Hora search everywhere!
Features
Portable 💼
- Support WebAssembly
- Support Windows, Linux and OS X
- Support no_std (WIP, not all features)
- Support IOS and Android (WIP)
- No heavy dependency, such as BLAS
Reliable and Productive ⭐
- Rust compiler secure all code
- Memory managed by Rust for all language libs such as horapy
- Broad testing coverage
- Well documented
- Elegant and simple API, easy to learn
Performant ⚡️
- SIMD-Accelerated (packed_simd)
- Stable algorithm implementation
- Multiple threads design
Multiple Languages Support✨
- Rust
- Python
- Nodejs
- Java (WIP)
- Ruby (WIP)
- Golang (WIP)
- Swift (WIP)
- Julia (WIP)
- R (WIP)
Multiple Distances Support 🧮
- Dot Product Distance
- Euclidean Distance
- Manhattan Distance
- Cosine Similarity
Installation
$ pip install horapy # recommend# or install from source$ git clone https://github.com/hora-search/horapy && cd horapy$ pip install maturin && maturin build --release
Demos
Choose one celebrity below…
all image data comes from [CelebA dataset], we use it only for algorithm demo, not any commercial activity.
Benchmark
by a AWS t2.medium (CPU: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz) [more]
Examples
import numpy as npfrom horapy import HNSWIndexdimension = 50n = 1000# init index instanceindex = HNSWIndex(dimension, "usize")samples = np.float32(np.random.rand(n, dimension))for i in range(0, len(samples)):# add nodeindex.add(np.float32(samples[i]), i)index.build("euclidean") # build indextarget = 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 {}has neighbors: {}".format(target, index, index.search(samples[target], 10))) # search
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