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
from langchain_community.vectorstores import FAISS
from langchain.schema import Document
import requests
import json
#from langchain.embeddings import HuggingFaceEmbeddings
from langchain.schema import Document
#from langchain.agents import create_retriever_tool


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 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)



# -------------------------------
# Step 1: Load documents from CSV file (max 165 rows)
# -------------------------------



# -------------------------------
# Step 1: Load JSON data from URL
# -------------------------------
jsonl_url = "https://huggingface.co/spaces/wt002/Final_Assignment_Project/blob/main/metedata.jsonl"  # Replace with your actual JSONL URL
response = requests.get(jsonl_url)

# Ensure the request was successful
if response.status_code != 200:
    raise Exception(f"Failed to load JSONL from {jsonl_url}. Status code: {response.status_code}")


# Ensure the request was successful
if response.status_code != 200:
    raise Exception(f"Failed to load JSONL from {jsonl_url}. Status code: {response.status_code}")

# Read and parse the JSONL file line by line
docs = []
for line_number, line in enumerate(response.text.splitlines(), 1):
    try:
        doc = json.loads(line)  # Parse each line as a separate JSON object
        content = doc.get('content', "").strip()
        if not content:
            continue  # Skip documents with no content

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

        # Convert the document into a Document object
        docs.append(Document(page_content=content, metadata=doc))

    except json.JSONDecodeError as e:
        print(f"Skipping malformed JSONL line at line {line_number}: {line}")
        print(f"Error: {e}")


# -------------------------------
# Step 2: Prepare documents
# -------------------------------
docs = []
for doc in data:
    # Ensure the document has 'content' field
    content = doc.get('content', "").strip()
    if not content:
        continue  # Skip documents with no content

    # Ensure unique ID for each document
    doc['id'] = str(uuid.uuid4())

    # Create Document objects from the data
    docs.append(Document(page_content=content, metadata=doc))

# -------------------------------
# Step 3: 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)

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

print("✅ FAISS index created and saved locally.")

# -------------------------------
# Step 4: 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)
        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()