X-Laravel/Embedding — Qdrant Plugin

Qdrant vector database driver for x-laravel/embedding.

How It Works

• Implements SimilarityDriver — registers as the qdrant driver, similarity search runs entirely in Qdrant using its native ANN (Approximate Nearest Neighbor) engine
• Implements VectorStore — writes embeddings to both the SQL embeddings table (for Eloquent relationships) and the Qdrant collection (for search)

Requirements

• PHP ^8.3
• Laravel ^12.0 | ^13.0
• x-laravel/embedding ^1.0
• Qdrant server (self-hosted or Qdrant Cloud)

Installation

composer require x-laravel/embedding-qdrant-plugin

The QdrantEmbeddingServiceProvider is auto-discovered and registers the qdrant driver automatically.

Setup

1. Configure x-laravel/embedding

Publish the config if you haven't already:

php artisan vendor:publish --tag=embedding-config

Set the similarity driver in config/embedding.php:

'similarity' => [
    'driver' => env('EMBEDDING_SIMILARITY_DRIVER', 'qdrant'),
],

2. Configure Qdrant

Set the following environment variables:

EMBEDDING_QDRANT_URL=http://localhost:6333
EMBEDDING_QDRANT_COLLECTION=embeddings
EMBEDDING_QDRANT_API_KEY=          # required for Qdrant Cloud

Or publish the config to config/embedding-qdrant.php for full customisation:

php artisan vendor:publish --tag=embedding-qdrant

3. Create the Qdrant collection and SQL table

php artisan migrate

This publishes both the SQL embeddings table migration (from the core package) and creates the Qdrant collection.

Note: The Qdrant collection is created with cosine similarity. The EMBEDDING_DIMENSIONS config value sets the vector size — it must match your AI model's output dimension.

4. Model

Follow the standard x-laravel/embedding setup. No Qdrant-specific changes are needed on your models.

use XLaravel\Embedding\Attributes\EmbedOn;
use XLaravel\Embedding\Concerns\Embeddable;
use XLaravel\Embedding\Contracts\HasEmbeddings;

#[EmbedOn(['title', 'body'])]
class Post extends Model implements HasEmbeddings
{
    use Embeddable;

    public function toEmbeddingText(): string
    {
        return $this->title.' '.$this->body;
    }
}

Usage

The driver is transparent — use the standard x-laravel/embedding API:

Post::similarToText('web framework', limit: 10);
Post::similarTo($vector, limit: 10, threshold: 0.8);
Post::rankByRelevance($posts, 'web framework');

$post->mostSimilar(limit: 5);
$post->similarityTo($otherPost);

All methods set a similarity_score float attribute on each returned model.

Testing

# Build first (once per PHP version)
DOCKER_BUILDKIT=0 docker compose --profile php83 build

# Run tests
docker compose --profile php83 up
docker compose --profile php84 up
docker compose --profile php85 up

License

This package is open-sourced software licensed under the MIT license.