veryfinal.py

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

# Load environment variables
load_dotenv()

# LangGraph imports
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver

# LangChain imports
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.tools import tool
from langchain_groq import ChatGroq
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from langchain_core.rate_limiters import InMemoryRateLimiter

# Tavily import
from tavily import TavilyClient

# Advanced Rate Limiter (SILENT)
class AdvancedRateLimiter:
    def __init__(self, requests_per_minute: int):
        self.requests_per_minute = requests_per_minute
        self.request_times = []
        
    def wait_if_needed(self):
        current_time = time.time()
        # Clean old requests (older than 1 minute)
        self.request_times = [t for t in self.request_times if current_time - t < 60]
        
        # Check if we need to wait
        if len(self.request_times) >= self.requests_per_minute:
            wait_time = 60 - (current_time - self.request_times[0]) + random.uniform(2, 8)
            time.sleep(wait_time)
        
        # Record this request
        self.request_times.append(current_time)

# Initialize rate limiters for free tiers
groq_limiter = AdvancedRateLimiter(requests_per_minute=30)
gemini_limiter = AdvancedRateLimiter(requests_per_minute=2)
nvidia_limiter = AdvancedRateLimiter(requests_per_minute=5)  # NVIDIA free tier
tavily_limiter = AdvancedRateLimiter(requests_per_minute=50)

# Initialize LangChain rate limiters for NVIDIA
nvidia_rate_limiter = InMemoryRateLimiter(
    requests_per_second=0.083,  # 5 requests per minute
    check_every_n_seconds=0.1,
    max_bucket_size=5
)

# Initialize LLMs with best free models
groq_llm = ChatGroq(
    model="llama-3.3-70b-versatile",
    api_key=os.getenv("GROQ_API_KEY"),
    temperature=0
)

gemini_llm = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash-thinking-exp",
    api_key=os.getenv("GOOGLE_API_KEY"),
    temperature=0
)

# Best NVIDIA models based on search results
nvidia_general_llm = ChatNVIDIA(
    model="meta/llama3-70b-instruct",  # Best general model from NVIDIA
    api_key=os.getenv("NVIDIA_API_KEY"),
    temperature=0,
    max_tokens=4000,
    rate_limiter=nvidia_rate_limiter
)

nvidia_code_llm = ChatNVIDIA(
    model="meta/codellama-70b",  # Best code generation model from NVIDIA
    api_key=os.getenv("NVIDIA_API_KEY"),
    temperature=0,
    max_tokens=4000,
    rate_limiter=nvidia_rate_limiter
)

nvidia_math_llm = ChatNVIDIA(
    model="mistralai/mixtral-8x22b-instruct-v0.1",  # Best reasoning model from NVIDIA
    api_key=os.getenv("NVIDIA_API_KEY"),
    temperature=0,
    max_tokens=4000,
    rate_limiter=nvidia_rate_limiter
)

# Initialize Tavily client
tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))

# Define State
class AgentState(TypedDict):
    messages: Annotated[List[HumanMessage | AIMessage], operator.add]
    query: str
    agent_type: str
    final_answer: str

# Custom Tools
@tool
def multiply_tool(a: float, b: float) -> float:
    """Multiply two numbers together"""
    return a * b

@tool
def add_tool(a: float, b: float) -> float:
    """Add two numbers together"""
    return a + b

@tool
def subtract_tool(a: float, b: float) -> float:
    """Subtract two numbers"""
    return a - b

@tool
def divide_tool(a: float, b: float) -> float:
    """Divide two numbers"""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def tavily_search_tool(query: str) -> str:
    """Search the web using Tavily for current information"""
    try:
        tavily_limiter.wait_if_needed()
        response = tavily_client.search(
            query=query,
            max_results=3,
            search_depth="basic",
            include_answer=False
        )
        
        # Format results
        results = []
        for result in response.get('results', []):
            results.append(f"Title: {result.get('title', '')}\nContent: {result.get('content', '')}")
        
        return "\n\n---\n\n".join(results)
        
    except Exception as e:
        return f"Tavily search failed: {str(e)}"

@tool
def wiki_search_tool(query: str) -> str:
    """Search Wikipedia for encyclopedic information"""
    try:
        time.sleep(random.uniform(1, 3))
        from langchain_community.document_loaders import WikipediaLoader
        loader = WikipediaLoader(query=query, load_max_docs=1)
        data = loader.load()
        return "\n\n---\n\n".join([doc.page_content[:1000] for doc in data])
    except Exception as e:
        return f"Wikipedia search failed: {str(e)}"

# Define tools for each agent type
math_tools = [multiply_tool, add_tool, subtract_tool, divide_tool]
research_tools = [tavily_search_tool, wiki_search_tool]
coordinator_tools = [tavily_search_tool, wiki_search_tool]

# Node functions
def router_node(state: AgentState) -> AgentState:
    """Route queries to appropriate agent type"""
    query = state["query"].lower()
    
    if any(word in query for word in ['calculate', 'math', 'multiply', 'add', 'subtract', 'divide', 'compute']):
        agent_type = "math"
    elif any(word in query for word in ['code', 'program', 'python', 'javascript', 'function', 'algorithm']):
        agent_type = "code"
    elif any(word in query for word in ['search', 'find', 'research', 'what is', 'who is', 'when', 'where']):
        agent_type = "research"
    else:
        agent_type = "coordinator"
    
    return {**state, "agent_type": agent_type}

def math_agent_node(state: AgentState) -> AgentState:
    """Mathematical specialist agent using NVIDIA Mixtral"""
    nvidia_limiter.wait_if_needed()
    
    system_message = SystemMessage(content="""You are a mathematical specialist with access to calculation tools.
    Use the appropriate math tools for calculations.
    Show your work step by step.
    Always provide precise numerical answers.
    Finish with: FINAL ANSWER: [numerical result]""")
    
    # Create math agent with NVIDIA's best reasoning model
    math_agent = create_react_agent(nvidia_math_llm, math_tools)
    
    # Process query
    messages = [system_message, HumanMessage(content=state["query"])]
    config = {"configurable": {"thread_id": "math_thread"}}
    
    try:
        result = math_agent.invoke({"messages": messages}, config)
        final_message = result["messages"][-1].content
        
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=final_message)],
            "final_answer": final_message
        }
    except Exception as e:
        error_msg = f"Math agent error: {str(e)}"
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=error_msg)],
            "final_answer": error_msg
        }

def code_agent_node(state: AgentState) -> AgentState:
    """Code generation specialist agent using NVIDIA CodeLlama"""
    nvidia_limiter.wait_if_needed()
    
    system_message = SystemMessage(content="""You are an expert coding AI specialist.
    Generate clean, efficient, and well-documented code.
    Explain your code solutions clearly.
    Always provide working code examples.
    Finish with: FINAL ANSWER: [your code solution]""")
    
    # Create code agent with NVIDIA's best code model
    code_agent = create_react_agent(nvidia_code_llm, [])
    
    # Process query
    messages = [system_message, HumanMessage(content=state["query"])]
    config = {"configurable": {"thread_id": "code_thread"}}
    
    try:
        result = code_agent.invoke({"messages": messages}, config)
        final_message = result["messages"][-1].content
        
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=final_message)],
            "final_answer": final_message
        }
    except Exception as e:
        error_msg = f"Code agent error: {str(e)}"
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=error_msg)],
            "final_answer": error_msg
        }

def research_agent_node(state: AgentState) -> AgentState:
    """Research specialist agent using Gemini"""
    gemini_limiter.wait_if_needed()
    
    system_message = SystemMessage(content="""You are a research specialist with access to web search and Wikipedia.
    Use appropriate search tools to gather comprehensive information.
    Always cite sources and provide well-researched answers.
    Synthesize information from multiple sources when possible.
    Finish with: FINAL ANSWER: [your researched answer]""")
    
    # Create research agent
    research_agent = create_react_agent(gemini_llm, research_tools)
    
    # Process query
    messages = [system_message, HumanMessage(content=state["query"])]
    config = {"configurable": {"thread_id": "research_thread"}}
    
    try:
        result = research_agent.invoke({"messages": messages}, config)
        final_message = result["messages"][-1].content
        
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=final_message)],
            "final_answer": final_message
        }
    except Exception as e:
        error_msg = f"Research agent error: {str(e)}"
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=error_msg)],
            "final_answer": error_msg
        }

def coordinator_agent_node(state: AgentState) -> AgentState:
    """Coordinator agent using NVIDIA Llama3"""
    nvidia_limiter.wait_if_needed()
    
    system_message = SystemMessage(content="""You are the main coordinator agent.
    Analyze queries and provide comprehensive responses.
    Use search tools for factual information when needed.
    Always finish with: FINAL ANSWER: [your final answer]""")
    
    # Create coordinator agent with NVIDIA's best general model
    coordinator_agent = create_react_agent(nvidia_general_llm, coordinator_tools)
    
    # Process query
    messages = [system_message, HumanMessage(content=state["query"])]
    config = {"configurable": {"thread_id": "coordinator_thread"}}
    
    try:
        result = coordinator_agent.invoke({"messages": messages}, config)
        final_message = result["messages"][-1].content
        
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=final_message)],
            "final_answer": final_message
        }
    except Exception as e:
        error_msg = f"Coordinator agent error: {str(e)}"
        return {
            **state,
            "messages": state["messages"] + [AIMessage(content=error_msg)],
            "final_answer": error_msg
        }

# Conditional routing function
def route_agent(state: AgentState) -> str:
    """Route to appropriate agent based on agent_type"""
    agent_type = state.get("agent_type", "coordinator")
    
    if agent_type == "math":
        return "math_agent"
    elif agent_type == "code":
        return "code_agent"
    elif agent_type == "research":
        return "research_agent"
    else:
        return "coordinator_agent"

# LangGraph Multi-Agent System
class LangGraphMultiAgentSystem:
    def __init__(self):
        self.request_count = 0
        self.last_request_time = time.time()
        self.graph = self._create_graph()
    
    def _create_graph(self) -> StateGraph:
        """Create the LangGraph workflow"""
        workflow = StateGraph(AgentState)
        
        # Add nodes
        workflow.add_node("router", router_node)
        workflow.add_node("math_agent", math_agent_node)
        workflow.add_node("code_agent", code_agent_node)
        workflow.add_node("research_agent", research_agent_node)
        workflow.add_node("coordinator_agent", coordinator_agent_node)
        
        # Add edges
        workflow.set_entry_point("router")
        workflow.add_conditional_edges(
            "router",
            route_agent,
            {
                "math_agent": "math_agent",
                "code_agent": "code_agent",
                "research_agent": "research_agent",
                "coordinator_agent": "coordinator_agent"
            }
        )
        
        # All agents end the workflow
        workflow.add_edge("math_agent", END)
        workflow.add_edge("code_agent", END)
        workflow.add_edge("research_agent", END)
        workflow.add_edge("coordinator_agent", END)
        
        # Compile the graph
        memory = MemorySaver()
        return workflow.compile(checkpointer=memory)
    
    def process_query(self, query: str) -> str:
        """Process query using LangGraph multi-agent system"""
        # Global rate limiting (SILENT)
        current_time = time.time()
        if current_time - self.last_request_time > 3600:
            self.request_count = 0
            self.last_request_time = current_time
        
        self.request_count += 1
        
        # Add delay between requests (SILENT)
        if self.request_count > 1:
            time.sleep(random.uniform(3, 10))
        
        # Initial state
        initial_state = {
            "messages": [HumanMessage(content=query)],
            "query": query,
            "agent_type": "",
            "final_answer": ""
        }
        
        # Configuration for the graph
        config = {"configurable": {"thread_id": f"thread_{self.request_count}"}}
        
        try:
            # Run the graph
            final_state = self.graph.invoke(initial_state, config)
            return final_state.get("final_answer", "No response generated")
            
        except Exception as e:
            return f"Error: {str(e)}"

# Main functions
def main(query: str) -> str:
    """Main function using LangGraph multi-agent system"""
    langgraph_system = LangGraphMultiAgentSystem()
    return langgraph_system.process_query(query)

def get_final_answer(query: str) -> str:
    """Extract only the FINAL ANSWER from the response"""
    full_response = main(query)
    
    if "FINAL ANSWER:" in full_response:
        final_answer = full_response.split("FINAL ANSWER:")[-1].strip()
        return final_answer
    else:
        return full_response.strip()

if __name__ == "__main__":
    # Test the LangGraph system - CLEAN OUTPUT ONLY
    result = get_final_answer("What are the names of the US presidents who were assassinated?")
    print(result)