Struct SparseVectorQuery 

pub struct SparseVectorQuery {
    pub field: Field,
    pub vector: Vec<(u32, f32)>,
    pub combiner: MultiValueCombiner,
}

Sparse vector query for similarity search

Fields

field: Field

Field containing the sparse vectors

vector: Vec<(u32, f32)>

Query vector as (dimension_id, weight) pairs

combiner: MultiValueCombiner

How to combine scores for multi-valued documents

Implementations

impl SparseVectorQuery

pub fn new(field: Field, vector: Vec<(u32, f32)>) -> Self

Create a new sparse vector query

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

Set the multi-value score combiner

pub fn from_indices_weights( field: Field, indices: Vec<u32>, weights: Vec<f32>, ) -> Self

Create from separate indices and weights vectors

pub fn from_text( field: Field, text: &str, tokenizer_name: &str, weighting: QueryWeighting, sparse_index: Option<&SparseIndex>, ) -> Result<Self>

Create from raw text using a HuggingFace tokenizer (single segment)

This method tokenizes the text and creates a sparse vector query. For multi-segment indexes, use from_text_with_stats instead.

Arguments

• field - The sparse vector field to search
• text - Raw text to tokenize
• tokenizer_name - HuggingFace tokenizer path (e.g., “bert-base-uncased”)
• weighting - Weighting strategy for tokens
• sparse_index - Optional sparse index for IDF lookup (required for IDF weighting)

pub fn from_text_with_stats( field: Field, text: &str, tokenizer: &HfTokenizer, weighting: QueryWeighting, global_stats: Option<&GlobalStats>, ) -> Result<Self>

Create from raw text using global statistics (multi-segment)

This is the recommended method for multi-segment indexes as it uses aggregated IDF values across all segments for consistent ranking.

Arguments

• field - The sparse vector field to search
• text - Raw text to tokenize
• tokenizer - Pre-loaded HuggingFace tokenizer
• weighting - Weighting strategy for tokens
• global_stats - Global statistics for IDF computation

pub fn from_text_with_tokenizer_bytes( field: Field, text: &str, tokenizer_bytes: &[u8], weighting: QueryWeighting, global_stats: Option<&GlobalStats>, ) -> Result<Self>

Create from raw text, loading tokenizer from index directory

This method supports the index:// prefix for tokenizer paths, loading tokenizer.json from the index directory.

Arguments

• field - The sparse vector field to search
• text - Raw text to tokenize
• tokenizer_bytes - Tokenizer JSON bytes (pre-loaded from directory)
• weighting - Weighting strategy for tokens
• global_stats - Global statistics for IDF computation

Trait Implementations

impl Clone for SparseVectorQuery

fn clone(&self) -> SparseVectorQuery

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SparseVectorQuery

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

Formats the value using the given formatter.

impl Query for SparseVectorQuery

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