Struct KnnQuery

pub struct KnnQuery { /* private fields */ }

Finds the k nearest vectors to a query vector, as measured by a similarity metric. knn query finds nearest vectors through approximate search on indexed dense_vectors. The preferred way to do approximate kNN search is through the top level knn section of a search request. knn query is reserved for expert cases, where there is a need to combine this query with other queries.

knn query doesn’t have a separate k parameter. k is defined by size parameter of a search request similar to other queries. knn query collects num_candidates results from each shard, then merges them to get the top size results.

To create a knn query:

Query::knn("test", vec![1.0, 2.0, 3.0]);

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

Implementations

impl KnnQuery

pub fn serialize<__S>( __self: &KnnQuery, __serializer: __S, ) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

impl KnnQuery

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 KnnQuery

fn clone(&self) -> KnnQuery

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for KnnQuery

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

Formats the value using the given formatter.

impl From<KnnQuery> for Option<Query>

fn from(q: KnnQuery) -> Self

Converts to this type from the input type.

impl From<KnnQuery> for Query

fn from(q: KnnQuery) -> Self

Converts to this type from the input type.

impl IntoIterator for KnnQuery

type Item = KnnQuery

The type of the elements being iterated over.

type IntoIter = IntoIter<<KnnQuery as IntoIterator>::Item>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl PartialEq<KnnQuery> for Query

fn eq(&self, other: &KnnQuery) -> 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 PartialEq<Query> for KnnQuery

fn eq(&self, other: &Query) -> 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 PartialEq for KnnQuery

fn eq(&self, other: &KnnQuery) -> 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 KnnQuery

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 KnnQuery