create_vectordb.py

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
import glob
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# 1. Load all files
filepaths = glob.glob("ratelist_offers.pdf")  # Adjust pattern if needed

all_documents = []
for path in filepaths:
    loader = PyPDFLoader(path)
    docs = loader.load()
    all_documents.extend(docs)

# 2. Chunk all documents
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=100
)
chunks = text_splitter.split_documents(all_documents)

# 3. Create embeddings
embeddings = OpenAIEmbeddings()

# 4. Store vectors in FAISS
faiss_index = FAISS.from_documents(chunks, embeddings)
faiss_index.save_local("faiss_index_store")