Stopping the Stream: A Pythonic Guide to Controlling OpenAI Responses

Hey there, Python devs! 👋

Let’s explore a practical approach to giving users control over stopping those AI-generated responses?

The Scenario

Imagine you're building a FastAPI application that uses OpenAI's API. You've got streaming responses working smoothly, but there's one thing missing: the ability for users to stop the stream mid-generation.

The Challenge

Stopping a stream isn't as straightforward as you might think. OpenAI's API keeps pumping out tokens, and you need a clean way to interrupt that flow without breaking your entire application.

The Solution

Here's a killer implementation that'll make your users happy:

import asyncio
from fastapi import FastAPI, WebSocket
from openai import AsyncOpenAI
from typing import Optional

class StreamController:
    def __init__(self):
        self.stop_generation = False

    def request_stop(self):
        self.stop_generation = True

class AIResponseGenerator:
    def __init__(self, client: AsyncOpenAI):
        self.client = client
        self.stream_controller = StreamController()

    async def generate_streaming_response(self, prompt: str):
        # Reset the stop flag
        self.stream_controller.stop_generation = False

        try:
            stream = await self.client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[{"role": "user", "content": prompt}],
                stream=True
            )

            full_response = ""
            for chunk in stream:
                # Check if stop was requested
                if self.stream_controller.stop_generation:
                    break

                if chunk.choices[0].delta.content:
                    content = chunk.choices[0].delta.content
                    full_response += content
                    yield content

        except Exception as e:
            print(f"Stream generation error: {e}")

    def stop_stream(self):
        # Trigger the stop mechanism
        self.stream_controller.request_stop()

Let's unpack what's happening here:

1. StreamController: This is our traffic cop. It manages a simple boolean flag to control stream generation.

2. AIResponseGenerator: The main class that handles AI response streaming.

   • Uses AsyncOpenAI for non-blocking API calls
   • Implements a generator that can be stopped mid-stream
   • Provides a stop_stream() method to interrupt generation

Pro Tips

• 🚀 Performance: This approach is memory-efficient and doesn't block the event loop.
• 🛡️ Error Handling: Includes basic error catching to prevent unexpected crashes.
• 🔧 Flexibility: Easy to adapt to different streaming scenarios.

Potential Improvements

• Add timeout mechanisms
• Implement more granular error handling
• Create a more sophisticated stop mechanism for complex streams

See u guys!