A k-nearest neighbor (kNN) search finds the k nearest vectors to a query vector, as measured by a similarity metric.
Common use cases for kNN include:
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Relevance ranking based on natural language processing (NLP) algorithms
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Product recommendations and recommendation engines
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Similarity search for images or videos
Learn more in the {ref}/knn-search.html[{es} core documentation].
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Tip
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Check out our {ref}/bring-your-own-vectors.html[hands-on tutorial] to learn how to ingest dense vector embeddings into Elasticsearch. |