Tutorial on implementing chat completion system with FastAPI and OpenAI integration, including streaming capability.

From Towards Data Science:

Implementing a chat completion system using FastAPI and Pydantic models involves creating a ChatMessage model for the client request and a ChatCompletionRequest model for the parameters like model, max_tokens, and temperature. The FastAPI endpoint ‘/chat/completions’ processes the request and generates a response based on the client’s messages, role, and content, echoing the last message.

To test the implementation, install FastAPI and OpenAI libraries, launch the server with uvicorn and create a chat completion using OpenAI’s Python client. Ensure the response from the server is correctly printed and all relevant attributes are as expected in the chat_completion object.

To improve efficiency, streaming the generated content back to the client in real-time can be implemented by modifying the endpoint to return a StreamingResponse when stream==True. Create a generator function to stream the response token by token, simulating a real-time chat interaction.

Testing the streaming implementation involves restarting the uvicorn server, opening a Python console, and creating a streaming chat completion using the OpenAI library. Each word in the server’s response should be printed slowly, mimicking token generation. Inspect the last chunk object to ensure the streaming is functioning as expected.

Putting it all together, the complete code for the server implementing chat completions with streaming capability using FastAPI, Pydantic models, and OpenAI integration is provided in the author’s code gist for reference and implementation verification.



Read more at Towards Data Science: How to build an OpenAI-compatible API | by Saar Berkovich | Mar, 2024