ES|QL KNN function

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field

Field that the query will target. knn function can be used with dense_vector or semantic_text fields. Other text fields are not allowed

query

Vector value to find top nearest neighbours for.

options

(Optional) kNN additional options as function named parameters. See knn query for more information.

Finds the k nearest vectors to a query vector, as measured by a similarity metric. knn function finds nearest vectors through approximate search on indexed dense_vectors or semantic_text fields.

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

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

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.

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.

from colors metadata _score
| where knn(rgb_vector, [0, 120, 0])
| sort _score desc, color asc