https://github.com/typesense/typesense-instantsearch-semantic-search-demo

A demo that shows how to build a semantic search experience with Typesense's vector search feature and Instantsearch.js
https://github.com/typesense/typesense-instantsearch-semantic-search-demo

algolia embeddings instantsearch instantsearch-js semantic-search sentence-bert typesense typesense-instantsearch-adapter

Last synced: 5 days ago
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A demo that shows how to build a semantic search experience with Typesense's vector search feature and Instantsearch.js

• Host: GitHub
• URL: https://github.com/typesense/typesense-instantsearch-semantic-search-demo
• Owner: typesense
• License: apache-2.0
• Created: 2023-03-23T05:13:41.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-08-22T00:30:52.000Z (about 1 year ago)
• Last Synced: 2024-05-01T09:38:47.080Z (7 months ago)
• Topics: algolia, embeddings, instantsearch, instantsearch-js, semantic-search, sentence-bert, typesense, typesense-instantsearch-adapter
• Language: JavaScript
• Homepage:
• Size: 12.5 MB
• Stars: 22
• Watchers: 4
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 📦 Semantic Search with Typesense + Instantsearch.js

This is a demo that shows how you can use [Typesense's](https://github.com/typesense/typesense) vector search feature, to build a semantic search experience.

**NOTE: ⚠️** This demo uses Typesense version 0.24.0 which did not have built-in embedding generation. But in v0.25.0, we've added built-in emebedding generation, which makes it even more easy to build semantic search out of the box, without the need for an external embedding service. Here's a newer demo that takes advantage of this feature: [https://github.com/typesense/showcase-hn-comments-semantic-search](https://github.com/typesense/showcase-hn-comments-semantic-search).

Learn more about Vector Search here: [https://typesense.org/docs/0.25.0/api/vector-search.html](https://typesense.org/docs/0.25.0/api/vector-search.html).

## Tech Stack

The app was built using the 

Typesense Adapter for InstantSearch.js.

## Repo structure

- `src/` and `index.html` - contain the frontend UI components.

- `scripts/indexer` - contains the script to index the book data into Typesense.

- `scripts/data` - contains a small sample subset of products.

- `server` - contains the API server that generates embeddings given a query.

## How to

Index sample dataset:

```shell

npm install

npm run typesenseServer

ln -s .env.development .env

npm run indexer

```

Start Embedding API server:

```shell

cd server

pip install -r requirements.txt

uvicorn main:app --reload

```

Start FE app:

```shell

npm start

```

Open http://localhost:3001 to see the app.

## Credits

The dataset used in this showcase is from Algolia's public set of datasets listed here: https://github.com/algolia/datasets