from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader # could have done any unstructured text loader like ppt and xlsx


from langchain.embeddings import HuggingFaceBgeEmbeddings # we can replace huggingface with facetransformers
from chainlit import cl
from langchain.vectorstores import FAISS

DATA_PATH = "data/"
DB_FAISS_PATH = "vectorstores/db_faiss"

#create vector database
def create_vector_db():
                                            # WE can change .pdf with any other unstructured text format
    loader = DirectoryLoader(DATA_PATH, glob="*.pdf", loader_cls = PyPDFLoader)
    documents = loader.load()

    text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=50)

    texts = text_splitter.split_documents(documents)

    embeddings = HuggingFaceBgeEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}) # change to GPU if you want

    # cuda is not supported in my MAC M1! SADLY.

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

if __name__ == "__main__":
    create_vector_db()
    cl.run()