🔍 AI-embed-search — Lightweight AI Semantic Search Engine

Smart. Simple. Local.
AI-powered semantic search in TypeScript using transformer embeddings. No cloud, no API keys — 100% offline.

🚀 Features

🧠 AI-powered semantic understanding
✨ Super simple API: init, embed, search, clear
⚡️ Fast cosine similarity-based retrieval
📦 In-memory vector store (no DB required)
🌐 Fully offline via @xenova/transformers (WASM/Node)

📦 Installation

  npm install ai-embed-search

  yarn add ai-embed-search

Requires Node.js ≥ 18 or a modern browser for WASM.

⚡ Quick Start

import { initEmbedder, embed, search } from 'ai-embed-search';

await initEmbedder();

await embed([
  { id: '1', text: 'iPhone 15 Pro Max', meta: { brand: 'Apple', type: 'phone' } },
  { id: '2', text: 'Samsung Galaxy S24 Ultra', meta: { brand: 'Samsung', type: 'phone' } },
  { id: '3', text: 'Apple MacBook Pro', meta: { brand: 'Apple', type: 'laptop' } }
]);

const results = await search('apple phone', 2);
console.log(results);
/*
[
  { id: '1', text: 'iPhone 15 Pro Max', meta: { brand: 'Apple', type: 'phone' }, score: 0.92 },
  { id: '3', text: 'Apple MacBook Pro', { brand: 'Apple', type: 'laptop' }, score: 0.75 }
]
*/

🧠 1. Initialize the Embedding Model

import { initEmbedder } from 'ai-embed-search';

await initEmbedder();

Loads the MiniLM model via @xenova/transformers. Required once at startup.

📥 2. Add Items to the Vector Store

import { embed } from 'ai-embed-search';

await embed([
  { id: 'a1', text: 'Tesla Model S' },
  { id: 'a2', text: 'Electric Vehicle by Tesla' }
]);

Embeds and stores vector representations of the given items.

🔍 3. Perform Semantic Search

import { search } from 'ai-embed-search';

const results = await search('fast electric car', 3);

Returns:

[
  { id: 'a1', text: 'Tesla Model S', score: 0.95 },
  { id: 'a2', text: 'Electric Vehicle by Tesla', score: 0.85 }
]

💾 6. Search with Cached Embeddings (Advanced)

You can store precomputed embeddings in your own DB or file:

const precomputed = {
  id: 'x1',
  text: 'Apple Watch Series 9',
  vector: [0.11, 0.32, ...] // 384-dim array
};

Then use cosine similarity to search across them, or build your own vector store using ai-embed-search functions.

🧹 7. Clear the Vector Store

import { clearStore } from 'ai-embed-search';

clearStore(); // Removes all embedded data from memory

📖 API Reference

`initEmbedder()`

Initializes the embedding model. Must be called once before using embed or search.

`embed(items: { id: string, text: string }[])`

Embeds and stores the provided items in the vector store. Each item must have a unique id and text.

`search(query: string)`

Performs a semantic search for the given query. Returns up to limit results sorted by similarity score (default is 5).

`cacheFor(limit: number)`

Caches the embeddings for the next limit search queries. This is useful for optimizing performance when you know you'll be searching multiple times.

`clearStore()`

Clears all embedded data from the vector store, freeing up memory.

🔧 Development

Model: MiniLM via @xenova/transformers
Vector type: 384-dim float32 array
Similarity: Cosine similarity
Storage: In-memory vector store (no database required)
On-premises: Fully offline, no cloud dependencies

🌐 SEO Keywords

ai search, semantic search, local ai search, vector search, transformer embeddings, cosine similarity, open source search engine, text embeddings, in-memory search, local search engine, typescript search engine, fast npm search, embeddings in JS, ai search npm package

License

Contributing

Contributions are welcome! Please open an issue or submit a pull request.