searchNearestVectors

fun searchNearestVectors(field: String, target: FloatArray, k: Int, acceptDocs: Bits?, visitedLimit: Int): TopDocs
fun searchNearestVectors(field: String, target: ByteArray, k: Int, acceptDocs: Bits?, visitedLimit: Int): TopDocs

Return the k nearest neighbor documents as determined by comparison of their vector values for this field, to the given vector, by the field's similarity function. The score of each document is derived from the vector similarity in a way that ensures scores are positive and that a larger score corresponds to a higher ranking.

The search is allowed to be approximate, meaning the results are not guaranteed to be the true k closest neighbors. For large values of k (for example when k is close to the total number of documents), the search may also retrieve fewer than k documents.

The returned TopDocs will contain a ScoreDoc for each nearest neighbor, sorted in order of their similarity to the query vector (decreasing scores). The [ ] contains the number of documents visited during the search. If the search stopped early because it hit visitedLimit, it is indicated through the relation TotalHits.Relation.GREATER_THAN_OR_EQUAL_TO.

Return

the k nearest neighbor documents, along with their (searchStrategy-specific) scores.

Parameters

field

the vector field to search

target

the vector-valued query

k

the number of docs to return

acceptDocs

Bits that represents the allowed documents to match, or null if they are all allowed to match.

visitedLimit

the maximum number of nodes that the search is allowed to visit


abstract fun searchNearestVectors(field: String, target: FloatArray, knnCollector: KnnCollector, acceptDocs: Bits?)
abstract fun searchNearestVectors(field: String, target: ByteArray, knnCollector: KnnCollector, acceptDocs: Bits?)

Return the k nearest neighbor documents as determined by comparison of their vector values for this field, to the given vector, by the field's similarity function. The score of each document is derived from the vector similarity in a way that ensures scores are positive and that a larger score corresponds to a higher ranking.

The search is allowed to be approximate, meaning the results are not guaranteed to be the true k closest neighbors. For large values of k (for example when k is close to the total number of documents), the search may also retrieve fewer than k documents.

The returned TopDocs will contain a ScoreDoc for each nearest neighbor, in order of their similarity to the query vector (decreasing scores). The TotalHits contains the number of documents visited during the search. If the search stopped early because it hit visitedLimit, it is indicated through the relation TotalHits.Relation.GREATER_THAN_OR_EQUAL_TO.

The behavior is undefined if the given field doesn't have KNN vectors enabled on its [ ]. The return value is never null.

Parameters

field

the vector field to search

target

the vector-valued query

knnCollector

collector with settings for gathering the vector results.

acceptDocs

Bits that represents the allowed documents to match, or null if they are all allowed to match.