prax-pgvector

pgvector integration for the Prax ORM — vector similarity search, embeddings, and index management for PostgreSQL.

This crate provides type-safe wrappers around pgvector functionality, integrating with the prax-postgres driver for seamless vector operations.

Features

• Vector types: [Embedding], [SparseEmbedding], [BinaryVector] (and [HalfEmbedding] with the halfvec feature)
• Distance metrics: L2, inner product, cosine, L1, Hamming, Jaccard
• Index management: IVFFlat and HNSW index creation with tuning parameters
• Query builder: Fluent API for vector similarity search
• Hybrid search: Combined vector + full-text search using Reciprocal Rank Fusion
• Filter integration: Vector filters for prax-query WHERE/ORDER BY clauses

Quick Start

use prax_pgvector::prelude::*;

// Create an embedding
let query = Embedding::new(vec![0.1, 0.2, 0.3, /* ... */]);

// Build a similarity search
let search = VectorSearchBuilder::new("documents", "embedding")
    .query(query)
    .metric(DistanceMetric::Cosine)
    .limit(10)
    .ef_search(200) // Tune HNSW recall
    .build();

let sql = search.to_sql();
// SELECT *, embedding <=> $1 AS distance FROM documents ORDER BY distance LIMIT 10

Index Management

use prax_pgvector::index::{VectorIndex, HnswConfig};
use prax_pgvector::DistanceMetric;

// Create an HNSW index
let index = VectorIndex::hnsw("idx_doc_embedding", "documents", "embedding")
    .metric(DistanceMetric::Cosine)
    .config(HnswConfig::high_recall())
    .concurrent()
    .build()
    .unwrap();

println!("{}", index.to_create_sql());
// CREATE INDEX CONCURRENTLY idx_doc_embedding ON documents
//   USING hnsw (embedding vector_cosine_ops)
//   WITH (m = 32, ef_construction = 128)

Feature Flags