Member-only story
Advanced Chainlit: Building Responsive Chat Apps with DeepSeek R1, LM Studio, and Ollama
Learn to Stream LLM Responses, Handle Interruptions, Manage Parallel User Requests, and Isolate Client Sessions in Chainlit
Published in
18 min read4 days ago
Using an LLM is straightforward — simply call its API and receive responses. However, managing LLM responses, especially lengthy ones, requires thoughtful adjustments to your UI. For instance, instead of waiting for the entire response to load, you should stream the output incrementally to provide a smoother user experience. Additionally, it’s crucial to handle user interruptions effectively, ensuring the application responds correctly when a user decides to stop or modify a request.
In this article, I will demonstrate how to:
- Stream LLM responses to deliver output in real-time, avoiding long wait times.
- Handle user interruptions gracefully, allowing users to stop or cancel requests without disrupting the application.
- Support parallel user requests to enable multiple users to interact with the LLM simultaneously.
- Isolate client sessions to ensure that actions in one session do not interfere with others.
