veryfinal.py

"""
Enhanced Multi-LLM Agent System with Question-Answering Capabilities
Supports Groq (Llama-3 8B/70B, DeepSeek), Google Gemini, NVIDIA NIM, and Agno-style agents
"""

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

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
from langchain_groq import ChatGroq

# Load environment variables
load_dotenv()

# Enhanced system prompt for question-answering tasks
ENHANCED_SYSTEM_PROMPT = (
    "You are a helpful assistant tasked with answering questions using a set of tools. "
    "You must provide accurate, comprehensive answers based on available information. "
    "When answering questions, follow these guidelines:\n"
    '1)You are a helpful assistant tasked with answering questions using a set of tools. 
    '2)Now, I will ask you a question. Report your thoughts, and finish your answer with the following template:' 
     'FINAL ANSWER: [YOUR FINAL ANSWER].'
    '3)YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.'
    '4)Your answer should only start with "FINAL ANSWER: ", then follows with the answer. '
)

# ---- Tool Definitions with Enhanced Docstrings ----
@tool
def multiply(a: int | float, b: int | float) -> int | float:
    """Multiply two numbers.
    
    Args:
        a: first int | float
        b: second int | float
    """
    return a * b

@tool
def add(a: int | float, b: int | float) -> int | float:
    """
    Adds two integers and returns the sum.
    
    Args:
        a (int): First integer
        b (int): Second integer
        
    Returns:
        int: Sum of a and b
    """
    return a + b

@tool
def subtract(a: int | float, b: int | float) -> int | float:
    """
    Subtracts the second integer from the first and returns the difference.
    
    Args:
        a (int): First integer (minuend)
        b (int): Second integer (subtrahend)
        
    Returns:
        int: Difference of a and b
    """
    return a - b

@tool
def divide(a: int | float, b: int | float) -> int | float:
    """
    Divides the first integer by the second and returns the quotient.
    
    Args:
        a (int): Dividend
        b (int): Divisor
        
    Returns:
        float: Quotient of a divided by b
        
    Raises:
        ValueError: If b is zero
    """
    if b == 0 or b==0.0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int | float, b: int | float) -> int | float:
    """
    Returns the remainder when dividing the first integer by the second.
    
    Args:
        a (int): Dividend
        b (int): Divisor
        
    Returns:
        int: Remainder of a divided by b
    """
    return a % b

@tool
def optimized_web_search(query: str) -> str:
    """
    Performs an optimized web search using TavilySearchResults.
    
    Args:
        query (str): Search query string
        
    Returns:
        str: Concatenated search results with URLs and content snippets
    """
    try:
        time.sleep(random.uniform(0.7, 1.5))
        docs = TavilySearchResults(max_results=3).invoke(query=query)
        return "\n\n---\n\n".join(
            f"<Doc url='{d.get('url','')}'>{d.get('content','')[:800]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Web search failed: {e}"

@tool
def optimized_wiki_search(query: str) -> str:
    """
    Performs an optimized Wikipedia search and returns content snippets.
    
    Args:
        query (str): Wikipedia search query
        
    Returns:
        str: Wikipedia content with source attribution
    """
    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}"

# ---- LLM Provider Integrations ----
try:
    from langchain_nvidia_ai_endpoints import ChatNVIDIA
    NVIDIA_AVAILABLE = True
except ImportError:
    NVIDIA_AVAILABLE = False

try:
    import google.generativeai as genai
    from langchain_google_genai import ChatGoogleGenerativeAI
    GOOGLE_AVAILABLE = True
except ImportError:
    GOOGLE_AVAILABLE = False

# ---- Enhanced Agent State ----
class EnhancedAgentState(TypedDict):
    """
    State structure for the enhanced multi-LLM agent system.
    
    Attributes:
        messages: List of conversation messages
        query: Current query string
        agent_type: Selected agent/LLM type
        final_answer: Generated response
        perf: Performance metrics
        agno_resp: Agno-style response metadata
        tools_used: List of tools used in processing
        reasoning: Step-by-step reasoning process
    """
    messages: Annotated[List[HumanMessage | AIMessage], operator.add]
    query: str
    agent_type: str
    final_answer: str
    perf: Dict[str, Any]
    agno_resp: str
    tools_used: List[str]
    reasoning: str

# ---- Enhanced Multi-LLM System ----
class EnhancedQuestionAnsweringSystem:
    """
    Advanced question-answering system that routes queries to appropriate LLM providers
    and uses tools to gather information for comprehensive answers.
    
    Features:
    - Multi-LLM routing (Groq, Google, NVIDIA)
    - Tool integration for web search and calculations
    - Structured reasoning and answer formatting
    - Performance monitoring
    """
    
    def __init__(self):
        """Initialize the enhanced question-answering system."""
        self.tools = [
            multiply, add, subtract, divide, modulus,
            optimized_web_search, optimized_wiki_search
        ]
        self.graph = self._build_graph()

    def _llm(self, model_name: str) -> ChatGroq:
        """
        Create a Groq LLM instance.
        
        Args:
            model_name (str): Model identifier
            
        Returns:
            ChatGroq: Configured Groq LLM instance
        """
        return ChatGroq(
            model=model_name,
            temperature=0,
            api_key=os.getenv("GROQ_API_KEY")
        )

    def _build_graph(self) -> StateGraph:
        """
        Build the LangGraph state machine with enhanced question-answering capabilities.
        
        Returns:
            StateGraph: Compiled graph with routing logic
        """
        # Initialize LLMs
        llama8_llm = self._llm("llama3-8b-8192")
        llama70_llm = self._llm("llama3-70b-8192")
        deepseek_llm = self._llm("deepseek-chat")

        def router(st: EnhancedAgentState) -> EnhancedAgentState:
            """
            Route queries to appropriate LLM based on complexity and content.
            
            Args:
                st (EnhancedAgentState): Current state
                
            Returns:
                EnhancedAgentState: Updated state with agent selection
            """
            q = st["query"].lower()
            
            # Route based on query characteristics
            if any(keyword in q for keyword in ["calculate", "compute", "math", "number"]):
                t = "llama70"  # Use more powerful model for calculations
            elif any(keyword in q for keyword in ["search", "find", "lookup", "wikipedia"]):
                t = "search_enhanced"  # Use search-enhanced processing
            elif "deepseek" in q or any(keyword in q for keyword in ["analyze", "reasoning", "complex"]):
                t = "deepseek"
            elif len(q.split()) > 20:  # Complex queries
                t = "llama70"
            else:
                t = "llama8"  # Default for simple queries
                
            return {**st, "agent_type": t, "tools_used": [], "reasoning": ""}

        def llama8_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process query with Llama-3 8B model."""
            t0 = time.time()
            try:
                sys = SystemMessage(content=ENHANCED_SYSTEM_PROMPT)
                res = llama8_llm.invoke([sys, HumanMessage(content=st["query"])])
                
                reasoning = "Used Llama-3 8B for efficient processing of straightforward query."
                
                return {**st,
                        "final_answer": res.content,
                        "reasoning": reasoning,
                        "perf": {"time": time.time() - t0, "prov": "Groq-Llama3-8B"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def llama70_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process query with Llama-3 70B model."""
            t0 = time.time()
            try:
                sys = SystemMessage(content=ENHANCED_SYSTEM_PROMPT)
                res = llama70_llm.invoke([sys, HumanMessage(content=st["query"])])
                
                reasoning = "Used Llama-3 70B for complex reasoning and detailed analysis."
                
                return {**st,
                        "final_answer": res.content,
                        "reasoning": reasoning,
                        "perf": {"time": time.time() - t0, "prov": "Groq-Llama3-70B"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def deepseek_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process query with DeepSeek model."""
            t0 = time.time()
            try:
                sys = SystemMessage(content=ENHANCED_SYSTEM_PROMPT)
                res = deepseek_llm.invoke([sys, HumanMessage(content=st["query"])])
                
                reasoning = "Used DeepSeek for advanced reasoning and analytical tasks."
                
                return {**st,
                        "final_answer": res.content,
                        "reasoning": reasoning,
                        "perf": {"time": time.time() - t0, "prov": "Groq-DeepSeek"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def search_enhanced_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Process query with search enhancement."""
            t0 = time.time()
            tools_used = []
            reasoning_steps = []
            
            try:
                # Determine if we need web search or Wikipedia
                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")
                    reasoning_steps.append("Searched Wikipedia for relevant information")
                else:
                    search_results = optimized_web_search.invoke({"query": query})
                    tools_used.append("web_search")
                    reasoning_steps.append("Performed web search for current information")
                
                # Enhance query with search results
                enhanced_query = f"""
                Original Query: {query}
                
                Search Results:
                {search_results}
                
                Based on the search results above, please provide a comprehensive answer to the original query.
                """
                
                sys = SystemMessage(content=ENHANCED_SYSTEM_PROMPT)
                res = llama70_llm.invoke([sys, HumanMessage(content=enhanced_query)])
                
                reasoning_steps.append("Used Llama-3 70B to analyze search results and generate comprehensive answer")
                reasoning = " -> ".join(reasoning_steps)
                
                return {**st,
                        "final_answer": res.content,
                        "tools_used": tools_used,
                        "reasoning": reasoning,
                        "perf": {"time": time.time() - t0, "prov": "Search-Enhanced-Llama70"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        # Build graph
        g = StateGraph(EnhancedAgentState)
        g.add_node("router", router)
        g.add_node("llama8", llama8_node)
        g.add_node("llama70", llama70_node)
        g.add_node("deepseek", deepseek_node)
        g.add_node("search_enhanced", search_enhanced_node)
        
        g.set_entry_point("router")
        g.add_conditional_edges("router", lambda s: s["agent_type"], {
            "llama8": "llama8",
            "llama70": "llama70",
            "deepseek": "deepseek",
            "search_enhanced": "search_enhanced"
        })
        
        for node in ["llama8", "llama70", "deepseek", "search_enhanced"]:
            g.add_edge(node, END)
            
        return g.compile(checkpointer=MemorySaver())

    def process_query(self, q: str) -> str:
        """
        Process a query through the enhanced question-answering system.
        
        Args:
            q (str): Input query
            
        Returns:
            str: Generated response with proper formatting
        """
        state = {
            "messages": [HumanMessage(content=q)],
            "query": q,
            "agent_type": "",
            "final_answer": "",
            "perf": {},
            "agno_resp": "",
            "tools_used": [],
            "reasoning": ""
        }
        cfg = {"configurable": {"thread_id": f"qa_{hash(q)}"}}
        
        try:
            out = self.graph.invoke(state, cfg)
            answer = out.get("final_answer", "").strip()
            
            # Ensure proper formatting
            if not answer.startswith("FINAL ANSWER:"):
                # Extract the actual answer if it's buried in explanation
                if "FINAL ANSWER:" in answer:
                    answer = answer.split("FINAL ANSWER:")[-1].strip()
                    answer = f"FINAL ANSWER: {answer}"
                else:
                    # Add FINAL ANSWER prefix if missing
                    answer = f"FINAL ANSWER: {answer}"
            
            return answer
        except Exception as e:
            return f"FINAL ANSWER: Error processing query: {e}"

def build_graph(provider: str | None = None) -> StateGraph:
    """
    Build and return the graph for the enhanced question-answering system.
    
    Args:
        provider (str | None): Provider preference (optional)
        
    Returns:
        StateGraph: Compiled graph instance
    """
    return EnhancedQuestionAnsweringSystem().graph

# ---- Main Question-Answering Interface ----
class QuestionAnsweringAgent:
    """
    Main interface for the question-answering agent system.
    """
    
    def __init__(self):
        """Initialize the question-answering agent."""
        self.system = EnhancedQuestionAnsweringSystem()
        
    def answer_question(self, question: str) -> str:
        """
        Answer a question using the enhanced multi-LLM system.
        
        Args:
            question (str): The question to answer
            
        Returns:
            str: Formatted answer with FINAL ANSWER prefix
        """
        return self.system.process_query(question)

if __name__ == "__main__":
    # Initialize the question-answering system
    qa_agent = QuestionAnsweringAgent()
    
    # Test with sample questions
    test_questions = [
        "How many studio albums were published by Mercedes Sosa between 2000 and 2009?",
        "What is 25 multiplied by 17?",
        "Find information about the capital of France on Wikipedia",
        "What is the population of Tokyo according to recent data?"
    ]
    
    print("=" * 80)
    print("Enhanced Question-Answering Agent System")
    print("=" * 80)
    
    for i, question in enumerate(test_questions, 1):
        print(f"\nQuestion {i}: {question}")
        print("-" * 60)
        answer = qa_agent.answer_question(question)
        print(answer)
        print()