Struct VectorSearchBuilder 

pub struct VectorSearchBuilder { /* private fields */ }

Fluent builder for vector search queries.

Examples

use prax_pgvector::query::VectorSearchBuilder;
use prax_pgvector::{Embedding, DistanceMetric};

let query = VectorSearchBuilder::new("documents", "embedding")
    .query(Embedding::new(vec![0.1, 0.2, 0.3]))
    .metric(DistanceMetric::Cosine)
    .limit(10)
    .ef_search(200)
    .build();

Implementations

impl VectorSearchBuilder

pub fn new(table: impl Into<String>, column: impl Into<String>) -> Self

Create a new search builder for a table and vector column.

pub fn query(self, embedding: Embedding) -> Self

Set the query vector.

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

Set the distance metric.

pub fn limit(self, limit: usize) -> Self

Set the result limit.

pub fn select(self, columns: &[&str]) -> Self

Set specific columns to select.

pub fn where_clause(self, condition: impl Into<String>) -> Self

Add a WHERE condition.

pub fn max_distance(self, distance: f64) -> Self

Set the maximum distance (radius search).

pub fn min_distance(self, distance: f64) -> Self

Set the minimum distance.

pub fn without_distance(self) -> Self

Don’t include the distance in the results.

pub fn distance_alias(self, alias: impl Into<String>) -> Self

Set a custom distance column alias.

pub fn then_order_by(self, clause: impl Into<String>) -> Self

Add an additional ORDER BY clause (after distance).

pub fn offset(self, offset: usize) -> Self

Set the offset for pagination.

pub fn probes(self, probes: usize) -> Self

Set the IVFFlat probes parameter.

pub fn ef_search(self, ef: usize) -> Self

Set the HNSW ef_search parameter.

pub fn build(self) -> VectorSearchQuery

Build the vector search query.

Panics

Panics if no query vector has been set. Use Self::try_build for a non-panicking alternative.

pub fn try_build(self) -> Option<VectorSearchQuery>

Try to build the vector search query.

Returns None if no query vector has been set.