veryfinal.py

"""
Final Working Multi-LLM Agent System
Robust fallback system that works even when Agno fails
"""

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
from langchain_groq import ChatGroq

load_dotenv()

# System prompt for proper question answering
SYSTEM_PROMPT = """You are a helpful assistant tasked with answering questions using available tools.

Guidelines:
1. Use available tools to gather information when needed
2. Provide precise, factual answers
3. For numbers: don't use commas or units unless specified
4. For strings: don't use articles or abbreviations, write digits in plain text
5. Always end with 'FINAL ANSWER: [YOUR ANSWER]'
6. Be concise but thorough
7. 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 web_search(query: str) -> str:
    """Search the web for information."""
    try:
        if os.getenv("TAVILY_API_KEY"):
            time.sleep(random.uniform(0.5, 1.0))
            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','')[:600]}</Doc>"
                for d in docs
            )
        else:
            return "Web search not available - no API key"
    except Exception as e:
        return f"Web search failed: {e}"

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for information."""
    try:
        time.sleep(random.uniform(0.3, 0.8))
        docs = WikipediaLoader(query=query, load_max_docs=2).load()
        return "\n\n---\n\n".join(
            f"<Doc src='Wikipedia'>{d.page_content[:800]}</Doc>"
            for d in docs
        )
    except Exception as e:
        return f"Wikipedia search failed: {e}"

# ---- Enhanced Agent State ----
class EnhancedAgentState(TypedDict):
    messages: Annotated[List[HumanMessage | AIMessage], operator.add]
    query: str
    agent_type: str
    final_answer: str
    perf: Dict[str, Any]
    tools_used: List[str]

# ---- Working Multi-LLM System ----
class WorkingMultiLLMSystem:
    """Reliable multi-LLM system that actually works"""
    
    def __init__(self):
        self.tools = [multiply, add, subtract, divide, modulus, web_search, wiki_search]
        self.graph = self._build_graph()
        print("✅ Working Multi-LLM System initialized")

    def _get_llm(self, model_name: str = "llama3-70b-8192"):
        """Get Groq LLM instance"""
        return ChatGroq(
            model=model_name,
            temperature=0,
            api_key=os.getenv("GROQ_API_KEY")
        )

    def _build_graph(self) -> StateGraph:
        """Build the working LangGraph system"""
        
        def router(st: EnhancedAgentState) -> EnhancedAgentState:
            """Route queries to appropriate processing"""
            q = st["query"].lower()
            
            if any(keyword in q for keyword in ["calculate", "multiply", "add", "subtract", "divide", "math"]):
                agent_type = "math"
            elif any(keyword in q for keyword in ["search", "find", "information", "about"]):
                agent_type = "search"
            elif any(keyword in q for keyword in ["wikipedia", "wiki"]):
                agent_type = "wiki"
            else:
                agent_type = "general"
            
            return {**st, "agent_type": agent_type, "tools_used": []}

        def math_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Handle mathematical queries"""
            t0 = time.time()
            try:
                llm = self._get_llm("llama3-70b-8192")
                
                enhanced_query = f"""
                Question: {st["query"]}
                
                This is a mathematical question. Please solve it step by step and provide the exact numerical answer.
                """
                
                sys_msg = SystemMessage(content=SYSTEM_PROMPT)
                response = llm.invoke([sys_msg, HumanMessage(content=enhanced_query)])
                
                answer = response.content.strip()
                if "FINAL ANSWER:" in answer:
                    answer = answer.split("FINAL ANSWER:")[-1].strip()
                
                return {**st, 
                       "final_answer": answer,
                       "perf": {"time": time.time() - t0, "provider": "Groq-Math"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def search_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Handle search queries"""
            t0 = time.time()
            try:
                # Perform web search
                search_results = web_search.invoke({"query": st["query"]})
                
                llm = self._get_llm("llama3-70b-8192")
                
                enhanced_query = f"""
                Question: {st["query"]}
                
                Search Results:
                {search_results}
                
                Based on the search results above, provide a direct answer to the question.
                """
                
                sys_msg = SystemMessage(content=SYSTEM_PROMPT)
                response = llm.invoke([sys_msg, HumanMessage(content=enhanced_query)])
                
                answer = response.content.strip()
                if "FINAL ANSWER:" in answer:
                    answer = answer.split("FINAL ANSWER:")[-1].strip()
                
                return {**st, 
                       "final_answer": answer,
                       "tools_used": ["web_search"],
                       "perf": {"time": time.time() - t0, "provider": "Groq-Search"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def wiki_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Handle Wikipedia queries"""
            t0 = time.time()
            try:
                # Perform Wikipedia search
                wiki_results = wiki_search.invoke({"query": st["query"]})
                
                llm = self._get_llm("llama3-70b-8192")
                
                enhanced_query = f"""
                Question: {st["query"]}
                
                Wikipedia Results:
                {wiki_results}
                
                Based on the Wikipedia information above, provide a direct answer to the question.
                """
                
                sys_msg = SystemMessage(content=SYSTEM_PROMPT)
                response = llm.invoke([sys_msg, HumanMessage(content=enhanced_query)])
                
                answer = response.content.strip()
                if "FINAL ANSWER:" in answer:
                    answer = answer.split("FINAL ANSWER:")[-1].strip()
                
                return {**st, 
                       "final_answer": answer,
                       "tools_used": ["wiki_search"],
                       "perf": {"time": time.time() - t0, "provider": "Groq-Wiki"}}
            except Exception as e:
                return {**st, "final_answer": f"Error: {e}", "perf": {"error": str(e)}}

        def general_node(st: EnhancedAgentState) -> EnhancedAgentState:
            """Handle general queries"""
            t0 = time.time()
            try:
                llm = self._get_llm("llama3-70b-8192")
                
                enhanced_query = f"""
                Question: {st["query"]}
                
                Please provide a direct, accurate answer to this question.
                """
                
                sys_msg = SystemMessage(content=SYSTEM_PROMPT)
                response = llm.invoke([sys_msg, HumanMessage(content=enhanced_query)])
                
                answer = response.content.strip()
                if "FINAL ANSWER:" in answer:
                    answer = answer.split("FINAL ANSWER:")[-1].strip()
                
                return {**st, 
                       "final_answer": answer,
                       "perf": {"time": time.time() - t0, "provider": "Groq-General"}}
            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("math", math_node)
        g.add_node("search", search_node)
        g.add_node("wiki", wiki_node)
        g.add_node("general", general_node)
        
        g.set_entry_point("router")
        g.add_conditional_edges("router", lambda s: s["agent_type"], {
            "math": "math",
            "search": "search", 
            "wiki": "wiki",
            "general": "general"
        })
        
        for node in ["math", "search", "wiki", "general"]:
            g.add_edge(node, END)
            
        return g.compile(checkpointer=MemorySaver())

    def process_query(self, query: str) -> str:
        """Process a query through the working system"""
        state = {
            "messages": [HumanMessage(content=query)],
            "query": query,
            "agent_type": "",
            "final_answer": "",
            "perf": {},
            "tools_used": []
        }
        config = {"configurable": {"thread_id": f"working_{hash(query)}"}}
        
        try:
            result = self.graph.invoke(state, config)
            answer = result.get("final_answer", "").strip()
            
            # Validation
            if not answer or answer == query or len(answer.strip()) == 0:
                return "Information not available"
            
            return answer
        except Exception as e:
            return f"Error processing query: {e}"

# ---- Compatibility Classes ----
class UnifiedAgnoEnhancedSystem:
    """Compatibility wrapper for the working system"""
    
    def __init__(self):
        print("Initializing working system...")
        self.agno_system = None  # Not using Agno
        self.working_system = WorkingMultiLLMSystem()
        self.graph = self.working_system.graph
    
    def process_query(self, query: str) -> str:
        return self.working_system.process_query(query)
    
    def get_system_info(self) -> Dict[str, Any]:
        return {
            "system": "working_multi_llm",
            "agno_available": False,
            "total_models": 1,
            "active_agents": ["math", "search", "wiki", "general"]
        }

# For backward compatibility
AgnoEnhancedAgentSystem = WorkingMultiLLMSystem
AgnoEnhancedModelManager = WorkingMultiLLMSystem

def build_graph(provider: str = "working"):
    """Build working graph"""
    system = WorkingMultiLLMSystem()
    return system.graph

if __name__ == "__main__":
    # Test the working system
    system = WorkingMultiLLMSystem()
    
    test_questions = [
        "How many studio albums were published by Mercedes Sosa between 2000 and 2009?",
        "What is 25 multiplied by 17?",
        "Who nominated the only Featured Article on English Wikipedia about a dinosaur?"
    ]
    
    print("Testing Working Multi-LLM System:")
    for i, question in enumerate(test_questions, 1):
        print(f"\nQuestion {i}: {question}")
        answer = system.process_query(question)
        print(f"Answer: {answer}")