# Quickstart

> 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'.

- Skill: `databricks/quickstart-4` (Agent Skill)
- Install (CLI): `npx skillmds@latest add databricks/quickstart-4`
- Raw SKILL.md: https://api.skillmd.com/api/skills/databricks/quickstart-4/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: databricks (https://skillmd.com/u/databricks)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/databricks/quickstart-4

---


# Quickstart: Databricks Chatbot App

Run the interactive setup script from the `e2e-chatbot-app-next/` directory:

```bash
./scripts/quickstart.sh
```

## What It Configures

The script walks through the following steps interactively:

1. **Prerequisites** — installs jq, nvm, Node 20, and the Databricks CLI if missing
2. **Databricks auth** — lets you select an existing profile or create a new one via `databricks auth login`
3. **Serving endpoint** — prompts for your agent endpoint name (Agent Bricks or custom agent); validates it exists
4. **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`
5. **App/bundle name** — optionally customize the app name (default: `db-chatbot-dev-<username>`, max 30 chars)
6. **Database** — optionally enable persistent chat history via a Lakebase instance (~5-10 min, costs apply)
7. **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:**
```bash
npx tsx scripts/get-experiment-id.ts --endpoint <your-serving-endpoint-name>
```

For Agent Bricks (Knowledge Assistant / Multi-Agent Supervisor):
```bash
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:
```yaml
        - 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:
```yaml
      - name: MLFLOW_EXPERIMENT_ID
        valueFrom: experiment
```

**5. Redeploy:**
```bash
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 |