ChatKit Backend - SSE and Conversation Persistence

SSE streaming for real-time AI chat responses in FastAPI is implemented by exposing a dedicated endpoint that streams OpenAI agent outputs directly to the client. This Skill provides that SSE streaming layer alongside persistent conversation history and message CRUD operations.

To persist chat conversation history using SQLModel and PostgreSQL, you need defined Conversation and Message database models paired with Alembic migrations. This Skill supplies those exact models and migrations to track and store chat interactions seamlessly.

Yes, you need Python 3.11+ and PostgreSQL to run this FastAPI chat backend. The implementation relies on SQLModel for database interactions and Alembic for migrations, requiring a PostgreSQL instance to manage persistent conversations and messages effectively.

To set up database migrations for FastAPI chat messages with Alembic, configure your SQLModel Conversation and Message schemas, then run the Alembic migration commands to generate and apply the tables to your PostgreSQL database before starting the Uvicorn server.

Yes, you can use this SSE backend with an existing frontend chat UI. It provides standard Server-Sent Events streaming through a specific endpoint and CRUD APIs, allowing easy integration with chatkit-frontend or similar chat interface components.