How to close a stream using chat_engine.stream_chat

[cxycxm]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

How to close a stream using chat_engine.stream_chat

[dosubot]

Hey @cxycxm! Nice to see you back 😊. Diving into your stream closing conundrum right now, expect an update in a flash!

[logan-markewich]

I actually don't think there is a way besides either exhausting the stream, or never continuing the stream

[cxycxm]

Can the client connection of this chat_engine be actively disconnected, similar to httpclient.close()

[logan-markewich]

Maybe? I think it really depends on the llm being used too. You'd have to do some digging into api docs there

[dosubot]

To actively disconnect or manage the lifecycle of a stream in chat_engine.stream_chat, you can utilize the reset method in the SimpleChatEngine class for resetting the chat engine's memory, which indirectly affects the stream by preparing it for a new session. This method doesn't directly close a stream in the conventional sense (like httpclient.close()), but it's a way to clear the session's state.

For a more direct approach to managing stream connections, especially for starting and stopping streams, the StreamingGeneratorCallbackHandler class within llama_index/core/langchain_helpers/streaming.py provides a structured mechanism. This class uses an event-driven approach with a Queue for tokens and an Event to signal the end of the stream, allowing for graceful handling of stream lifecycle events.

Events such as StreamChatStartEvent and StreamChatEndEvent are also crucial for lifecycle management, signaling the beginning and end of a chat stream session. While these events don't execute disconnection logic themselves, they provide a framework for implementing such functionality, allowing for custom logic to be executed at the start or end of a chat session.

In summary, while there's no explicit close method for streams in chat_engine.stream_chat, using the reset method, along with lifecycle management techniques provided by StreamingGeneratorCallbackHandler and stream lifecycle events, can serve to manage stream connections effectively.

Details

Sources

• llama-index-core/llama_index/core/chat_engine/simple.py
• llama-index-legacy/llama_index/legacy/chat_engine/simple.py
• llama-index-core/llama_index/core/chat_engine/types.py
• llama-index-core/llama_index/core/instrumentation/events/chat_engine.py
• llama-index-core/llama_index/core/base/llms/base.py
• llama-index-integrations/llms/llama-index-llms-nvidia-triton/llama_index/llms/nvidia_triton/utils.py
• llama-index-legacy/llama_index/legacy/llms/nvidia_triton_utils.py
• llama-index-core/llama_index/core/langchain_helpers/streaming.py
• llama-index-legacy/llama_index/legacy/chat_engine/condense_question.py
• llama-index-core/llama_index/core/chat_engine/context.py
• llama-index-core/llama_index/core/instrumentation/events/llm.py

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[RussellLuo]

Maybe we can add a close method to StreamingAgentChatResponse by leveraging generator.close(), for example:

class StreamingAgentChatResponse:
    ...

    def close(self) -> None:
        if self.chat_stream is not None:
            # Here we assume that `self.chat_stream` is a generator,
            # so this will trigger a GeneratorExit in the generator function.
            self.chat_stream.close()

Then we also need to handle GeneratorExit in the relevant llm's stream_chat() method. Take OpenAI for example, we might update its _stream_chat() as below:

def _stream_chat(
    self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
    ...

    def gen() -> ChatResponseGen:
        ...

        stream = client.chat.completions.create(
            messages=message_dicts,
            stream=True,
            **self._get_model_kwargs(**kwargs),
        )
        for response in stream:
            try:
                ...

                yield ChatResponse(
                    message=ChatMessage(
                        role=role,
                        content=content,
                        additional_kwargs=additional_kwargs,
                    ),
                    delta=content_delta,
                    raw=response,
                    additional_kwargs=self._get_response_token_counts(response),
                )
            except GeneratorExit:
                # Interrupt the stream by closing the connection.
                # see https://github.com/openai/openai-python/issues/969#issuecomment-1857158754
                stream.response.close()

                # Then exit gracefully.
                return

    return gen()

[logan-markewich]

This would need to be implemented for every streaming llm 😅

[logan-markewich]

But, makes sense

[RussellLuo]

This would need to be implemented for every streaming llm 😅

Yes, both the sync and async versions need to be modified. Perhaps we can leverage some decorators, although they may not be helpful in all cases.

[cxycxm]

thanks