ridegenie 0.1.1

One-line installer for the India RideGenie Databricks demo (data + Genie + AI/BI dashboard + app).

🛺 ridegenie — India RideGenie demo, installed in one line

A dbdemos-style installer for the India RideGenie demo: natural-language geospatial analytics on synthetic Indian ride-hailing data — 200K trips · 160+ cities · ₹ fares · UPI payments.

One call provisions the whole thing into your Databricks workspace:

#	Asset	What it does
1	Unity Catalog data layer	rides_enriched, ride_zones, pickup_hotspots (~200K rides, PIN-code geo)
2	Genie Space	Ask in plain English — Genie writes governed SQL (₹, cities, surge, payments)
3	AI/BI Dashboard	INR KPIs, demand-by-hour, surge, vehicle/payment mix + a live pickup-hotspot map
4	RideGenie App	FastAPI + Leaflet — draw a region on an India map and ask Genie about it

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Quick start (Databricks notebook)

%pip install ridegenie
dbutils.library.restartPython()

import ridegenie
ridegenie.install("india-ridegenie")

Want the bleeding edge (unreleased main)? %pip install git+https://github.com/anuj1303/ridegenie-demo.git

Not on Free Edition? The default catalog workspace only exists on Free Edition. On any other workspace, pass your own catalog: ridegenie.install("india-ridegenie", catalog="your_catalog"). (If you skip this, the installer fails fast and lists the catalogs available to you.)

That's it. The installer prints links to your Genie Space, Dashboard, and App when it finishes.

Prerequisite: a running SQL Warehouse (the Serverless Starter Warehouse is enough). Works on Databricks Free Edition and any AWS/Azure/GCP workspace.

Prefer not to write code? Import notebooks/00-START-HERE-RideGenie.py into your workspace and Run All.

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What install() does (à la dbdemos)

1. Resolves context — current user, workspace host, and a SQL warehouse (auto-picks a running one, or starts/creates one).
2. Builds the data layer — creates the schema + volume, uploads the bundled (gzipped) CSVs, and runs a templated setup.sql to build the tables/views.
3. Creates the Genie Space — registers the tables with format-assistance + entity-matching and applies domain instructions (INR, surge, payments).
4. Creates & publishes the AI/BI dashboard — one shared dataset drives cross-filtering across KPIs, charts, and the hotspot map.
5. Deploys the app — creates the app, uploads the source, attaches sql-warehouse + genie resources (IDs auto-injected), grants the app's service principal data access, and deploys.

Everything is workspace-portable — table names, warehouse, Genie/app resource IDs are resolved at install time via {{CATALOG}} / {{SCHEMA}} / {{VOLUME}} templating. No hard-coded IDs.

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Options

ridegenie.install(
    "india-ridegenie",
    catalog="workspace",              # target UC catalog
    schema="india_rides_workshop",    # target UC schema
    warehouse_id=None,                # default: first running/available warehouse
    install_app=True,                 # set False to skip the FastAPI app
    profile=None,                     # only when running locally (Databricks CLI profile)
    overwrite=False,
)

Run locally (outside a notebook) against a CLI profile:

import ridegenie
ridegenie.install("india-ridegenie", profile="FreeEdition")

---

Package layout

ridegenie/
├── conf/india-ridegenie.json     # demo manifest (the "bundle_config")
├── installer.py                  # the Installer orchestrator + public install()
├── sql/setup.sql                 # templated data-layer DDL
├── genie/instructions.md         # Genie domain instructions
├── dashboards/india_ridegenie.lvdash.json   # AI/BI dashboard definition
├── data/*.csv.gz                 # pre-built synthetic data (uploaded to a Volume)
└── app/                          # FastAPI + Leaflet app source
notebooks/00-START-HERE-RideGenie.py   # one-click bootstrap notebook
scripts/                          # build_zones.py / generate_rides.py (regenerate the data)

Regenerating the data (optional)

The repo ships pre-built data. To rebuild it from source (GeoNames India PIN codes):

cd scripts
python3 build_zones.py       # downloads GeoNames, builds ride_zones.csv
python3 generate_rides.py    # builds rides.csv (200K trips)
gzip -c ride_zones.csv > ../ridegenie/data/ride_zones.csv.gz
gzip -c rides.csv      > ../ridegenie/data/rides.csv.gz

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Built for Databricks Field Engineering. Synthetic data — no PII. PIN codes from GeoNames (India).