certex

certex is the open-source implementation of CertEx (Certified Exclusion), exact k-nearest-neighbor search on NVIDIA GPUs. It returns the exact k nearest neighbors of every query, at the speed of a half-precision screen: a lower bound evaluated on the tensor cores, with its rounding error folded into the same product, proves that almost every stored vector cannot be a neighbor, and only the few survivors are verified with exact double-precision distances.

Every result equals exhaustive search in double precision. No setting trades accuracy for speed: there is no graph and no recall setting, options such as k-means lists change only the speed, and additions and removals take effect at once, also while queries run. On one H100, certex answers 176,700 exact 10-NN queries per second over BIGANN-10M and searches 500 million BIGANN vectors exactly within 71 GB of device memory. It supports Euclidean, cosine, and inner-product metrics, filtered search by label, and host-memory storage for collections larger than GPU memory, and it is a drop-in for cuVS (C++, C, and Python), Faiss, torch.topk, and the sparse-attention top-k of FlashInfer.

Installation

pip install torch --index-url https://download.pytorch.org/whl/cu129   # PyTorch for CUDA 12.9, as cuVS needs
pip install certex            # Linux, Python >= 3.11, CUDA 12, an NVIDIA GPU of compute capability >= 8.0

Quickstart

One call returns the exact nearest neighbors:

import certex

D, I = certex.search(xb, xq, k=10)  # numpy, torch, or cupy; exact neighbors, best first
D, I = certex.search(xb, k=10)  # all k nearest neighbors of every vector of xb
certex.verify(xb, xq, I)  # 1.0: sampled queries match exhaustive search in float64

For repeated searches and updates, keep an index:

index = certex.Index(dim=768, metric="cosine")  # "l2", "cosine", or "ip"
index.add(xb)  # numpy, torch, or cupy; call again to insert
D, I = index.search(xq, k=10)  # exact neighbors, best first

tagged = certex.Index(dim=768)  # filtered search: one integer label per vector and query
tagged.add(xb, labels=lb)
D, I = tagged.search(xq, k=10, labels=lq)  # exact k nearest vectors of each query's label

index.remove(ids)  # removed vectors never come back
index.save("vectors.certex")  # certex.Index.load restores it without a rebuild

Interfaces

certex keeps the calls of the libraries that it replaces, so that moving code to exact search changes little more than an import. tests/test_dropin.py checks the Python interfaces against the original libraries.

Interface	Drop-in for	Example
certex.cuvs (Python)	cuvs.neighbors.brute_force	from_cuvs.py
certex.faiss (Python)	faiss.IndexFlatL2, faiss.IndexFlatIP	from_faiss.py
certex.topk, certex.KeyCache	torch.topk(q @ keys.mT, k)	sparse_attention.py
certex.flashinfer	FlashInfer top_k_page_table_transform, top_k_ragged_transform, top_k	flashinfer_sparse_attention.py
cuvs::neighbors::certex (C++)	the cuVS index modules (build, extend, search)	cpp/exact_search.cu
cuvsCertex* (C)	the cuVS C API	c/exact_search.c

cuVS and Faiss from Python:

from certex import cuvs as certex_cuvs  # instead of cuvs.neighbors.brute_force

index = certex_cuvs.build(dataset, metric="sqeuclidean")
distances, neighbors = certex_cuvs.search(index, queries, k)

from certex.faiss import IndexCertExL2  # instead of faiss.IndexFlatL2

index = IndexCertExL2(d)
index.add(xb)
D, I = index.search(xq, k)

Sparse attention in PyTorch and FlashInfer, with the exact top-k keys of every query:

scores, ids = certex.topk(q, keys, k=64)  # instead of torch.topk(q @ keys.mT, 64)

cache = certex.KeyCache(n_kv_heads=8, head_dim=128, k=2048)  # keys that grow during decoding
cache.append(keys)  # (n_kv_heads, n, head_dim)
scores, ids = cache.topk(q, k=2048)

from certex import flashinfer as certex_fi  # instead of the top-k transforms of FlashInfer

sel = certex_fi.ExactTopK(num_kv_heads=8, head_dim=128)
sel.append(0, k_new)  # keys of sequence slot 0, (n, num_kv_heads, head_dim)
out = sel.page_table_transform(q, page_table, k=2048, page_size=64)  # int32 physical positions

C++ and C, with the conventions of cuVS:

#include <cuvs/neighbors/certex.hpp>

auto index = cuvs::neighbors::certex::build(res, cuvs::neighbors::certex::index_params{}, dataset);
cuvs::neighbors::certex::search(res, cuvs::neighbors::certex::search_params{}, index, queries, neighbors, distances);

#include <cuvs/neighbors/certex.h>

cuvsCertexBuild(res, index_params, &dataset, index);
cuvsCertexSearch(res, search_params, index, &queries, &neighbors, &distances, filter);

Both examples build with the C++ library (cmake -S cpp -B build, see docs/cpp.md) as build/certex_example_cpp and build/certex_example_c; docs/cpp.md and docs/c.md describe the two APIs.

Documentation

The documentation covers installation, the user guide (metrics and storage, insertions and removals, filtered search, persistence, adapters), how certified exclusion works, benchmarks, and the API reference. Build it locally with pip install -e ".[docs]" && mkdocs serve.

Runnable examples are in examples/: one-call search, exact ground truth for evaluating approximate indexes, an exact k-NN graph with near-duplicate removal, filtered search, and every interface of the table above. The Zarc paper describes the design of the library (search pipeline, kernels, memory tiers, updates, metrics, and filters), proves that each mechanism keeps every answer exact, and reports its evaluation.

Benchmarks

Dataset	Width	Vectors in	Device memory	Queries per second	Exact queries
BIGANN-10M	123	host	5.1 GB	176,700	100%
BIGANN-100M	123	host	29.0 GB	18,700	100%
BIGANN-500M	59	host	71 GB	5,233	100%
BIGANN-10M, filtered at 10%, 1%, 0.1%	123	device		324,400, 392,000, 402,700	100%

One H100 NVL, batches of 2,048 queries, k = 10, anchor stride 16 (32 at 500M). These are the measurements of the Zarc paper; docs/benchmarks.md lists more configurations.

certex is a CUDA C++ library laid out as a cuVS module (cuvs::neighbors::certex, in cpp/), with a Python binding.

Contributing

Contributions are welcome; see CONTRIBUTING.md (commits are signed off under the DCO) and the Code of Conduct. Report security issues as described in SECURITY.md.

Citation

The library is described in the Zarc paper, and its certified screening comes from the CertEx paper:

@misc{zhao2026zarc,
  author       = {Dongfang Zhao},
  title        = {{Zarc: Exact Nearest Neighbors over a Billion Vectors in Seconds}},
  howpublished = {Zenodo preprint},
  year         = {2026},
  doi          = {10.5281/zenodo.23674583},
  url          = {https://doi.org/10.5281/zenodo.23674583}
}

@article{zhao2026certex,
  author  = {Dongfang Zhao},
  title   = {{CertEx: Exact Visual Retrieval at Scale by Certified Exclusion}},
  journal = {arXiv preprint arXiv:2610.21325},
  year    = {2026}
}

License

Apache License 2.0; see LICENSE and NOTICE.