This release is a pre-release and may not be stable for production use.
QaNN
QaNN (Quantized Nearest Neighbors) is a Python library for fast nearest-neighbor search over float32 vectors. It offers exact search, inverted-file (IVF) search, product quantization (PQ) compression and exact re-ranking, all implemented in multithreaded, SIMD-accelerated C++ and driven from NumPy.
Alpha: under active development. The API may change between releases, and indexes cannot be saved or loaded yet.
Install
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).
Quick start
import numpy as np, qann
x = np.random.rand(100_000, 128).astype(np.float32)
ivf = qann.IVFIndex(128, nlist=1024, nprobe=16)
ivf.enable_pq(16) # store each vector in 16 bytes
ivf.train(x, seed=1)
index = qann.RefineIndex(ivf, k_factor=10) # re-rank PQ candidates exactly
index.add(x)
ids, dists = index.batch_query(x[:5], 10) # both shaped (5, 10)
Conventions
- Vectors are passed as 2D NumPy arrays of shape
(n, dim), except single vectors (query,PQCodebookmethods), which are(dim,). float32 C-contiguous arrays are used without copying; anything else (float64, Fortran order, strided slices) is converted first. - IDs are assigned in insertion order: the first vector added gets id 0, the next id 1, and so on across all
addcalls. - Distances are squared Euclidean (L2) distances, smallest first. For cosine similarity, L2-normalize vectors before adding and querying.
- Results from
batch_queryare anint64id array and afloat32distance array, both shaped(num_queries, k). Rows with fewer thankhits are padded with id-1and distanceinf.
Indexes
Every index has the same core methods:
| Method | Description |
|---|---|
add(data) |
Add an (n, dim) array of vectors. |
batch_query(queries, k) |
k nearest neighbors for each row of an (nq, dim) array, run in parallel. Returns (ids, dists). |
query(query, k) |
k nearest neighbors for a single (dim,) vector such as x[i]. Returns 1D (ids, dists). |
size() |
Number of vectors added. |
dim() |
Vector dimension. |
FlatIndex(dim)
Exact brute-force search: no training, 100% recall, and query cost that grows linearly with the number of vectors. Good for up to a few hundred thousand vectors, or as ground truth when measuring recall.
index = qann.FlatIndex(128)
index.add(x)
ids, dists = index.batch_query(x[:10], 10)
IVFIndex(dim, nlist, nprobe=10)
Partitions vectors into nlist clusters with k-means and, at query time, scans only the nprobe clusters closest to the query. Much faster than flat search at a small cost in recall.
| Member | Description |
|---|---|
train(data, max_iters=25, seed=None) |
Learn the cluster centroids from a sample of at least nlist vectors. Call once, before add. Pass seed for reproducible results. |
enable_pq(num_subspaces, centroids_per_subspace=256) |
Compress stored vectors with product quantization. Call before train. |
nprobe |
Property: clusters scanned per query. Can be changed at any time; higher means better recall and slower queries. |
pq_distance |
Property: how PQ codes are scored, qann.PQDistance.ADC (default, more accurate) or qann.PQDistance.SDC. |
nlist must be between 100 and 65535, and nprobe between 1 and nlist. A common starting point is nlist around sqrt(n) to 4 * sqrt(n) and nprobe at 1% to 5% of nlist.
With PQ (enable_pq), each vector is split into num_subspaces slices and each slice is stored as one byte, so a 128-dim float32 vector (512 bytes) with 16 subspaces takes 16 bytes. dim must be divisible by num_subspaces. Distances become approximate, which lowers recall; wrap the index in a RefineIndex to recover it.
ivf = qann.IVFIndex(128, nlist=1024, nprobe=16)
ivf.enable_pq(16)
ivf.train(x, seed=1)
ivf.add(x)
ivf.nprobe = 32 # trade speed for recall
RefineIndex(base, k_factor=10)
Wraps a trained, empty approximate index. Each query fetches k * k_factor candidates from base, then re-ranks them with exact distances against full-precision copies of the vectors. Add vectors through the RefineIndex, not the base. k_factor is a property and can be changed at any time.
This is the usual way to combine PQ's speed with near-exact ranking. It keeps the original vectors in memory alongside the PQ codes.
make_index(type, dim, opts=IndexOptions())
Builds an index from a type and an options object, returning a FlatIndex or IVFIndex:
opts = qann.IndexOptions()
opts.nlist, opts.nprobe, opts.pq_subspaces = 1024, 16, 16
ivf = qann.make_index(qann.IndexType.IVF, 128, opts)
ivf.train(x)
IndexType is Flat or IVF. IndexOptions fields and defaults: capacity (Flat initial reserve, 1024), nlist (100), nprobe (10), pq_subspaces (0, meaning no PQ) and pq_centroids (256).
Product quantization
PQCodebook(dim, num_subspaces, centroids_per_subspace=256) exposes the quantizer used inside IVFIndex for direct use.
| Method | Description |
|---|---|
train(data, max_iters=25, seed=None) |
Run k-means in each subspace over an (n, dim) array. |
encode(vec) |
Encode a (dim,) vector into a uint8 array of num_subspaces codes. |
compute_adc_table(query) |
Precompute distances from a (dim,) query to every centroid; reuse it across many codes. |
distance_adc(table, code) |
Approximate squared L2 between the table's query and a code (asymmetric). |
distance_sdc(query_code, code) |
Approximate squared L2 between two codes (symmetric). |
dim(), num_subspaces(), centroids_per_subspace() |
Shape of the codebook. |
pq = qann.PQCodebook(128, 16)
pq.train(x, seed=1)
codes = [pq.encode(v) for v in x[:1000]]
table = pq.compute_adc_table(x[0])
dists = [pq.distance_adc(table, c) for c in codes]
Utilities
| Function | Description |
|---|---|
l2_distance(a, b) |
Row-wise squared L2 distance between two (m, dim) arrays; returns shape (m,). |
cosine_distance(a, b) |
Row-wise cosine distance (1 minus cosine similarity) between two (m, dim) arrays. |
set_num_threads(n) |
Threads used for batch queries, adding with PQ and PQ training. 0 (default) uses every core; 1 runs serially. |
num_threads() |
Thread count currently in effect. |
__version__ |
Installed version. |
More
See ARCHITECTURE.md for the design, a file-by-file reference and SIFT1M benchmarks.
1 maintainer
Metadata
1 maintainer
Release files for qann 0.1.0a2
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