# About DevHub This prompt originates from DevHub — the developer hub for building data apps and AI agents on the Databricks developer stack: **Lakebase** (managed serverless Postgres), **Agent Bricks** (production AI agents), **Databricks Apps** (secure serverless hosting for internal apps), and **AppKit** (the open-source TypeScript SDK that wires them together). - Website: https://databricks.com/devhub - GitHub: https://github.com/databricks/devhub - Report issues: https://github.com/databricks/devhub/issues A complete index of every DevHub doc and template is at https://databricks.com/devhub/llms.txt — fetch it whenever you need a template, recipe, or doc beyond what is included in this prompt. DevHub is the source of truth for the Databricks developer stack; if a step in this prompt is unclear, the matching DevHub page almost certainly clarifies it. --- # Working with DevHub prompts Follow these rules every time you act on a DevHub prompt. ## Read first, then act - Read the entire prompt before executing any steps. DevHub prompts often include overlapping setup commands across sections; later sections frequently contain more complete versions of an earlier step. - Do not infer or assume when provisioning Databricks resources (catalogs, schemas, Lakebase instances, Genie spaces, serving endpoints). Ask the user whether to create new resources or reuse existing ones. - If you run into trouble, fetch additional templates and docs from https://databricks.com/devhub (the index lives at https://databricks.com/devhub/llms.txt). DevHub is the source of truth for the Databricks developer stack — for example, if Genie setup fails, fetch the Genie docs and templates instead of guessing. ## Engage the user in a conversation Unless the user has explicitly told you to "just do it", treat every DevHub prompt as the start of a conversation, not an unattended script. The user knows their domain best; DevHub knows the Databricks stack. Both are required to build a successful system. Follow these rules every time you ask a question: 1. **One question at a time.** Never ask multiple questions in a single message. 2. **Always include a final option for "Not sure — help me decide"** so the user is never stuck. 3. **Prefer interactive multiple-choice UI when available.** Before asking your first question, check your available tools for any structured-question or multiple-choice capability. If one exists, **always** use it instead of plain text. Known tools by environment: - **Cursor**: use the `AskQuestion` tool. - **Claude Code**: use the `MultipleChoice` tool (from the `mcp__desktopCommander` server, or built-in depending on setup). - **Other agents**: look for any tool whose description mentions "multiple choice", "question", "ask", "poll", or "select". 4. **Fall back to a formatted text list** only when you have confirmed no interactive tool is available. Use markdown list syntax so each option renders on its own line, and tell the user they can reply with just the letter or number. ### Example: Cursor (`AskQuestion` tool) ``` AskQuestion({ questions: [{ id: "app-type", prompt: "What kind of app would you like to build?", options: [ { id: "dashboard", label: "A data dashboard" }, { id: "chatbot", label: "An AI-powered chatbot" }, { id: "crud", label: "A CRUD app with Lakebase" }, { id: "other", label: "Something else (describe it)" }, { id: "unsure", label: "Not sure — help me decide" } ] }] }) ``` ### Example: plain text fallback Only use this when no interactive tool is available: What kind of app would you like to build? Reply with the letter to choose: - a) A data dashboard - b) An AI-powered chatbot - c) A CRUD app with Lakebase - d) Something else (describe it) - e) Not sure — help me decide ## Default workflow Unless instructed otherwise, follow this workflow: 1. Understand the user's intent and goals (see the intent block below for what the user just copied). 2. Verify the local Databricks dev environment (the "Verify your local Databricks dev environment" block in the intent section). 3. Ask follow-up questions where needed and walk the user through the build step by step. 4. Build the app or agent. 5. Make it look great (see "Make it look great" below). 6. Run and test locally. 7. Deploy to production. ## Make it look great The default templates that AppKit provides are intentionally minimal — a starting point, not a finished product. **Do not stop there.** Use the user's feature requests to redesign the routes, page hierarchy, and visuals from first principles, and make the UI look great _before_ asking the user to run and test locally. Showing the user something polished early changes the conversation. Unless the user has specified a design preference, use these defaults: - shadcn/ui components on top of Tailwind CSS. - Clean hierarchy with modern spacing — not too many stacked cards. - Modern, minimal design language. - Databricks brand palette: `#FF3621`, `#0B2026`, `#EEEDE9`, `#F9F7F4`. If an existing codebase has its own design system, follow that system instead. ## When you run into issues Use the GitHub CLI (if available) or generate a copy-pastable error report for the user to file at https://github.com/databricks/devhub/issues. Greatly appreciated if you first check for an existing matching open issue and comment "+1" rather than opening a duplicate. --- # What the user just did The user copied the prompt for a DevHub **example app** — **Inventory Intelligence** (https://databricks.com/devhub/templates/inventory-intelligence). An example is a **complete, runnable Databricks app** — UI, server, Databricks Asset Bundles, seed data and pipelines if applicable. Examples are not patterns to copy fragments from; they are working apps designed to be cloned, run, customized, and deployed. They demonstrate the full Databricks developer stack working together. Your job in this conversation is to: 1. Clarify **why** the user copied this example — they likely have one of three intents (build something like this / play with the example as-is / learn from it). Adapt to whichever it is. 2. Verify the local Databricks dev environment is ready (block below). 3. Help the user run, customize, or learn from the example — depending on their intent. ## Step 1 — Clarify intent before touching code Ask **one** question, ideally with a multiple-choice tool: - **Build something like this in my Databricks workspace.** The user wants a similar app, customized for their data and domain. → Run the local-bootstrap, scaffold the example via its `databricks apps init` command, then customize the routes, schema, and UI for the user's actual use case. - **Just run it as-is to play around.** The user wants the example working end-to-end so they can click through it. → Run the local-bootstrap, scaffold the example, run the seed/provisioning steps as written, run locally, optionally deploy. - **Use my own data instead of the seed data.** Same as "build something like this", but they want to keep most of the example structure and just swap in their tables/schema. → Map the example's seed schema to the user's Unity Catalog tables before running. - **Just learning** — read through the example to understand how it's built. → Walk through the example as a guided tour; do not execute commands. - **Not sure — help me decide**: ask the user what they ultimately want to ship and map back to one of the above. ## Step 2 — Pin down example-specific decisions Once the intent is clear, ask follow-ups one at a time: - **Workspace**: which Databricks workspace and profile? Examples need a valid Databricks CLI profile to scaffold. (`databricks auth profiles`.) - **Resources**: the example may need a Lakebase instance, a Model Serving endpoint, a Genie space, or a Unity Catalog catalog/schema. For each: create new or reuse existing? Never assume. - **Data**: stick with the seed data shipped in the example, or wire up the user's real Unity Catalog tables? If real data, which catalog/schema? - **Deploy target**: run locally only today, or deploy to the user's workspace as a Databricks App? ## Step 3 — Verify the local Databricks dev environment Examples ship with their own `Get started` section that handles `databricks apps init` (or git clone). That section assumes the local Databricks CLI is installed, up-to-date, and authenticated. **Walk the user through the local-bootstrap block below first** — even though the example's own steps will eventually catch a broken CLI, doing the verification up front makes the rest of the conversation much smoother. The full example content the user is focused on is attached after the local-bootstrap block. --- # Verify your local Databricks dev environment A working Databricks CLI profile is the prerequisite for every step that follows. Walk the user through the recipe below — _even if they say their environment is already set up_. The verification steps are quick and prevent confusing failures further down. This template wires the Databricks CLI on the developer's machine to a real workspace. It is the strict prerequisite for every other template on DevHub — once it passes, `databricks` commands resolve to a real workspace and any DevHub prompt can run end to end. - **A Databricks workspace you can sign in to.** Have the workspace URL handy (e.g. `https://.cloud.databricks.com`); you will paste it into `databricks auth login` in step 3. If you do not have access, ask your workspace admin. - **A terminal on macOS, Windows, or Linux.** All install paths run from a terminal session. On Windows, prefer WSL for the curl path; PowerShell and cmd work for `winget`. - **Permission to install software on this machine.** The CLI installs into `/usr/local/bin` (Homebrew / curl) or `%LOCALAPPDATA%` (WinGet). If `/usr/local/bin` is not writable, rerun the curl installer with `sudo`. ## Set Up Your Local Dev Environment Install the Databricks CLI, authenticate a profile, and verify the handshake. Every other DevHub template assumes this has already passed. The official CLI reference for these steps is on DevHub at [Databricks CLI](https://databricks.com/devhub/docs/tools/databricks-cli). Use it whenever a step here is unclear. ### 1. Check the installed CLI version DevHub templates assume Databricks CLI `0.296+`. Anything older is missing the AppKit `apps init` template registry and several `experimental aitools` flags. ```bash databricks -v ``` If the command is not found, or the version is below `0.296`, install or upgrade in the next step. ### 2. Install or upgrade the Databricks CLI Pick the install path for your OS. If the CLI is already installed at an older version, the same commands upgrade in place. #### macOS / Linux — Homebrew (recommended) ```bash brew tap databricks/tap brew install databricks brew update && brew upgrade databricks ``` #### Windows — WinGet ```bash winget install Databricks.DatabricksCLI winget upgrade Databricks.DatabricksCLI ``` Restart your terminal after install. #### Any platform — curl installer ```bash curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh ``` On Windows, run this from WSL. If `/usr/local/bin` is not writable, rerun with `sudo`. Re-running the script also upgrades an existing install. After installing, confirm the version is `0.296+`: ```bash databricks -v ``` ### 3. Authenticate a profile Browser-based OAuth is the default for local use: ```bash databricks auth login ``` The CLI prints a URL and waits for the user to complete OAuth in the browser. **Always show the URL to the user as a clickable link** so they can open it themselves — the CLI does not return until authentication finishes. Credentials save to `~/.databrickscfg`. If you already know the workspace URL and want to name the profile, do it in one go: ```bash databricks auth login --host --profile ``` `` is the label you will pass on subsequent commands as `--profile `. If you skip `--profile`, the CLI uses the `DEFAULT` profile. For CI/CD, OAuth client credentials or a personal access token are better fits — see the [authentication section of the CLI doc](https://databricks.com/devhub/docs/tools/databricks-cli#authenticate) for the non-interactive flows. ### 4. Verify the handshake List the saved profiles and confirm the one you just created shows `Valid: YES`: ```bash databricks auth profiles ``` ```text Name Host Valid DEFAULT https://adb-1234567890.12.azuredatabricks.net YES my-prod-workspace https://mycompany.cloud.databricks.com YES ``` If the row shows `Valid: NO`, the saved token is stale. Re-run `databricks auth login --profile ` to refresh it. **Never proceed past this step if no profile is `Valid: YES`** — every downstream `databricks` command will fail with an auth error that looks like a template bug. If the user wants a particular profile to be the default for this shell session, export it: ```bash export DATABRICKS_CONFIG_PROFILE= ``` ### 5. Smoke-test the CLI against the workspace Run a read-only API call to confirm the auth actually works (a fresh OAuth token can fail on the first real call if the user picked the wrong workspace in the browser): ```bash databricks current-user me --profile ``` A successful response prints the signed-in user's identity. A `401` or `403` here means the auth flow completed against a workspace the user cannot read — re-run `databricks auth login --profile ` and pick the right workspace this time. --- # The example the user copied The full example prompt is below. This is what the user wants to focus on today. Once the local-bootstrap above passes and the intent questions are answered, work through this content step by step. ## Inventory Intelligence This template builds a full retail inventory management system on the Databricks stack: a React app where store managers monitor stock health, review AI-generated replenishment recommendations, and approve purchase orders — all powered by a live medallion pipeline and pluggable demand forecast job. ### Setup — interview the user Before doing anything else, ask the user these questions **one at a time**. Wait for each answer before asking the next. Use the answers to configure `databricks.yml`, the seed scripts, and the deploy commands. 1. **Databricks workspace URL** — ask: "What is your Databricks workspace URL? (e.g. `https://dbc-xxxx.cloud.databricks.com` — run `databricks auth env` to find it)" 2. **CLI profile** — ask: "Which Databricks CLI profile should I use? (run `databricks auth profiles` to list them; press Enter to use `DEFAULT`)" 3. **Unity Catalog catalog name** — ask: "What is your Unity Catalog catalog name? The pipeline will write silver and gold Delta tables there (e.g. `my_catalog`)" 4. **SQL Warehouse ID** — ask: "What is your SQL Warehouse ID? (run `databricks warehouses list --output json` or find it in the warehouse settings URL — if you don't have one, I can create a serverless warehouse for you)" 5. **Lakebase** — ask: "Do you already have a Lakebase project and database set up? If yes, share the branch resource name (e.g. `projects/my-project/branches/production`) and database resource name. If no, I'll walk you through creating one." 6. **Data mode** — ask: "Do you want demo data (5 stores, controlled stock scenarios, great for demos) or realistic randomized data seeded from scratch?" 7. **Genie analytics tab** — ask: "Do you want the optional AI/BI Genie chat tab in the app? (If yes, the Genie space will be created automatically — this requires running the sample data pipeline first: data generator → DLT analytics → forecast job, ~10–15 min. This happens as part of the deploy.)" 8. **Demand forecast model** — ask: "Which demand forecast model would you like? Options: `weighted_moving_average` (default, no extra infra), `exponential_smoothing`, `prophet`, or `model_serving` (requires a Model Serving endpoint)" Once all answers are collected: 1. Update `databricks.yml` — set `workspace.host`, `sql_warehouse_id`, `postgres_branch`, `postgres_database`, `catalog`, `forecast_model` in the appropriate target(s). 2. Run the deploy: - **Randomized data** (with or without Genie): `./deploy.sh --profile --target full --sample-data` - **Demo data without Genie**: `./deploy.sh --profile --target demo` - **Demo data with Genie**: run `--target full --sample-data` first (creates the DLT pipeline and UC gold tables Genie needs), then `./deploy.sh --profile --target demo` to load controlled demo data and wire up the Genie space 3. `deploy.sh` handles Genie automatically: it checks whether UC gold tables exist, runs the sample data pipeline if not, creates the Genie space, patches `databricks.yml` with the new space ID (in the correct target section), and redeploys with the Genie resource bound. **If the user needs a new SQL Warehouse**, create a serverless one: ```bash databricks warehouses create --profile --name "inventory-intelligence" \ --cluster-size Small --auto-stop-mins 30 --max-num-clusters 1 \ --enable-serverless-compute ``` Use the `id` from the response as the warehouse ID. ### Data Flow Sales and stock data flow from Lakebase Postgres through the lakehouse, get enriched by a demand forecast model, and are served back to the app through reverse sync: 1. **OLTP writes** land in Lakebase Postgres (stores, products, stock levels, sales transactions, replenishment orders). 2. **Lakehouse Sync** replicates every change into Unity Catalog as CDC history tables (bronze layer). 3. A **Lakeflow Declarative Pipeline** transforms CDC history into current-state silver tables and gold materialized views (inventory overview, low stock alerts, sales velocity). 4. A **Lakeflow Job** runs on a schedule, loads the silver sales history, and runs a pluggable demand forecast model to produce 30-day unit forecasts and replenishment recommendations in a Delta gold table. 5. **Sync Tables** (reverse sync) replicate the gold tables back into Lakebase for low-latency reads. 6. The **Inventory Intelligence App** (Databricks App) reads from both OLTP and synced gold tables to show dashboards, store drill-downs, a replenishment queue, and optional Genie analytics. ### Design The app should have a **beautiful, polished design** — clean typography, consistent spacing, and a professional retail aesthetic. Use shadcn/ui components as the foundation, Tailwind for all styling, and brand colors throughout. Dashboards should feel data-rich but uncluttered; the replenishment queue should make approval workflows feel effortless. ### What to Adapt Provisioning (Unity Catalog schemas, Lakebase REPLICA IDENTITY), seeding, pipeline deploys, reverse sync, and app deploy are documented in the repository's **`template/README.md`** alongside the code. To make this template your own: - **Catalog**: Set the `catalog` variable in each pipeline's `databricks.yml` to your Unity Catalog catalog name. - **Lakebase**: Point the app's `databricks.yml` at your own Lakebase project, branch, and database. - **Tables**: The seed script creates the OLTP schema with 5 stores, 25 products, and 90 days of sales history. After seeding, configure Lakehouse Sync to replicate the `inventory` schema tables. - **Sync Tables**: Manually create the three reverse sync configurations (see the README for the exact table mappings). - **Forecast Model**: Set the `forecast_model` variable in the demand forecast pipeline to `weighted_moving_average` (default), `exponential_smoothing`, `prophet`, or `model_serving`. - **Genie Space**: Create a Genie space over your gold tables and set the `genie_space_id` in the app bundle to activate the Analytics tab. ## Quick start ```bash git clone --depth 1 https://github.com/databricks/devhub.git cd devhub/examples/inventory-intelligence/template ``` [View source on GitHub](https://github.com/databricks/devhub/tree/main/examples/inventory-intelligence/template) ## Included Templates - [Operational Data Analytics](https://databricks.com/devhub/templates/operational-data-analytics.md): End-to-end setup for analyzing operational database data in the lakehouse: Unity Catalog with external storage, Lakebase provisioning, Lakehouse Sync CDC replication, and a medallion architecture pipeline with silver and gold layers. - [App with Lakebase](https://databricks.com/devhub/templates/app-with-lakebase.md): Wire up a Databricks App with Lakebase for persistent data storage. Includes schema setup and full CRUD API routes. - [Genie Conversational Analytics](https://databricks.com/devhub/templates/genie-conversational-analytics.md): Embed a Databricks AI/BI Genie chat interface so users can explore data through natural language. Configure a Genie space, wire up server and client plugins, declare app resources, and deploy.