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Hora Search Everywhere!
Hora is an approximate nearest neighbor search algorithm (wiki) library. We implement all code in Rust🦀 for reliability, high level abstraction and high speeds comparable to C++.
Hora, 「ほら」 in Japanese, sounds like [hōlə], and means Wow, You see! or Look at that!. The name is inspired by a famous Japanese song 「小さな恋のうた」.
👩 Face-Match [online demo], have a try!
🍷 Dream wine comments search [online demo], have a try!
-
Performant ⚡️
- SIMD-Accelerated (packed_simd)
- Stable algorithm implementation
- Multiple threads design
-
Supports Multiple Languages ☄️
PythonJavascriptJavaGo(WIP)Ruby(WIP)Swift(WIP)R(WIP)Julia(WIP)- Can also be used as a service
-
Supports Multiple Indexes 🚀
-
Portable 💼
- Supports
WebAssembly - Supports
Windows,LinuxandOS X - Supports
IOSandAndroid(WIP) - Supports
no_std(WIP, partial) - No heavy dependencies, such as
BLAS
- Supports
-
Reliability 🔒
Rustcompiler secures all code- Memory managed by
Rustfor all language libraries such asPython's - Broad testing coverage
-
Supports Multiple Distances 🧮
Dot Product DistanceEuclidean DistanceManhattan DistanceCosine Similarity
-
Productive ⭐
- Well documented
- Elegant, simple and easy to learn API
Rust
in Cargo.toml
[dependencies]
hora = "0.1.1"Python
$ pip install horapyJavascript (WebAssembly)
$ npm i horajsBuilding from source
$ git clone https://github.com/hora-search/hora
$ cargo build
by aws t2.medium (CPU: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz) more information
Rust example [more info]
use hora::core::ann_index::ANNIndex;
use rand::{thread_rng, Rng};
use rand_distr::{Distribution, Normal};
pub fn demo() {
let n = 1000;
let dimension = 64;
// make sample points
let mut samples = Vec::with_capacity(n);
let normal = Normal::new(0.0, 10.0).unwrap();
for _i in 0..n {
let mut sample = Vec::with_capacity(dimension);
for _j in 0..dimension {
sample.push(normal.sample(&mut rand::thread_rng()));
}
samples.push(sample);
}
// init index
let mut index = hora::index::hnsw_idx::HNSWIndex::<f32, usize>::new(
dimension,
&hora::index::hnsw_params::HNSWParams::<f32>::default(),
);
for (i, sample) in samples.iter().enumerate().take(n) {
// add point
index.add(sample, i).unwrap();
}
index.build(hora::core::metrics::Metric::Euclidean).unwrap();
let mut rng = thread_rng();
let target: usize = rng.gen_range(0..n);
// 523 has neighbors: [523, 762, 364, 268, 561, 231, 380, 817, 331, 246]
println!(
"{:?} has neighbors: {:?}",
target,
index.search(&samples[target], 10) // search for k nearest neighbors
);
}thank @vaaaaanquish for this complete pure Rust 🦀 image search example, For more information about this example, you can click Pure Rust な近似最近傍探索ライブラリ hora を用いた画像検索を実装する
Python example [more info]
import numpy as np
from horapy import HNSWIndex
dimension = 50
n = 1000
# init index instance
index = HNSWIndex(dimension, "usize")
samples = np.float32(np.random.rand(n, dimension))
for i in range(0, len(samples)):
# add node
index.add(np.float32(samples[i]), i)
index.build("euclidean") # build index
target = np.random.randint(0, n)
# 410 in Hora ANNIndex <HNSWIndexUsize> (dimension: 50, dtype: usize, max_item: 1000000, n_neigh: 32, n_neigh0: 64, ef_build: 20, ef_search: 500, has_deletion: False)
# has neighbors: [410, 736, 65, 36, 631, 83, 111, 254, 990, 161]
print("{} in {} \nhas neighbors: {}".format(
target, index, index.search(samples[target], 10))) # searchJavaScript example [more info]
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();
})();Java example [more info]
public void demo() {
final int dimension = 2;
final float variance = 2.0f;
Random fRandom = new Random();
BruteForceIndex bruteforce_idx = new BruteForceIndex(dimension); // init index instance
List<float[]> tmp = new ArrayList<>();
for (int i = 0; i < 5; i++) {
for (int p = 0; p < 10; p++) {
float[] features = new float[dimension];
for (int j = 0; j < dimension; j++) {
features[j] = getGaussian(fRandom, (float) (i * 10), variance);
}
bruteforce_idx.add("bf", features, i * 10 + p); // add point
tmp.add(features);
}
}
bruteforce_idx.build("bf", "euclidean"); // build index
int search_index = fRandom.nextInt(tmp.size());
// nearest neighbor search
int[] result = bruteforce_idx.search("bf", 10, tmp.get(search_index));
// [main] INFO com.hora.app.ANNIndexTest - demo bruteforce_idx[7, 8, 0, 5, 3, 9, 1, 6, 4, 2]
log.info("demo bruteforce_idx" + Arrays.toString(result));
}
private static float getGaussian(Random fRandom, float aMean, float variance) {
float r = (float) fRandom.nextGaussian();
return aMean + r * variance;
}- Full test coverage
- Implement EFANNA algorithm to achieve faster KNN graph building
- Swift support and iOS/macOS deployment example
- Support
R - support
mmap
-
Hora's implementation is strongly inspired by these libraries.Faissfocuses more on the GPU scenerio, andHorais lighter than Faiss (no heavy dependencies).Horaexpects to support more languages, and everything related to performance will be implemented by Rust🦀.Annoyonly supports theLSH (Random Projection)algorithm.ScaNNandFaissare less user-friendly, (e.g. lack of documentation).- Hora is ALL IN RUST 🦀.
-
MilvusandValdalso support multiple languages, but serve as a service instead of a libraryMilvusis built upon some libraries such asFaiss, whileHorais a library with all the algorithms implemented itself
We appreciate your participation!
We are glad to have you participate, any contributions are welcome, including documentations and tests.
You can create a Pull Request or Issue on GitHub, and we will review it as soon as possible.
We use GitHub issues for tracking suggestions and bugs.
git clone https://github.com/hora-search/horacargo buildcargo test --libcd examples
cargo runThe entire repository is licensed under the Apache License.
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