agent.py

import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Set protobuf implementation to avoid C++ extension issues
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"

# Load keys from environment
hf_token = os.getenv("HUGGINGFACE_INFERENCE_TOKEN")
serper_api_key = os.getenv("SERPER_API_KEY")

# ---- Imports ----
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.schema import Document
import json
import requests
from typing import List, Dict, Any
import re
import math
from datetime import datetime

# ---- Enhanced Tools ----

@tool
def multiply(a: float, b: float) -> float:
    """Multiply two numbers"""
    return a * b

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

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

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

@tool
def modulus(a: int, b: int) -> int:
    """Calculate modulus of two integers"""
    return a % b

@tool
def power(a: float, b: float) -> float:
    """Calculate a raised to the power of b"""
    return a ** b

@tool
def square_root(a: float) -> float:
    """Calculate square root of a number"""
    return math.sqrt(a)

@tool
def factorial(n: int) -> int:
    """Calculate factorial of a number"""
    if n < 0:
        raise ValueError("Factorial is not defined for negative numbers")
    if n == 0 or n == 1:
        return 1
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

@tool
def gcd(a: int, b: int) -> int:
    """Calculate greatest common divisor"""
    while b:
        a, b = b, a % b
    return a

@tool
def lcm(a: int, b: int) -> int:
    """Calculate least common multiple"""
    return abs(a * b) // gcd(a, b)

@tool
def percentage(part: float, whole: float) -> float:
    """Calculate percentage"""
    return (part / whole) * 100

@tool
def compound_interest(principal: float, rate: float, time: float, n: int = 1) -> float:
    """Calculate compound interest"""
    return principal * (1 + rate/n) ** (n * time)

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for information"""
    try:
        search_docs = WikipediaLoader(query=query, load_max_docs=3).load()
        if not search_docs:
            return "No Wikipedia results found."
        
        formatted = "\n\n---\n\n".join([
            f'<Document source="{doc.metadata.get("source", "Wikipedia")}" title="{doc.metadata.get("title", "Unknown")}"/>\n{doc.page_content[:2000]}\n</Document>'
            for doc in search_docs
        ])
        return formatted
    except Exception as e:
        return f"Wikipedia search error: {str(e)}"

@tool
def web_search(query: str) -> str:
    """Search the web using Tavily"""
    try:
        search_docs = TavilySearchResults(max_results=3).invoke(query=query)
        if not search_docs:
            return "No web search results found."
        
        formatted = "\n\n---\n\n".join([
            f'<Document source="{doc.get("url", "Unknown")}" title="{doc.get("title", "Unknown")}"/>\n{doc.get("content", "")[:2000]}\n</Document>'
            for doc in search_docs
        ])
        return formatted
    except Exception as e:
        return f"Web search error: {str(e)}"

@tool
def arxiv_search(query: str) -> str:
    """Search ArXiv for academic papers"""
    try:
        search_docs = ArxivLoader(query=query, load_max_docs=2).load()
        if not search_docs:
            return "No ArXiv results found."
        
        formatted = "\n\n---\n\n".join([
            f'<Document source="{doc.metadata.get("source", "ArXiv")}" title="{doc.metadata.get("Title", "Unknown")}"/>\n{doc.page_content[:1500]}\n</Document>'
            for doc in search_docs
        ])
        return formatted
    except Exception as e:
        return f"ArXiv search error: {str(e)}"

@tool
def serper_search(query: str) -> str:
    """Enhanced web search using Serper API"""
    if not serper_api_key:
        return "Serper API key not available"
    
    try:
        url = "https://google.serper.dev/search"
        payload = json.dumps({
            "q": query,
            "num": 5
        })
        headers = {
            'X-API-KEY': serper_api_key,
            'Content-Type': 'application/json'
        }
        
        response = requests.request("POST", url, headers=headers, data=payload)
        results = response.json()
        
        if 'organic' not in results:
            return "No search results found"
        
        formatted = "\n\n---\n\n".join([
            f'<Document source="{result.get("link", "Unknown")}" title="{result.get("title", "Unknown")}"/>\n{result.get("snippet", "")}\n</Document>'
            for result in results['organic'][:3]
        ])
        return formatted
    except Exception as e:
        return f"Serper search error: {str(e)}"

# ---- Embedding & Vector Store Setup ----
def setup_vector_store():
    try:
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
        
        # Check if metadata.jsonl exists and load it
        if os.path.exists('metadata.jsonl'):
            json_QA = []
            with open('metadata.jsonl', 'r') as jsonl_file:
                for line in jsonl_file:
                    if line.strip():  # Skip empty lines
                        json_QA.append(json.loads(line))
            
            if json_QA:
                documents = [
                    Document(
                        page_content=f"Question: {sample.get('Question', '')}\n\nFinal answer: {sample.get('Final answer', '')}",
                        metadata={"source": sample.get("task_id", "unknown")}
                    )
                    for sample in json_QA if sample.get('Question') and sample.get('Final answer')
                ]
                
                if documents:
                    vector_store = Chroma.from_documents(
                        documents=documents,
                        embedding=embeddings,
                        persist_directory="./chroma_db",
                        collection_name="my_collection"
                    )
                    vector_store.persist()
                    print(f"Vector store created with {len(documents)} documents")
                    return vector_store
        
        # Create empty vector store if no data
        vector_store = Chroma(
            embedding_function=embeddings,
            persist_directory="./chroma_db",
            collection_name="my_collection"
        )
        print("Empty vector store created")
        return vector_store
        
    except Exception as e:
        print(f"Vector store setup error: {e}")
        # Return a dummy vector store function
        return None

vector_store = setup_vector_store()

@tool
def similar_question_search(query: str) -> str:
    """Search for similar questions in the knowledge base"""
    if not vector_store:
        return "Vector store not available"
    
    try:
        matched_docs = vector_store.similarity_search(query, 3)
        if not matched_docs:
            return "No similar questions found"
        
        formatted = "\n\n---\n\n".join([
            f'<Document source="{doc.metadata.get("source", "Unknown")}" />\n{doc.page_content[:1000]}\n</Document>'
            for doc in matched_docs
        ])
        return formatted
    except Exception as e:
        return f"Similar question search error: {str(e)}"

# ---- Enhanced System Prompt ----
system_prompt = """
You are an expert assistant capable of solving complex questions using available tools. You have access to:

1. Mathematical tools: add, subtract, multiply, divide, modulus, power, square_root, factorial, gcd, lcm, percentage, compound_interest
2. Search tools: wiki_search, web_search, arxiv_search, serper_search, similar_question_search

IMPORTANT INSTRUCTIONS:
1. Break down complex questions into smaller steps
2. Use tools systematically to gather information and perform calculations
3. For mathematical problems, show your work step by step
4. For factual questions, search for current and accurate information
5. Cross-reference information from multiple sources when possible
6. Be precise with numbers - avoid rounding unless necessary

When providing your final answer, use this exact format:
FINAL ANSWER: [YOUR ANSWER]

Rules for the final answer:
- Numbers: Use plain digits without commas, units, or symbols (unless specifically requested)
- Strings: Use exact names without articles or abbreviations
- Lists: Comma-separated values following the above rules
- Be concise and accurate

Think step by step and use the available tools to ensure accuracy.
"""

sys_msg = SystemMessage(content=system_prompt)

# ---- Enhanced Tool List ----
tools = [
    # Math tools
    multiply, add, subtract, divide, modulus, power, square_root, 
    factorial, gcd, lcm, percentage, compound_interest,
    # Search tools
    wiki_search, web_search, arxiv_search, serper_search, similar_question_search
]

# ---- Graph Definition ----
def build_graph(provider: str = "huggingface"):
    """Build the agent graph with improved HuggingFace model"""
    
    if provider == "huggingface":
        # Use a more capable model from HuggingFace
        try:
            # Try with a well-supported model first
            endpoint = HuggingFaceEndpoint(
                repo_id="google/flan-t5-base",  # This model works well with the current setup
                temperature=0.1,
                huggingfacehub_api_token=hf_token,
                max_new_tokens=512,
                task="text2text-generation"
            )
            llm = ChatHuggingFace(llm=endpoint)
        except Exception as e:
            print(f"Failed to initialize google/flan-t5-base: {e}")
            # Fallback to another model
            try:
                endpoint = HuggingFaceEndpoint(
                    repo_id="microsoft/DialoGPT-medium",
                    temperature=0.1,
                    huggingfacehub_api_token=hf_token,
                    max_new_tokens=512
                )
                llm = ChatHuggingFace(llm=endpoint)
            except Exception as e2:
                print(f"Failed to initialize DialoGPT-medium: {e2}")
                # Final fallback
                endpoint = HuggingFaceEndpoint(
                    repo_id="bigscience/bloom-560m",
                    temperature=0.1,
                    huggingfacehub_api_token=hf_token,
                    max_new_tokens=256
                )
                llm = ChatHuggingFace(llm=endpoint)
    else:
        raise ValueError("Only 'huggingface' provider is supported in this version.")

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        """Enhanced assistant node with better error handling"""
        try:
            messages = state["messages"]
            response = llm_with_tools.invoke(messages)
            return {"messages": [response]}
        except Exception as e:
            print(f"Assistant error: {e}")
            # Fallback response
            fallback_msg = HumanMessage(content=f"I encountered an error: {str(e)}. Let me try a simpler approach.")
            return {"messages": [fallback_msg]}

    def retriever(state: MessagesState):
        """Enhanced retriever with better context injection"""
        messages = state["messages"]
        user_query = messages[-1].content if messages else ""
        
        # Try to find similar questions
        context_messages = [sys_msg]
        
        if vector_store:
            try:
                similar = vector_store.similarity_search(user_query, k=2)
                if similar:
                    context_msg = HumanMessage(
                        content=f"Here are similar questions for context:\n\n{similar[0].page_content}"
                    )
                    context_messages.append(context_msg)
            except Exception as e:
                print(f"Retriever error: {e}")
        
        return {"messages": context_messages + messages}

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

    return builder.compile()