{"id":39946,"url":"https://github.com/currentslab/awesome-vector-search","name":"awesome-vector-search","description":"Collections of vector search related libraries, service and research papers","projects_count":72,"last_synced_at":"2026-06-07T01:00:33.938Z","repository":{"id":37772696,"uuid":"356254457","full_name":"currentslab/awesome-vector-search","owner":"currentslab","description":"Collections of vector search related libraries, service and research papers","archived":false,"fork":false,"pushed_at":"2024-08-06T16:07:09.000Z","size":90,"stargazers_count":1562,"open_issues_count":19,"forks_count":116,"subscribers_count":34,"default_branch":"main","last_synced_at":"2026-05-21T16:52:31.178Z","etag":null,"topics":["awesome","awesome-list","knn-search","machine-learning","nearest-neighbor-search","search-engine","similarity-search","vector","vector-search","vector-search-engine"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/currentslab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-04-09T11:59:34.000Z","updated_at":"2026-05-16T08:24:37.000Z","dependencies_parsed_at":"2024-03-23T02:47:16.552Z","dependency_job_id":"3863bc97-6480-425d-be51-8796a9b742a9","html_url":"https://github.com/currentslab/awesome-vector-search","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/currentslab/awesome-vector-search","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/currentslab%2Fawesome-vector-search","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/currentslab%2Fawesome-vector-search/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/currentslab%2Fawesome-vector-search/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/currentslab%2Fawesome-vector-search/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/currentslab","download_url":"https://codeload.github.com/currentslab/awesome-vector-search/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/currentslab%2Fawesome-vector-search/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34005030,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-06T02:00:07.033Z","response_time":107,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T19:26:05.668Z","updated_at":"2026-06-07T01:00:33.938Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Awesome Vector Search Engine"],"sub_categories":["Research Papers","Library","Cloud Service","Standalone Service"],"readme":"## Awesome Vector Search Engine\n\n\n\u003e A curated list of awesome vector search framework/engine, library, cloud service and research papers to vector similarity search\n\n\n### Standalone Service\n- [Apache Cassandra 5.0 – Vector search (cep-30), Strict Serialisable ACID (cep-15), horizontally scaling database](https://cassandra.apache.org)\n- [Qdrant -  Vector Similarity Search Engine with extended filtering support](https://github.com/qdrant/qdrant)\n- [Vald - A Highly Scalable Distributed Vector Search Engine](https://github.com/vdaas/vald)\n- [Milvus - A cloud-native vector database with high-performance and high scalability.](https://github.com/milvus-io/milvus)\n- [Weaviate - A cloud-native, real-time vector search engine](https://github.com/semi-technologies/weaviate)\n- [OpenDistro Elasticsearch KNN - A machine learning plugin which supports an approximate k-NN search algorithm for Open Distro for Elasticsearch](https://github.com/opendistro-for-elasticsearch/k-NN)\n- [Elastiknn - Elasticsearch plugin for nearest neighbor search](https://github.com/alexklibisz/elastiknn)\n- [Epsilla -  A High Performance Vector Database Management System, Hippocampus For AI](https://github.com/epsilla-cloud/vectordb)\n- [Vearch - A scalable distributed system for efficient similarity search of deep learning vectors](https://github.com/vearch/vearch)\n- [pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database](https://github.com/netrasys/pgANN)\n- [Jina - Jina allows you to build deep learning-powered search-as-a-service.](https://github.com/jina-ai/jina)\n- [Infinity -  The AI-native database built for LLM applications, providing incredibly fast vector and full-text search](https://github.com/infiniflow/infinity)\n- [Aquila DB - Distribution focused k-NN search algorithm](https://github.com/Aquila-Network/AquilaDB)\n- [Redis HNSW - A redis module for similarity search based on HNSW](https://github.com/zhao-lang/redis_hnsw)\n- [Solr - Apache Solr](https://github.com/apache/solr) - [has a Dense Vector Search feature as of Solr 9.0](https://solr.apache.org/guide/solr/latest/query-guide/dense-vector-search.html)\n- [Marqo - A semantic search engine which supports tensor search (sequence of vectors)](https://github.com/marqo-ai/marqo)\n- [txtai - Build semantic search applications and workflows](https://github.com/neuml/txtai)\n- [Semantra - A multipurpose tool for semantically searching documents.](https://github.com/freedmand/semantra)\n- [SuperDuperDB - Bring AI to your favorite database](https://github.com/SuperDuperDB/superduperdb)\n- [TensorDB - High Performance Vector Database Supporting Heterogeneous Computing](https://www.actionsky.com/tensorDB)\n- [JVector - a pure Java, zero dependency, embedded vector search engine, used by DataStax Astra DB and Apache Cassandra.](https://github.com/jbellis/jvector/)\n- [VQLite - Simple and Lightweight Vector Search Engine](https://github.com/VQLite/VQLite)\n- [Vexvault - 100% browser based, open source, scalable, simple, zero-cost vector search](https://github.com/Xyntopia/vexvault)\n- [Vespa.ai - Text search engine and ... fast approximate vector search (ANN)](https://github.com/vespa-engine) \n- [Vespa's large-scale ANN search using HNSW-IF indexes is described here](https://blog.vespa.ai/vespa-hybrid-billion-scale-vector-search/)\n\n### Library\n- [LangStream - LangStream is an open-source project that combines the best of event-based architectures with the latest Gen AI technologies.](https://langstream.ai)\n- [CassIO - CassIO is the ultimate solution for seamlessly integrating Apache Cassandra® with generative artificial intelligence and other machine learning workloads](https://cassio.org)\n- [JVector - A pure Java, zero dependency, embedded vector search engine used by some of the advanced distributed databases such as DataStax Astra DB \u0026 Apache Cassandra\u0026trade;](https://github.com/jbellis/jvector)\n- [Faiss - A library for efficient similarity search and clustering of dense vectors](https://github.com/facebookresearch/faiss)\n- [Distributed Faiss - Work with FAISS indexes which don't fit into a single server memory](https://github.com/facebookresearch/distributed-faiss)\n- [Autofaiss - Automatically create Faiss knn indices](https://github.com/criteo/autofaiss)\n- [ScaNN - A library efficient vector similarity search at scale. ](https://github.com/google-research/google-research/tree/master/scann)\n- [NMSLIB - Non-Metric Space Library, an efficient similarity search library for generic non-metric spaces](https://github.com/nmslib/nmslib)\n- [Annoy - C++ library with Python bindings to search for points](https://github.com/spotify/annoy)\n- [FLANN - Library written in C++ and contains bindings for the following languages: C, MATLAB, Python, and Ruby](http://www.cs.ubc.ca/research/flann/)\n- [LLM App - Open-source Python library for a real-time data KNN (K-Nearest Neighbors) indexing](https://github.com/pathwaycom/llm-app)\n- [MRPT - Fast nearest neighbor search with random projection](https://github.com/teemupitkanen/mrpt)\n- [RPForest - Python library for approximate nearest neighbours search](https://github.com/lyst/rpforest)\n- [pgvector - Open-source vector similarity search extension for Postgres](https://github.com/pgvector/pgvector)\n- [PASE - Ultra-High-Dimensional approximate nearest neighbor search extension for Postgres](https://github.com/alipay/PASE)\n- [Pyserini - Toolkit for reproducible information retrieval research with sparse and dense representations](https://github.com/castorini/pyserini)\n- [NGT - Provides commands and a library for performing high-speed approximate nearest neighbor ](https://github.com/yahoojapan/NGT)\n- [NearPy - Approximate search using different locality-sensitive hashing methods](http://pixelogik.github.io/NearPy/)\n- [TOROS N2 - lightweight approximate Nearest Neighbor library](https://github.com/kakao/n2)\n- [PUFFINN - Parameterless and Universal Fast FInding of Nearest Neighbors](https://github.com/puffinn/puffinn)\n- [SPTAG - A distributed approximate nearest neighborhood search (ANN) library ](https://github.com/microsoft/SPTAG)\n- [PyNNDescent - A python nearest neighbor descent for approximate k nearest neighbors](https://github.com/lmcinnes/pynndescent)\n- [TarsosLSH - A Java library implementing practical nearest neighbour search algorithm for multidimensional vectors ](https://github.com/JorenSix/TarsosLSH)\n- [TorchPQ - Efficient implementations of Product Quantization and its variants using Pytorch and CUDA](https://github.com/DeMoriarty/TorchPQ)\n- [Granne  - Graph-based retrieval of approximate nearest neighbors witten in rust ](https://github.com/granne/granne)\n- [Embeddinghub - A database built for machine learning embeddings](https://github.com/featureform/embeddinghub)\n- [Hora - Efficient approximate nearest neighbor search algorithm collections library written in Rust](https://github.com/hora-search/hora)\n- [Voy - A WASM vector similarity search engine written in Rust](https://github.com/tantaraio/voy)\n- [Chroma - The open-source embedding database for building LLM apps in Python or JavaScript with memory](https://github.com/chroma-core/chroma)\n- [USearch - Smaller \u0026 Faster Vector Search Engine for C++, Python, JavaScript, Rust, Java, GoLang, Wolfram](https://github.com/unum-cloud/usearch)\n- [Golang vector stores collection - Chroma, PGVector interfaces](https://github.com/urjitbhatia/vectorstores)\n- [Scalable Vector Search (SVS) - A performance library for vector similarity search](https://github.com/IntelLabs/ScalableVectorSearch)\n\n### Cloud Service\n\n- [Epsilla Cloud - The fully managed serverless vector database with 10X faster, cheaper and better.](https://cloud.epsilla.com)\n- [DataStax Astra Vector - Multi-cloud, serverless vector DBaaS](https://www.datastax.com/products/vector-search)\n- [Relevance AI - Vector Platform From Experimentation To Deployment](https://relevance.ai/vectors/)\n- [Pinecone - Managed vector search with filtering, live index updates, horizontal scaling, and a lot more](https://www.pinecone.io)\n- [MyScale - A managed vector database based on ClickHouse](https://myscale.com)\n- [Redis Cloud - Managed vector database in Redis](https://redis.com/cloud)\n- [Zilliz Cloud - Cloud-native service for Milvus](https://zilliz.com/cloud)\n\n### Research Papers\n\nList of methods on how approximate vector search algorithm can be implemented more effciently.\n\n- [SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search - NEURIPS 2021](https://proceedings.neurips.cc/paper/2021/hash/299dc35e747eb77177d9cea10a802da2-Abstract.html)\n- [Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors - ECCV 2018](https://openaccess.thecvf.com/content_ECCV_2018/html/Dmitry_Baranchuk_Revisiting_the_Inverted_ECCV_2018_paper.html)\n- [Accelerating Large-Scale Inference with Anisotropic Vector Quantization](https://arxiv.org/abs/1908.10396)\n- [Billion-scale similarity search with GPUs](https://arxiv.org/abs/1702.08734)\n- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://arxiv.org/abs/1603.09320)\n- [Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Data](https://arxiv.org/abs/1810.07355)\n- [On Approximately Searching for Similar Word Embeddings - ACL 2016](https://www.aclweb.org/anthology/P16-1214.pdf)\n\n[![CC0](https://i.creativecommons.org/p/zero/1.0/88x31.png)](https://creativecommons.org/publicdomain/zero/1.0/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/currentslab%2Fawesome-vector-search/projects"}