| name | quickstart |
| description | Set up the Databricks chatbot app for local development and deployment. Use when: (1) First time setup, (2) User says 'quickstart', 'set up', 'authenticate', or 'configure databricks', (3) No .env file exists, (4) User says 'enable feedback', 'feedback widget', or 'MLFLOW_EXPERIMENT_ID'. |
Quickstart: Databricks Chatbot App
Run the interactive setup script from the e2e-chatbot-app-next/ directory:
./scripts/quickstart.sh
What It Configures
The script walks through the following steps interactively:
- Prerequisites — installs jq, nvm, Node 20, and the Databricks CLI if missing
- Databricks auth — lets you select an existing profile or create a new one via
databricks auth login
- Serving endpoint — prompts for your agent endpoint name (Agent Bricks or custom agent); validates it exists
- Feedback widget — if the endpoint has a linked MLflow experiment, offers to enable thumbs up/down feedback (default: yes). This automatically:
- Sets
MLFLOW_EXPERIMENT_ID in .env
- Uncomments the experiment resource in
databricks.yml
- Uncomments
MLFLOW_EXPERIMENT_ID in app.yaml
- App/bundle name — optionally customize the app name (default:
db-chatbot-dev-<username>, max 30 chars)
- Database — optionally enable persistent chat history via a Lakebase instance (~5-10 min, costs apply)
- Deploy — runs
databricks bundle deploy and starts the app
Enabling Feedback After Initial Setup
If you skipped feedback during quickstart, or ran the script before feedback support was added, enable it manually:
1. Find the experiment name for your endpoint:
npx tsx scripts/get-experiment-id.ts --endpoint <your-serving-endpoint-name>
For Agent Bricks (Knowledge Assistant / Multi-Agent Supervisor):
npx tsx scripts/get-experiment-id.ts --agent-brick <agent-brick-name>
2. Set MLFLOW_EXPERIMENT_ID in .env (for local dev):
MLFLOW_EXPERIMENT_ID=<experiment-name-from-step-1>
3. Configure databricks.yml — uncomment and fill in the experiment resource:
- name: experiment
description: "MLflow experiment for collecting user feedback"
experiment:
name: "<experiment-name-from-step-1>"
permission: CAN_EDIT
4. Configure app.yaml — uncomment the env var:
- name: MLFLOW_EXPERIMENT_ID
valueFrom: experiment
5. Redeploy:
databricks bundle deploy
databricks bundle run databricks_chatbot
Troubleshooting
| Issue | Solution |
|---|
| Feedback widget not showing after setup | Restart dev server; env vars are read at startup |
get-experiment-id.ts fails with auth error | Run databricks auth login first |
| No experiment found for endpoint | Only custom agents and Agent Bricks endpoints have linked experiments; Foundation Model endpoints do not support feedback |
| Feedback submission returns 403 | App service principal is missing CAN_EDIT on the experiment — check permission: CAN_EDIT in databricks.yml |
| "Instance name is not unique" on deploy | Run ./scripts/cleanup-database.sh to remove the old database instance |
