GSI's Elasticsearch k-NN Plugin Application Brief
■ The GSI Elasticsearch k-NN plugin allows you to perform nearest neighbor vector similarity search using Elasticsearch’s dense_vector type. The plugin provides a high-performance, low-latency, low-power, billion-scale vector similarity search solution that allows users to combine traditional Elasticsearch text filters with vector search queries for a more advanced search.
■ Elasticsearch was originally designed as a text and document search engine. The GSI Elasticsearch k-NN plugin expands Elasticsearch’s ability to search beyond just text. The plugin opens the door to other data types like images, video, audio—any data type that can be represented as a compact, semantically rich numeric vector. Vectors can be used to search for the most similar items (nearest neighbors) to a query and can accelerate applications such as visual search, face recognition, natural language processing (NLP), and recommendation systems.
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Please see the document for details |
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English Chinese Chinese and English Japanese |
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2021/3/11 |
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628 KB |
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