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Javascript bidding for the Hora Approximate Nearest Neighbor Search, in WebAssembly way.

• Performant ⚡️

  • SIMD-Accelerated (packed_simd)
  • Stable algorithm implementation
  • Multiple threads design

• Multiple Indexes Support 🚀

  • Hierarchical Navigable Small World Graph Index(HNSWIndex) (detail)
  • Satellite System Graph (SSGIndex) (detail)
  • Product Quantization Inverted File(PQIVFIndex) (detail)
  • Random Projection Tree(RPTIndex) (LSH, WIP)
  • BruteForce (BruteForceIndex) (naive implementation with SIMD)

• Reliability 🔒

  • Rust compiler secure all code
  • Memory managed by Rust for all language libs such as Python lib
  • Broad testing coverage

• Multiple Distances Support 🧮

  • Dot Product Distance
    • 
  • Euclidean Distance
    • 
  • Manhattan Distance
    • 
  • Cosine Similarity
    • 

• Productive ⭐

  • Well documented
  • Elegant and simple API, easy to learn

by aws t2.medium (CPU: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz) more information

npm i horajs

import * as horajs from "horajs";

const demo = () => {
    const dimension = 50;
    var bf_idx = horajs.BruteForceIndexUsize.new(dimension);
    // var hnsw_idx = horajs.HNSWIndexUsize.new(dimension, 1000000, 32, 64, 20, 500, 16, false);
    for (var i = 0; i < 1000; i++) {
        var feature = [];
        for (var j = 0; j < dimension; j++) {
            feature.push(Math.random());
        }
        bf_idx.add(feature, i); // add point 
    }
    bf_idx.build("euclidean"); // build index
    var feature = [];
    for (var j = 0; j < dimension; j++) {
        feature.push(Math.random());
    }
    console.log("bf result", bf_idx.search(feature, 10)); //bf result Uint32Array(10) [704, 113, 358, 835, 408, 379, 117, 414, 808, 826]
}

(async () => {
    await horajs.default();
    await horajs.init_env();
    demo();
})();

The entire repo is under Apache License.