Laravel Vector Metrics

Laravel 13 already supports vector distance queries on MariaDB and Postgres, but only with cosine distance. This package adds Euclidean (L2) distance, and lets you choose the metric and the MariaDB M parameter for vector indexes.

It adds macros only. No framework class is replaced.

Requirements

• PHP 8.3+, Laravel 13
• MariaDB 11.7+ or Postgres with pgvector

Installation

composer require rhaima96/laravel-vector-metrics

The service provider is registered automatically.

Usage

Supported metrics: cosine, euclidean.

Migrations

Schema::create('documents', function (Blueprint $table) {
    $table->id();
    $table->vector('embedding', 1536);

    // MariaDB: VECTOR INDEX ... M=16 DISTANCE=euclidean
    // Postgres: USING hnsw (embedding vector_l2_ops)
    $table->vectorIndexUsing('embedding', 'euclidean', m: 16);
});

m is MariaDB's graph degree (3–200, default 6). Postgres ignores it.

Queries

Document::query()
    ->select('id', 'title')
    ->selectVectorDistanceUsing('euclidean', 'embedding', $vector)   // adds embedding_distance
    ->whereVectorDistanceUsing('euclidean', 'embedding', $vector, 0.8)
    ->orderByVectorDistanceUsing('euclidean', 'embedding', $vector)
    ->limit(10)
    ->get();

$vector is an array of floats or an Arrayable.

On MariaDB, a vector index is only used when the query's distance function matches the index's DISTANCE. Create the index with the same metric you query with.

Testing

composer update
vendor/bin/phpunit

The integration tests run only when DB_CONNECTION is mariadb or pgsql (see .github/workflows/tests.yml).

License

MIT