OpenSearch k-NN enables you to run the nearest neighbor search on billions of documents across thousands of dimensions with the same ease as running any regular OpenSearch query. You can use aggregations and filter clauses to further refine your similarity search operations. k-NN similarity search powers use cases such as product recommendations, fraud detection, image and video search, related document search, and more.
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This project uses two similarity search libraries to perform Approximate Nearest Neighbor Search: the Apache 2.0-licensed Non-Metric Space Library and the MIT licensed Faiss library. Thank you to all who have contributed to those projects including Bilegsaikhan Naidan, Leonid Boytsov, Yury Malkov and David Novak for nmslib and Hervé Jégou, Matthijs Douze, Jeff Johnson and Lucas Hosseini for Faiss.
This project has adopted the Amazon Open Source Code of Conduct. For more information see the Code of Conduct FAQ, or contact opensource-codeofconduct@amazon.com with any additional questions or comments.
This project is licensed under the Apache v2.0 License.
Copyright OpenSearch Contributors. See NOTICE for details.
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