Elastiknn (https://elastiknn.com/) is an open-source Elasticsearch plugin for exact and approximate nearest neighbor search.
Methods like word2vec and neural nets can convert various data modalities (text, images, users, items, etc.) into numerical vectors (i.e., embeddings), enabling data scientists and engineers to use nearest neighbor queries to search for semantically similar data (e.g., similar documents, images, users, etc.). Elasticsearch is a ubiquitous search solution, but its support for vector search is still evolving. Elastiknn fills the gap by bringing efficient exact and approximate vector search to Elasticsearch. This enables an enhanced search experience by combining traditional queries (e.g., products matching <some text query>) with nearest neighbor search queries (e.g., products with images similar to <a user-provided image>).
In this talk, Alex will present the features of Elastiknn, some example use-cases, and a few of the interesting engineering challenges in building it.