veryfinal.py

import os, json, time, random
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Imports
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
from langchain_groq import ChatGroq
from langchain_nvidia_ai_endpoints import ChatNVIDIA
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_community.vectorstores import FAISS
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import JSONLoader
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_core.rate_limiters import InMemoryRateLimiter

# Rate limiters for different providers
groq_rate_limiter = InMemoryRateLimiter(
    requests_per_second=0.5,  # 30 requests per minute
    check_every_n_seconds=0.1,
    max_bucket_size=10
)

google_rate_limiter = InMemoryRateLimiter(
    requests_per_second=0.33,  # 20 requests per minute
    check_every_n_seconds=0.1,
    max_bucket_size=10
)

nvidia_rate_limiter = InMemoryRateLimiter(
    requests_per_second=0.25,  # 15 requests per minute
    check_every_n_seconds=0.1,
    max_bucket_size=10
)

# Initialize individual LLMs
groq_llm = ChatGroq(
    model="llama-3.3-70b-versatile",
    temperature=0,
    api_key=os.getenv("GROQ_API_KEY"),
    rate_limiter=groq_rate_limiter,
    max_retries=2,
    request_timeout=60
)

nvidia_llm = ChatNVIDIA(
    model="meta/llama-3.1-405b-instruct",
    temperature=0,
    api_key=os.getenv("NVIDIA_API_KEY"),
    rate_limiter=nvidia_rate_limiter,
    max_retries=2
)

# Create LLM tools that can be selected by the agent
@tool
def groq_reasoning_tool(query: str) -> str:
    """Use Groq's Llama model for fast reasoning, mathematical calculations, and logical problems.
    Best for: Math problems, logical reasoning, quick calculations, code generation.
    
    Args:
        query: The question or problem to solve
    """
    try:
        time.sleep(random.uniform(1, 2))  # Rate limiting
        response = groq_llm.invoke([HumanMessage(content=query)])
        return f"Groq Response: {response.content}"
    except Exception as e:
        return f"Groq tool failed: {str(e)}"


@tool
def nvidia_specialist_tool(query: str) -> str:
    """Use NVIDIA's large model for specialized tasks, technical questions, and domain expertise.
    Best for: Technical questions, specialized domains, scientific problems, detailed analysis.
    
    Args:
        query: The specialized question or technical problem
    """
    try:
        time.sleep(random.uniform(2, 4))  # Rate limiting
        response = nvidia_llm.invoke([HumanMessage(content=query)])
        return f"NVIDIA Response: {response.content}"
    except Exception as e:
        return f"NVIDIA tool failed: {str(e)}"

# Define calculation tools
@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:
    """Add two numbers.
    
    Args:
        a: first int | float
        b: second int | float
    """
    return a + b

@tool
def subtract(a: int | float , b: int | float) -> int | float:
    """Subtract two numbers.
    
    Args:
        a: first int | float
        b: second int | float
    """
    return a - b

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

@tool
def modulus(a: int | float, b: int | float) -> int | float:
    """Get the modulus of two numbers.
    
    Args:
        a: first int | float
        b: second int | float
    """
    return a % b

# Define search tools
@tool
def wiki_search(query: str) -> str:
    """Search the wikipedia for a query and return the first paragraph
    args:
        query: the query to search for
    """
    try:
        loader = WikipediaLoader(query=query, load_max_docs=1)
        data = loader.load()
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'\n{doc.page_content}\n'
                for doc in data
            ])
        return formatted_search_docs
    except Exception as e:
        return f"Wikipedia search failed: {str(e)}"

@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.
    
    Args:
        query: The search query.
    """
    try:
        time.sleep(random.uniform(1, 3))
        search_docs = TavilySearchResults(max_results=3).invoke(query=query)
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'\n{doc.get("content", "")}\n'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"Web search failed: {str(e)}"

@tool
def arxiv_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query.
    """
    try:
        search_docs = ArxivLoader(query=query, load_max_docs=3).load()
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'\n{doc.page_content[:1000]}\n'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"ArXiv search failed: {str(e)}"

# Load and process your JSONL data
jq_schema = """
{
  page_content: .Question,
  metadata: {
    task_id: .task_id,
    Level: .Level,
    Final_answer: ."Final answer",
    file_name: .file_name,
    Steps: .["Annotator Metadata"].Steps,
    Number_of_steps: .["Annotator Metadata"]["Number of steps"],
    How_long: .["Annotator Metadata"]["How long did this take?"],
    Tools: .["Annotator Metadata"].Tools,
    Number_of_tools: .["Annotator Metadata"]["Number of tools"]
  }
}
"""

# Load documents and create vector database
json_loader = JSONLoader(file_path="metadata.jsonl", jq_schema=jq_schema, json_lines=True, text_content=False)
json_docs = json_loader.load()

# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=200)
json_chunks = text_splitter.split_documents(json_docs)

# Create vector database
database = FAISS.from_documents(json_chunks, NVIDIAEmbeddings())

# Create retriever and retriever tool
retriever = database.as_retriever(search_type="similarity", search_kwargs={"k": 3})

retriever_tool = create_retriever_tool(
    retriever=retriever,
    name="question_search",
    description="Search for similar questions and their solutions from the knowledge base."
)

# Combine all tools including LLM tools
tools = [
    # Math tools
    multiply,
    add,
    subtract,
    divide,
    modulus,
    
    # Search tools
    wiki_search,
    web_search,
    arxiv_search,
    retriever_tool,
    
    # LLM tools - agent can choose which LLM to use
    groq_reasoning_tool,
    nvidia_specialist_tool
]

# Use a lightweight coordinator LLM (Groq for speed)
coordinator_llm = ChatGroq(
    model="llama-3.3-70b-versatile",
    temperature=0,
    api_key=os.getenv("GROQ_API_KEY"),
    rate_limiter=groq_rate_limiter
)

# Create memory for conversation
memory = MemorySaver()

# Create the agent with coordinator LLM
agent_executor = create_react_agent(
    model=coordinator_llm,
    tools=tools,
    checkpointer=memory
)

# Enhanced robust agent run
def robust_agent_run(query, thread_id="robust_conversation", max_retries=3):
    """Run agent with error handling, rate limiting, and LLM tool selection"""
    
    for attempt in range(max_retries):
        try:
            config = {"configurable": {"thread_id": f"{thread_id}_{attempt}"}}
            
            system_msg = SystemMessage(content='''You are a helpful assistant with access to multiple specialized LLM tools and other utilities. 

AVAILABLE LLM TOOLS:
- groq_reasoning_tool: Fast reasoning, math, calculations, code (use for quick logical problems)
- google_analysis_tool: Complex analysis, creative tasks, detailed explanations (use for comprehensive analysis)
- nvidia_specialist_tool: Technical questions, specialized domains, scientific problems (use for expert-level tasks)

TOOL SELECTION STRATEGY:
- For math/calculations: Use basic math tools (add, multiply, etc.) OR groq_reasoning_tool for complex math
- For factual questions: Use web_search, wiki_search, or arxiv_search first
- For analysis/reasoning: Choose the most appropriate LLM tool based on complexity
- For technical/scientific: Use nvidia_specialist_tool
- For creative/comprehensive: Use google_analysis_tool
- For quick logical problems: Use groq_reasoning_tool

Always finish with: FINAL ANSWER: [YOUR FINAL ANSWER]
Your answer should be a number OR few words OR comma separated list as appropriate.''')
            
            user_msg = HumanMessage(content=query)
            result = []
            
            print(f"Attempt {attempt + 1}: Processing query with multi-LLM agent...")
            
            for step in agent_executor.stream(
                {"messages": [system_msg, user_msg]}, 
                config, 
                stream_mode="values"
            ):
                result = step["messages"]
                
            final_response = result[-1].content if result else "No response generated"
            print(f"Query processed successfully on attempt {attempt + 1}")
            return final_response
            
        except Exception as e:
            error_msg = str(e).lower()
            
            if any(keyword in error_msg for keyword in ['rate limit', 'too many requests', '429', 'quota exceeded']):
                wait_time = (2 ** attempt) + random.uniform(1, 3)
                print(f"Rate limit hit on attempt {attempt + 1}. Waiting {wait_time:.2f} seconds...")
                time.sleep(wait_time)
                
                if attempt == max_retries - 1:
                    return f"Rate limit exceeded after {max_retries} attempts: {str(e)}"
                continue
                
            elif any(keyword in error_msg for keyword in ['api', 'connection', 'timeout', 'service unavailable']):
                wait_time = (2 ** attempt) + random.uniform(0.5, 1.5)
                print(f"API error on attempt {attempt + 1}. Retrying in {wait_time:.2f} seconds...")
                time.sleep(wait_time)
                
                if attempt == max_retries - 1:
                    return f"API error after {max_retries} attempts: {str(e)}"
                continue
                
            else:
                return f"Error occurred: {str(e)}"
    
    return "Maximum retries exceeded"

# Main function with request tracking
request_count = 0
last_request_time = time.time()

def main(query: str) -> str:
    """Main function to run the multi-LLM agent"""
    global request_count, last_request_time
    
    current_time = time.time()
    
    # Reset counter every minute
    if current_time - last_request_time > 60:
        request_count = 0
        last_request_time = current_time
    
    request_count += 1
    print(f"Processing request #{request_count} with multi-LLM agent")
    
    # Add delay between requests
    if request_count > 1:
        time.sleep(random.uniform(2, 5))
    
    return robust_agent_run(query)

if __name__ == "__main__":
    # Test the multi-LLM agent
    result = main("What are the names of the US presidents who were assassinated?")
    print(result)