create_retriever.py

from langchain.schema import Document
import pickle
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
import os
from typing import Iterable
import json
from tqdm import tqdm
from langchain_huggingface import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L12-v2")

def parent_retriever(chroma_path, embeddings):
    parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000,
                                                    chunk_overlap=500)

    # create the child documents - The small chunks
    child_splitter = RecursiveCharacterTextSplitter(chunk_size=300,
                                                    chunk_overlap=50)

    # The storage layer for the parent chunks
    store = InMemoryStore()

    vectorstore = Chroma(collection_name="full_documents",
                        embedding_function=embeddings,
                        persist_directory=chroma_path)
    retriever = ParentDocumentRetriever(
        vectorstore=vectorstore,
        docstore=store,
        child_splitter=child_splitter,
        parent_splitter=parent_splitter,
        search_kwargs={"k": 5})
    return retriever

def save_to_pickle(obj, filename):
    '''
    save docstore as pickle file
    '''
    with open(filename, "wb") as file:
        pickle.dump(obj, file, pickle.HIGHEST_PROTOCOL)

retriever_repos = parent_retriever('ohw_proj_chorma_db',embeddings=embedding)
def load_docs_from_jsonl(file_path)->Iterable[Document]:
    array = []
    with open(file_path, 'r') as jsonl_file:
        for line in jsonl_file:
            data = json.loads(line)
            obj = Document(**data)
            array.append(obj)
    return array
documents = load_docs_from_jsonl('project_readmes.json')
for i in tqdm(range(0,len(documents))):
    retriever_repos.add_documents([documents[i]])
save_to_pickle(retriever_repos.docstore.store, 'ohw_proj_chorma_db.pcl')