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Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity searches using exact and approximate algorithms. Methods like word2vec and convolutional neural nets can convert many data modalities (text, images, users, items, etc.) into numerical vectors, such that pairwise distance computations on the vectors correspond to semantic similarity of the original data. Elasticsearch is a ubiquitous search solution, but its support for vectors is limited. This plugin fills the gap by bringing efficient exact and approximate vector search to Elasticsearch. This enables users to combine traditional queries (e.g., “some product”) with vector search queries (e.g., an image (vector) of a product) for an enhanced search experience.
Features
- Elasticsearch plugin for similarity search on dense floating point and sparse boolean vectors
- Comprehensive documentation
- Datatypes to efficiently store dense and sparse numerical vectors in Elasticsearch documents, including multiple vectors per document
- Exact nearest neighbor queries for five similarity functions: L1, L2, Cosine, Jaccard, and Hamming
- Approximate queries using Locality Sensitive Hashing for L2, Cosine, Jaccard, and Hamming similarity
- Integration of nearest neighbor queries with standard Elasticsearch queries
Project Samples
