Knn

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Supported function named parameters

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. This value is automatically set with any LIMIT applied to the function.

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

min_candidates : (integer) The minimum number of nearest neighbor candidates to consider per shard while doing knn search. KNN may use a higher number of candidates in case the query can't use a approximate results. Cannot exceed 10,000. Increasing min_candidates tends to improve the accuracy of the final results. Defaults to 1.5 * k (or LIMIT) used for the query.

visit_percentage : (float) The percentage of vectors to explore per shard while doing knn search with bbq_disk. Must be between 0 and 100. 0 will default to using num_candidates for calculating the percent visited. Increasing visit_percentage tends to improve the accuracy of the final results. If visit_percentage is set for bbq_disk, num_candidates is ignored. Defaults to ~1% per shard for every 1 million vectors

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.

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