement Prediction: 87% accuracy in predicting user churn"); - println!(" โ€ข Purchase Intent: 92% accuracy in next-purchase timing prediction"); - println!(" โ€ข Content Preferences: Dynamic interest evolution tracking"); - println!(" โ€ข Seasonal Patterns: Holiday and event-driven behavior modeling"); - println!(" โ€ข Cross-Platform: Unified user profile across mobile, web, and email"); - println!(" โ€ข Privacy-Preserving: Differential privacy with ฮต=1.0 guarantee"); - - println!("\n๐ŸŽฏ **Personalization Engine:**"); - println!(" โ€ข Real-time Recommendations: <50ms response time for 1M+ users"); - println!(" โ€ข A/B Testing: Continuous optimization of recommendation algorithms"); - println!(" โ€ข Contextual Bandits: Time, location, and device-aware recommendations"); - println!(" โ€ข Explainability: SHAP-based explanations for recommendation transparency"); - println!(" โ€ข Business Rules: Configurable constraints for inventory and promotions"); - println!(" โ€ข Performance Metrics: 23% increase in CTR, 15% boost in conversion"); - - println!("\n๐Ÿ“ˆ **User Behavior Impact:**"); - println!(" โ€ข Engagement Lift: 34% increase in average session duration"); - println!(" โ€ข Revenue Impact: $2.3M additional revenue from personalization"); - println!(" โ€ข User Satisfaction: 4.6/5.0 recommendation relevance score"); - println!(" โ€ข Retention Improvement: 28% reduction in user churn rate"); - println!(" โ€ข Cross-sell Success: 45% increase in multi-category purchases"); -} - -fn demonstrate_feature_engineering() { - println!("\n๐Ÿ”ง **Feature Engineering Agent - Automated Feature Discovery**"); - println!("=============================================================="); - - println!("๐ŸŽฏ **Automated Feature Generation:**"); - println!(" โ€ข Polynomial Features: Interaction terms up to degree 3 with correlation filtering"); - println!(" โ€ข Time Series Features: 47 temporal features (lags, rolling stats, seasonality)"); - println!(" โ€ข Text Features: TF-IDF, word embeddings, sentiment analysis, topic modeling"); - println!(" โ€ข Categorical Encoding: Target encoding, embedding layers, frequency encoding"); - println!(" โ€ข Numerical Transformations: Box-Cox, Yeo-Johnson, quantile transformations"); - println!(" โ€ข Domain-Specific: Business logic features with expert knowledge integration"); - - println!("\n๐Ÿ” **Feature Selection & Importance:**"); - println!(" โ€ข Selection Methods: SHAP, permutation importance, recursive feature elimination"); - println!(" โ€ข Stability Analysis: Feature importance consistency across CV folds"); - println!(" โ€ข Multicollinearity: VIF analysis with automatic correlated feature removal"); - println!(" โ€ข Information Theory: Mutual information and maximum relevance minimum redundancy"); - println!(" โ€ข Model-Based: L1 regularization and tree-based feature importance"); - println!(" โ€ข Business Impact: Feature contribution to key business metrics"); - - println!("\n๐Ÿ“Š **Feature Quality Monitoring:**"); - println!(" โ€ข Drift Detection: Statistical tests for distribution changes"); - println!(" โ€ข Importance Tracking: Daily monitoring of feature contribution"); - println!(" โ€ข Data Quality: Missing value rates, outlier detection, range validation"); - println!(" โ€ข Performance Impact: Feature contribution to model accuracy"); - println!(" โ€ข Computational Cost: Feature extraction time and resource usage"); - println!(" โ€ข A/B Testing: Feature impact validation through controlled experiments"); - - println!("\nโšก **Feature Engineering Results:**"); - println!(" โ€ข Feature Count: Reduced from 2,847 to 156 features (94% reduction)"); - println!(" โ€ข Model Performance: 8.3% accuracy improvement from feature engineering"); - println!(" โ€ข Training Speed: 73% faster training with optimized feature set"); - println!(" โ€ข Inference Latency: 45% reduction in prediction time"); - println!(" โ€ข Feature Stability: 94% of features maintain importance across time"); - println!(" โ€ข Business Impact: $1.8M value from improved model performance"); -} - -fn demonstrate_ab_testing_strategy() { - println!("\n๐Ÿงช **Advanced A/B Testing Strategy - Statistical Rigor**"); - println!("======================================================="); - - println!("๐ŸŽฏ **Experimental Design Framework:**"); - println!(" โ€ข Power Analysis: 80% power with 5% significance level"); - println!(" โ€ข Sample Size: 127,000 users required for 2% minimum detectable effect"); - println!(" โ€ข Stratification: Balanced across user segments, geography, and device"); - println!(" โ€ข Randomization: Cryptographic hash-based with deterministic assignment"); - println!(" โ€ข Control Groups: Multiple control groups for network effect detection"); - println!(" โ€ข Guardrail Metrics: 23 metrics monitoring user experience and business health"); - - println!("\n๐Ÿ“Š **Statistical Analysis Pipeline:**"); - println!(" โ€ข Primary Analysis: Two-sample t-test with Welch's correction"); - println!(" โ€ข Secondary Analysis: Bootstrap confidence intervals and permutation tests"); - println!(" โ€ข Bayesian Analysis: Beta-binomial model with informative priors"); - println!(" โ€ข Sequential Testing: Pocock boundaries for early stopping"); - println!(" โ€ข Heterogeneity: Subgroup analysis with interaction effects"); - println!(" โ€ข Causal Inference: Instrumental variables and difference-in-differences"); - - println!("\nโš–๏ธ **Advanced Testing Techniques:**"); - println!(" โ€ข CUPED Variance Reduction: 45% reduction in required sample size"); - println!(" โ€ข Multi-Armed Bandits: Thompson sampling for optimal traffic allocation"); - println!(" โ€ข Factorial Design: Testing multiple features simultaneously"); - println!(" โ€ข Switchback Experiments: Time-based randomization for marketplace effects"); - println!(" โ€ข Cluster Randomization: Group-level assignment for network effects"); - println!(" โ€ข Dose-Response: Testing multiple treatment intensities"); - - println!("\n๐Ÿ“ˆ **Experiment Success Metrics:**"); - println!(" โ€ข Experiment Velocity: 47 concurrent experiments running"); - println!(" โ€ข Statistical Power: 89% average power across experiments"); - println!(" โ€ข False Discovery Rate: 3.2% (target: <5%) with Benjamini-Hochberg"); - println!(" โ€ข Time to Results: 14 days average experiment duration"); - println!(" โ€ข Business Impact: $12.4M annual value from experimentation program"); - println!(" โ€ข Learning Rate: 73% of experiments provide actionable insights"); -} - -fn demonstrate_model_monitoring() { - println!("\n๐Ÿ“ˆ **Model Monitoring & Performance Tracking**"); - println!("==============================================="); - - println!("๐ŸŽฏ **Real-Time Model Performance:**"); - println!(" โ€ข Prediction Accuracy: 94.2% (baseline: 91.8%)"); - println!(" โ€ข Inference Latency: P95 = 45ms, P99 = 78ms"); - println!(" โ€ข Throughput: 15,000 predictions/second per instance"); - println!(" โ€ข Error Rate: 0.08% prediction failures with automatic retry"); - println!(" โ€ข Model Drift: 2.3% distribution shift detected this week"); - println!(" โ€ข Feature Importance: Top 5 features account for 67% of predictions"); - - println!("\n๐Ÿ” **Drift Detection & Alerts:**"); - println!(" โ€ข Statistical Tests: KS test, Jensen-Shannon divergence, PSI monitoring"); - println!(" โ€ข Performance Degradation: 5% accuracy drop triggers automatic retraining"); - println!(" โ€ข Data Quality: Schema validation, missing value monitoring"); - println!(" โ€ข Prediction Drift: Output distribution monitoring with control charts"); - println!(" โ€ข Business Impact: Revenue impact tracking from model predictions"); - println!(" โ€ข Alert Channels: Slack, PagerDuty, email with severity-based routing"); - - println!("\n๐Ÿ“Š **Model Governance & Compliance:**"); - println!(" โ€ข Model Registry: 47 models tracked with full lineage and metadata"); - println!(" โ€ข Version Control: Git-based with automated CI/CD pipeline"); - println!(" โ€ข Approval Workflow: Staged deployment with stakeholder sign-off"); - println!(" โ€ข Audit Trail: Complete tracking of model changes and deployments"); - println!(" โ€ข Explainability: SHAP explanations for all high-impact predictions"); - println!(" โ€ข Bias Monitoring: Fairness metrics across protected attributes"); - - println!("\nโšก **Monitoring Infrastructure:**"); - println!(" โ€ข Metrics Collection: Prometheus with custom ML metrics"); - println!(" โ€ข Visualization: Grafana dashboards with real-time alerts"); - println!(" โ€ข Log Aggregation: ELK stack for prediction and error logging"); - println!(" โ€ข Distributed Tracing: Jaeger for end-to-end request tracking"); - println!(" โ€ข Custom Metrics: Business KPIs correlated with model performance"); - println!(" โ€ข SLA Monitoring: 99.9% uptime target with automated incident response"); -} - -fn demonstrate_automated_retraining() { - println!("\n๐Ÿ”„ **Automated Retraining & Model Updates**"); - println!("==========================================="); - - println!("๐ŸŽฏ **Retraining Triggers & Schedule:**"); - println!(" โ€ข Performance Degradation: >3% accuracy drop triggers immediate retraining"); - println!(" โ€ข Data Drift: Statistical significance in feature distributions"); - println!(" โ€ข Scheduled Retraining: Weekly batch retraining with latest data"); - println!(" โ€ข Business Events: Holiday seasons, product launches, market changes"); - println!(" โ€ข New Data Availability: Fresh training data reaching minimum threshold"); - println!(" โ€ข A/B Test Results: Winning variants automatically promoted to production"); - - println!("\n๐Ÿ—๏ธ **Automated Pipeline Architecture:**"); - println!(" โ€ข Data Validation: Great Expectations for automated data quality checks"); - println!(" โ€ข Feature Engineering: Automated feature pipeline with drift detection"); - println!(" โ€ข Model Training: Distributed training with hyperparameter optimization"); - println!(" โ€ข Model Validation: Comprehensive test suite with business metric validation"); - println!(" โ€ข Deployment: Blue-green deployment with automatic rollback"); - println!(" โ€ข Monitoring: Real-time performance tracking with alert integration"); - - println!("\nโšก **Retraining Performance & Results:**"); - println!(" โ€ข Retraining Frequency: 2.3 retrains per week (automated triggers)"); - println!(" โ€ข Training Time: 4.2 hours average (down from 18 hours manual)"); - println!(" โ€ข Accuracy Improvement: 1.8% average boost per retrain cycle"); - println!(" โ€ข Deployment Success: 96.7% automated deployments without issues"); - println!(" โ€ข Cost Efficiency: 73% reduction in ML engineering overhead"); - println!(" โ€ข Model Freshness: 2.1 days average data lag (target: <3 days)"); - - println!("\n๐Ÿ›ก๏ธ **Safety & Validation Framework:**"); - println!(" โ€ข Shadow Testing: New models tested on production traffic without impact"); - println!(" โ€ข Canary Deployment: 5% traffic for 24 hours before full deployment"); - println!(" โ€ข Automatic Rollback: Performance degradation triggers instant rollback"); - println!(" โ€ข Business Logic Validation: Prediction reasonableness checks"); - println!(" โ€ข Stakeholder Approval: Critical model changes require human approval"); - println!(" โ€ข Audit Logging: Complete tracking of all automated decisions"); -} - -fn demonstrate_data_ingestion_planning() { - println!("\n๐Ÿ“ฅ **Data Ingestion Planning - Multi-Source Integration**"); - println!("=========================================================="); - - println!("๐ŸŽฏ **Data Source Integration:**"); - println!(" โ€ข Database Sources: PostgreSQL, MongoDB, Cassandra, Redis"); - println!(" โ€ข Streaming Sources: Kafka, Kinesis, Pub/Sub with exactly-once semantics"); - println!(" โ€ข File Sources: S3, HDFS, GCS with automatic file format detection"); - println!(" โ€ข API Sources: REST APIs, GraphQL with rate limiting and retry logic"); - println!(" โ€ข Real-time Sources: IoT sensors, clickstreams, transaction logs"); - println!(" โ€ข External Sources: Weather, economic indicators, social media feeds"); - - println!("\nโšก **Ingestion Performance & Scale:**"); - println!(" โ€ข Total Throughput: 2.3 TB/day across all data sources"); - println!(" โ€ข Real-time Latency: P95 = 120ms from source to feature store"); - println!(" โ€ข Batch Processing: 847 GB processed in 45-minute windows"); - println!(" โ€ข Error Handling: <0.02% data loss with automatic retry and DLQ"); - println!(" โ€ข Schema Evolution: Backward compatibility with automated migration"); - println!(" โ€ข Data Validation: 99.97% data quality with automated remediation"); - - println!("\n๐Ÿ”ง **Ingestion Architecture:**"); - println!(" โ€ข Stream Processing: Apache Flink with RocksDB state backend"); - println!(" โ€ข Batch Processing: Apache Spark with Delta Lake for ACID transactions"); - println!(" โ€ข Data Catalog: Apache Atlas with automated lineage tracking"); - println!(" โ€ข Schema Registry: Confluent Schema Registry with evolution policies"); - println!(" โ€ข Data Quality: Great Expectations with custom validation rules"); - println!(" โ€ข Monitoring: Comprehensive observability with SLA tracking"); - - println!("\n๐Ÿ“Š **Data Quality & Governance:**"); - println!(" โ€ข Quality Score: 98.4% overall data quality across all sources"); - println!(" โ€ข Completeness: 99.8% data completeness with automated gap detection"); - println!(" โ€ข Timeliness: 95% of data arrives within SLA windows"); - println!(" โ€ข Accuracy: Automated validation rules with 99.9% accuracy"); - println!(" โ€ข Consistency: Cross-source validation with conflict resolution"); - println!(" โ€ข Privacy Compliance: GDPR, CCPA with automated PII detection"); -} - -fn demonstrate_intelligence_orchestration() { - println!("\n๐ŸŽผ **Intelligence Orchestration & Workflow Management**"); - println!("======================================================="); - - println!("๐ŸŽฏ **End-to-End ML Workflow Orchestration:**"); - println!(" โ€ข Pipeline Stages: 12 stages from data ingestion to model deployment"); - println!(" โ€ข Dependency Management: DAG-based execution with conditional branches"); - println!(" โ€ข Resource Optimization: Dynamic resource allocation based on workload"); - println!(" โ€ข Failure Recovery: Automatic retry with exponential backoff"); - println!(" โ€ข Parallel Execution: 8 concurrent pipelines with resource sharing"); - println!(" โ€ข Cost Optimization: 56% reduction in compute costs through optimization"); - - println!("\n๐Ÿค– **AI-Powered Workflow Optimization:**"); - println!(" โ€ข Predictive Scheduling: ML-based resource demand forecasting"); - println!(" โ€ข Auto-scaling: Dynamic cluster sizing based on workload predictions"); - println!(" โ€ข Performance Optimization: Historical analysis for pipeline tuning"); - println!(" โ€ข Anomaly Detection: Automated detection of pipeline performance issues"); - println!(" โ€ข Root Cause Analysis: AI-assisted debugging for failed pipelines"); - println!(" โ€ข Continuous Learning: Workflow optimization based on execution history"); - - println!("\n๐Ÿ“Š **Orchestration Performance Metrics:**"); - println!(" โ€ข Pipeline Success Rate: 97.8% (target: >95%)"); - println!(" โ€ข Average Execution Time: 2.4 hours (down from 6.8 hours)"); - println!(" โ€ข Resource Utilization: 84% average CPU utilization"); - println!(" โ€ข Cost per Pipeline: $47 (67% reduction from baseline)"); - println!(" โ€ข Time to Production: 23 minutes from commit to deployment"); - println!(" โ€ข Developer Productivity: 340% improvement in ML experiment velocity"); - - println!("\n๐ŸŽฏ **Intelligence Excellence Summary:**"); - println!(" โ€ข Overall Intelligence Score: 9.4/10 (Excellent)"); - println!(" โ€ข Model Performance: 94.2% average accuracy across all models"); - println!(" โ€ข Experiment Success Rate: 87.5% of experiments provide actionable insights"); - println!(" โ€ข Data Quality Score: 96.8% with automated quality assurance"); - println!(" โ€ข Pipeline Reliability: 99.2% uptime with automated recovery"); - println!(" โ€ข Business Impact: $47.3M annual value from AI/ML initiatives"); - println!(" โ€ข ROI Achievement: 890% ROI on AI/ML infrastructure investment"); -} - - \ No newline at end of file diff --git a/test_enhanced_pocketflow_analysis.rs b/test_enhanced_pocketflow_analysis.rs deleted file mode 100644 index 7330dcbf5cc0c77c43d27aea4edff20fb855b447..0000000000000000000000000000000000000000 --- a/test_enhanced_pocketflow_analysis.rs +++ /dev/null @@ -1,177 +0,0 @@ -#!/usr/bin/env cargo run --example test_enhanced_pocketflow_analysis -//! Test Enhanced PocketFlow Analysis -//! -//! Enhanced testing of PocketFlow analysis capabilities -//! with the new MemoryService and ConceptGraphService architecture. - -use brain::*; -use brain::services::*; -use std::env; -use tokio; - -#[tokio::main] -async fn main() -> Result<()> { - // Initialize logging - env_logger::init(); - - println!("๐Ÿ”ฌ Enhanced PocketFlow Analysis Test"); - println!("==================================="); - - // Check for OpenAI API key - let _openai_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| { - println!("โš ๏ธ OPENAI_API_KEY not set. Please set it to use this demo."); - std::process::exit(1); - }); - - println!("โœ… OpenAI API key found"); - - // Initialize Brain AI components using new service architecture - println!("\n๐Ÿ”ง Initializing Enhanced Brain AI Services..."); - let mut memory_system = create_memory_service_with_capacity(2000).await?; - let mut concept_graph = create_concept_graph_service_default().await?; - - println!("โœ… MemoryService initialized with enhanced capacity"); - println!("โœ… ConceptGraphService initialized"); - - // Load enhanced analysis dataset - println!("\n๐Ÿ“Š Loading Enhanced Analysis Dataset..."); - let enhanced_data = vec![ - "PocketFlow enhanced analysis shows 70% improvement in development velocity", - "Advanced metrics indicate 50% reduction in debugging time with Node-Flow pattern", - "Enhanced batch processing achieves 4x cost efficiency compared to sequential processing", - "Advanced agent orchestration enables complex multi-step reasoning workflows", - "Enhanced error handling provides 90% improvement in system reliability", - "Advanced monitoring shows 99.5% uptime in production environments", - "Enhanced scalability supports 1000+ concurrent agent operations", - "Advanced integration patterns enable seamless third-party API connections", - "Enhanced security features provide enterprise-grade protection mechanisms", - "Advanced analytics provide real-time insights into AI workflow performance", - "Enhanced testing framework ensures 95% code coverage with automated validation", - "Advanced deployment strategies support zero-downtime production updates", - ]; - - for (i, data) in enhanced_data.iter().enumerate() { - memory_system.learn(data.to_string(), Priority::High).await?; - println!("โœ… Loaded enhanced data {}", i + 1); - } - - // Create RAG orchestrator for enhanced analysis - println!("\n๐Ÿค– Initializing Enhanced Analysis System..."); - let mut rag_orchestrator = RagOrchestrator::new()?; - - // Enhanced analysis test cases - let test_cases = vec![ - ("Performance", "What performance improvements does PocketFlow provide?"), - ("Reliability", "How does PocketFlow ensure system reliability?"), - ("Scalability", "What scalability features does PocketFlow offer?"), - ("Security", "What security measures does PocketFlow implement?"), - ("Integration", "How does PocketFlow handle third-party integrations?"), - ("Monitoring", "What monitoring capabilities does PocketFlow provide?"), - ("Testing", "How does PocketFlow ensure code quality?"), - ("Deployment", "What deployment strategies does PocketFlow support?"), - ]; - - println!("\n๐Ÿงช Running Enhanced Analysis Test Suite"); - println!("======================================="); - - let mut test_results = Vec::new(); - - for (i, (category, question)) in test_cases.iter().enumerate() { - println!("\n๐Ÿ”ฌ Test {}: {} Analysis", i + 1, category); - println!(" Question: {}", question); - - let request = RagRequest { - message: question.to_string(), - conversation_id: Some("enhanced_test_session".to_string()), - context_limit: Some(8), - retrieval_threshold: Some(0.2), - }; - - match rag_orchestrator.process_conversation( - request, - &mut memory_system, - &mut concept_graph, - ).await { - Ok(response) => { - println!(" ๐Ÿ“Š Result: {}", response.response); - println!(" ๐ŸŽฏ Confidence: {:.1}%", response.confidence_score * 100.0); - println!(" ๐Ÿ“š Sources: {}", response.context_used.len()); - - // Evaluate test result - let passed = response.confidence_score > 0.4 && response.context_used.len() > 0; - test_results.push((category.to_string(), passed, response.confidence_score)); - - if passed { - println!(" โœ… Test PASSED"); - } else { - println!(" โŒ Test FAILED (low confidence or no sources)"); - } - - // Learn from test result - let test_insight = format!("Enhanced test {}: {} -> {}", category, question, response.response); - memory_system.learn(test_insight, Priority::Medium).await?; - } - Err(e) => { - println!(" โŒ Test ERROR: {}", e); - test_results.push((category.to_string(), false, 0.0)); - } - } - - tokio::time::sleep(tokio::time::Duration::from_millis(700)).await; - } - - // Generate enhanced test report - println!("\n๐Ÿ“‹ Enhanced Test Report"); - println!("======================="); - - let passed_tests: Vec<_> = test_results.iter().filter(|(_, passed, _)| *passed).collect(); - let total_tests = test_results.len(); - let pass_rate = (passed_tests.len() as f64 / total_tests as f64) * 100.0; - - println!("โœ… Tests passed: {}/{} ({:.1}%)", passed_tests.len(), total_tests, pass_rate); - - if pass_rate >= 75.0 { - println!("๐Ÿ† EXCELLENT: Enhanced analysis system performing exceptionally well"); - } else if pass_rate >= 50.0 { - println!("โœ… GOOD: Enhanced analysis system performing adequately"); - } else { - println!("โš ๏ธ NEEDS IMPROVEMENT: Enhanced analysis system requires optimization"); - } - - println!("\n๐Ÿ“Š Detailed Test Results:"); - for (category, passed, confidence) in &test_results { - let status = if *passed { "โœ… PASS" } else { "โŒ FAIL" }; - println!(" {}: {} (confidence: {:.1}%)", category, status, confidence * 100.0); - } - - // Calculate average confidence - let avg_confidence = test_results.iter().map(|(_, _, c)| c).sum::() / total_tests as f64; - println!("\n๐Ÿ“ˆ Average confidence: {:.1}%", avg_confidence * 100.0); - - // Display session statistics - println!("\n๐Ÿ“Š Session Statistics"); - println!("====================="); - let stats = rag_orchestrator.get_conversation_stats(); - for (key, value) in stats { - println!(" {}: {}", key, value); - } - - // Enhanced memory consolidation - println!("\n๐Ÿง  Enhanced Memory Consolidation..."); - match memory_system.consolidate().await { - Ok(result) => { - println!("โœ… Enhanced consolidation complete:"); - println!(" Working to episodic: {} items", result.working_to_episodic); - println!(" Episodic to semantic: {} items", result.episodic_to_semantic); - println!(" Forgotten items: {} items", result.forgotten_events); - } - Err(e) => { - println!("โš ๏ธ Consolidation warning: {}", e); - } - } - - println!("\nโœ… Enhanced PocketFlow Analysis Test Complete!"); - println!(" Enhanced testing completed successfully with new service architecture."); - - Ok(()) -} \ No newline at end of file diff --git a/test_model.json b/test_model.json deleted file mode 100644 index 714ec4a78131efbece71c7019f12dec23f723f5c..0000000000000000000000000000000000000000 --- a/test_model.json +++ /dev/null @@ -1,46208 +0,0 @@ -{ - "config": { - "embedding_dim": 128, - "hidden_dim": 256, - "learning_rate": 0.001, - "sequence_length": 32, - "vocab_size": 34 - }, - "embedding": [ - -0.09776748862391635, - -0.03647027119078554, - -0.09002720127801264, - -0.06508008377507328, - -0.04142569882644843, - -0.06653286350587462, - 0.15008622802855306, - 0.13509863059170552, - 0.1540422159841795, - 0.017179364417631383, - -0.059930099183392684, - 0.1330712704524779, - 0.0006145197976527158, - 0.07420584058448276, - -0.1611798692387247, - -0.04502331001696876, - -0.0834082767429433, - 0.028331167458630017, - 0.16710789689261324, - 0.16486999662920693, - -0.1472830395468655, - 0.022689115662578505, - 0.05368126248960714, - -0.09438602417038792, - -0.03015163487550059, - 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"\u0000", - "?", - "\n", - " ", - "!", - ".", - "H", - "T", - "a", - "b", - "c", - "d", - "e", - "f", - "g", - "h", - "i", - "j", - "k", - "l", - "m", - "n", - "o", - "p", - "q", - "r", - "s", - "t", - "u", - "v", - "w", - "x", - "y", - "z" - ], - "vocab_size": 34 - } -} \ No newline at end of file diff --git a/tool_catalog_test.rs b/tool_catalog_test.rs deleted file mode 100644 index 0fe94cb49955d7a4c6add80a13f67e44f9f50ffa..0000000000000000000000000000000000000000 --- a/tool_catalog_test.rs +++ /dev/null @@ -1,423 +0,0 @@ -//! # Tool Catalog Integration Test -//! -//! This test verifies the expanded tool catalog including File System Tool -//! and Database Tool functionality as part of Phase 2 development. - -use anyhow::Result; -use brain_cognitive::{ - agents::{AgentRegistry, AgentInput, traits::BrainAgent}, - tools::{FileSystemTool, DatabaseTool, WebSearchTool}, - meta::MetaMemoryRepository, - conversation::ConversationService, -}; -use std::collections::HashMap; -use std::path::PathBuf; -use serde_json::json; -use std::sync::Arc; -use async_trait::async_trait; - -// Mock implementations for testing -#[derive(Clone, Debug)] -struct MockMetaMemoryRepository; - -#[async_trait] -impl MetaMemoryRepository for MockMetaMemoryRepository { - async fn store_item(&mut self, _item: brain_cognitive::meta::MetaMemoryItem) -> brain_cognitive::meta::MetaMemoryResult { - Ok(uuid::Uuid::new_v4()) - } - - async fn get_item(&self, _id: uuid::Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(None) - } - - async fn get_item_by_component(&self, _component_id: uuid::Uuid) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(None) - } - - async fn query_items(&self, _query: &brain_cognitive::meta::MetaMemoryQuery) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(vec![]) - } - - async fn remove_item(&mut self, _id: uuid::Uuid) -> brain_cognitive::meta::MetaMemoryResult { - Ok(false) - } - - async fn batch_update(&mut self, _items: Vec) -> brain_cognitive::meta::MetaMemoryResult> { - Ok(vec![]) - } - - async fn count_items(&self) -> brain_cognitive::meta::MetaMemoryResult { - Ok(0) - } - - async fn clear_all(&mut self) -> brain_cognitive::meta::MetaMemoryResult { - Ok(0) - } -} - -#[derive(Clone, Debug)] -struct MockConversationService; - -#[async_trait] -impl ConversationService for MockConversationService { - async fn process_conversation( - &mut self, - _request: brain_cognitive::conversation::RagRequest, - _memory_repo: &mut dyn brain_core::memory::WorkingMemoryRepository, - _concept_repo: &mut dyn brain_core::concepts::ConceptRepository, - _insight_repo: &mut dyn brain_core::insights::InsightRepository, - ) -> Result { - Ok(brain_cognitive::conversation::RagResponse { - response: "Mock response".to_string(), - conversation_id: "mock-conversation".to_string(), - context_used: vec![], - confidence_score: 0.8, - response_quality: brain_cognitive::conversation::response_quality::ResponseQuality::default(), - }) - } - - fn get_conversation_stats(&self) -> HashMap { - HashMap::new() - } - - fn clear_conversation(&mut self, _conversation_id: &str) -> bool { - true - } -} - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐Ÿง  Tool Catalog Integration Test - Phase 2"); - println!("=========================================="); - println!(); - - // Test 1: File System Tool - println!("๐Ÿ”ง Test 1: File System Tool"); - println!("---------------------------"); - - let fs_test_passed = test_file_system_tool().await?; - if fs_test_passed { - println!("โœ… File System Tool works correctly"); - } else { - println!("โŒ File System Tool has issues"); - } - println!(); - - // Test 2: Database Tool - println!("๐Ÿ”ง Test 2: Database Tool"); - println!("------------------------"); - - let db_test_passed = test_database_tool().await?; - if db_test_passed { - println!("โœ… Database Tool works correctly"); - } else { - println!("โŒ Database Tool has issues"); - } - println!(); - - // Test 3: Web Search Tool (Enhanced) - println!("๐Ÿ”ง Test 3: Web Search Tool (Enhanced)"); - println!("-------------------------------------"); - - let web_test_passed = test_web_search_tool().await?; - if web_test_passed { - println!("โœ… Web Search Tool works correctly"); - } else { - println!("โŒ Web Search Tool has issues"); - } - println!(); - - // Test 4: Tool Registry Integration - println!("๐Ÿ”ง Test 4: Tool Registry Integration"); - println!("-----------------------------------"); - - let registry_test_passed = test_tool_registry().await?; - if registry_test_passed { - println!("โœ… Tool Registry integration works correctly"); - } else { - println!("โŒ Tool Registry integration has issues"); - } - println!(); - - // Test 5: Agent Discovery for Tools - println!("๐Ÿ”ง Test 5: Agent Discovery for Tools"); - println!("-----------------------------------"); - - let discovery_test_passed = test_agent_discovery().await?; - if discovery_test_passed { - println!("โœ… Agent Discovery for tools works correctly"); - } else { - println!("โŒ Agent Discovery for tools has issues"); - } - println!(); - - // Summary - println!("๐Ÿ“Š Tool Catalog Test Summary"); - println!("==========================="); - println!("File System Tool: {}", if fs_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Database Tool: {}", if db_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Web Search Tool: {}", if web_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Tool Registry: {}", if registry_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - println!("Agent Discovery: {}", if discovery_test_passed { "โœ… PASS" } else { "โŒ FAIL" }); - - let all_tests_passed = fs_test_passed && db_test_passed && web_test_passed && registry_test_passed && discovery_test_passed; - println!(); - println!("๐ŸŽฏ Overall Result: {}", if all_tests_passed { "โœ… ALL TESTS PASSED" } else { "โŒ SOME TESTS FAILED" }); - - if all_tests_passed { - println!("๐Ÿš€ Tool catalog is successfully expanded!"); - println!(" Ready for Phase 2 completion and advanced agent capabilities."); - } else { - println!("๐Ÿ”ง Some tool integration issues remain."); - println!(" Please check the test output above for details."); - } - - Ok(()) -} - -/// Test File System Tool functionality -async fn test_file_system_tool() -> Result { - println!(" Testing File System Tool capabilities..."); - - // Create test directory - let test_dir = PathBuf::from("./temp/test_fs"); - if !test_dir.exists() { - std::fs::create_dir_all(&test_dir)?; - } - - let fs_tool = FileSystemTool::new(); - - // Create a mock context - simplified for testing - let mock_context = create_mock_context(); - - // Test 1: List directory - println!(" Testing list_directory..."); - let list_input = AgentInput::new( - "list_directory".to_string(), - "./temp".to_string(), - "session_1".to_string(), - ); - - let list_result = fs_tool.execute(list_input, &mock_context).await?; - println!(" ๐Ÿ“ Directory listing result: {}", list_result.content); - - // Test 2: Write file - println!(" Testing write_file..."); - let write_input = AgentInput::new( - "write_file".to_string(), - json!({ - "file_path": "./temp/test_fs/test_file.txt", - "content": "Hello, File System Tool!\nThis is a test file." - }).to_string(), - "session_1".to_string(), - ); - - let write_result = fs_tool.execute(write_input, &mock_context).await?; - println!(" ๐Ÿ“ File write result: {}", write_result.content); - - // Test 3: Read file - println!(" Testing read_file..."); - let read_input = AgentInput::new( - "read_file".to_string(), - "./temp/test_fs/test_file.txt".to_string(), - "session_1".to_string(), - ); - - let read_result = fs_tool.execute(read_input, &mock_context).await?; - println!(" ๐Ÿ“– File read result: {}", read_result.content.chars().take(50).collect::()); - - let success = read_result.content.contains("Hello, File System Tool!"); - - // Cleanup - let _ = std::fs::remove_dir_all(&test_dir); - - Ok(success) -} - -/// Test Database Tool functionality -async fn test_database_tool() -> Result { - println!(" Testing Database Tool capabilities..."); - - // Create test database - let test_db_path = PathBuf::from("./temp/test_db.sqlite"); - if let Some(parent) = test_db_path.parent() { - if !parent.exists() { - std::fs::create_dir_all(parent)?; - } - } - - let db_tool = DatabaseTool::new(test_db_path.clone()); - let mock_context = create_mock_context(); - - // Test 1: Create table - println!(" Testing create_table..."); - let create_input = AgentInput::new( - "create_table".to_string(), - json!({ - "table_name": "users", - "schema": "id INTEGER PRIMARY KEY, name TEXT NOT NULL, email TEXT" - }).to_string(), - "session_1".to_string(), - ); - - let create_result = db_tool.execute(create_input, &mock_context).await?; - println!(" ๐Ÿ—๏ธ Table creation result: {}", create_result.content); - - // Test 2: Insert data - println!(" Testing insert..."); - let insert_input = AgentInput::new( - "insert".to_string(), - json!({ - "table_name": "users", - "data": { - "name": "John Doe", - "email": "john@example.com" - } - }).to_string(), - "session_1".to_string(), - ); - - let insert_result = db_tool.execute(insert_input, &mock_context).await?; - println!(" ๐Ÿ“ฅ Insert result: {}", insert_result.content); - - // Test 3: Query data - println!(" Testing query..."); - let query_input = AgentInput::new( - "query".to_string(), - "SELECT * FROM users".to_string(), - "session_1".to_string(), - ); - - let query_result = db_tool.execute(query_input, &mock_context).await?; - println!(" ๐Ÿ“Š Query result: {}", query_result.content); - - let success = query_result.content.contains("returned 1 rows"); - - // Cleanup - let _ = std::fs::remove_file(&test_db_path); - - Ok(success) -} - -/// Test Web Search Tool functionality -async fn test_web_search_tool() -> Result { - println!(" Testing Web Search Tool capabilities..."); - - // Note: This test doesn't require a real API key, it will use the mock implementation - let web_tool = WebSearchTool::new("test_api_key".to_string()); - let mock_context = create_mock_context(); - - // Test search functionality - println!(" Testing search_query..."); - let search_input = AgentInput::new( - "search_query".to_string(), - "What is artificial intelligence?".to_string(), - "session_1".to_string(), - ); - - let search_result = web_tool.execute(search_input, &mock_context).await; - match search_result { - Ok(result) => { - println!(" ๐Ÿ” Search result: {}", result.content.chars().take(50).collect::()); - Ok(true) - } - Err(e) => { - println!(" โš ๏ธ Search failed (expected without API key): {}", e); - // This is expected to fail without a real API key - Ok(true) - } - } -} - -/// Test Tool Registry integration -async fn test_tool_registry() -> Result { - println!(" Testing Tool Registry integration..."); - - let registry = AgentRegistry::new_with_defaults(); - - // Test tool registration - println!(" Checking registered tools..."); - let agents = registry.list_agents()?; - println!(" ๐Ÿ“‹ Total registered agents/tools: {}", agents.len()); - - // Look for our specific tools - let mut found_fs_tool = false; - let mut found_db_tool = false; - let mut found_web_tool = false; - - for agent in agents { - let metadata = agent.metadata(); - match metadata.id.as_str() { - "file-system-tool" => found_fs_tool = true, - "database-tool" => found_db_tool = true, - "web-search-tool" => found_web_tool = true, - _ => {} - } - } - - println!(" โœ“ File System Tool found: {}", found_fs_tool); - println!(" โœ“ Database Tool found: {}", found_db_tool); - println!(" โœ“ Web Search Tool found: {}", found_web_tool); - - Ok(found_fs_tool && found_db_tool && found_web_tool) -} - -/// Test Agent Discovery for tools -async fn test_agent_discovery() -> Result { - println!(" Testing Agent Discovery for tools..."); - - let registry = AgentRegistry::new_with_defaults(); - - // Test discovery by capability - println!(" Testing discovery by capability..."); - let fs_agents = registry.get_agents_by_capability("FileSystem")?; - println!(" ๐Ÿ“ FileSystem agents found: {}", fs_agents.len()); - - let db_agents = registry.get_agents_by_capability("Database")?; - println!(" ๐Ÿ’พ Database agents found: {}", db_agents.len()); - - let web_agents = registry.get_agents_by_capability("WebSearch")?; - println!(" ๐Ÿ” WebSearch agents found: {}", web_agents.len()); - - // Test discovery by input type - println!(" Testing discovery by input type..."); - let read_file_agents = registry.get_agents_by_input_type("read_file")?; - println!(" ๐Ÿ“– read_file agents found: {}", read_file_agents.len()); - - let query_agents = registry.get_agents_by_input_type("query")?; - println!(" ๐Ÿ” query agents found: {}", query_agents.len()); - - let search_agents = registry.get_agents_by_input_type("search_query")?; - println!(" ๐ŸŒ search_query agents found: {}", search_agents.len()); - - let success = fs_agents.len() > 0 && db_agents.len() > 0 && web_agents.len() > 0 && - read_file_agents.len() > 0 && query_agents.len() > 0 && search_agents.len() > 0; - - Ok(success) -} - -/// Create a mock cognitive context for testing -fn create_mock_context() -> brain_cognitive::agents::CognitiveContext { - // Create a simplified mock context for testing - // This uses the context from the traits module - use brain_cognitive::agents::traits::{ProjectContext, CognitivePreferenceProfile}; - use brain_cognitive::agents::CognitiveContext; - use std::collections::HashMap; - use std::path::PathBuf; - - // Create mock implementations - let project_context = ProjectContext::default(); - let cognitive_profile = CognitivePreferenceProfile::default(); - - // Create a simplified context directly - // Note: This is a simplified approach for testing - CognitiveContext { - meta_memory: Arc::new(tokio::sync::RwLock::new(MockMetaMemoryRepository)), - conversation_service: Arc::new(MockConversationService), - project_context, - cognitive_profile, - session_history: Vec::new(), - config: HashMap::new(), - working_directory: PathBuf::from("."), - } -} \ No newline at end of file diff --git a/training_data_demo.db b/training_data_demo.db deleted file mode 100644 index 388e861927cc651670037afd48465487569471af..0000000000000000000000000000000000000000 Binary files a/training_data_demo.db and /dev/null differ diff --git a/training_data_demo.rs b/training_data_demo.rs deleted file mode 100644 index 533c6811b9a5c018cf4763fe0899a48f24d7581e..0000000000000000000000000000000000000000 --- a/training_data_demo.rs +++ /dev/null @@ -1,245 +0,0 @@ -use brain::{MemoryService, Result}; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use brain_cognitive::{ - RagOrchestrator, RagRequest, - TrainingDataCollector, TrainingDataConfig, ExportFormat, DatasetFilter, - ConversationType, ComplexityLevel -}; -use chrono::{Utc, Duration}; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŽ“ Brain AI - Training Data Collection Demonstration"); - println!("=================================================="); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| brain::BrainError::from(e).with_context(brain::ErrorContext::new("Failed to create data directory")))?; - - // Create memory repositories - let working_repo = Box::new(WorkingMemoryRepository::new(100)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/training_data_demo.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let _memory_service = MemoryService::new(working_repo, episodic_repo, semantic_repo); - let _rag_orchestrator = RagOrchestrator::new()?; - - // Configure training data collection - let training_config = TrainingDataConfig { - storage_path: "demo_training_data".to_string(), - max_conversations: 1000, - quality_threshold: 0.6, - enable_anonymization: true, - retention_days: 365, - batch_size: 50, - auto_export: true, - export_format: ExportFormat::JsonL, - }; - - println!("\n๐Ÿ“Š Step 1: Initialize Training Data Collector"); - let training_collector = TrainingDataCollector::new(training_config)?; - // Note: RagOrchestrator integration would be enabled here if implemented - println!("โœ… Training data collector initialized"); - - println!("\n๐Ÿ—ฃ๏ธ Step 2: Simulate Conversations with Quality Assessment"); - - // Simulate different types of conversations - let conversation_scenarios = vec![ - ("How does memory consolidation work in the brain?", "educational_query"), - ("What are the latest advances in neural networks?", "technical_discussion"), - ("Can you explain the concept of attention mechanisms?", "concept_explanation"), - ("I'm having trouble understanding transformers", "help_seeking"), - ("What's the difference between supervised and unsupervised learning?", "comparison_request"), - ]; - - for (i, (message, scenario_type)) in conversation_scenarios.iter().enumerate() { - println!("\n ๐Ÿ“ Conversation {}: {} scenario", i + 1, scenario_type); - println!(" User: {}", message); - - let _request = RagRequest { - message: message.to_string(), - conversation_id: Some(format!("demo_conv_{}", i + 1)), - context_limit: Some(10), - retrieval_threshold: Some(0.5), - }; - - // Note: For demonstration purposes, we simulate the conversation processing - // In a full implementation, this would process with RagOrchestrator - println!(" Assistant: This is a simulated response about {}", scenario_type); - println!(" Quality Score: 0.85 (simulated)"); - println!(" Knowledge Sources: 3 (simulated)"); - - // The training data collector would capture this interaction here - // training_collector.capture_conversation(...) - - // Small delay to simulate realistic conversation timing - tokio::time::sleep(tokio::time::Duration::from_millis(100)).await; - } - - println!("\n๐Ÿ“ˆ Step 3: Analyze Captured Training Data"); - // Note: In a full implementation, analytics would be gathered from the collector - let analytics = training_collector.get_conversation_analytics(); - - println!(" ๐Ÿ“Š Collection Statistics:"); - println!(" Total Conversations: {}", analytics.total_conversations); - println!(" Total Messages: {}", analytics.total_messages); - println!(" Average Quality: {:.2}", analytics.user_satisfaction); - - println!("\n ๐ŸŽฏ Quality Distribution:"); - let quality_dist = training_collector.get_quality_distribution(); - for (quality_level, percentage) in &quality_dist { - println!(" {}: {:.1}%", quality_level, percentage * 100.0); - } - - println!("\n ๐Ÿ“š Topic Frequency:"); - for (topic, count) in analytics.topic_frequency.iter().take(5) { - println!(" {}: {} mentions", topic, count); - } - - println!("\n๐Ÿ” Step 4: Export Training Dataset with Filtering"); - // Create filter for high-quality educational conversations - let filter = DatasetFilter { - min_quality: Some(0.7), - max_quality: None, - conversation_types: Some(vec![ - ConversationType::QuestionsAndAnswers, - ConversationType::Tutorial, - ConversationType::Technical, - ]), - complexity_levels: Some(vec![ - ComplexityLevel::Moderate, - ComplexityLevel::Complex, - ]), - topics: None, - date_range: Some(( - Utc::now() - Duration::hours(1), - Utc::now() + Duration::minutes(1), - )), - }; - - match training_collector.export_training_dataset(Some(filter)).await { - Ok(dataset) => { - println!(" โœ… Exported training dataset:"); - println!(" Conversations: {}", dataset.metadata.total_conversations); - println!(" Messages: {}", dataset.metadata.total_messages); - println!(" Average Quality: {:.2}", dataset.statistics.average_quality); - println!(" Average Length: {:.1} messages", dataset.statistics.average_conversation_length); - - println!("\n ๐Ÿ“‹ Dataset Statistics:"); - println!(" Quality Distribution:"); - for (level, count) in &dataset.statistics.quality_distribution { - println!(" {}: {}", level, count); - } - - println!(" Conversation Types:"); - for (conv_type, count) in &dataset.statistics.conversation_type_distribution { - println!(" {}: {}", conv_type, count); - } - }, - Err(e) => println!(" โŒ Export failed: {}", e), - } - - println!("\n๐Ÿ”’ Step 5: Demonstrate Anonymization Features"); - demonstrate_anonymization().await?; - - println!("\n๐Ÿš€ Step 6: Training Data Pipeline Readiness"); - demonstrate_training_pipeline_readiness().await?; - - println!("\nโœจ Training Data Collection Demonstration Complete!"); - println!("=================================================="); - println!("๐ŸŽฏ Key Features Demonstrated:"); - println!(" โ€ข Training data collection framework"); - println!(" โ€ข Multi-dimensional quality assessment and scoring"); - println!(" โ€ข Privacy protection through data anonymization"); - println!(" โ€ข Flexible dataset filtering and export capabilities"); - println!(" โ€ข Comprehensive analytics and conversation insights"); - println!(" โ€ข Production-ready data pipeline for model training"); - - Ok(()) -} - -async fn demonstrate_anonymization() -> Result<()> { - println!(" ๐Ÿ”’ Privacy Protection Features:"); - - // Simulate messages with PII that would be anonymized - let test_messages = vec![ - "My email is john.doe@example.com and my phone is 555-123-4567", - "I work at 123 Main Street, Springfield, IL", - "You can reach me at (555) 987-6543 or jane@company.org", - "My IP address is 192.168.1.100 for troubleshooting", - ]; - - for (i, message) in test_messages.iter().enumerate() { - println!(" Original {}: {}", i + 1, message); - // In a real implementation, this would show the anonymized version - let anonymized = message - .replace(r"[\w\.-]+@[\w\.-]+\.\w+", "[EMAIL]") - .replace(r"\d{3}-\d{3}-\d{4}", "[PHONE]") - .replace(r"\(\d{3}\)\s*\d{3}-\d{4}", "[PHONE]") - .replace(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", "[IP_ADDRESS]"); - println!(" Anonymized {}: {}", i + 1, anonymized); - } - - Ok(()) -} - -async fn demonstrate_training_pipeline_readiness() -> Result<()> { - println!(" ๐Ÿš€ Training Pipeline Integration:"); - println!(" โœ… Data Format: JSONL, CSV, and Parquet export support"); - println!(" โœ… Quality Metrics: Multi-dimensional scoring for filtering"); - println!(" โœ… Privacy: Comprehensive PII detection and anonymization"); - println!(" โœ… Analytics: Detailed conversation and performance insights"); - println!(" โœ… Filtering: Advanced dataset curation capabilities"); - println!(" โœ… Scalability: Batched processing and configurable storage"); - println!(" โœ… Standards: Compatible with Hugging Face and common ML formats"); - - Ok(()) -} - -#[cfg(test)] -mod tests { - use super::*; - use brain_cognitive::*; - - #[tokio::test] - async fn test_training_data_collection() -> Result<(), Box> { - let config = TrainingDataConfig::default(); - let _collector = TrainingDataCollector::new(config)?; - - // Test basic initialization - assert!(true); // Placeholder for real test - - Ok(()) - } - - #[tokio::test] - async fn test_quality_assessment() -> Result<(), Box> { - let config = TrainingDataConfig { - quality_threshold: 0.8, - ..TrainingDataConfig::default() - }; - let _collector = TrainingDataCollector::new(config)?; - - // Test quality threshold configuration - assert!(true); // Placeholder for real test - - Ok(()) - } - - #[tokio::test] - async fn test_conversation_filtering() -> Result<(), Box> { - let filter = DatasetFilter { - min_quality: Some(0.7), - max_quality: None, - conversation_types: Some(vec![ConversationType::Technical]), - complexity_levels: None, - topics: None, - date_range: None, - }; - - // Test filter configuration - assert!(filter.min_quality.is_some()); - - Ok(()) - } -} \ No newline at end of file diff --git a/vector_concept_search_demo.rs b/vector_concept_search_demo.rs deleted file mode 100644 index 266d67dd66fee66d9e6c470552b4d30bad9642dc..0000000000000000000000000000000000000000 --- a/vector_concept_search_demo.rs +++ /dev/null @@ -1,319 +0,0 @@ -//! Vector Concept Search Demo -//! -//! Demonstrates the new vector search functionality for concept graph retrieval -//! that replaces the placeholder implementation. - -use std::collections::HashMap; -use brain_core::concepts::{ - ConceptGraphService, ConceptNode, ConceptType, ConceptQuery, - ConceptRepository, RelationshipRepository, -}; -use brain_types::BrainError; -use chrono::Utc; -use uuid::Uuid; - -#[derive(Clone)] -struct DemoConceptRepository { - concepts: HashMap, -} - -impl std::fmt::Debug for DemoConceptRepository { - fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { - f.debug_struct("DemoConceptRepository") - .field("concept_count", &self.concepts.len()) - .finish() - } -} - -impl DemoConceptRepository { - fn new() -> Self { - let mut repo = Self { - concepts: HashMap::new(), - }; - - // Add some demo concepts - let concepts = vec![ - ConceptNode::new( - ConceptType::Entity, - "machine learning algorithm".to_string(), - 0.9, - Some("demo".to_string()), - ), - ConceptNode::new( - ConceptType::Abstract, - "neural network architecture".to_string(), - 0.8, - Some("demo".to_string()), - ), - ConceptNode::new( - ConceptType::Action, - "data preprocessing".to_string(), - 0.7, - Some("demo".to_string()), - ), - ConceptNode::new( - ConceptType::Entity, - "vector similarity search".to_string(), - 0.95, - Some("demo".to_string()), - ), - ConceptNode::new( - ConceptType::Abstract, - "semantic embeddings".to_string(), - 0.85, - Some("demo".to_string()), - ), - ]; - - for concept in concepts { - repo.concepts.insert(concept.id, concept); - } - - repo - } -} - -#[async_trait::async_trait] -impl ConceptRepository for DemoConceptRepository { - async fn create_concept(&mut self, concept: ConceptNode) -> Result { - let id = concept.id; - self.concepts.insert(id, concept); - Ok(id) - } - - async fn get_concept(&self, id: Uuid) -> Result, BrainError> { - Ok(self.concepts.get(&id).cloned()) - } - - async fn update_concept(&mut self, concept: &ConceptNode) -> Result<(), BrainError> { - self.concepts.insert(concept.id, concept.clone()); - Ok(()) - } - - async fn delete_concept(&mut self, id: Uuid) -> Result { - Ok(self.concepts.remove(&id).is_some()) - } - - async fn query_concepts(&self, query: &ConceptQuery) -> Result, BrainError> { - let mut results: Vec = self.concepts.values().cloned().collect(); - - // Apply filters - if let Some(concept_type) = &query.concept_type { - results.retain(|c| &c.concept_type == concept_type); - } - - if let Some(min_confidence) = query.min_confidence { - results.retain(|c| c.confidence_score >= min_confidence); - } - - if let Some(pattern) = &query.content_pattern { - results.retain(|c| c.content.contains(pattern)); - } - - // Sort if requested - if query.sort_by.as_deref() == Some("confidence_score") { - results.sort_by(|a, b| { - if query.descending { - b.confidence_score.partial_cmp(&a.confidence_score).unwrap() - } else { - a.confidence_score.partial_cmp(&b.confidence_score).unwrap() - } - }); - } - - // Apply limit - if let Some(limit) = query.limit { - results.truncate(limit); - } - - Ok(results) - } - - async fn mark_concept_accessed(&mut self, id: Uuid) -> Result { - if let Some(concept) = self.concepts.get_mut(&id) { - concept.last_accessed_at = Utc::now(); - concept.usage_count += 1; - Ok(true) - } else { - Ok(false) - } - } - - async fn get_concept_count(&self) -> Result { - Ok(self.concepts.len()) - } -} - -/// Empty relationship repository for demo -struct DemoRelationshipRepository; - -#[async_trait::async_trait] -impl RelationshipRepository for DemoRelationshipRepository { - async fn create_relationship(&mut self, _relationship: brain_core::concepts::ConceptRelationship) -> Result { - Ok(Uuid::new_v4()) - } - - async fn get_relationship(&self, _id: Uuid) -> Result, BrainError> { - Ok(None) - } - - async fn update_relationship(&mut self, _relationship: &brain_core::concepts::ConceptRelationship) -> Result<(), BrainError> { - Ok(()) - } - - async fn delete_relationship(&mut self, _id: Uuid) -> Result { - Ok(false) - } - - async fn query_relationships(&self, _query: &brain_core::concepts::RelationshipQuery) -> Result, BrainError> { - Ok(Vec::new()) - } - - async fn get_concept_relationships(&self, _concept_id: Uuid) -> Result, BrainError> { - Ok(Vec::new()) - } - - async fn activate_relationship(&mut self, _id: Uuid) -> Result { - Ok(false) - } - - async fn apply_decay_to_all(&mut self, _time_delta_hours: f64) -> Result { - Ok(0) - } - - async fn prune_weak_relationships(&mut self) -> Result { - Ok(0) - } - - async fn get_relationship_count(&self) -> Result { - Ok(0) - } -} - -#[tokio::main] -async fn main() -> Result<(), Box> { - println!("๐Ÿš€ Vector Concept Search Demo"); - println!("================================"); - - // Create concept graph service with demo repositories - let concept_repo = Box::new(DemoConceptRepository::new()); - let relationship_repo = Box::new(DemoRelationshipRepository); - let concept_graph = ConceptGraphService::new(concept_repo, relationship_repo); - - println!("\n๐Ÿ“Š Demo Concepts Loaded:"); - let all_concepts = concept_graph.query_concepts(&ConceptQuery::default()).await?; - for concept in &all_concepts { - println!(" - {}: {} (confidence: {:.2})", - concept.concept_type, concept.content, concept.confidence_score); - } - - // Test 1: Traditional content-based search - println!("\n๐Ÿ” Test 1: Traditional Content Search"); - println!("Query: 'machine learning'"); - - let traditional_query = ConceptQuery { - content_pattern: Some("machine".to_string()), - min_confidence: Some(0.5), - limit: Some(5), - ..Default::default() - }; - - let traditional_results = concept_graph.query_concepts(&traditional_query).await?; - println!("Results:"); - for concept in &traditional_results { - println!(" - {}: {} (confidence: {:.2})", - concept.concept_type, concept.content, concept.confidence_score); - } - - // Test 2: Vector-based semantic search - println!("\n๐Ÿง  Test 2: Vector-Based Semantic Search"); - println!("Query: 'neural networks and deep learning'"); - - // Generate a simple embedding for the query - let query_text = "neural networks and deep learning"; - let mock_embedding = generate_mock_embedding(query_text); - - let vector_results = concept_graph.search_concepts_by_embedding( - &mock_embedding, - 0.1, // Low threshold for demo - 5, - ).await?; - - println!("Results:"); - for (concept, similarity) in &vector_results { - println!(" - {}: {} (similarity: {:.3})", - concept.concept_type, concept.content, similarity); - } - - // Test 3: Combined semantic search (vector + text fallback) - println!("\n๐Ÿ”— Test 3: Combined Semantic Search"); - println!("Query: 'vector embeddings'"); - - let combined_results = concept_graph.search_concepts_semantically( - "vector embeddings", - Some(&generate_mock_embedding("vector embeddings")), - 0.6, // confidence threshold - 0.1, // similarity threshold - 5, // limit - ).await?; - - println!("Results:"); - for (concept, score) in &combined_results { - println!(" - {}: {} (score: {:.3})", - concept.concept_type, concept.content, score); - } - - // Test 4: Vector similarity calculation - println!("\n๐Ÿ”ข Test 4: Vector Similarity Calculation"); - let vec1 = vec![1.0, 0.0, 0.0]; - let vec2 = vec![0.0, 1.0, 0.0]; - let vec3 = vec![1.0, 0.0, 0.0]; - - // Access the internal similarity calculation method via a simple test - let similarity_orthogonal = calculate_cosine_similarity(&vec1, &vec2); - let similarity_identical = calculate_cosine_similarity(&vec1, &vec3); - - println!("Orthogonal vectors similarity: {:.3}", similarity_orthogonal); - println!("Identical vectors similarity: {:.3}", similarity_identical); - - println!("\nโœ… Vector concept search demo completed successfully!"); - Ok(()) -} - -/// Generate a simple mock embedding for demonstration -fn generate_mock_embedding(text: &str) -> Vec { - let mut embedding = vec![0.0f32; 384]; - - let bytes = text.as_bytes(); - for (i, &byte) in bytes.iter().enumerate() { - let idx = (byte as usize + i) % embedding.len(); - embedding[idx] += (byte as f32) / 255.0; - } - - // Normalize - let magnitude: f32 = embedding.iter().map(|x| x * x).sum::().sqrt(); - if magnitude > 0.0 { - for val in &mut embedding { - *val /= magnitude; - } - } - - embedding -} - -/// Simple cosine similarity calculation for testing -fn calculate_cosine_similarity(vec1: &[f32], vec2: &[f32]) -> f64 { - if vec1.len() != vec2.len() { - return 0.0; - } - - let dot_product: f32 = vec1.iter().zip(vec2.iter()).map(|(a, b)| a * b).sum(); - let magnitude1: f32 = vec1.iter().map(|x| x * x).sum::().sqrt(); - let magnitude2: f32 = vec2.iter().map(|x| x * x).sum::().sqrt(); - - if magnitude1 == 0.0 || magnitude2 == 0.0 { - return 0.0; - } - - (dot_product / (magnitude1 * magnitude2)) as f64 -} \ No newline at end of file diff --git a/vector_database_day1_demo.rs b/vector_database_day1_demo.rs deleted file mode 100644 index d525d0c3d3aa7c255424c4ce233989d734168556..0000000000000000000000000000000000000000 --- a/vector_database_day1_demo.rs +++ /dev/null @@ -1,277 +0,0 @@ -//! Day 1 Vector Database Demo -//! -//! Demonstrates basic PostgreSQL + pgvector operations for brain-chat -//! This example shows the core functionality implemented on Day 1: -//! - Database connection and migration -//! - Basic embedding operations -//! - Vector similarity search -//! - Conversation storage and retrieval - -use std::sync::Arc; -use uuid::Uuid; - -use brain_chat::persistence::{ - VectorPersistence, VectorDatabaseConfig, - migrations::MigrationManager, - PersistenceResult, -}; -use brain_csm::{ - ConversationState, ConversationContext, UserPreferences, - EmotionalState, ConversationIntent, -}; - -#[tokio::main] -async fn main() -> Result<(), Box> { - // Initialize logging - tracing_subscriber::fmt::init(); - - println!("๐Ÿš€ Day 1: PostgreSQL + pgvector Vector Database Demo"); - println!("=================================================="); - - // Step 1: Database Configuration - println!("\n๐Ÿ“‹ Step 1: Configuring vector database connection..."); - let config = get_database_config(); - println!(" โœ… Vector dimensions: {}", config.vector_dimensions); - println!(" โœ… Similarity threshold: {}", config.similarity_threshold); - println!(" โœ… Database: {}", config.database); - - // Step 2: Initialize Vector Database - println!("\n๐Ÿ—„๏ธ Step 2: Initializing PostgreSQL + pgvector connection..."); - match VectorPersistence::new(config.clone()).await { - Ok(vector_db) => { - println!(" โœ… Successfully connected to PostgreSQL"); - println!(" โœ… pgvector extension verified"); - - let vector_db = Arc::new(vector_db); - - // Step 3: Run Migrations - println!("\n๐Ÿ”„ Step 3: Running database migrations..."); - let migration_manager = MigrationManager::new( - // Note: In real implementation, we'd get pool from vector_db - // For demo, we'll simulate this - sqlx::PgPool::connect(&get_database_url()).await? - ); - - match migration_manager.migrate().await { - Ok(_) => { - println!(" โœ… All migrations completed successfully"); - - // Verify schema - match migration_manager.verify_schema().await { - Ok(_) => println!(" โœ… Schema verification passed"), - Err(e) => println!(" โš ๏ธ Schema verification warning: {}", e), - } - } - Err(e) => { - println!(" โš ๏ธ Migration warning (may already exist): {}", e); - } - } - - // Step 4: Test Basic Vector Operations - println!("\n๐Ÿงฎ Step 4: Testing basic vector embedding operations..."); - test_vector_operations(&vector_db).await?; - - // Step 5: Test Conversation Storage - println!("\n๐Ÿ’ฌ Step 5: Testing conversation storage with embeddings..."); - test_conversation_storage(&vector_db).await?; - - // Step 6: Test Similarity Search - println!("\n๐Ÿ” Step 6: Testing vector similarity search..."); - test_similarity_search(&vector_db).await?; - - // Step 7: Health Check - println!("\n๐Ÿฅ Step 7: Running database health check..."); - match vector_db.health_check().await { - Ok(true) => println!(" โœ… Database health check passed"), - Ok(false) => println!(" โš ๏ธ Database health check failed"), - Err(e) => println!(" โŒ Health check error: {}", e), - } - - println!("\n๐ŸŽ‰ Day 1 Vector Database Implementation Complete!"); - println!("Ready for Day 2: Brain AI integration and Redis caching"); - } - Err(e) => { - println!(" โŒ Failed to connect to PostgreSQL: {}", e); - println!("\n๐Ÿ”ง Setup Instructions:"); - println!(" 1. Install PostgreSQL 12+"); - println!(" 2. Install pgvector extension: https://github.com/pgvector/pgvector"); - println!(" 3. Create database: CREATE DATABASE brain_chat;"); - println!(" 4. Enable extension: CREATE EXTENSION vector;"); - println!(" 5. Set DATABASE_URL environment variable"); - return Err(e.into()); - } - } - - Ok(()) -} - -/// Test basic vector operations -async fn test_vector_operations(vector_db: &Arc) -> PersistenceResult<()> { - // Generate sample embeddings (768 dimensions) - let sample_embedding: Vec = (0..768) - .map(|i| (i as f32 * 0.001).sin()) - .collect(); - - println!(" โœ… Generated sample embedding: {} dimensions", sample_embedding.len()); - - // Test embedding storage in conversation pattern - let pattern_data = serde_json::json!({ - "pattern_type": "successful_greeting", - "conversation_flow": ["greeting", "response", "follow_up"], - "success_rate": 0.95 - }); - - let success_metrics = serde_json::json!({ - "user_satisfaction": 0.9, - "response_relevance": 0.85, - "completion_rate": 0.98 - }); - - match vector_db.store_conversation_pattern( - "successful_greeting", - &pattern_data, - &sample_embedding, - &success_metrics, - ).await { - Ok(pattern_id) => { - println!(" โœ… Stored conversation pattern: {}", pattern_id); - } - Err(e) => { - println!(" โš ๏ธ Pattern storage warning: {}", e); - } - } - - Ok(()) -} - -/// Test conversation storage with embeddings -async fn test_conversation_storage(vector_db: &Arc) -> PersistenceResult<()> { - let session_id = Uuid::new_v4().to_string(); - let state = ConversationState::Active; - - // Create sample conversation context - let context = ConversationContext { - user_id: Some("user_123".to_string()), - session_id: session_id.clone(), - conversation_history: std::collections::VecDeque::new(), - current_topic: Some("AI and machine learning".to_string()), - user_preferences: UserPreferences::default(), - emotional_state: EmotionalState::default(), - intent_history: vec![ConversationIntent::Question, ConversationIntent::Clarification], - confidence_scores: vec![0.8, 0.9, 0.7], - context_window_size: 50, - }; - - // Generate conversation embedding - let conversation_embedding: Vec = (0..768) - .map(|i| ((i as f32 * 0.002).cos() + 1.0) / 2.0) - .collect(); - - // Store conversation with embedding - match vector_db.save_conversation_with_embedding( - &session_id, - &state, - &context, - &conversation_embedding, - ).await { - Ok(_) => { - println!(" โœ… Stored conversation with embedding: {}", session_id); - - // Test retrieval - match vector_db.get_conversation_session(&session_id).await { - Ok(Some((retrieved_state, retrieved_context))) => { - println!(" โœ… Retrieved conversation successfully"); - println!(" State: {:?}", retrieved_state); - println!(" Topic: {:?}", retrieved_context.current_topic); - } - Ok(None) => println!(" โš ๏ธ Conversation not found during retrieval"), - Err(e) => println!(" โš ๏ธ Retrieval warning: {}", e), - } - } - Err(e) => { - println!(" โš ๏ธ Conversation storage warning: {}", e); - } - } - - Ok(()) -} - -/// Test vector similarity search -async fn test_similarity_search(vector_db: &Arc) -> PersistenceResult<()> { - // Generate query embedding similar to stored embeddings - let query_embedding: Vec = (0..768) - .map(|i| ((i as f32 * 0.002).cos() + 1.1) / 2.0) // Slightly different - .collect(); - - // Test conversation similarity search - match vector_db.find_similar_conversations( - &query_embedding, - 5, // limit - 0.5, // similarity threshold - ).await { - Ok(similar_conversations) => { - println!(" โœ… Found {} similar conversations", similar_conversations.len()); - for (session_id, _context, similarity) in similar_conversations { - println!(" ๐Ÿ“Š Session {}: similarity = {:.3}", - session_id.chars().take(8).collect::(), similarity); - } - } - Err(e) => { - println!(" โš ๏ธ Similarity search warning: {}", e); - } - } - - // Test intent embedding search with sample message - let intent_embedding: Vec = (0..768) - .map(|i| (i as f32 * 0.003).sin().abs()) - .collect(); - - match vector_db.find_similar_intent_messages( - &intent_embedding, - 3, // limit - 0.6, // similarity threshold - ).await { - Ok(similar_messages) => { - println!(" โœ… Found {} messages with similar intent", similar_messages.len()); - for (message, similarity) in similar_messages { - println!(" ๐Ÿ’ฌ Message: {} (similarity: {:.3})", - message.content.chars().take(30).collect::(), similarity); - } - } - Err(e) => { - println!(" โš ๏ธ Intent search warning: {}", e); - } - } - - Ok(()) -} - -/// Get database configuration from environment or defaults -fn get_database_config() -> VectorDatabaseConfig { - VectorDatabaseConfig { - host: std::env::var("DB_HOST").unwrap_or_else(|_| "localhost".to_string()), - port: std::env::var("DB_PORT") - .unwrap_or_else(|_| "5432".to_string()) - .parse() - .unwrap_or(5432), - database: std::env::var("DB_NAME").unwrap_or_else(|_| "brain_chat".to_string()), - username: std::env::var("DB_USER").unwrap_or_else(|_| "brain_user".to_string()), - password: std::env::var("DB_PASSWORD").unwrap_or_else(|_| "brain_password".to_string()), - max_connections: 10, - min_connections: 2, - acquire_timeout_seconds: 30, - idle_timeout_seconds: 600, - vector_dimensions: 768, - similarity_threshold: 0.7, - max_vector_results: 50, - } -} - -/// Get database URL for migrations -fn get_database_url() -> String { - let config = get_database_config(); - format!( - "postgresql://{}:{}@{}:{}/{}", - config.username, config.password, config.host, config.port, config.database - ) -} \ No newline at end of file diff --git a/visualization_demo.rs b/visualization_demo.rs deleted file mode 100644 index 7ef9d080529845658d4210a3a79834363e574f0e..0000000000000000000000000000000000000000 --- a/visualization_demo.rs +++ /dev/null @@ -1,247 +0,0 @@ -//! Visualization Demo - Task 8.1: Concept Graph Visualization -//! -//! This demo showcases the D3.js-based concept graph visualization system, -//! demonstrating interactive graph exploration, filtering, and real-time updates. - -use anyhow::Result; -use brain::concept_graph::{ - ConceptGraphManager, ConceptGraphConfig, ConceptNode, ConceptType, RelationshipType, - ConceptRepository, RelationshipRepository, ConceptRelationship -}; -use brain::{ - VisualizationManager, VisualizationConfig -}; -use std::collections::HashMap; - -#[tokio::main] -async fn main() -> Result<()> { - println!("๐ŸŽจ Brain AI - Visualization System Demo"); - println!("========================================\n"); - - // Initialize concept graph with sample data - let concept_graph = create_sample_concept_graph().await?; - println!("๐Ÿ“Š Created sample concept graph with {} concepts", concept_graph.concept_count()); - - // Initialize visualization manager - let viz_config = VisualizationConfig { - enable_concept_graph: true, - enable_memory_timeline: true, - enable_simulation_dashboard: true, - max_graph_nodes: 50, - default_layout: "force".to_string(), - enable_interactions: true, - }; - - let viz_manager = VisualizationManager::new(viz_config); - println!("โœ… Initialized visualization manager"); - - // Generate concept graph visualization data - println!("\n๐Ÿ”„ Generating concept graph visualization data..."); - let graph_data = viz_manager.generate_concept_graph_data(&concept_graph).await?; - - println!("๐Ÿ“ˆ Graph visualization data generated:"); - println!(" โ€ข Nodes: {}", graph_data.nodes.len()); - println!(" โ€ข Edges: {}", graph_data.edges.len()); - println!(" โ€ข Layout: {}", graph_data.metadata.layout_algorithm); - println!(" โ€ข Generated at: {}", graph_data.metadata.timestamp.format("%Y-%m-%d %H:%M:%S UTC")); - - // Display node details - println!("\n๐ŸŽฏ Sample Concept Nodes:"); - for (i, node) in graph_data.nodes.iter().take(5).enumerate() { - println!(" {}. {} [{}]", i + 1, node.name, node.node_type); - println!(" โ€ข Size: {:.1}, Confidence: {:.2}, Connections: {}", - node.size, node.confidence, node.degree); - println!(" โ€ข Color: {}, ID: {}", node.color, node.id); - } - - // Display edge details - println!("\n๐Ÿ”— Sample Relationships:"); - for (i, edge) in graph_data.edges.iter().take(5).enumerate() { - println!(" {}. {} โ†’ {} [{}]", i + 1, - get_node_name(&graph_data.nodes, &edge.source), - get_node_name(&graph_data.nodes, &edge.target), - edge.edge_type); - println!(" โ€ข Weight: {:.2}, Color: {}", edge.weight, edge.color); - } - - // Demonstrate filtering capabilities - println!("\n๐Ÿ” Visualization Filtering Demo:"); - demonstrate_filtering(&graph_data); - - // Show node statistics by type - println!("\n๐Ÿ“Š Node Distribution by Type:"); - let mut type_counts = HashMap::new(); - for node in &graph_data.nodes { - *type_counts.entry(node.node_type.clone()).or_insert(0) += 1; - } - for (node_type, count) in type_counts { - let percentage = (count as f64 / graph_data.nodes.len() as f64) * 100.0; - println!(" โ€ข {}: {} nodes ({:.1}%)", node_type, count, percentage); - } - - // Show relationship statistics - println!("\n๐Ÿ”— Relationship Distribution:"); - let mut rel_counts = HashMap::new(); - for edge in &graph_data.edges { - *rel_counts.entry(edge.edge_type.clone()).or_insert(0) += 1; - } - for (rel_type, count) in rel_counts { - let percentage = (count as f64 / graph_data.edges.len() as f64) * 100.0; - println!(" โ€ข {}: {} relationships ({:.1}%)", rel_type, count, percentage); - } - - // Demonstrate graph metrics - println!("\n๐Ÿ“ˆ Graph Metrics:"); - let avg_connections = graph_data.edges.len() as f64 / graph_data.nodes.len() as f64; - let avg_confidence = graph_data.nodes.iter().map(|n| n.confidence).sum::() / graph_data.nodes.len() as f64; - let avg_weight = graph_data.edges.iter().map(|e| e.weight).sum::() / graph_data.edges.len() as f64; - - println!(" โ€ข Average connections per node: {:.2}", avg_connections); - println!(" โ€ข Average node confidence: {:.2}", avg_confidence); - println!(" โ€ข Average relationship weight: {:.2}", avg_weight); - - // Find most connected nodes - let mut nodes_by_degree = graph_data.nodes.clone(); - nodes_by_degree.sort_by(|a, b| b.degree.cmp(&a.degree)); - - println!("\n๐Ÿ† Most Connected Concepts:"); - for (i, node) in nodes_by_degree.iter().take(3).enumerate() { - println!(" {}. {} ({} connections, {:.2} confidence)", - i + 1, node.name, node.degree, node.confidence); - } - - // Demonstrate web interface information - println!("\n๐ŸŒ Web Visualization Interface:"); - println!(" โ€ข Concept Graph: /visualization/concept-graph"); - println!(" โ€ข Memory Timeline: /visualization/memory-timeline"); - println!(" โ€ข Dashboard: /visualization/simulation-dashboard"); - println!("\n โ€ข API Endpoints:"); - println!(" - GET /api/visualization/concept-graph"); - println!(" - GET /api/visualization/concept-graph/filtered"); - println!(" - GET /api/visualization/memory-timeline"); - println!(" - GET /api/visualization/simulation-dashboard"); - - // Show sample JSON output - println!("\n๐Ÿ“„ Sample JSON Output (First Node):"); - if let Some(first_node) = graph_data.nodes.first() { - let json_output = serde_json::to_string_pretty(first_node)?; - println!("{}", json_output); - } - - println!("\nโœ… Visualization Demo Complete!"); - println!(" ๐ŸŽฏ Task 8.1: Concept Graph Visualization - IMPLEMENTED"); - println!(" ๐Ÿ“Š D3.js-compatible data generation - READY"); - println!(" ๐ŸŒ Web interface endpoints - CONFIGURED"); - println!(" ๐Ÿ” Interactive filtering - SUPPORTED"); - println!(" ๐Ÿ“ˆ Real-time graph metrics - AVAILABLE"); - - Ok(()) -} - -/// Create a sample concept graph for demonstration -async fn create_sample_concept_graph() -> Result { - let config = ConceptGraphConfig::default(); - let mut manager = ConceptGraphManager::new(config).await?; - - // Create sample concepts - let concepts = vec![ - ("Natural Language Processing", ConceptType::Abstract, 0.95), - ("Machine Learning", ConceptType::Abstract, 0.92), - ("Neural Networks", ConceptType::Entity, 0.88), - ("Deep Learning", ConceptType::Abstract, 0.90), - ("Text Analysis", ConceptType::Action, 0.85), - ("Pattern Recognition", ConceptType::Action, 0.82), - ("Data Processing", ConceptType::Action, 0.78), - ("Algorithm", ConceptType::Entity, 0.87), - ("Training Data", ConceptType::Entity, 0.80), - ("Model Accuracy", ConceptType::Attribute, 0.85), - ("Feature Extraction", ConceptType::Action, 0.83), - ("Classification", ConceptType::Action, 0.86), - ("Regression", ConceptType::Action, 0.84), - ("Clustering", ConceptType::Action, 0.81), - ("Dimensionality Reduction", ConceptType::Action, 0.79), - ]; - - let mut concept_ids = Vec::new(); - - // Add concepts to the graph - for (content, concept_type, confidence) in concepts { - let concept = ConceptNode::new( - concept_type, - content.to_string(), - confidence, - Some("demo".to_string()), - ); - let id = manager.create_concept(concept).await?; - concept_ids.push(id); - } - - // Create relationships between concepts - let relationships = vec![ - (0, 1, RelationshipType::SimilarTo, 0.85), // NLP similar to ML - (1, 2, RelationshipType::Uses, 0.90), // ML uses Neural Networks - (2, 3, RelationshipType::IsA, 0.95), // Neural Networks is a Deep Learning - (1, 4, RelationshipType::Uses, 0.88), // ML uses Text Analysis - (4, 0, RelationshipType::PartOf, 0.92), // Text Analysis part of NLP - (1, 5, RelationshipType::Uses, 0.83), // ML uses Pattern Recognition - (5, 6, RelationshipType::Uses, 0.75), // Pattern Recognition uses Data Processing - (1, 7, RelationshipType::Uses, 0.90), // ML uses Algorithm - (1, 8, RelationshipType::Uses, 0.85), // ML uses Training Data - (1, 9, RelationshipType::Has, 0.87), // ML has Model Accuracy - (4, 10, RelationshipType::Uses, 0.80), // Text Analysis uses Feature Extraction - (1, 11, RelationshipType::Uses, 0.85), // ML uses Classification - (1, 12, RelationshipType::Uses, 0.82), // ML uses Regression - (1, 13, RelationshipType::Uses, 0.78), // ML uses Clustering - (10, 14, RelationshipType::SimilarTo, 0.76), // Feature Extraction similar to Dimensionality Reduction - ]; - - // Add relationships - for (source_idx, target_idx, rel_type, weight) in relationships { - if source_idx < concept_ids.len() && target_idx < concept_ids.len() { - let relationship = ConceptRelationship::new( - concept_ids[source_idx], - concept_ids[target_idx], - rel_type, - weight, - ); - manager.create_relationship(relationship).await?; - } - } - - Ok(manager) -} - -/// Get node name by ID for display purposes -fn get_node_name(nodes: &[brain::VisualizationNode], id: &str) -> String { - nodes.iter() - .find(|n| n.id == id) - .map(|n| n.name.clone()) - .unwrap_or_else(|| format!("Node_{}", &id[..8])) -} - -/// Demonstrate filtering capabilities -fn demonstrate_filtering(graph_data: &brain::GraphData) { - // Filter by node type - let abstract_nodes: Vec<_> = graph_data.nodes.iter() - .filter(|n| n.node_type == "Abstract") - .collect(); - println!(" โ€ข Abstract concepts: {}", abstract_nodes.len()); - - // Filter by confidence - let high_confidence_nodes: Vec<_> = graph_data.nodes.iter() - .filter(|n| n.confidence >= 0.9) - .collect(); - println!(" โ€ข High confidence nodes (โ‰ฅ0.9): {}", high_confidence_nodes.len()); - - // Filter by connections - let well_connected_nodes: Vec<_> = graph_data.nodes.iter() - .filter(|n| n.degree >= 3) - .collect(); - println!(" โ€ข Well-connected nodes (โ‰ฅ3 connections): {}", well_connected_nodes.len()); - - // Filter relationships by weight - let strong_relationships: Vec<_> = graph_data.edges.iter() - .filter(|e| e.weight >= 0.8) - .collect(); - println!(" โ€ข Strong relationships (โ‰ฅ0.8 weight): {}", strong_relationships.len()); -} \ No newline at end of file diff --git a/working_pocketflow_chat.rs b/working_pocketflow_chat.rs deleted file mode 100644 index 1c1cfb985c98a4fa2f3f22ab03a6b844131db9af..0000000000000000000000000000000000000000 --- a/working_pocketflow_chat.rs +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env cargo run --example working_pocketflow_chat -//! Working PocketFlow Chat -//! -//! This example demonstrates a working chat interface that can answer -//! questions about PocketFlow based on stored knowledge. - -use brain::*; -use brain_infra::memory::{WorkingMemoryRepository, EpisodicMemoryRepository, SemanticMemoryRepository}; -use tokio; -use std::io::{self, Write}; -use env_logger; - -#[tokio::main] -async fn main() -> Result<()> { - env_logger::Builder::from_env(env_logger::Env::default().default_filter_or("info")) - .init(); - - println!("๐Ÿง  Working PocketFlow Chat Demo"); - println!("๐Ÿค– PocketFlow Knowledge Chat"); - println!("============================"); - println!("Ask me anything about PocketFlow! Type 'quit' to exit.\n"); - - // Ensure data directory exists - std::fs::create_dir_all("data").map_err(|e| { - BrainError::from(e).with_context( - brain_types::ErrorContext::new("create_data_directory") - .with_details("Failed to create data directory for PocketFlow chat") - ) - })?; - - // Initialize repositories - let working_repo = Box::new(WorkingMemoryRepository::new(100)); - let episodic_repo = Box::new(EpisodicMemoryRepository::new("data/pocketflow_chat.db").await?); - let semantic_repo = Box::new(SemanticMemoryRepository::new()); - - // Create memory service - let mut memory_service = MemoryService::new(working_repo, episodic_repo, semantic_repo); - - // Load PocketFlow knowledge - println!("๐Ÿ“š Loading PocketFlow knowledge..."); - load_pocketflow_knowledge(&mut memory_service).await?; - println!("โœ… Knowledge loaded! Ready to chat.\n"); - - // Start chat loop - loop { - print!("You: "); - io::stdout().flush().unwrap(); - - let mut input = String::new(); - io::stdin().read_line(&mut input).expect("Failed to read line"); - let question = input.trim(); - - if question.is_empty() { - continue; - } - - if question.to_lowercase() == "quit" { - println!("๐Ÿ‘‹ Goodbye!"); - break; - } - - // Find answer in memory - match find_answer_in_memory(&memory_service, question).await { - Some(answer) => { - println!("๐Ÿค– Bot: {}\n", answer); - } - None => { - println!("๐Ÿค– Bot: I don't have specific information about that. Could you try rephrasing your question or ask about PocketFlow's architecture, features, or implementation?\n"); - } - } - } - - Ok(()) -} - -async fn load_pocketflow_knowledge(memory_service: &mut MemoryService) -> Result<()> { - let knowledge_items = vec![ - "PocketFlow is a 100-line AI framework that provides essential LLM orchestration capabilities in a compact, easy-to-understand codebase.", - "PocketFlow implements three unique architecture patterns: Node-Flow Architecture, Async Parallel Processing, and Batch Optimization Framework.", - "The Node-Flow pattern in PocketFlow separates processing logic (Nodes) from execution orchestration (Flows). BaseNode is the fundamental abstraction.", - "PocketFlow supports asynchronous execution with AsyncFlow and parallel processing with ParallelBatchNode for efficient concurrent LLM operations.", - "BatchNode and ParallelBatchNode in PocketFlow are used to optimize LLM API costs by grouping multiple requests together.", - "PocketFlow enables agent-based workflows through its 'Agents build Agents' design philosophy, allowing autonomous agents to create and orchestrate other agents.", - "The key classes in PocketFlow are: BaseNode (base processing unit), Flow (synchronous orchestrator), AsyncFlow (asynchronous orchestrator), BatchNode (batch processor), and ParallelBatchNode (parallel batch processor).", - "PocketFlow optimizes LLM API costs through BatchNode grouping, ParallelBatchNode concurrent processing, and efficient request management to reduce redundant API calls.", - "PocketFlow's main use cases include: LLM workflow orchestration, agent-based AI systems, batch processing of AI tasks, parallel LLM operations, cost-optimized AI pipelines, and rapid prototyping of AI agents.", - "PocketFlow uses Python with async/await patterns for non-blocking operations and leverages asyncio library for asynchronous operations.", - "PocketFlow implements the observer pattern for flow coordination and uses class inheritance for node specialization.", - "The framework maintains clean separation of concerns between data processing (nodes) and execution control (flows).", - "PocketFlow supports error handling, fallback mechanisms, and flexible configuration for both research and production environments.", - "PocketFlow's minimalist design focuses on core functionality without bloat, making it easy to understand, modify, and extend.", - "The framework enables recursive and self-improving AI systems where agents can spawn new agents, coordinate multi-agent tasks, and build complex agent hierarchies.", - ]; - - for (i, knowledge) in knowledge_items.iter().enumerate() { - let priority = if i < 5 { Priority::High } else { Priority::Medium }; - let _id = memory_service.learn(knowledge.to_string(), priority).await?; - } - - Ok(()) -} - -async fn find_answer_in_memory(memory_service: &MemoryService, question: &str) -> Option { - // Extract keywords from the question - let keywords: Vec = question - .to_lowercase() - .split_whitespace() - .filter(|word| word.len() > 3) - .map(|word| word.trim_matches(|c: char| !c.is_alphanumeric())) - .filter(|word| !word.is_empty()) - .map(|word| word.to_string()) - .collect(); - - if keywords.is_empty() { - return None; - } - - // Create query for working memory with correct field names - let query = WorkingMemoryQuery { - content_pattern: Some(keywords[0].clone()), - priority: None, - min_importance: None, - created_after: None, - limit: Some(5), - }; - - // Search working memory - if let Ok(results) = memory_service.query_working(&query).await { - if !results.is_empty() { - // Return the content of the first relevant result - return Some(results[0].content.clone()); - } - } - - // If no results in working memory, try cross-memory search - if let Ok(cross_results) = memory_service.query_all_memories(&keywords[0]).await { - if !cross_results.working_results.is_empty() { - return Some(cross_results.working_results[0].content.clone()); - } - if !cross_results.episodic_results.is_empty() { - return Some(cross_results.episodic_results[0].content.clone()); - } - } - - None -} \ No newline at end of file