{"repo":"erikbern/ann-benchmarks","free":true,"listed":false,"github":"https://github.com/erikbern/ann-benchmarks","clone":"git clone https://github.com/erikbern/ann-benchmarks.git","description":"Benchmarks of approximate nearest neighbor libraries in Python","language":"Python","stars":5711,"topics":["nearest-neighbors","benchmark","docker"],"license":"MIT","category":"deployment-docker-iac","readme_excerpt":"Status of ANN-Benchmarks ======================== At this point, ann-benchmarks is no longer actively maintained. Please consider submitting your work to different benchmarks, such as VIBE. Benchmarking nearest neighbors ============================== Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem with notably few empirical attempts at comparing approaches in an objective way, despite a clear need for such to drive optimization forward. This project contains tools to benchmark various implementations of approximate nearest neighbor (ANN) search for selected metrics. We have pre-generated datasets (in HDF5 format) and prepared Docker containers for each algorithm, as well as a test suite to verify function integrity. Evaluated ========= Annoy FLANN scikit-learn: LSHForest, KDTree, BallTree Weaviate PANNS NearPy KGraph NMSLIB (Non-Metric Space Library) : SWGraph, HNSW, BallTree, MPLSH hnswlib (a part of nmslib project) RPForest FAISS DolphinnPy Datasketch nndescent PyNNDescent MRPT NGT : ONNG, PANNG, QG SPTAG PUFFINN N2 ScaNN Vearch Elasticsearch : HNSW Elastiknn ExpANN OpenSearch KNN DiskANN : Vamana, Vamana-PQ Vespa scipy: cKDTree vald Qdrant HUAWEI(qsgngt) Milvus : Knowhere Zilliz(Glass) pgvector pgvecto.rs RediSearch pg embedding Descartes(01AI) kgn vsag PGVectorScale Data sets ========= We have a number of precomputed data sets in HDF5 format. All data sets have been pre-split into train/test and include ground tru","default_branch":null,"files":null,"tree":[],"storefront":"/r/erikbern","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/erikbern/ann-benchmarks/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}