from datetime import datetime, date, timedelta
from typing import Optional as _Optional
import json
import httpx
from urllib.parse import urljoin
from llama_index.llms.groq import Groq
import asyncio
import random
import os
from dotenv import load_dotenv
load_dotenv(dotenv_path="./.env.local")
from agents import function_tool , RunContextWrapper
from .VectorDBManagers import VectorDBManager

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
CHROMA_DB_PATH = os.path.join(BASE_DIR, "chromafast_db")
WEBSITE_PATH = os.path.join(BASE_DIR, "website")

manager = VectorDBManager(db_path=CHROMA_DB_PATH, collection_name="DB_collection")
if not os.path.exists(CHROMA_DB_PATH) or manager.is_collection_empty():
    print("🆕 No existing embeddings found. Building new Chroma DB...")
    manager.build_index_from_documents(WEBSITE_PATH)
else:
    print("📂 Existing Chroma DB found. Loading it...")
    manager.load_existing_index()


@function_tool(
        name_override="suggestion_ragtool",
            description_override="""
        Name:    suggestion_ragtool
        Query the company's knowledge base for information.

        Description:
        all Question except any meeting , call , invitation like schedule 
        Searches the company's internal knowledge base to provide informative, paragraph-style answers 
        related to services, policies, technologies, or any general information embedded in the vector store.

    Parameters:
        context (RunContextWrapper): The openai session context used for communicating with the user.
        query (str): The question or query asked by the user about the company.

    Returns:
        str: 
        """,
        )
async def suggestion_ragtool(ctx: RunContextWrapper, query: str) :
    """
    🔍 Tool Name: suggestion_ragtool

    Description:
        all Question except any meeting , call , invitation like schedule 
        Searches the company's internal knowledge base to provide informative, paragraph-style answers 
        related to services, policies, technologies, or any general information embedded in the vector store.

    Parameters:
        context (RunContext): The Openai session context used for communicating with the user.
        query (str): The question or query asked by the user about the company.

    Returns:
        str:
    """
    
    try:   
        print(f"Answering from knowledgebase: {query}")
        
        
        

        res = await manager.aquery(query)
        
        
        print("Query result:", res)
        result=str(res)
        return result 
    except Exception as e:
        print(f"Error: {e}")
        return f"❌ Failed: Unable to answer the question. {str(e)}"