Pinned MiniLM product retrieval in the browser, with a remixable JSON catalogue

Madhvansh · July 19, 2026, 11:49am

I built a small MIT-licensed search lab that runs MiniLM feature extraction in
the browser with Transformers.js. The model revision and library version are
pinned, queries stay on-device, and the 20-product catalogue can be replaced
without a backend or API key.

The larger repository contains an Amazon ESCI retrieve-and-rank reference and a
TREC run preflight for CI. The browser lab is deliberately labelled as a
general-purpose MiniLM demonstration, not an ESCI-trained benchmark.

Try it: NECS Browser Lab — neural product search in your browser

Remix guide:

github.com/Madhvansh/Neural-E-Commerce-Search

docs/FORK_THE_LAB.md

v0.3.0

# Put neural search on your own catalogue

The browser lab has no project backend, API key, database, or training step. A
fork can become a small semantic-search demo for your own catalogue in about
five minutes.

1. [Fork the repository](https://github.com/Madhvansh/Neural-E-Commerce-Search/fork).
2. Replace the 20 sample products in `docs/catalog.json` with your own public,
   non-sensitive catalogue data. Keep the same five fields. The exact contract
   is published as [`docs/catalog.schema.json`](catalog.schema.json), and the
   lab now reports a readable error instead of silently accepting malformed data.
3. In the fork, enable GitHub Pages with **Deploy from a branch**, branch
   `main`, folder `/docs`.
4. Open `/lab.html` on your Pages site and try a query.

```json
[
  {
    "id": "your-product-id",
    "title": "Product name",

This file has been truncated. show original

I would value first-query and cached-query latency, plus the least relevant top
result for one example search. Browser/model details make the report useful.

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Pinned MiniLM product retrieval in the browser, with a remixable JSON catalogue

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