Struct Knn

pub struct Knn { /* private fields */ }

Performs a k-nearest neighbor (kNN) search and returns the matching documents.

The kNN search API performs a k-nearest neighbor (kNN) search on a dense_vector field. Given a query vector, it finds the k closest vectors and returns those documents as search hits.

Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like most kNN algorithms, HNSW is an approximate method that sacrifices result accuracy for improved search speed. This means the results returned are not always the true k closest neighbors.

The kNN search API supports restricting the search using a filter. The search will return the top k documents that also match the filter query.

To create a knn search with a query vector or query vector builder:

Search::new()
    .knn(Knn::query_vector("test1", vec![1.0, 2.0, 3.0]))
    .knn(Knn::query_vector_builder("test3", TextEmbedding::new("my-text-embedding-model", "The opposite of pink")));

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-knn-query.html

Implementations

impl Knn

pub fn query_vector<T>(field: T, query_vector: Vec<f32>) -> Self

where T: ToString,

Creates an instance of Knn search with query vector

• field - The name of the vector field to search against. Must be a dense_vector field with indexing enabled.
• query_vector - Query vector. Must have the same number of dimensions as the vector field you are searching against.

pub fn query_vector_builder<T, U>(field: T, query_vector_builder: U) -> Self

where T: ToString, U: Into<QueryVectorBuilder>,

Creates an instance of Knn search with query vector builder

• field - The name of the vector field to search against. Must be a dense_vector field with indexing enabled.
• query_vector_builder - A configuration object indicating how to build a query_vector before executing the request.

pub fn k(self, k: u32) -> Self

Number of nearest neighbors to return as top hits. This value must be less than num_candidates.

Defaults to size.

pub fn num_candidates(self, num_candidates: u32) -> Self

The number of nearest neighbor candidates to consider per shard. Cannot exceed 10,000. Elasticsearch collects num_candidates results from each shard, then merges them to find the top results. Increasing num_candidates tends to improve the accuracy of the final results. Defaults to Math.min(1.5 * size, 10_000).

pub fn filter<T>(self, filter: T) -> Self

where T: Into<Query>,

Query to filter the documents that can match. The kNN search will return the top documents that also match this filter. The value can be a single query or a list of queries. If filter is not provided, all documents are allowed to match.

The filter is a pre-filter, meaning that it is applied during the approximate kNN search to ensure that num_candidates matching documents are returned.

pub fn similarity(self, similarity: f32) -> Self

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. The matched documents are then scored according to similarity and the provided boost is applied.

pub fn boost<T>(self, boost: T) -> Self

where T: AsPrimitive<f32>,

Floating point number used to decrease or increase the relevance scores of a query. Defaults to 1.0.

You can use the boost parameter to adjust relevance scores for searches containing two or more queries.

Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

pub fn name<S>(self, name: S) -> Self

where S: ToString,

You can use named queries to track which queries matched returned documents. If named queries are used, the response includes a matched_queries property for each hit.

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html#named-queries

Trait Implementations

impl Clone for Knn

fn clone(&self) -> Knn

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Knn

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for Knn

fn eq(&self, other: &Knn) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for Knn

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for Knn