Laravel Related Content

Build related content links using vector embeddings and pgvector for Laravel.

Features

• 🔗 Pre-computed Related Links - Related content is calculated on save, not on every page load
• 🚀 Fast Lookups - O(1) relationship queries instead of real-time similarity search
• 🔄 Cross-Model Relationships - Find related content across different model types (Blog → Events → Questions)
• 🧠 Multiple Embedding Providers - Support for OpenAI and Ollama
• 📦 Queue Support - Process embeddings in the background
• 🔍 Semantic Search - Search content by meaning, not just keywords

Requirements

• PHP 8.3+
• Laravel 11 or 12
• PostgreSQL with pgvector extension

Installation

1. Install pgvector extension in PostgreSQL

CREATE EXTENSION IF NOT EXISTS vector;

2. Install the package via Composer

composer require vlados/laravel-related-content

3. Publish the config and migrations

php artisan vendor:publish --tag="related-content-config"
php artisan vendor:publish --tag="related-content-migrations"
php artisan migrate

4. Configure your environment

# Embedding provider (openai or ollama)
RELATED_CONTENT_PROVIDER=openai

# OpenAI settings
OPENAI_API_KEY=your-api-key
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
OPENAI_EMBEDDING_DIMENSIONS=1536

# Or Ollama settings
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_EMBEDDING_MODEL=nomic-embed-text

Usage

1. Add the trait to your models

use Vlados\LaravelRelatedContent\Concerns\HasRelatedContent;

class BlogPost extends Model
{
    use HasRelatedContent;

    /**
     * Define which fields should be embedded.
     */
    public function embeddableFields(): array
    {
        return ['title', 'excerpt', 'content'];
    }
}

2. Configure models for cross-model relationships

In config/related-content.php:

'models' => [
    \App\Models\BlogPost::class,
    \App\Models\Event::class,
    \App\Models\Question::class,
],

3. Related content is automatically synced on save

$post = BlogPost::create([
    'title' => 'Electric Vehicle Charging Guide',
    'content' => '...',
]);

// Embedding is generated and related content is found automatically

4. Retrieve related content

// Get all related content
$related = $post->getRelatedModels();

// Get related content of a specific type
$relatedEvents = $post->getRelatedOfType(Event::class);

// Get the raw relationship with similarity scores
$post->relatedContent()->with('related')->get();

5. Use in Blade templates

@if($post->relatedContent->isNotEmpty())
    <div class="related-content">
        <h3>Related Content</h3>
        @foreach($post->getRelatedModels(5) as $item)
            <a href="{{ $item->url }}">{{ $item->title }}</a>
        @endforeach
    </div>
@endif

Artisan Commands

Rebuild embeddings and related content

# Process models missing embeddings (default behavior)
php artisan related-content:rebuild

# Process a specific model (missing only)
php artisan related-content:rebuild "App\Models\BlogPost"

# Force regenerate all embeddings
php artisan related-content:rebuild --force

# Process synchronously (instead of queuing)
php artisan related-content:rebuild --sync

# With custom chunk size
php artisan related-content:rebuild --chunk=50

Semantic Search

You can also use the package for semantic search:

use Vlados\LaravelRelatedContent\Services\RelatedContentService;

$service = app(RelatedContentService::class);

// Search across all embeddable models
$results = $service->search('electric vehicle charging');

// Search specific model types
$results = $service->search('charging stations', [
    \App\Models\Event::class,
    \App\Models\BlogPost::class,
]);

Configuration

return [
    // Embedding provider: 'openai' or 'ollama'
    'provider' => env('RELATED_CONTENT_PROVIDER', 'openai'),

    // Provider-specific settings
    'providers' => [
        'openai' => [
            'api_key' => env('OPENAI_API_KEY'),
            'base_url' => env('OPENAI_BASE_URL', 'https://api.openai.com/v1'),
            'model' => env('OPENAI_EMBEDDING_MODEL', 'text-embedding-3-small'),
            'dimensions' => env('OPENAI_EMBEDDING_DIMENSIONS', 1536),
        ],
        'ollama' => [
            'base_url' => env('OLLAMA_BASE_URL', 'http://localhost:11434'),
            'model' => env('OLLAMA_EMBEDDING_MODEL', 'nomic-embed-text'),
            'dimensions' => env('OLLAMA_EMBEDDING_DIMENSIONS', 768),
        ],
    ],

    // Maximum related items per model
    'max_related_items' => 10,

    // Minimum similarity threshold (0-1)
    'similarity_threshold' => 0.5,

    // Queue settings
    'queue' => [
        'connection' => 'default',
        'name' => 'default',
    ],

    // Models to include in cross-model relationships
    'models' => [],

    // Database table names
    'tables' => [
        'embeddings' => 'embeddings',
        'related_content' => 'related_content',
    ],
];

Events

The package dispatches events you can listen to:

use Vlados\LaravelRelatedContent\Events\RelatedContentSynced;

class HandleRelatedContentSynced
{
    public function handle(RelatedContentSynced $event): void
    {
        // $event->model - The model that was synced
    }
}

How It Works

1. On Model Save: When a model with HasRelatedContent is saved, a job is dispatched
2. Generate Embedding: The job generates a vector embedding from the model's embeddable fields
3. Find Similar: Uses pgvector to find similar content across all configured models
4. Store Links: Stores the related content relationships in the related_content table
5. Fast Retrieval: When displaying related content, it's a simple database lookup (no API calls)

Bidirectional Relationships

Related content works in both directions automatically. When a new BlogPost is saved and finds an Event as related, the Event will also show the BlogPost in its related content - without needing to re-sync the Event.

This is achieved by querying both directions:

• Forward: where this model is the source
• Reverse: where this model is the related target

Results are deduplicated and sorted by similarity score.

Performance

• Embedding Generation: ~200-500ms per model (depends on text length and provider)
• Related Content Lookup: ~5ms (simple database query)
• Storage: ~6KB per embedding (1536 dimensions x 4 bytes)

License

MIT License. See LICENSE for more information.

Credits

• Vladislav Stoitsov