Enum QueryExpr 

pub enum QueryExpr {
    Points {
        ids: Vec<PointId>,
    },
    Nearest {
        input: QueryInput,
        using: Option<VectorTarget>,
        prefetch: Vec<Prefetch>,
        mmr: Option<Box<MmrConfig>>,
    },
    Recommend {
        positive: Vec<QueryInput>,
        negative: Vec<QueryInput>,
        strategy: Option<RecommendStrategy>,
        using: Option<VectorTarget>,
        prefetch: Vec<Prefetch>,
    },
    Context {
        pairs: Vec<ContextPair>,
        using: Option<VectorTarget>,
        prefetch: Vec<Prefetch>,
    },
    Discover {
        target: QueryInput,
        context: Vec<ContextPair>,
        using: Option<VectorTarget>,
        prefetch: Vec<Prefetch>,
    },
    OrderBy {
        field: String,
        direction: OrderDirection,
    },
    SampleRandom,
    Fusion {
        method: FusionMethod,
        prefetch: Vec<Prefetch>,
    },
    Formula {
        expression: Box<FormulaExpr>,
        defaults: Vec<(String, Value)>,
        prefetch: Vec<Prefetch>,
    },
    RelevanceFeedback {
        target: QueryInput,
        feedback: Vec<FeedbackItem>,
        strategy: FeedbackStrategy,
        using: Option<VectorTarget>,
        prefetch: Vec<Prefetch>,
    },
    Hybrid {
        text: String,
        model: Option<String>,
        dense_vector: Option<String>,
        sparse_vector: Option<String>,
        fusion: FusionMethod,
    },
    Rerank {
        input: QueryInput,
        model: String,
        using: Option<VectorTarget>,
        prefetch: Vec<Prefetch>,
    },
    CrossRerank {
        query: String,
        model: String,
        field: Option<String>,
        prefetch: Vec<Prefetch>,
    },
}

Variants

Points

Fields

ids: Vec<PointId>

Nearest

Fields

input: QueryInput

using: Option<VectorTarget>

prefetch: Vec<Prefetch>

mmr: Option<Box<MmrConfig>>

Recommend

Fields

positive: Vec<QueryInput>

negative: Vec<QueryInput>

strategy: Option<RecommendStrategy>

using: Option<VectorTarget>

prefetch: Vec<Prefetch>

Context

Fields

pairs: Vec<ContextPair>

using: Option<VectorTarget>

prefetch: Vec<Prefetch>

Discover

Fields

target: QueryInput

context: Vec<ContextPair>

using: Option<VectorTarget>

prefetch: Vec<Prefetch>

OrderBy

Fields

field: String

direction: OrderDirection

SampleRandom

Fusion

Fields

method: FusionMethod

prefetch: Vec<Prefetch>

Formula

Fields

expression: Box<FormulaExpr>

defaults: Vec<(String, Value)>

prefetch: Vec<Prefetch>

RelevanceFeedback

Fields

target: QueryInput

feedback: Vec<FeedbackItem>

strategy: FeedbackStrategy

using: Option<VectorTarget>

prefetch: Vec<Prefetch>

Hybrid

Fields

text: String

model: Option<String>

dense_vector: Option<String>

sparse_vector: Option<String>

fusion: FusionMethod

Rerank

Fields

input: QueryInput

model: String

using: Option<VectorTarget>

prefetch: Vec<Prefetch>

CrossRerank

Cross-encoder pair rerank: score query against PREFETCH document texts. Not sent to Qdrant as MaxSim — executor scores client-side then reorders.

Fields

query: String

Query string scored against each document.

model: String

Cross-encoder model id (e.g. bge-reranker-base).

field: Option<String>

Payload field holding document text (default "text" at resolve time).

prefetch: Vec<Prefetch>

Trait Implementations

impl Clone for QueryExpr

fn clone(&self) -> QueryExpr

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for QueryExpr

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

Formats the value using the given formatter.

impl PartialEq for QueryExpr

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

Equality operator ==.

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

Inequality operator !=.

impl StructuralPartialEq for QueryExpr