// This is generated by ESQL's AbstractFunctionTestCase. Do no edit it. See ../README.md for how to regenerate it.

*Supported function named parameters*

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name | types | description
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](docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring) 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.
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