v0.6 : Streaming doesn't work for ConversationalRetrievalChain

[Shivanandroy]

I implemented a ConversationalRetrievalChain and streaming the final output to the UI seems to be problematic.

1. When you ask the first question: you get the nice streaming response.
2. But when I ask the followup question: It seems to output the condensed version of the question? and not the final streaming output.

Am I missing something?

here's my code:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.document_loaders import PyPDFLoader
import os
import chainlit as cl
from langchain.prompts import PromptTemplate
from langchain.vectorstores import FAISS

print(cl.__version__)

system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.

Example of your response should be:

The answer is foo
SOURCES: xyz

Begin!
----------------
{summaries}"""
messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}

@cl.on_chat_start
async def start():
    files = None

    # Wait for the user to upload a file
    while files == None:
        files = await cl.AskFileMessage(
            content="Hey, Welcome to ChatPDF!\n\nChatPDF is a smart, user-friendly tool that integrates state-of-the-art AI models with text extraction and embedding capabilities to create a unique, conversational interaction with your PDF documents.\n\nSimply upload your PDF, ask your questions, and ChatPDF will deliver the most relevant answers directly from your document.\n\nPlease upload a PDF file to begin!",max_size_mb=100, accept=["application/pdf"]
        ).send()

    file = files[0]

    msg = cl.Message(content=f'''Processing "{file.name}"...''')
    await msg.send()

    #

    with open(os.path.join(file.name), "wb") as f:
        f.write(file.content)

    print(file.name)

    loader = PyPDFLoader(file.name)
    pages = loader.load_and_split()

    # add page split info
    # Initialize a dictionary to keep track of duplicate page numbers
    page_counts = {}

    for document in pages:
        page_number = document.metadata['page']

        # If this is the first occurrence of this page number, initialize its count to 1
        # Otherwise, increment the count for this page number
        page_counts[page_number] = page_counts.get(page_number, 0) + 1

        # Create the page split info string
        page_split_info = f"Page-{page_number+1}.{page_counts[page_number]}"

        # Add the page split info to the document's metadata
        document.metadata['page_split_info'] = page_split_info

    # Create a Chroma vector store
    embeddings = OpenAIEmbeddings()
    docsearch = await cl.make_async(FAISS.from_documents)(pages, embeddings)

    # define memory
    memory = ConversationBufferWindowMemory(
        k=5,
        memory_key='chat_history', 
        return_messages=True, 
        output_key='answer'
        )

    # Create a chain that uses the Chroma vector store
    chain = ConversationalRetrievalChain.from_llm(
        ChatOpenAI(temperature=0, model="gpt-4", streaming=True),
        chain_type="stuff",
        retriever=docsearch.as_retriever(search_kwargs={'k':5}),
        memory=memory,
        return_source_documents=True,
    )

    # Save the metadata and texts in the user session
    # cl.user_session.set("metadatas", metadatas)
    cl.user_session.set("texts", pages)

    # Let the user know that the system is ready
    msg.content = f''' "{file.name}" processed. You can now ask questions!'''
    await msg.update()

    cl.user_session.set('chain', chain)

@cl.on_message
async def process_response(message):
    chain = cl.user_session.get("chain")
    cb = cl.AsyncLangchainCallbackHandler(
        stream_final_answer=True, 
        answer_prefix_tokens=["FINAL", "ANSWER"]
    )
    cb.answer_reached = True
    res = await chain.acall(message, callbacks=[cb])

    answer = res["answer"]
    source_documents = res['source_documents']
    content = [source_documents[i].page_content for i in range(len(source_documents))]
    name = [source_documents[i].metadata['page_split_info'] for i in range(len(source_documents))]
    source_elements = [
        cl.Text(content=content[i], name=name[i]) for i in range(len(source_documents))
        ]

    if source_documents:
        answer += f"\n\nSources: {', '.join([source_documents[i].metadata['page_split_info'] for i in range(len(source_documents))])}"
    else:
        answer += "\n\nNo sources found"

    if cb.has_streamed_final_answer:
        cb.final_stream.elements = source_elements
        await cb.final_stream.update()
    else:
        await cl.Message(content=answer, elements=source_elements).send()

[Shivanandroy]

@willydouhard : I think, it doesn't work when the chain takes 2 steps for final answer, instead of one.

[ramnes]

What if you remove cb.answer_reached = True?

[willydouhard]

Yeah in this case you should remove cb.answer_reached = True but you will need to provide the final answer tokens instead. All LangChain chains do not have a consistent final answer token delimiter though. In those cases final answer streaming is not possible.