veryfinal.py

"""LangGraph Agent with FAISS Vector Store and Custom Tools"""
import os, time, random
from dotenv import load_dotenv
from typing import List, Dict, Any, TypedDict, Annotated
import operator

# LangGraph imports
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langgraph.checkpoint.memory import MemorySaver

# LangChain imports
from langchain_core.messages import SystemMessage, HumanMessage
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_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_community.vectorstores import FAISS
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
from langchain.tools.retriever import create_retriever_tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import JSONLoader

load_dotenv()

# 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
groq_limiter = AdvancedRateLimiter(requests_per_minute=30)
gemini_limiter = AdvancedRateLimiter(requests_per_minute=2)
nvidia_limiter = AdvancedRateLimiter(requests_per_minute=5)

# Custom Tools
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers.
    Args:
        a: first int
        b: second int
    """
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two numbers.
    
    Args:
        a: first int
        b: second int
    """
    return a + b

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

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

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

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.
    
    Args:
        query: The search query."""
    try:
        time.sleep(random.uniform(1, 3))
        search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
                for doc in search_docs
            ])
        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(2, 5))
        search_docs = TavilySearchResults(max_results=3).invoke(query=query)
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'<Document source="{doc.get("url", "")}" />\n{doc.get("content", "")}\n</Document>'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"Web search failed: {str(e)}"

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    try:
        time.sleep(random.uniform(1, 4))
        search_docs = ArxivLoader(query=query, load_max_docs=3).load()
        formatted_search_docs = "\n\n---\n\n".join(
            [
                f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
                for doc in search_docs
            ])
        return formatted_search_docs
    except Exception as e:
        return f"ArXiv search failed: {str(e)}"

# Load and process JSONL data for FAISS vector store
def setup_faiss_vector_store():
    """Setup FAISS vector database from JSONL metadata"""
    try:
        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
        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 FAISS vector store
        embeddings = NVIDIAEmbeddings(
            model="nvidia/nv-embedqa-e5-v5",
            api_key=os.getenv("NVIDIA_API_KEY")
        )
        vector_store = FAISS.from_documents(json_chunks, embeddings)
        
        return vector_store
    except Exception as e:
        print(f"FAISS vector store setup failed: {e}")
        return None

# Load system prompt
try:
    with open("system_prompt.txt", "r", encoding="utf-8") as f:
        system_prompt = f.read()
except FileNotFoundError:
    system_prompt = """You are a helpful assistant tasked with answering questions using a set of tools. 
    Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
    FINAL ANSWER: [YOUR FINAL ANSWER]. 
    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.
    Your answer should only start with "FINAL ANSWER: ", then follows with the answer."""

# System message
sys_msg = SystemMessage(content=system_prompt)

# Setup FAISS vector store and retriever
vector_store = setup_faiss_vector_store()
if vector_store:
    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 3})
    retriever_tool = create_retriever_tool(
        retriever=retriever,
        name="Question_Search",
        description="A tool to retrieve similar questions from a vector store.",
    )
else:
    retriever_tool = None

# All tools
all_tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
]

if retriever_tool:
    all_tools.append(retriever_tool)

# Build graph function
def build_graph(provider: str = "groq"):
    """Build the LangGraph with rate limiting"""
    
    # Initialize LLMs with best free models
    if provider == "google":
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-thinking-exp", temperature=0)
    elif provider == "groq":
        llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0)
    elif provider == "nvidia":
        llm = ChatNVIDIA(model="meta/llama-3.1-70b-instruct", temperature=0)
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'nvidia'.")
    
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(all_tools)

    # Node functions
    def assistant(state: MessagesState):
        """Assistant node with rate limiting"""
        if provider == "groq":
            groq_limiter.wait_if_needed()
        elif provider == "google":
            gemini_limiter.wait_if_needed()
        elif provider == "nvidia":
            nvidia_limiter.wait_if_needed()
            
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    def retriever_node(state: MessagesState):
        """Retriever node"""
        if vector_store and len(state["messages"]) > 0:
            try:
                similar_questions = vector_store.similarity_search(state["messages"][-1].content, k=1)
                if similar_questions:
                    example_msg = HumanMessage(
                        content=f"Here I provide a similar question and answer for reference: \n\n{similar_questions[0].page_content}",
                    )
                    return {"messages": [sys_msg] + state["messages"] + [example_msg]}
            except Exception as e:
                print(f"Retriever error: {e}")
        
        return {"messages": [sys_msg] + state["messages"]}

    # Build graph
    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever_node)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(all_tools))
    builder.add_edge(START, "retriever")
    builder.add_edge("retriever", "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")

    # Compile graph with memory
    memory = MemorySaver()
    return builder.compile(checkpointer=memory)

# Test
if __name__ == "__main__":
    question = "What are the names of the US presidents who were assassinated?"
    # Build the graph
    graph = build_graph(provider="groq")
    # Run the graph
    messages = [HumanMessage(content=question)]
    config = {"configurable": {"thread_id": "test_thread"}}
    result = graph.invoke({"messages": messages}, config)
    for m in result["messages"]:
        m.pretty_print()