from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

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

#model path:
#https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q8_0.bin

#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()