Struct DenseVectorQuery 

pub struct DenseVectorQuery {
    pub field: Field,
    pub vector: Vec<f32>,
    pub nprobe: usize,
    pub rerank_factor: usize,
    pub combiner: MultiValueCombiner,
}

Dense vector query for similarity search

Fields

field: Field

Field containing the dense vectors

vector: Vec<f32>

Query vector

nprobe: usize

Number of clusters to probe (for IVF indexes)

rerank_factor: usize

Re-ranking factor (multiplied by k for candidate selection)

combiner: MultiValueCombiner

How to combine scores for multi-valued documents

Implementations

impl DenseVectorQuery

pub fn new(field: Field, vector: Vec<f32>) -> Self

Create a new dense vector query

pub fn with_nprobe(self, nprobe: usize) -> Self

Set the number of clusters to probe (for IVF indexes)

pub fn with_rerank_factor(self, factor: usize) -> Self

Set the re-ranking factor

pub fn with_combiner(self, combiner: MultiValueCombiner) -> Self

Set the multi-value score combiner

Trait Implementations

impl Clone for DenseVectorQuery

fn clone(&self) -> DenseVectorQuery

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DenseVectorQuery

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

Formats the value using the given formatter.

impl Query for DenseVectorQuery

fn scorer<'a>( &self, reader: &'a SegmentReader, limit: usize, ) -> ScorerFuture<'a>

Create a scorer for this query against a single segment (async)

fn count_estimate<'a>(&self, _reader: &'a SegmentReader) -> CountFuture<'a>

Estimated number of matching documents in a segment (async)

fn as_term_query_info(&self) -> Option<TermQueryInfo>

Return term info if this is a simple term query eligible for WAND optimization