from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from dotenv import load_dotenv
import os
import logging


load_dotenv()

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def load_embeddings():
    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})

def load_vector_database(embeddings):
    try:
        db = FAISS.load_local("vectorstore/db_faiss", embeddings, allow_dangerous_deserialization=True)
        logger.info("Vector database loaded successfully!")
        return db
    except Exception as e:
        logger.error(f"Failed to load vector database: {e}")
        raise e

embeddings = load_embeddings()
vector_db = load_vector_database(embeddings)