VelesDB WASM

WebAssembly build of VelesDB - vector search in the browser.

Features

• In-browser vector search - No server required
• SIMD optimized - Uses WASM SIMD128 for fast distance calculations
• Multiple metrics - Cosine, Euclidean, Dot Product
• Lightweight - Minimal bundle size

Installation

npm install velesdb-wasm

Usage

import init, { VectorStore } from 'velesdb-wasm';

async function main() {
  // Initialize WASM module
  await init();

  // Create a vector store (768 dimensions, cosine similarity)
  const store = new VectorStore(768, 'cosine');

  // Insert vectors
  store.insert(1, new Float32Array([0.1, 0.2, ...]));
  store.insert(2, new Float32Array([0.3, 0.4, ...]));

  // Search for similar vectors
  const query = new Float32Array([0.15, 0.25, ...]);
  const results = store.search(query, 5); // Top 5 results

  // Results: [[id, score], [id, score], ...]
  console.log(results);
}

main();

API

VectorStore

class VectorStore {
  // Create a new store
  constructor(dimension: number, metric: 'cosine' | 'euclidean' | 'dot');

  // Properties
  readonly len: number;
  readonly is_empty: boolean;
  readonly dimension: number;

  // Methods
  insert(id: number, vector: Float32Array): void;
  search(query: Float32Array, k: number): Array<[number, number]>;
  remove(id: number): boolean;
  clear(): void;
  memory_usage(): number;
}

Distance Metrics

Use Cases

• Browser-based RAG - 100% client-side semantic search
• Offline-first apps - Works without internet
• Privacy-preserving AI - Data never leaves the browser
• Electron/Tauri apps - Desktop AI without a server

Building from Source

# Install wasm-pack
cargo install wasm-pack

# Build for browser
wasm-pack build --target web

# Build for Node.js
wasm-pack build --target nodejs

Performance

Typical latencies on modern browsers:

License

Business Source License 1.1 (BSL-1.1)

See LICENSE for details.