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  • Datatypes to efficiently store floating-point and boolean vectors in Elasticsearch documents.
  • Exact nearest neighbor queries for five similarity functions: L1, L2, Angular, Jaccard, and Hamming.
  • Approximate nearest neighbor queries using Locality Sensitive Hashing and related algorithms for all five similarity functions.
  • Combine nearest neighbor queries with standard Elasticsearch queries.
  • Implemented using standard Elasticsearch and Lucene constructs, so indexing and queries scale horizontally with Elasticsearch.

Use Cases

  • Horizontally scalable nearest neighbor search
  • Visual similarity search
  • Word and document embedding search


Elastiknn is very much a work in progress. I appreciate any feedback over on the Github repo.