Enable response streaming

[artmoskvin]

Currently, AI class handles only batch completions. To improve UX it should also support streaming.

[devbuddytest[bot]]

Here is the plan for implementation:

1. Modify the AI class in autocoder/ai.py to support response streaming. This could be done by adding a new method stream_call which will take in a list of BaseMessage and return a generator instead of a string. The generator will yield responses as they are generated by the AI model.

2. Update the Orchestrator class in autocoder/agent/orchestrator.py to handle the streaming responses from the AI class. This could involve modifying the run method to call the new stream_call method on the AI instance and handle the responses as they are yielded.

3. Update the CodeGenerator class in autocoder/agent/code.py to handle streaming responses. This could involve modifying the generate_code method to handle a generator of responses instead of a single string response.

4. Test the new streaming functionality by running a task through the Orchestrator and ensuring that responses are streamed correctly.

5. Document the changes and update any necessary user guides or API documentation.

[artmoskvin]

@devbuddytest could you be more specific on the updates in the Orchestrator and the CodeGenerator classes? What exactly do you propose to change?

[devbuddytest[bot]]

Based on the current implementation of the Orchestrator and CodeGenerator classes, here are the specific changes I propose:

For the Orchestrator class:

1. Modify the run method to handle streaming responses from the AI class. This could involve changing the call to self.code_generator.run(plan) to something like for response in self.code_generator.run(plan): handle_response(response). The handle_response method would be a new method responsible for handling each individual response from the AI.

For the CodeGenerator class:

1. Modify the run method to return a generator of responses instead of a single response. This could involve changing the call to self.ai.call(messages) to yield from self.ai.call(messages).

2. Modify the generate_code method to return a generator of responses instead of a single response. This could involve changing the call to self.ai.call(messages) to yield from self.ai.call(messages).

3. Update the init_messages method to handle a generator of responses instead of a single response. This could involve changing the call to self.ai.call(messages) to yield from self.ai.call(messages).

These changes would allow the Orchestrator and CodeGenerator classes to handle streaming responses from the AI class, improving the user experience by providing responses as they are generated rather than all at once.

[artmoskvin]

@devbuddytest I'm not sure I follow. The CodeGenerator.run method doesn't use self.ai at all.

[devbuddytest[bot]]

I apologize for the confusion. You're right, the CodeGenerator.run method doesn't directly use self.ai. It's the generate_code method that interacts with the AI. Here's the revised plan:

For the Orchestrator class:

1. No changes are needed in the Orchestrator class as it only calls the run method of the CodeGenerator class.

For the CodeGenerator class:

1. Modify the generate_code method to handle streaming responses from the AI class. This could involve changing the call to self.ai.call(messages) to something like for response in self.ai.call(messages): handle_response(response). The handle_response method would be a new method responsible for handling each individual response from the AI.

2. Update the init_messages method to handle a generator of responses instead of a single response. This could involve changing the call to self.ai.call(messages) to yield from self.ai.call(messages).

These changes would allow the CodeGenerator class to handle streaming responses from the AI class, improving the user experience by providing responses as they are generated rather than all at once.

[artmoskvin]

@devbuddytest no worries! Now it makes sense. Except the init_messages doesn't use self.ai either. So I don't think it requires any update.

[devbuddytest[bot]]

You're absolutely right, and I appreciate your patience. If init_messages doesn't interact with self.ai, then it indeed wouldn't require updates in the context of enabling response streaming. Let's correct and simplify the plan focusing on the relevant parts:

For the CodeGenerator class:

1. Implement Streaming in AI Calls: Modify the generate_code method (or wherever the AI interaction occurs) to support streaming. If generate_code directly or indirectly calls a method that interacts with self.ai, adjust that interaction to handle streaming. This could involve iterating over a generator of responses from the AI and processing them as they arrive.

2. Process Streamed Responses: Implement logic within generate_code or the relevant method to process each response from the AI as it's received. This could involve appending the responses to a buffer, processing them in real-time, or directly passing them to the next component that requires the AI's output.

3. Error Handling and Timeout: Ensure that the streaming implementation gracefully handles errors and timeouts. This might involve setting up exception handling around the streaming loop and defining a timeout for the AI response stream.

4. Testing: Thoroughly test the new streaming functionality to ensure it works as expected. This includes testing for various sizes of input, handling of partial responses, and proper error handling.

5. Documentation: Update the documentation to reflect the new streaming capabilities and provide examples on how to use the updated generate_code method.

By focusing on these steps, you'll be able to implement response streaming in a way that improves the user experience by providing more immediate feedback from the AI, without unnecessary modifications to unrelated parts of the code.

[artmoskvin]

@devbuddytest cool! could you summarize our discussion into a final plan? also, please be concise.

[devbuddytest[bot]]

Certainly! Here's a concise final plan for enabling response streaming:

1. Modify AI Interaction: Update the method in CodeGenerator that interacts with self.ai to support streaming. This involves changing the AI call to handle a generator of responses, processing each response as it arrives.

2. Process Streamed Responses: Implement logic to handle each streamed response from the AI in real-time within the CodeGenerator class.

3. Error Handling: Ensure robust error handling and timeouts for the streaming process to manage exceptions and long waits gracefully.

4. Testing: Conduct thorough tests to verify the streaming functionality works as expected under various conditions.

5. Documentation: Update the project documentation to reflect the new streaming capabilities and provide usage examples.

[artmoskvin]

@devbuddytest wait what about the original plan for updating the AI class?

[devbuddytest[bot]]

Apologies for the oversight. Let's integrate that crucial step into the final plan. Here's the revised and concise plan, including the necessary update to the AI class:

1. Update AI Class for Streaming: Enhance the AI class to support response streaming. Add a new method or modify the existing call method to yield responses as they are generated, instead of returning a batch completion.

2. Modify AI Interaction in CodeGenerator: Adjust the method in CodeGenerator that interacts with self.ai to handle streaming. Process each response from the AI as it arrives, leveraging the updated streaming capability of the AI class.

3. Error Handling and Robustness: Implement comprehensive error handling and timeout mechanisms for the streaming interaction, ensuring the system can gracefully handle exceptions and delays.

4. Testing: Perform extensive testing to ensure the streaming functionality operates correctly across different scenarios and inputs.

5. Documentation Update: Revise the documentation to include details about the new streaming feature, usage examples, and any necessary API changes.

This plan ensures a cohesive approach to enabling response streaming, starting from the AI class update and extending through how it's utilized in the CodeGenerator, including all necessary support mechanisms like error handling, testing, and documentation.