How to get streamed output chunk by chunk using flask api:Llama

[bsg321]

[dosubot[bot]]

Hey there, @bsg321! 👋 I'm Dosu, a friendly bot here to help you navigate through bugs, answer your questions, and guide you on your journey to becoming a contributor. While we're waiting for a human maintainer to join us, I'm here to assist you with your issue. Let's take a closer look at how we can get your Flask API to stream output chunk by chunk, just as you need. Hang tight, a detailed answer is on its way!

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I did a small research and it seems Flask already supports generator-based streaming.

And LangChain in general have support for streaming too: https://python.langchain.com/docs/modules/model_io/chat/streaming/

But for RetrievalQA it may be a little tricky. I got lost in #4950

[bsg321]

When running without Flask, streaming output is generated directly in the terminal. However, in a Flask API, I'm unsure how to send a streaming response from there.

[dosubot[bot]]

Based on your requirements, you want to stream the output of the LangChain components in real-time through a Flask API. To achieve this, you can utilize Flask's support for generator-based streaming and adapt the LangChain components to yield results as they become available.

However, the RetrievalQA component in LangChain, as it currently stands, does not directly support streaming. It retrieves and processes documents asynchronously, but it waits for all results to be ready before returning them. You would need to modify or wrap the RetrievalQA component's methods to yield results as they become available. This might involve adjusting how documents are retrieved and processed.

Here's a high-level approach to achieve real-time streaming of the output:

1. Adapt the Retrieval Process for Streaming: Modify or wrap the RetrievalQA component's methods to yield results as they become available rather than waiting for all results to be ready. This might involve adjusting how documents are retrieved and processed.

2. Use Flask's Streaming Response: Flask allows you to stream responses to the client by yielding data chunks. You can create a generator function that interacts with your modified RetrievalQA component, yielding results as they become available.

Here's an example of how you might implement a Flask route to stream responses from your question-answering service:

from flask import Flask, Response, request
app = Flask(__name__)

def generate_answer_stream(question):
    # This is a placeholder for interacting with your modified RetrievalQA component.
    # You would replace this with actual calls to your component, yielding results as they become available.
    for chunk in modified_retrieval_qa_process(question):
        yield chunk

@app.route('/answer', methods=['POST'])
def answer():
    data = request.json
    question = data.get('question')
    return Response(generate_answer_stream(question), content_type='text/plain')

if __name__ == '__main__':
    app.run(debug=True)

In this example, generate_answer_stream is a generator function that yields chunks of the answer as they become available. The answer route receives a question from the client, then streams the response back using Response(generate_answer_stream(question), content_type='text/plain').

Please note that the actual implementation of streaming within the RetrievalQA component or its adaptation for streaming is not provided in the context. You would need to implement the logic for yielding results as they become available within the generate_answer_stream function or a similar mechanism adapted to your specific use case.

Regarding the StreamingStdOutCallbackHandler, it is designed to interact with Language Model (LLM) components that support streaming outputs. It provides several methods to capture and access the output of LangChain components, specifically focusing on streaming outputs to the standard output (stdout). The primary method that directly deals with streaming output to stdout is on_llm_new_token, which ensures that each new token generated by the LLM is immediately written to the standard output. However, integrating this with Flask's streaming response might require additional adjustments, as the handler writes directly to stdout and does not provide a built-in way to capture the output for other uses.

You can find more details about the StreamingStdOutCallbackHandler and its methods in the LangChain repository.

I hope this helps! If you have further questions or need more specific guidance, please provide additional details about your use case and the specific LangChain components you're using.

Sources

• How to stream response data of load_qa_chain in python flask
• How return LLMChain.run() stream data?? it did not store stream text in variable rather it just print??
• How do you stream a langchain response from Flask to React?
• DOC: How to use stream output while calling batch
• libs/langchain/langchain/chains/retrieval_qa/base.py
• libs/langchain/langchain/schema/callbacks/streaming_stdout.py
• libs/core/langchain_core/callbacks/streaming_stdout.py

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