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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()
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