Quickstart: Python

The full quickstart — install, connect, register, search — lives in the repo’s cookbook tree under cookbook/quickstart/ with a runnable quickstart.py that’s exercised end-to-end on every PR by tests/cookbook_smoke.py. This page mirrors the cookbook’s overview so the mdBook site renders a self-contained quickstart; the cookbook is the source of truth.

Goal: a fresh user goes from pip install jammi-ai to a successful vector query in five minutes. The end-to-end script lives next to this file in quickstart.py — copy-paste it, run it, then read the four step-by-step pages for the explanation.

Steps

1. Install — pip install jammi-ai
2. Connect — open a session against a local artifact dir
3. Register a source — attach a Parquet file
4. Generate embeddings + search — build a vector index and run a similarity query

Run it

python cookbook/quickstart/quickstart.py

Expected output: a header row and three top-3 matches with cosine similarity scores. The script exits 0 in under 30 seconds on CPU.

Production substitution

The script uses the local cookbook/fixtures/tiny_bert/ model (32-dim, 88 KB, single-layer) so the example needs no network access. In a real workload you would swap in a Hub model — for example sentence-transformers/all-MiniLM-L6-v2 (384-dim, English) — by changing the MODEL constant. Everything else stays the same.