# 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'

# # Create vector database
# 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()