tools.py

import os
from llama_index.core import VectorStoreIndex
from llama_index.core import SimpleDirectoryReader
from agents import function_tool
import openai
from openai import OpenAI
import openai
import base64 # Added for b64 decoding
import uuid   # Added for unique filenames
from pathlib import Path
import dotenv
from typing import Optional
from pydantic import BaseModel
# import requests # Ensure this is removed if no longer used
dotenv.load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

try:
    client = OpenAI() # Assumes OPENAI_API_KEY is set in environment
except Exception as e:
    print(f"CRITICAL: Failed to initialize OpenAI client: {e}.")
    client = None




    

# Load and index FAQ documents once
documents = SimpleDirectoryReader(input_dir="data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()


@function_tool
def faq_search(query: str) -> str:
    """
    A search tool that gives access to your FAQ knowledge base.
    Provide a natural language query.
    Returns summarized answers using LlamaIndex.
    Retrieves the top 5 most relevant document chunks from FAQ base,
    including source file and page info.
    """
    response = query_engine.query(query)
    return str(response)