db.py

# ingest.py
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

DATA_PATH = "data/"
DB_FAISS_PATH = "vectorstore/db_faiss"

def create_vector_db():
    loader = DirectoryLoader(
        DATA_PATH, glob="*.pdf", loader_cls=PyPDFLoader
    )

    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    texts = text_splitter.split_documents(documents)

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

    db = FAISS.from_documents(texts, embeddings)
    db.save_local(DB_FAISS_PATH)

if __name__ == "__main__":
    create_vector_db()