[D]: Best nearest neighbour search for high dimensions

This page summarizes the projects mentioned and recommended in the original post on /r/MachineLearning

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  • faiss

    A library for efficient similarity search and clustering of dense vectors.

  • If you need large scale (1000+ dimension, millions+ source points, >1000 queries per second) and accept imperfect results / approximate nearest neighbors, then other people have already mentioned some of the best libraries (FAISS, Annoy).

  • annoy

    Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

  • If you need large scale (1000+ dimension, millions+ source points, >1000 queries per second) and accept imperfect results / approximate nearest neighbors, then other people have already mentioned some of the best libraries (FAISS, Annoy).

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    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • ann-benchmarks

    Benchmarks of approximate nearest neighbor libraries in Python

  • Look at ANN Benchmarks - there are quite a few indexes tested on various datasets.

  • pynndescent

    A Python nearest neighbor descent for approximate nearest neighbors

  • I'll assume this is the link to pynndescent, looks cool! Thanks for sharing. I haven't used it before. Also seems like it's an approximate nearest neighbor algorithm, just FYI for others seeing this.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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