elasticsearch/docs/reference/query-languages/esql/_snippets/functions/functionNamedParams/knn.md

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Supported function named parameters

num_candidates : (integer) The number of nearest neighbor candidates to consider per shard while doing knn search. Cannot exceed 10,000. Increasing num_candidates tends to improve the accuracy of the final results. Defaults to 1.5 * k

boost : (float) Floating point number used to decrease or increase the relevance scores of the query.Defaults to 1.0.

k : (integer) The number of nearest neighbors to return from each shard. Elasticsearch collects k results from each shard, then merges them to find the global top results. This value must be less than or equal to num_candidates. Defaults to 10.

rescore_oversample : (double) Applies the specified oversampling for rescoring quantized vectors. See oversampling and rescoring quantized vectors for details.

similarity : (double) The minimum similarity required for a document to be considered a match. The similarity value calculated relates to the raw similarity used, not the document score.