AI Chat App

A streaming AI chat app on Databricks: a user sends a message, the server authenticates with the Databricks CLI profile (or a service-principal token in production), calls an AI Gateway chat endpoint via the OpenAI-compatible provider, and streams the answer back token-by-token. Chat sessions and messages are persisted in Lakebase Postgres so conversations survive page refreshes and redeploys.

How the steps fit together

Work through the steps in the order below. Each one adds one concrete piece; by the end you have a deployable app. Your installed Databricks agent skills provide the implementation patterns for each step.

1. Spin Up a Databricks App — scaffold a fresh AppKit Databricks App with databricks apps init (the meta-prompt above already verifies the CLI profile via Set Up Your Local Dev Environment).
2. Query AI Gateway Endpoints — pick a chat model (e.g. databricks-gpt-5-4-mini) and wire up createOpenAI() with the AI Gateway base URL.
3. Streaming AI Chat with Model Serving — add the /api/chat route with streamText() and a useChat UI backed by TextStreamChatTransport.
4. Create a Lakebase Instance — provision a managed Postgres project, branch, and endpoint; capture the connection values.
5. Lakebase Data Persistence — add the lakebase() plugin, schema setup, and CRUD plumbing against your new project.
6. Lakebase Agent Memory — create the chat.chats and chat.messages tables and persist each turn of every conversation.

Before you start

Every step below lists its own workspace-feature checks. Combined, the app needs a Databricks CLI profile that can reach Model Serving (AI Gateway foundation-model endpoints), Lakebase Postgres, and Databricks Apps. Run each step's prerequisite checks upfront so you do not hit gated features mid-build.