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<h2 id="use-in-vector-databases">Use in vector databases</h2> <p>HNSW is a key method for approximate nearest neighbor search in high-dimensional vector databases, for example in the context of embeddings from neural networks in large language models. Databases that use HNSW as search index include: </p> <ul><li><a href="/facts/Apache_Lucene/tPQ0D39Y">Apache Lucene</a> Vector Search</li> <li><a href="/facts/Chroma_(vector_database)/CHxDfAE7">Chroma</a> <a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a></li> <li>Qdrant <a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a></li> <li>Vespa</li> <li>Vearch Gamma</li> <li>Weaviate</li> <li>pgvector <a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a></li> <li><a href="/facts/MariaDB/QWobVFIq">MariaDB</a><a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a></li> <li><a href="/facts/MongoDB/9myQKGoY">MongoDB</a> Atlas<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a></li> <li><a href="/facts/ClickHouse/gorE2TXd">ClickHouse</a><a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a></li> <li><a href="/facts/Milvus_(vector_database)/xGNfeBlU">Milvus</a><a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a></li> <li><a href="/facts/DuckDB/GkXvzpBx">DuckDB</a><a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a></li> <li>Kuzu<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a></li></ul> <p>Several of these use either the hnswlib library<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> provided by the original authors, or the FAISS library. </p> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Malkov, Yury A; Yashunin, Dmitry A (1 April 2020). "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs". IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (4): 824–836. arXiv:1603.09320. doi:10.1109/TPAMI.2018.2889473. PMID 30602420. <a href="/wiki/IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligence" target="_blank">/wiki/IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligence</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Malkov, Yury A; Yashunin, Dmitry A (1 April 2020). "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs". IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (4): 824–836. arXiv:1603.09320. doi:10.1109/TPAMI.2018.2889473. PMID 30602420. <a href="/wiki/IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligence" target="_blank">/wiki/IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligence</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Malkov, Yury; Ponomarenko, Alexander; Logvinov, Andrey; Krylov, Vladimir (2012). "Scalable Distributed Algorithm for Approximate Nearest Neighbor Search Problem in High Dimensional General Metric Spaces". In Navarro, Gonzalo; Pestov, Vladimir (eds.). Similarity Search and Applications. Lecture Notes in Computer Science. Vol. 7404. Berlin, Heidelberg: Springer. pp. 132–147. doi:10.1007/978-3-642-32153-5_10. ISBN 978-3-642-32153-5. <a href="978-3-642-32153-5" target="_blank">978-3-642-32153-5</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Aumüller, Martin; Bernhardsson, Erik; Faithfull, Alexander (2017). "ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms". In Beecks, Christian; Borutta, Felix; Kröger, Peer; Seidl, Thomas (eds.). Similarity Search and Applications. Lecture Notes in Computer Science. Vol. 10609. Cham: Springer International Publishing. pp. 34–49. arXiv:1807.05614. doi:10.1007/978-3-319-68474-1_3. ISBN 978-3-319-68474-1. Republished as: Aumüller, Martin; Bernhardsson, Erik; Faithfull, Alexander (2020). "ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms". Information Systems. 87: 101374. arXiv:1807.05614. doi:10.1016/j.is.2019.02.006. <a href="978-3-319-68474-1" target="_blank">978-3-319-68474-1</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>"ANN-Benchmarks". ann-benchmarks.com. Retrieved 2024-03-19. <a href="https://ann-benchmarks.com/" target="_blank">https://ann-benchmarks.com/</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>"pgvector Documentation on IVFFlat". github.com/pgvector. Retrieved 2025-03-21. <a href="https://github.com/pgvector/pgvector?tab=readme-ov-file#ivfflat" target="_blank">https://github.com/pgvector/pgvector?tab=readme-ov-file#ivfflat</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>"Chroma Documentation". docs.trychroma.com. Retrieved 2025-03-19. <a href="https://docs.trychroma.com/docs/collections/configure" target="_blank">https://docs.trychroma.com/docs/collections/configure</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>"Qdrant Documentation". qdrant.tech/. Retrieved 2025-03-19. <a href="https://qdrant.tech/documentation/concepts/indexing/" target="_blank">https://qdrant.tech/documentation/concepts/indexing/</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>"pgvector Repository". github.com/pgvector. Retrieved 2025-03-19. <a href="https://github.com/pgvector/pgvector?tab=readme-ov-file#hnsw" target="_blank">https://github.com/pgvector/pgvector?tab=readme-ov-file#hnsw</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>"MariaDB Vector". MariaDB.org. Retrieved 2024-07-30. <a href="https://mariadb.org/projects/mariadb-vector/" target="_blank">https://mariadb.org/projects/mariadb-vector/</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>"MongoDB Atlas Vector Search". mongodb.com. Retrieved 2024-09-17. <a href="https://www.mongodb.com/products/platform/atlas-vector-search" target="_blank">https://www.mongodb.com/products/platform/atlas-vector-search</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>"Exact and Approximate Nearest Neighbor Search in ClickHouse". clickhouse.com. 21 Apr 2025. Retrieved 2025-04-21. <a href="https://clickhouse.com/docs/engines/table-engines/mergetree-family/annindexes" target="_blank">https://clickhouse.com/docs/engines/table-engines/mergetree-family/annindexes</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>"How to Pick a Vector Index in Your Milvus Instance: A Visual Guide". zilliz.com. Retrieved 2024-10-10. <a href="https://zilliz.com/learn/how-to-pick-a-vector-index-in-milvus-visual-guide" target="_blank">https://zilliz.com/learn/how-to-pick-a-vector-index-in-milvus-visual-guide</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>"Vector Similarity Search in DuckDB". duckdb.org. 3 May 2024. Retrieved 2025-02-20. <a href="https://duckdb.org/2024/05/03/vector-similarity-search-vss.html" target="_blank">https://duckdb.org/2024/05/03/vector-similarity-search-vss.html</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> <li id="fn:15"><p>"Vector search". kuzudb.com. 15 April 2025. <a href="https://docs.kuzudb.com/extensions/vector/" target="_blank">https://docs.kuzudb.com/extensions/vector/</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></p></li> <li id="fn:16"><p>nmslib/hnswlib, nmslib, 2024-03-18, retrieved 2024-03-19 <a href="https://github.com/nmslib/hnswlib" target="_blank">https://github.com/nmslib/hnswlib</a> <a href="#fnref:16" class="footnote-back-ref">↩</a></p></li> </ol>