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Hora Search Everywhere!
Hora is an approximate nearest neighbor search algorithm (wiki) library. We implement all code in Rust๐ฆ
for reliability, high level abstraction and high speeds comparable to C++
.
Hora, ใใปใใ
in Japanese, sounds like [hลlษ]
, and means Wow
, You see!
or Look at that!
. The name is inspired by a famous Japanese song ใๅฐใใชๆใฎใใใ
.
๐ฉ Face-Match [online demo], have a try!
๐ท Dream wine comments search [online demo], have a try!
-
Performant โก๏ธ
- SIMD-Accelerated (packed_simd)
- Stable algorithm implementation
- Multiple threads design
-
Supports Multiple Languages โ๏ธ
Python
Javascript
Java
Go
(WIP)Ruby
(WIP)Swift
(WIP)R
(WIP)Julia
(WIP)- Can also be used as a service
-
Supports Multiple Indexes ๐
-
Portable ๐ผ
- Supports
WebAssembly
- Supports
Windows
,Linux
andOS X
- Supports
IOS
andAndroid
(WIP) - Supports
no_std
(WIP, partial) - No heavy dependencies, such as
BLAS
- Supports
-
Reliability ๐
Rust
compiler secures all code- Memory managed by
Rust
for all language libraries such asPython's
- Broad testing coverage
-
Supports Multiple Distances ๐งฎ
-
Productive โญ
- Well documented
- Elegant, simple and easy to learn API
Rust
in Cargo.toml
[dependencies]
hora = "0.1.1"
Python
$ pip install horapy
Javascript (WebAssembly)
$ npm i horajs
Building 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
);
}
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))) # search
JavaScript
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.Faiss
focuses more on the GPU scenerio, andHora
is lighter than Faiss (no heavy dependencies).Hora
expects to support more languages, and everything related to performance will be implemented by Rust๐ฆ.Annoy
only supports theLSH (Random Projection)
algorithm.ScaNN
andFaiss
are less user-friendly, (e.g. lack of documentation).- Hora is ALL IN RUST ๐ฆ.
-
Milvus
andVald
also support multiple languages, but serve as a service instead of a libraryMilvus
is built upon some libraries such asFaiss
, whileHora
is a library with all the algorithms implemented itself
We appreciate your help!
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/hora
cargo build
cargo test --lib
cd examples
cargo run
The entire repository is licensed under the Apache License.