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.

Install

pip install qann

Wheels are published for Linux (x86_64, aarch64) and macOS (Apple Silicon, Intel) on Python 3.10 to 3.14. 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, PQCodebook methods), 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 by default: the first vector added gets id 0, the next id 1, and so on across all add calls. Create an index with custom_ids=True to use your own ids instead; see Custom ids and deleting.
• Distances are squared Euclidean (L2) distances by default, or cosine distances with metric=qann.Metric.Cosine (see Metrics); smallest first either way.
• Results from batch_query are an int64 id array and a float32 distance array, both shaped (num_queries, k). Rows with fewer than k hits are padded with id -1 and distance inf.

Indexes

Every index has the same core methods:

FlatIndex(dim, *, custom_ids=False)

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, *, custom_ids=False)

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.

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

Fast-scan

enable_pq(num_subspaces, fast_scan=True) stores each slice in 4 bits (16 centroids per slice) and scores 32 vectors at a time with a single SIMD table lookup per slice (NEON on ARM, SSSE3 on x86). Scanning the codes is about 6x faster than regular PQ at the same bytes per vector. Use twice the num_subspaces you would with regular PQ to keep the memory and most of the accuracy, and pair it with a RefineIndex:

ivf = qann.IVFIndex(128, nlist=1024, nprobe=16)
ivf.enable_pq(64, fast_scan=True)          # 64 x 4 bits = 32 bytes per vector
ivf.train(x, seed=1)
refine = qann.RefineIndex(ivf, k_factor=16)
refine.add(x)

• On SIFT1M with re-ranking (k_factor=16), 64 subspaces with fast-scan match 32 regular subspaces (recall@10 0.931 at nprobe=16, 0.995 at nprobe=64) and beat 16 regular ones, while scanning several times faster.
• Without a RefineIndex, recall is lower than regular PQ at the same memory, and the returned distances are approximate: the lookup tables are rounded to 6 bits.
• num_subspaces must be even and at most 256, centroids_per_subspace must be 16 (the default with fast_scan), and pq_distance stays ADC: setting SDC raises ValueError.

Rotation

enable_pq(..., rotate=True) makes train() also learn a rotation of the vectors (PCA, with the variance spread evenly over the subspaces), and every vector is rotated before IVF and PQ see it. Rotations don't change distances, so results stay valid; only PQ's error changes. It helps when dimensions are correlated across subspaces, so plain PQ spends bits encoding the same information twice:

ivf.enable_pq(64, fast_scan=True, rotate=True)

• On SIFT1M with fast-scan, recall@10 at nprobe=16 goes from 0.635 to 0.718 without re-ranking, and k_factor=4 reaches what k_factor=8 did (0.988 vs 0.956 at nprobe=64). With re-ranking that is about 5% more queries per second at the same recall, after the cost of rotating each query.
• It does nothing for embeddings whose dimensions are already uncorrelated (GloVe: within 0.004 either way), and with heavy re-ranking (k_factor=16) there is little left to gain. Measure on your data.
• Training takes a little longer (0.4 s more on SIFT1M), and the index stores one dim x dim matrix (64 KB at 128 dimensions).

SQ

enable_sq(8) stores each vector as one byte per dimension (on SIFT, 128 bytes instead of 512) and enable_sq(4) as half a byte; train also learns each dimension's range. Unlike PQ there are no codebooks, and 8 bits loses little recall, so it needs no RefineIndex: it is the IVF option for high recall without keeping the raw vectors in memory. 4 bits loses more and should be wrapped in a RefineIndex(k_factor=8).

ivf = qann.IVFIndex(128, nlist=1024, nprobe=16)
ivf.enable_sq(8)
ivf.train(x, seed=1)
ivf.add(x)

On SIFT1M (all cores, busy machine), 8 bits reached recall@10 0.925 at nprobe=16 and 0.985 at 64 (floats: 0.932 and 0.995) at 1.3 to 1.4x the queries per second of float lists, in a quarter of the memory; 4 bits + RefineIndex(k_factor=8) matched the floats' recall exactly at the same speed-up. On one thread the gain mostly disappears: SQ helps because the float scan is limited by memory bandwidth. For the fastest queries when raw vectors fit in memory, fast-scan PQ + RefineIndex is still quicker, and HNSW-SQ8 is faster at the same recall for about twice the memory.

HNSWIndex(dim, M=16, ef_construction=200, *, custom_ids=False, seed=None)

A graph index (Hierarchical Navigable Small World): every vector is linked to M near neighbors (2 * M on the bottom level), and a query walks the graph toward the query vector. No training; add builds the graph on all cores. It stores vectors in full, so distances are exact, and it needs no representative training sample, at the cost of more memory per vector (the vector plus its links) and slower adds. With enable_sq() it stores 8- or 4-bit codes per dimension instead (see below).

index = qann.HNSWIndex(128, M=16, ef_construction=200)
index.add(x)
index.ef_search = 128   # trade speed for recall
ids, dists = index.batch_query(q, 10)

On SIFT1M (1M vectors), M=16, ef_construction=200 reaches recall@10 0.960 at ef_search=64 (about 80 µs per query on one thread) and 0.995 at 256 (about 340 µs). On all cores, with the machine busy, the float graph built in 75 s and reached 0.996 at 11.4k queries/s; with 8-bit SQ and re-ranking (below) it reached 0.996 at 15.8k/s, close to IVF + fast-scan + RefineIndex at 0.995 and 16.9k/s, which built in 5 s. Below 0.96 recall HNSW is the faster of the two on SIFT1M. On GloVe-100 word embeddings (cosine) the order flips: IVF + fast-scan + RefineIndex was about twice as fast up to 0.94 recall, and HNSW with M=16 needed ef_search=1024 to reach 0.95. Benchmark on your own data before choosing. Choose IVF + fast-scan when you can train on representative data and build time matters; choose HNSW when you can't train up front, or when vectors keep arriving that the IVF clusters were not trained on.

• Deleting marks vectors deleted; queries still route through them, so the graph stays connected, but never return them. compact() rebuilds the graph from the remaining vectors, which takes about as long as building it again, so call it after many deletes rather than after each one.
• Builds are parallel, so the graph differs slightly from run to run. With seed and qann.set_num_threads(1), the build is reproducible.
• Choosing M: the default 16 suits data like SIFT. For high-dimensional embeddings (word or sentence vectors), use M=32 to 48 with ef_construction=300 to 400: on GloVe-100, M=48 answered 1.5x more queries per second than M=16 at 0.90 and 0.95 recall, and only M=48 reached 0.99. It builds about 5x slower and adds 256 bytes of links per vector.

Scalar quantization (compressed HNSW). enable_sq(8) maps each dimension's trained range onto 256 evenly spaced values, so a vector takes dim bytes instead of 4 * dim (on SIFT, 128 instead of 512); enable_sq(4) uses 16 values packed two per byte (64 bytes on SIFT). Distances become approximate, so wrap the index in a RefineIndex to re-rank with exact ones: k_factor=2 is enough for 8 bits; 4 bits needs 4 on SIFT and 8 on GloVe word embeddings. Values outside the training sample's range are clamped, so train on a representative sample (or on the vectors themselves).

index = qann.HNSWIndex(128)
index.enable_sq(8)
index.train(x)
refine = qann.RefineIndex(index, k_factor=2)
refine.add(x)

On SIFT1M with the machine busy, at ef_search=256: 8 bits + RefineIndex(k_factor=2) matched the float graph's recall (0.996) at 1.4x its queries per second on all cores (15.8k vs 11.4k) and 1.7x on one thread, and 4 bits + k_factor=4 did nearly the same (0.995, 15.6k). The RefineIndex keeps full vectors too, so the memory saving needs the plain SQ index: 8 bits alone reaches 0.986, 4 bits alone only 0.86, so 4 bits really needs re-ranking. 4 bits is no faster than 8, since searches wait on memory latency rather than bytes moved; choose it to halve code memory. On GloVe-100 (cosine), 8 bits + k_factor=2 again matched the floats exactly, while 4 bits alone reached only 0.70 and needed k_factor=8.

RefineIndex(base, k_factor=10, *, custom_ids=False)

Wraps a trained, empty approximate index created without custom ids (and never compacted) (give the RefineIndex its own custom_ids instead). Each query fetches k * k_factor candidates from base, then re-ranks them with exact distances against full-precision copies of the vectors. Add, remove and compact through the RefineIndex: while it wraps a base, calling add, remove or compact on the base itself raises ValueError (it would put the two out of step), and a base can only be wrapped by one RefineIndex at a time. Tuning the base (nprobe, pq_distance) is fine. k_factor is a property and can be changed at any time, and the read-only base property returns the wrapped index, e.g. to change nprobe.

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, IVFIndex or HNSWIndex:

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, IVF or HNSW. IndexOptions fields and defaults: metric (Metric.L2), plus capacity (Flat initial reserve, 1024), nlist (100), nprobe (10), pq_subspaces (0, meaning no PQ), pq_centroids (256) and ivf_sq_bits (0; 8 or 4 calls enable_sq()) for IVF, and hnsw_m (16), ef_construction (200), ef_search (64) and hnsw_sq_bits (0 for floats; 8 or 4 calls enable_sq()) for HNSW.

Metrics

Every index takes metric=qann.Metric.L2 (the default) or metric=qann.Metric.Cosine, fixed at construction and saved with the index. Most text and image embedding models are meant to be compared by cosine similarity, so use Cosine for them:

index = qann.HNSWIndex(384, metric=qann.Metric.Cosine)
index.add(embeddings)
ids, dists = index.batch_query(query_embeddings, 10)   # dists = 1 - cosine similarity

• Distances are 1 - cosine similarity, from 0 (same direction) to 2 (opposite), matching qann.cosine_distance. Similarity is 1 - dist.
• Vector length doesn't matter: each vector is scaled to length 1 when it's added and each query when it's run, so you don't need to normalize anything yourself. A zero vector has no direction, so adding or querying one raises ValueError.
• It works with every index, including PQ, fast-scan and HNSW. IVF learns its clusters on the scaled vectors, and a RefineIndex uses its base's metric. index.metric reports it.
• Inner product (for vectors whose length carries meaning) isn't supported yet.

Custom ids and deleting

By default an index numbers vectors 0, 1, 2, ... in the order they're added. To use your own ids (database keys, document ids, ...), create the index with custom_ids=True and pass one id per vector:

index = qann.FlatIndex(128, custom_ids=True)
index.add(vectors, ids=doc_ids)     # doc_ids: one int per row
ids, dists = index.batch_query(queries, 10)   # ids are your doc_ids

index.remove([doc_ids[0], doc_ids[1]])        # returns 2

• Ids can be a NumPy array of any integer type, a list or a range. They must be >= 0 (-1 marks missing results) and unique within the index; a bad batch raises an error and adds nothing.
• Every add must pass ids on a custom_ids=True index, and an index created without it refuses them.
• remove(ids) works with or without custom ids. Unknown and already deleted ids are skipped, so deleting twice is harmless. A deleted id can be added again, with a new vector.
• Deleted vectors keep their memory until compact(): queries skip them, but they still take space and scan time. compact() frees them in one pass, keeping every id and every query result the same, so call it after a large batch of deletes. It doesn't retrain anything. (HNSWIndex is the exception: it rebuilds its graph, so ids stay the same but results can shift slightly.)
• Default ids never repeat. New vectors continue after the highest id ever assigned, even after deletes and compact() (delete ids 0 to 9, compact, add one more, and it gets id 10).
• For make_index, set IndexOptions.custom_ids.

Filtered search

To search only some of the vectors ("nearest products in this category", "documents this user may see"), pass the ids that may be returned as allowed:

in_stock = product_ids[stock > 0]   # computed from your own metadata
ids, dists = index.batch_query(queries, 10, allowed=in_stock)

• Ids from your own data: keep metadata wherever you already do and turn the condition into a list of ids, or store it in the index and filter with where (see Metadata). allowed takes the same kinds of ids as remove (any integer array, list or range), with or without custom ids, and one set applies to every row of a batch_query.
• Unknown and deleted ids in allowed are ignored. An empty set returns no results; fewer than k allowed vectors gives fewer results (rows padded with -1 / inf).
• Filtering happens during the search, not after it, so a selective filter still returns k results instead of the few that happen to be in an unfiltered top k.
• Works on every index. FlatIndex scores only the allowed vectors. IVFIndex skips the others while scanning, and if its nprobe lists hold fewer than k allowed vectors it keeps probing the next nearest lists until it has k. HNSWIndex walks its graph through every node but returns only allowed ones; when few are allowed (below about sqrt(ef_search * M * size), ~3% of SIFT1M), walking the graph would cost more than scoring the allowed vectors directly, so it does that instead, which is also exact. A RefineIndex passes the filter to its base and re-ranks as usual.
• Recall by index: FlatIndex is exact, and HNSWIndex holds or gains recall as the filter narrows. IVFIndex loses recall as it narrows (on SIFT1M at nprobe=16: 0.93 unfiltered, 0.85 with 5% allowed, 0.52 with 0.1%), because the nearest allowed vectors spread over more lists than nprobe; raise nprobe, or for very selective filters prefer HNSWIndex or FlatIndex. Full numbers are in ARCHITECTURE.md.
• Cost is a pass over allowed per call (to look up each id), plus one bit per vector in the index.

Metadata

Store attributes with each vector and filter on them directly:

index.add(vectors, metadata={"category": categories, "price": prices, "year": years})

f = qann.field
ids, dists = index.batch_query(queries, 10, where=(f("category") == "shoes") & (f("price") < 50))
ids, dists = index.batch_query(queries, 10, where=f("year").isin([2024, 2025]) | f("price").is_null())

• Columns are named and hold ints, floats or strings, one value per vector, as NumPy arrays or lists (bool arrays become ints). A column's type is fixed by the first add that has it.
• Missing values: a column added later is null for the vectors already in the index, and an add that leaves a column out stores null for its vectors. Nulls fail every comparison and isin, so ~(f("price") < 50) includes vectors with no price; use f("price").is_null() to select or exclude them.
• Filters: ==, !=, <, <=, >, >=, .isin([...]) and .is_null(), combined with & (and), | (or) and ~ (not). Parenthesize comparisons, since & binds tighter than <: (f("a") < 1) & (f("b") == 2). Python's and, or, not and chained comparisons such as 0 < f("a") < 5 raise TypeError instead of silently dropping part of the filter.
• Types: ints and floats compare as numbers; strings compare with strings (< is alphabetical). Filtering on a column the index doesn't have, or comparing a number column with a string, raises ValueError.
• Same search as allowed: a where filter becomes the set of matching vectors and runs the filtered search for every index, with the same recall behavior. With both where and allowed, only vectors passing both are returned.
• Cost: each query call scans the columns its filter names once, shared by every row of a batch_query: about 0.4 ms per condition per million vectors. Strings are stored once per distinct value, plus 4 bytes per vector; ints and floats take 8 bytes per vector; each column also takes 1 byte per vector for its null flag.
• Kept with the index: remove and compact keep metadata with its vector, and save / qann.load store it. A RefineIndex holds the metadata itself; add through it as usual.

Saving and loading

Any index can be saved to a file and loaded back, including its training, so it doesn't have to be retrained or re-added each run:

index.save("vectors.qann")          # str or pathlib.Path

index = qann.load("vectors.qann")   # returns a FlatIndex, IVFIndex, HNSWIndex or RefineIndex
ids, dists = index.batch_query(queries, 10)

• Everything is restored: vectors, ids (including custom ids), deletions, IVF centroids, PQ codebooks, HNSW graphs and SQ ranges, the metric and settings such as nprobe, pq_distance, precomputed_tables, ef_search and k_factor. A loaded index gives identical results and keeps accepting add and remove.
• A RefineIndex file contains its base index, so loading one restores both. Tune the base through loaded.base, e.g. loaded.base.nprobe = 32.
• Saving is safe to interrupt: the file is written next to the target as <path>.tmp and renamed into place when complete, so a failed save never leaves a partial file or destroys an existing one.
• Bad files raise RuntimeError: a missing, truncated or corrupted file, or one that isn't a QaNN index, is rejected instead of loading garbage.
• Files are versioned. Newer releases keep loading files saved by older ones (files from 0.1.0a4, 0.1.0a6, 0.1.0a7, 0.1.0a10, 0.1.0a11 and 0.1.0a12 still load). A file saved by a newer release than the one loading it raises RuntimeError rather than being misread.

PQCodebook on its own can't be saved yet; save the IVFIndex that uses it instead.

Product quantization

PQCodebook(dim, num_subspaces, centroids_per_subspace=256) exposes the quantizer used inside IVFIndex for direct use.

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

Threads and type hints

• Thread-safe. Queries, adds and training release Python's GIL while they run, so other Python threads keep running, and several threads can query the same index at once. An add, train or setting change waits for running queries on that index to finish (and blocks new ones until it's done), so concurrent use never sees a half-updated index. This also covers the base of a RefineIndex, e.g. ivf.nprobe = 32 while queries run through the RefineIndex wrapping ivf.
• Typed. Wheels include type stubs, so editors autocomplete the API and type checkers such as mypy and Pylance/pyright check calls to it.

More

See ARCHITECTURE.md for the design, a file-by-file reference and SIFT1M benchmarks.