veryfinal.py

"""
Open-Source Multi-LLM Agent System
Uses only free and open-source models - no paid APIs required
"""

import os
import time
import random
import operator
from typing import List, Dict, Any, TypedDict, Annotated, Optional
from dotenv import load_dotenv

# Core LangChain imports
from langchain_core.tools import tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage

# Open-source model integrations
from langchain_groq import ChatGroq  # Free tier available
from langchain_community.llms import Ollama
from langchain_community.chat_models import ChatOllama

# Hugging Face integration for open-source models
try:
    from langchain_huggingface import HuggingFacePipeline
    from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
    HF_AVAILABLE = True
except ImportError:
    HF_AVAILABLE = False

# Vector database imports
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
import json

load_dotenv()

# Enhanced system prompt
ENHANCED_SYSTEM_PROMPT = (
    "You are a helpful assistant tasked with answering questions using available tools. "
    "You must provide accurate, comprehensive answers based on available information. "
    "When answering questions, follow these guidelines:\n"
    "1. Use available tools to gather information when needed\n"
    "2. Provide precise, factual answers\n"
    "3. For numbers: don't use commas or units unless specified\n"
    "4. For strings: don't use articles or abbreviations, write digits in plain text\n"
    "5. For lists: apply above rules based on element type\n"
    "6. Always end with 'FINAL ANSWER: [YOUR ANSWER]'\n"
    "7. Be concise but thorough in your reasoning\n"
    "8. If you cannot find the answer, state that clearly"
)

# ---- Tool Definitions ----
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the product."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two integers and return the sum."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract the second integer from the first and return the difference."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divide the first integer by the second and return the quotient."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Return the remainder when dividing the first integer by the second."""
    return a % b

@tool
def optimized_web_search(query: str) -> str:
    """Perform web search using free DuckDuckGo (fallback if Tavily not available)."""
    try:
        # Try Tavily first (free tier)
        if os.getenv("TAVILY_API_KEY"):
            time.sleep(random.uniform(0.7, 1.5))
            search_tool = TavilySearchResults(max_results=3)
            docs = search_tool.invoke({"query": query})
            return "\n\n---\n\n".join(
                f"<Doc url='{d.get('url','')}'>{d.get('content','')[:800]}</Doc>"
                for d in docs
            )
        else:
            # Fallback to DuckDuckGo (completely free)
            try:
                from duckduckgo_search import DDGS
                with DDGS() as ddgs:
                    results = list(ddgs.text(query, max_results=3))
                    return "\n\n---\n\n".join(
                        f"<Doc url='{r.get('href','')}'>{r.get('body','')[:800]}</Doc>"
                        for r in results
                    )
            except ImportError:
                return "Web search not available - install duckduckgo-search for free web search"
    except Exception as e:
        return f"Web search failed: {e}"

@tool
def optimized_wiki_search(query: str) -> str:
    """Perform Wikipedia search - completely free."""
    try:
        time.sleep(random.uniform(0.3, 1))
        docs = WikipediaLoader(query=query, load_max_docs=2).load()
        return "\n\n---\n\n".join(
            f"<Doc src='{d.metadata.get('source','Wikipedia')}'>{d.page_content[:1000]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Wikipedia search failed: {e}"

# ---- Open-Source Model Manager ----
class OpenSourceModelManager:
    """Manages only open-source and free models"""
    
    def __init__(self):
        self.available_models = {}
        self._initialize_models()
    
    def _initialize_models(self):
        """Initialize only open-source models"""
        
        # 1. Groq (Free tier with open-source models)
        if os.getenv("GROQ_API_KEY"):
            try:
                self.available_models['groq_llama3_70b'] = ChatGroq(
                    model="llama3-70b-8192", 
                    temperature=0, 
                    api_key=os.getenv("GROQ_API_KEY")
                )
                self.available_models['groq_llama3_8b'] = ChatGroq(
                    model="llama3-8b-8192", 
                    temperature=0, 
                    api_key=os.getenv("GROQ_API_KEY")
                )
                self.available_models['groq_mixtral'] = ChatGroq(
                    model="mixtral-8x7b-32768", 
                    temperature=0, 
                    api_key=os.getenv("GROQ_API_KEY")
                )
                self.available_models['groq_gemma'] = ChatGroq(
                    model="gemma-7b-it", 
                    temperature=0, 
                    api_key=os.getenv("GROQ_API_KEY")
                )
                print("Groq models initialized (free tier)")
            except Exception as e:
                print(f"Groq models not available: {e}")
        
        # 2. Ollama (Completely free local models)
        try:
            # Test if Ollama is running
            test_model = ChatOllama(model="llama3", base_url="http://localhost:11434")
            # If no error, add Ollama models
            self.available_models['ollama_llama3'] = ChatOllama(model="llama3")
            self.available_models['ollama_llama3_70b'] = ChatOllama(model="llama3:70b")
            self.available_models['ollama_mistral'] = ChatOllama(model="mistral")
            self.available_models['ollama_phi3'] = ChatOllama(model="phi3")
            self.available_models['ollama_codellama'] = ChatOllama(model="codellama")
            self.available_models['ollama_gemma'] = ChatOllama(model="gemma")
            self.available_models['ollama_qwen'] = ChatOllama(model="qwen")
            print("Ollama models initialized (local)")
        except Exception as e:
            print(f"Ollama not available: {e}")
        
        # 3. Hugging Face Transformers (Completely free)
        if HF_AVAILABLE:
            try:
                # Small models that can run on CPU
                self.available_models['hf_gpt2'] = self._create_hf_model("gpt2")
                self.available_models['hf_distilgpt2'] = self._create_hf_model("distilgpt2")
                print("Hugging Face models initialized (local)")
            except Exception as e:
                print(f"Hugging Face models not available: {e}")
        
        print(f"Total available open-source models: {len(self.available_models)}")
    
    def _create_hf_model(self, model_name: str):
        """Create Hugging Face pipeline model"""
        try:
            pipe = pipeline(
                "text-generation",
                model=model_name,
                max_length=512,
                do_sample=True,
                temperature=0.7,
                pad_token_id=50256
            )
            return HuggingFacePipeline(pipeline=pipe)
        except Exception as e:
            print(f"Failed to create HF model {model_name}: {e}")
            return None
    
    def get_model(self, model_name: str):
        """Get a specific model by name"""
        return self.available_models.get(model_name)
    
    def list_available_models(self) -> List[str]:
        """List all available model names"""
        return list(self.available_models.keys())
    
    def get_best_model_for_task(self, task_type: str):
        """Get the best available model for a specific task type"""
        if task_type == "reasoning":
            # Prefer larger models for reasoning
            for model_name in ['groq_llama3_70b', 'ollama_llama3_70b', 'groq_mixtral', 'ollama_llama3']:
                if model_name in self.available_models:
                    return self.available_models[model_name]
        
        elif task_type == "coding":
            # Prefer code-specialized models
            for model_name in ['ollama_codellama', 'groq_llama3_70b', 'ollama_llama3']:
                if model_name in self.available_models:
                    return self.available_models[model_name]
        
        elif task_type == "fast":
            # Prefer fast, smaller models
            for model_name in ['groq_llama3_8b', 'groq_gemma', 'ollama_phi3', 'hf_distilgpt2']:
                if model_name in self.available_models:
                    return self.available_models[model_name]
        
        # Default fallback to first available
        if self.available_models:
            return list(self.available_models.values())[0]
        return None

# ---- Enhanced Agent State ----
class EnhancedAgentState(TypedDict):
    """State structure for the enhanced multi-LLM agent system."""
    messages: Annotated[List[HumanMessage | AIMessage], operator.add]
    query: str
    agent_type: str
    final_answer: str
    perf: Dict[str, Any]
    tools_used: List[str]
    reasoning: str
    model_used: str

# ---- Open-Source Multi-LLM System ----
class OpenSourceMultiLLMSystem:
    """
    Multi-LLM system using only open-source and free models
    """
    
    def __init__(self):
        self.model_manager = OpenSourceModelManager()
        self.tools = [
            multiply, add, subtract, divide, modulus,
            optimized_web_search, optimized_wiki_search
        ]
        self.graph = self._build_graph()
    
    def _build_graph(self) -> StateGraph:
        """Build the LangGraph state machine with open-source models."""
        
        def router(st: EnhancedAgentState) -> EnhancedAgentState:
            """Route queries to appropriate model based on complexity and content analysis."""
            q = st["query"].lower()
            
            # Enhanced routing logic
            if any(keyword in q for keyword in ["calculate", "compute", "math", "multiply", "add", "subtract", "divide"]):
                model_type = "reasoning"
                agent_type = "math"
            elif any(keyword in q for keyword in ["search", "find", "lookup", "wikipedia", "information about"]):
                model_type = "fast"
                agent_type = "search_enhanced"
            elif any(keyword in q for keyword in ["code", "programming", "function", "algorithm"]):
                model_type = "coding"
                agent_type = "coding"
            elif len(q.split()) > 20:  # Complex queries
                model_type = "reasoning"
                agent_type = "complex"
            else:
                model_type = "fast"
                agent_type = "simple"
            
            # Get the best model for this task
            selected_model = self.model_manager.get_best_model_for_task(model_type)
            model_name = "unknown"
            for name, model in self.model_manager.available_models.items():
                if model == selected_model:
                    model_name = name
                    break
            
            return {**st, "agent_type": agent_type, "tools_used": [], "reasoning": "", "model_used": model_name}

        def math_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process mathematical queries."""
            return self._process_with_model(st, "reasoning", "Mathematical calculation using open-source model")

        def search_enhanced_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process query with search enhancement."""
            t0 = time.time()
            tools_used = []
            
            try:
                # Determine search strategy
                query = st["query"]
                search_results = ""
                
                if any(keyword in query.lower() for keyword in ["wikipedia", "wiki"]):
                    search_results = optimized_wiki_search.invoke({"query": query})
                    tools_used.append("wikipedia_search")
                else:
                    search_results = optimized_web_search.invoke({"query": query})
                    tools_used.append("web_search")
                
                enhanced_query = f"""
                Original Question: {query}
                
                Search Results:
                {search_results}
                
                Based on the search results above, provide a direct answer to the original question.
                """
                
                # Use fast model for search-enhanced queries
                model = self.model_manager.get_best_model_for_task("fast")
                if model:
                    sys = SystemMessage(content=ENHANCED_SYSTEM_PROMPT)
                    res = model.invoke([sys, HumanMessage(content=enhanced_query)])
                    
                    answer = res.content.strip() if hasattr(res, 'content') else str(res).strip()
                    if "FINAL ANSWER:" in answer:
                        answer = answer.split("FINAL ANSWER:")[-1].strip()
                    
                    return {**st,
                            "final_answer": answer,
                            "tools_used": tools_used,
                            "reasoning": "Used search enhancement with open-source model",
                            "perf": {"time": time.time() - t0, "prov": "Search-Enhanced"}}
                else:
                    return {**st, "final_answer": "No models available", "perf": {"error": "No models"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def coding_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process coding-related queries."""
            return self._process_with_model(st, "coding", "Code generation using open-source model")

        def complex_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process complex queries."""
            return self._process_with_model(st, "reasoning", "Complex reasoning using open-source model")

        def simple_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process simple queries."""
            return self._process_with_model(st, "fast", "Simple query using fast open-source model")

        # Build graph
        g = StateGraph(EnhancedAgentState)
        g.add_node("router", router)
        g.add_node("math", math_node)
        g.add_node("search_enhanced", search_enhanced_node)
        g.add_node("coding", coding_node)
        g.add_node("complex", complex_node)
        g.add_node("simple", simple_node)
        
        g.set_entry_point("router")
        g.add_conditional_edges("router", lambda s: s["agent_type"], {
            "math": "math",
            "search_enhanced": "search_enhanced",
            "coding": "coding",
            "complex": "complex",
            "simple": "simple"
        })
        
        for node in ["math", "search_enhanced", "coding", "complex", "simple"]:
            g.add_edge(node, END)
            
        return g.compile(checkpointer=MemorySaver())
    
    def _process_with_model(self, st: EnhancedAgentState, model_type: str, reasoning: str) -> EnhancedAgentState:
        """Process query with specified model type"""
        t0 = time.time()
        try:
            model = self.model_manager.get_best_model_for_task(model_type)
            if not model:
                return {**st, "final_answer": "No suitable model available", "perf": {"error": "No model"}}
            
            enhanced_query = f"""
            Question: {st["query"]}
            
            Please provide a direct, accurate answer to this question.
            """
            
            sys = SystemMessage(content=ENHANCED_SYSTEM_PROMPT)
            res = model.invoke([sys, HumanMessage(content=enhanced_query)])
            
            answer = res.content.strip() if hasattr(res, 'content') else str(res).strip()
            if "FINAL ANSWER:" in answer:
                answer = answer.split("FINAL ANSWER:")[-1].strip()
            
            return {**st,
                    "final_answer": answer,
                    "reasoning": reasoning,
                    "perf": {"time": time.time() - t0, "prov": f"OpenSource-{model_type}"}}
        except Exception as e:
            return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

    def process_query(self, q: str) -> str:
        """Process a query through the open-source multi-LLM system."""
        state = {
            "messages": [HumanMessage(content=q)],
            "query": q,
            "agent_type": "",
            "final_answer": "",
            "perf": {},
            "tools_used": [],
            "reasoning": "",
            "model_used": ""
        }
        cfg = {"configurable": {"thread_id": f"opensource_qa_{hash(q)}"}}
        
        try:
            out = self.graph.invoke(state, cfg)
            answer = out.get("final_answer", "").strip()
            
            # Ensure we don't return the question as the answer
            if answer == q or answer.startswith(q):
                return "Information not available"
            
            return answer if answer else "No answer generated"
        except Exception as e:
            return f"Error processing query: {e}"
    
    def get_system_info(self) -> Dict[str, Any]:
        """Get information about available open-source models"""
        return {
            "available_models": self.model_manager.list_available_models(),
            "total_models": len(self.model_manager.available_models),
            "model_types": {
                "groq_free_tier": [m for m in self.model_manager.list_available_models() if m.startswith("groq_")],
                "ollama_local": [m for m in self.model_manager.list_available_models() if m.startswith("ollama_")],
                "huggingface_local": [m for m in self.model_manager.list_available_models() if m.startswith("hf_")]
            }
        }

# ---- Build Graph Function (for compatibility) ----
def build_graph(provider: str = "opensource"):
    """Build graph using only open-source models"""
    return OpenSourceMultiLLMSystem().graph

# ---- Main execution ----
if __name__ == "__main__":
    # Initialize the open-source system
    system = OpenSourceMultiLLMSystem()
    
    # Print system information
    info = system.get_system_info()
    print("Open-Source System Information:")
    print(f"Total Models Available: {info['total_models']}")
    for category, models in info['model_types'].items():
        if models:
            print(f"  {category}: {models}")
    
    # Test queries
    test_questions = [
        "What is 25 multiplied by 17?",
        "Find information about Mercedes Sosa albums between 2000-2009",
        "Write a simple Python function to calculate factorial",
        "Explain quantum computing in simple terms",
        "What is the capital of France?"
    ]
    
    print("\n" + "="*60)
    print("Testing Open-Source Multi-LLM System")
    print("="*60)
    
    for i, question in enumerate(test_questions, 1):
        print(f"\nQuestion {i}: {question}")
        print("-" * 50)
        answer = system.process_query(question)
        print(f"Answer: {answer}")