altor-vec

<b>Client-side vector search. Rust + WASM. 54KB. Sub-millisecond.</b>






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Zero server. Zero API keys. Zero per-query cost. Your users' data never leaves their browser.

altor-vec is an HNSW vector similarity search engine written in Rust that compiles to 54KB of WebAssembly. Search 10,000 vectors in under 1ms — entirely client-side.

Why altor-vec?

You're paying Algolia $0.50 per 1,000 searches and sending your users' queries to a third party.

With altor-vec, search runs in the browser. $0 per query. Forever.

	altor-vec	Algolia	Voy	Orama
Runs client-side	Yes	No	Yes	Yes
Binary size	54KB gz	N/A	75KB gz	~2KB*
Algorithm	HNSW	BM25	k-d tree	Brute-force
p95 latency	0.6ms	~50ms (network)	~2ms	~5ms
Per-query cost	$0	$0.50/1K	$0	Free tier

_{*Orama's 2KB is keyword search only; vector search adds significant size.}

Get started in 30 seconds

npm install altor-vec

import init, { WasmSearchEngine } from 'altor-vec';

await init();

// Load a pre-built index
const resp = await fetch('/index.bin');
const engine = new WasmSearchEngine(new Uint8Array(await resp.arrayBuffer()));

// Search — returns in <1ms
const results = JSON.parse(engine.search(queryEmbedding, 5));
// => [[nodeId, distance], ...]

That's it. No server to deploy. No API key to manage. No billing to worry about.

Benchmarks

**Latency** (10K vectors, 384d) | Environment | p95 | |-------------|-----| | Chrome | **0.60ms** | | Node.js | **0.50ms** | | Native Rust | **0.26ms** |

**Size** | Asset | Size | |-------|------| | `.wasm` gzipped | **54KB** | | `.wasm` raw | 117KB | | Index (10K/384d) | 17MB |

Use with a Web Worker (recommended for production)

Keep the main thread free — especially important on mobile:

// worker.js
import init, { WasmSearchEngine } from 'altor-vec';

let engine;
self.onmessage = async (e) => {
  if (e.data.type === 'init') {
    await init();
    const resp = await fetch(e.data.indexUrl);
    engine = new WasmSearchEngine(new Uint8Array(await resp.arrayBuffer()));
    postMessage({ type: 'ready', count: engine.len() });
  }
  if (e.data.type === 'search') {
    const results = JSON.parse(engine.search(new Float32Array(e.data.query), e.data.topK));
    postMessage({ type: 'results', results });
  }
};

// main.js — UI stays buttery smooth
const worker = new Worker('worker.js', { type: 'module' });
worker.postMessage({ type: 'init', indexUrl: '/index.bin' });

API

Method	Description
new WasmSearchEngine(bytes)	Load a serialized index
.from_vectors(flat, dims, m, ef_construction, ef_search)	Build index from vectors
.search(query, topK)	Search → JSON [[id, dist], ...]
.add_vectors(flat, dims)	Add vectors to existing index
.to_bytes()	Serialize index
.len()	Vector count
.free()	Free WASM memory

Parameters:

Param	Default	What it does
m	16	Connections per node. Higher = better recall, more RAM
ef_construction	200	Build-time beam width. Higher = better index, slower build
ef_search	50	Search-time beam width. Higher = better recall, slower search

Works with any embedding model

Model	Dims	Where it runs
all-MiniLM-L6-v2	384	Browser (Transformers.js)
text-embedding-3-small	1536	OpenAI API
embed-english-v3	1024	Cohere API

Fully client-side with Transformers.js — no API calls at all:

import { pipeline } from '@huggingface/transformers';

const embed = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
const output = await embed('your query', { pooling: 'mean', normalize: true });
const results = JSON.parse(engine.search(new Float32Array(output.data), 5));

How it works

altor-vec uses HNSW (Hierarchical Navigable Small World) — the same algorithm behind Pinecone, Qdrant, and pgvector. HNSW builds a multi-layer graph where each node is a vector and edges connect nearby neighbors. Upper layers act as express lanes for coarse navigation; the bottom layer contains all vectors for fine-grained search. A query enters at the top and greedily descends to find the nearest neighbors in O(log n) time.

All vectors are L2-normalized at insert time, so dot product distance equals cosine similarity — no extra computation at search time.

Architecture

src/
├── lib.rs              # Public API re-exports
├── distance.rs         # Dot product, normalization (auto-vectorizes with SIMD)
└── hnsw/
    ├── mod.rs           # HnswIndex: API + serialization
    ├── graph.rs         # Layered graph structure
    ├── search.rs        # Greedy beam search
    └── construction.rs  # HNSW insert + random layer selection

wasm/
└── src/lib.rs          # WasmSearchEngine (wasm-bindgen wrapper)

Build from source

cargo test                # run tests
cargo bench               # run benchmarks
cd wasm && wasm-pack build --target web --release  # build WASM

Contributing

We welcome contributions! See CONTRIBUTING.md for build instructions, code style, and PR process.

License

MIT

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Built by altor-lab

_{npm · Issues · Contact}

Need managed semantic search? Embedding pipeline, index building, CDN delivery?

anshul@altorlab.dev