Multi-model AI workspace · 2024
Chat Assistant
A multi-model AI workspace for switching language models while preserving conversation context and delivering fast, streamed responses in one focused interface.
- Role
- AI integration, product engineering
- Duration
- 3 weeks
Think with the right intelligence.
Chat Assistant explores a simple idea: the model should be a tool choice, not a product boundary. Users can keep one coherent thread while selecting the model that fits the work in front of them.
The context
Different language models excel at different tasks, but moving between separate tools breaks conversational context and forces users to repeat information.
The approach
I built a lightweight Streamlit workspace backed by GroqCloud and LangChain, with model selection, low-latency streaming, and an explicit memory layer for ongoing conversations.
Architecture
A simplified view of how the main product layers exchange data.
Responsibilities and data flow are shown here without infrastructure noise.
Built with
Core features
- 01
Instant switching across supported language models
- 02
Streaming, low-latency responses
- 03
Persistent conversational context
- 04
Focused interface with minimal setup
What required care
- Creating consistent output behavior across different models
- Managing context length without making memory opaque
- Keeping the interface responsive during streamed output
What the work clarified
- Model choice is most useful when the trade-off is legible to the user.
- Visible memory controls build trust in context-aware products.
Key product flows
Three direct paths that show how the product helps someone move from intent to outcome.
Conversation view
A focused product flow designed around one clear user outcome.
Model selection
A focused product flow designed around one clear user outcome.
Context controls
A focused product flow designed around one clear user outcome.