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horapy

[Homepage] [Document] [Examples] [Hora]

Python binding for the Hora Approximate Nearest Neighbor Search

Key Features

• 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)

• Portable 💼

  • Support no_std (WIP, partial)
  • Support Windows, Linux and OS X
  • Support IOS and Android (WIP)
  • No heavy dependency, such as BLAS

• Reliability 🔒

  • Rust compiler secure all code
  • Memory managed by Rust
  • 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

Benchmark

<img src="asset/fashion-mnist-784-euclidean_10_euclidean.png"/>

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

Installation

pip install horapy

Example

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

License

The entire repo is under Apache License.