ingest.py

import box
import yaml
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.embeddings.sentence_transformer import (
    SentenceTransformerEmbeddings,
)
from langchain.vectorstores import Chroma

# Import config vars
with open('config.yml', 'r', encoding='utf8') as ymlfile:
    cfg = box.Box(yaml.safe_load(ymlfile))


def run_ingest():
    loader = DirectoryLoader(cfg.DATA_PATH,
                             glob='*.pdf',
                             loader_cls=PyPDFLoader)

    documents = loader.load()
    print("documents",documents)
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=20,length_function =len,add_start_index = True)
    text = text_splitter.split_documents(documents)
    embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2",model_kwargs={'device': 'cpu'})
    # load it into Chroma
    # save to disk
    db2 = Chroma.from_documents(text, embedding_function, persist_directory="./vectorestore/chroma")

if __name__ == "__main__":
    run_ingest()