This release is a pre-release and may not be stable for production use.
QaNN
QaNN (Quantized Nearest Neighbors) is a C++20 vector search library (Flat, IVF, product quantization, exact re-ranking) with Python bindings.
Alpha: under active development. The API may change between releases, and indexes cannot be saved or loaded yet.
pip install qann
Wheels are published for Linux (x86_64, aarch64) and macOS (Apple Silicon, Intel) on Python 3.9 to 3.13. x86_64 builds require AVX2 and FMA. To build from source instead, run pip install . in a checkout; this needs CMake 3.20+, a C++20 compiler and, on Linux, OpenBLAS (optional but much faster training).
import numpy as np, qann
x = np.random.rand(10_000, 128).astype(np.float32)
ivf = qann.IVFIndex(128, nlist=100, nprobe=10)
ivf.enable_pq(16)
ivf.train(x, seed=1)
refine = qann.RefineIndex(ivf, k_factor=10)
refine.add(x)
ids, dists = refine.batch_query(x[:5], 10) # (5, 10) arrays
See ARCHITECTURE.md for design, benchmarks and build details.
1 maintainer
Metadata
1 maintainer
Release files for qann 0.1.0a1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qann-0.1.0a1.tar.gz | 46.6 kB | Details |
Built distributions (wheels)
Total release size: 86.9 MB
Release files / qann-0.1.0a1.tar.gz
| Download URL | qann-0.1.0a1.tar.gz |
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| Size | 46.6 kB |
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / qann-0.1.0a1-cp39-cp39-macosx_11_0_x86_64.whl
| Download URL | qann-0.1.0a1-cp39-cp39-macosx_11_0_x86_64.whl |
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| Size | 102.7 kB |
| Tags | CPython 3.9 macOS 11.0+ x86-64 |
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Transparency logRelease files / qann-0.1.0a1-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | qann-0.1.0a1-cp39-cp39-macosx_11_0_arm64.whl |
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| Size | 90.3 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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twine/7.0.0 CPython/3.13.14
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