I'm facing an issue where the cl.Message().update() method is not updating the message content asynchronously as expected in my file processing application.

Here's the relevant part of my code:

@cl.on_chat_start
async def start():
    # ...

    msg = cl.Message(content=f"Processing `{file.name}`...", disable_feedback=True)
    await msg.send()

    # Process the file and split into chunks
    chunks = split_into_chunks(file)

    msg.content = f"Creating chunks for `{file.name}`..."
    await msg.update()

    # Store the data in form of embeddings
    vectordb = store_embeddings(chunks)

    msg.content = f"Creating embeddings for `{file.name}`. . ."
    await msg.update()

    # ...
    msg.content = f"`{file.name}` processed. You can now ask questions!"
    await msg.update()

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I have implemented the code for ConversationalRetrievalChain as per the official documentation provided by Chainlit, but it seems that the memory retention functionality is not working as expected. Despite following the guidelines, the bot fails to retain memory, which impacts its conversational capabilities.

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Approaches to Solve this Issue:

1. Use LLMChain

• Official Docs: Link
• Example Usage: Link
• Medium link: Link

2. Use load_qa_chain

• Github Issue Docs: Link
• Stack Overflow: Link
• Example Usage: Link

3. Use RunnablePassthrough (Using Approach 1 from app.py)

• Official Docs 1: Link
• Official Docs 2: Link

Change create_stuff_documents_chain()

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