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Businesses are increasingly relying on intelligent search capabilities to enhance customer experience, automate insights, and unlock the potential of unstructured information. Elasticsearch, a leading distributed search and analytics engine, is at the heart of many such systems. One of its powerful and lesser-known capabilities is support for k-nearest neighbors (k-NN) search, a method particularly useful for similarity-based retrieval in domains such as semantic search, recommendation engines, and image recognition.
This article delves into what Elasticsearch and k-NN search are, how the two are integrated, and how to configure and optimize k-NN in Elasticsearch for real-world applications.
Click through for a high-level primer on the topic, as well as a few links to additional resources.