agent.py

# agent.py

import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
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 SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client
from sentence_transformers import SentenceTransformer
from langchain.embeddings.base import Embeddings
from typing import List
import numpy as np


import pandas as pd
import uuid
import requests
import json
from langchain_core.documents import Document
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings


from tools import (
    multiply, add, subtract, divide, modulus,
    wiki_search, web_search, arvix_search
)



load_dotenv()

@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) -> int:
    """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."""
    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 {"wiki_results": formatted_search_docs}

@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.
    
    Args:
        query: The search query."""
    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
    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 {"web_results": formatted_search_docs}

@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    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 {"arvix_results": formatted_search_docs}



# -----------------------------
# Load configuration from YAML
# -----------------------------
with open("config.yaml", "r") as f:
    config = yaml.safe_load(f)

provider = config["provider"]
#prompt_path = config["system_prompt_path"]
enabled_tool_names = config["tools"]


# -----------------------------
# Load system prompt
# -----------------------------
# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

# System message
sys_msg = SystemMessage(content=system_prompt)


# -----------------------------
# Map tool names to functions
# -----------------------------
tool_map = {
    "multiply": multiply,
    "add": add,
    "subtract": subtract,
    "divide": divide,
    "modulus": modulus,
    "wiki_search": wiki_search,
    "web_search": web_search,
    "arvix_search": arvix_search,
}

tools = [tool_map[name] for name in enabled_tool_names]


# -------------------------------
# Step 2: Load the JSON file or tasks (Replace this part if you're loading tasks dynamically)
# -------------------------------
# Here we assume the tasks are already fetched from a URL or file.
# For now, using an example JSON array directly. Replace this with the actual loading logic.

tasks = [
    {
        "task_id": "8e867cd7-cff9-4e6c-867a-ff5ddc2550be",
        "question": "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of English Wikipedia.",
        "Level": "1",
        "file_name": ""
    },
    {
        "task_id": "a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
        "question": "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?",
        "Level": "1",
        "file_name": ""
    }
]

# -------------------------------
# Step 3: Create Documents from Each JSON Object
# -------------------------------
docs = []
for task in tasks:
    # Debugging: Print the keys of each task to ensure 'question' exists
    print(f"Keys in task: {task.keys()}")

    # Ensure the required field 'question' exists
    if 'question' not in task:
        print(f"Skipping task with missing 'question' field: {task}")
        continue

    content = task.get('question', "").strip()
    if not content:
        print(f"Skipping task with empty 'question': {task}")
        continue

    # Add unique ID to each document
    task['id'] = str(uuid.uuid4())

    # Create a document from the task data
    docs.append(Document(page_content=content, metadata=task))



# -------------------------------
# Step 4: Set up HuggingFace Embeddings and FAISS VectorStore
# -------------------------------
# Initialize HuggingFace Embedding model
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

# Create FAISS VectorStore from documents
vector_store = FAISS.from_documents(docs, embedding_model)
#vector_store = FAISS.load_local("faiss_index", embedding_model)

# Save the FAISS index locally
vector_store.save_local("faiss_index")



# -------------------------------
# Step 5: Create Retriever Tool (for use in LangChain)
# -------------------------------
retriever = vector_store.as_retriever()

# Create the retriever tool
question_retriever_tool = create_retriever_tool(
    retriever=retriever,
    name="Question_Search",
    description="A tool to retrieve documents related to a user's question."
)





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

# Build graph function
def build_graph(provider: str = "google"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "google":
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        # Groq https://console.groq.com/docs/models
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
    elif provider == "huggingface":
        # TODO: Add huggingface endpoint
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                temperature=0,
            ),
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    def retriever(state: MessagesState):
        """Retriever node"""
        similar_question = vector_store.similarity_search(state["messages"][0].content)
    
        if not similar_question:
            example_msg = HumanMessage(content="No similar question found.")
        else:
            example_msg = HumanMessage(
                content=f"Here I provide a similar question and answer for reference:\n\n{similar_question[0].page_content}",
            )
    
        return {"messages": [sys_msg] + state["messages"] + [example_msg]}


    
    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(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
    return builder.compile()