src/chains/qa_chain_openai.py

from langchain_chroma import Chroma                       # Chroma moved here
from langchain_openai import OpenAIEmbeddings, ChatOpenAI # OpenAI moved here
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from src.agents.prompts import RAG_PROMPT
import os
import shutil


def build_openai_rag_chain_and_llm(pdf_path: str):
    # Load and split documents
    loader = PyPDFLoader(pdf_path)
    documents = loader.load()
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    texts = splitter.split_documents(documents)

    # # Create old-style Chroma client (in-memory, no tenant/db)
    # chroma_client = chromadb.Client(Settings(anonymized_telemetry=False))
    
    # # Vectorstore with explicit client
    # vectorstore = Chroma.from_documents(
    #     texts,
    #     embedding=OpenAIEmbeddings(),
    #     client=chroma_client
    # )
    
    if os.path.exists(".chroma"):
        shutil.rmtree(".chroma")
    vectorstore = Chroma.from_documents(texts,embedding=OpenAIEmbeddings())
    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

    # Build chain
    llm = ChatOpenAI(model_name="gpt-4", temperature=0, streaming=True)
    qa_chain = create_stuff_documents_chain(llm=llm, prompt=RAG_PROMPT)
    rag_chain = create_retrieval_chain(retriever, qa_chain)
    return rag_chain, llm