Struct MultiVectorSearchRequest 

pub struct MultiVectorSearchRequest {

    pub positive_vectors: Vec<Vec<f32>>,
    pub positive_weights: Option<Vec<f32>>,
    pub negative_vectors: Option<Vec<Vec<f32>>>,
    pub negative_weights: Option<Vec<f32>>,
    pub top_k: u32,
    pub distance_metric: DistanceMetric,
    pub score_threshold: Option<f32>,
    pub enable_mmr: bool,
    pub mmr_lambda: f32,
    pub include_metadata: bool,
    pub include_vectors: bool,
    pub filter: Option<Value>,
    pub consistency: ReadConsistency,
    pub staleness_config: Option<StalenessConfig>,
}

Request for multi-vector search with positive and negative vectors

Fields

positive_vectors: Vec<Vec<f32>>

Positive vectors to search towards (required, at least one)

positive_weights: Option<Vec<f32>>

Weights for positive vectors (optional, defaults to equal weights)

negative_vectors: Option<Vec<Vec<f32>>>

Negative vectors to search away from (optional)

negative_weights: Option<Vec<f32>>

Weights for negative vectors (optional, defaults to equal weights)

top_k: u32

Number of results to return

distance_metric: DistanceMetric

Distance metric to use

score_threshold: Option<f32>

Minimum score threshold

enable_mmr: bool

Enable MMR (Maximal Marginal Relevance) for diversity

mmr_lambda: f32

Lambda parameter for MMR (0 = max diversity, 1 = max relevance)

include_metadata: bool

Include metadata in results

include_vectors: bool

Include vectors in results

filter: Option<Value>

Optional metadata filter

consistency: ReadConsistency

Read consistency level

staleness_config: Option<StalenessConfig>

Staleness configuration for bounded staleness reads

Implementations

impl MultiVectorSearchRequest

pub fn new(positive_vectors: Vec<Vec<f32>>) -> Self

Create a new multi-vector search request with positive vectors

pub fn with_top_k(self, top_k: u32) -> Self

Set the number of results to return

pub fn with_positive_weights(self, weights: Vec<f32>) -> Self

Add weights for positive vectors

pub fn with_negative_vectors(self, vectors: Vec<Vec<f32>>) -> Self

Add negative vectors to search away from

pub fn with_negative_weights(self, weights: Vec<f32>) -> Self

Add weights for negative vectors

pub fn with_distance_metric(self, metric: DistanceMetric) -> Self

Set distance metric

pub fn with_score_threshold(self, threshold: f32) -> Self

Set minimum score threshold

pub fn with_mmr(self, lambda: f32) -> Self

Enable MMR for diversity

pub fn include_metadata(self, include: bool) -> Self

Set whether to include metadata

pub fn include_vectors(self, include: bool) -> Self

Set whether to include vectors

pub fn with_filter(self, filter: Value) -> Self

Add a filter

pub fn with_consistency(self, consistency: ReadConsistency) -> Self

Set read consistency level

Trait Implementations

impl Clone for MultiVectorSearchRequest

fn clone(&self) -> MultiVectorSearchRequest

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MultiVectorSearchRequest

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for MultiVectorSearchRequest

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for MultiVectorSearchRequest

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

where __S: Serializer,

Serialize this value into the given Serde serializer.