Graph Library for Approximate Similarity Search

pyglass is a library for fast inference of graph index for approximate similarity search.

Features

• Supports multiple graph algorithms, like HNSW and NSG.
• Supports multiple hardware platforms, like X86 and ARM. Support for GPU is on the way
• No third-party library dependencies, does not rely on OpenBLAS / MKL or any other computing framework.
• Sophisticated memory management and data structure design, very low memory footprint.
• It's high performant.

Installation

Installation from Wheel

pyglass can be installed using pip as follows:

pip3 install glassppy

Installation from Source

If there's some problem when installing from wheel, you can try to build from source.

sudo apt-get update && sudo apt-get install -y build-essential git python3 python3-distutils python3-venv

pip3 install numpy
pip3 install pybind11

bash build.sh

Quick Tour

A runnable demo is at examples/demo.ipynb. It's highly recommended to try it.

Usage

Import library

>>> import glassppy as glass

Load Data

>>> n, d = 10000, 128
>>> X = np.random.randn(n, d)
>>> Y = np.random.randn(d)

Create Index pyglass supports HNSW and NSG index currently

>>> index = glass.Index(index_type="HNSW", dim=d, metric="L2", R=32, L=50)
>>> index = glass.Index(index_type="NSG", dim=d, metric="L2", R=32, L=50)

Build Graph

>>> graph = index.build(X)

Create Searcher Searcher accepts level parameter as the optimization level. You can set level as 0 or 1 or 2. The higher the level, the faster the searching, but it may cause unstable recall.

>>> optimize_level = 2
>>> searcher = glass.Searcher(graph=graph, data=X, metric="L2", level=optimize_level)
>>> searcher.set_ef(32)

(Optional) Optimize Searcher

>>> searcher.optimize()

Searching

>>> ret = searcher.search(query=Y, k=10)
>>> print(ret)

Performance

Glass is among one of the top performant ann algorithms on ann-benchmarks

fashion-mnist-784-euclidean

gist-960-euclidean

sift-128-euclidean

Quick Benchmark

1. Change configuration file examples/config.json
2. Run benchmark

python3 examples/main.py

3. You could check plots on results folder