It is tedious, inconsistent, and frequently incorrect to manually tag blog posts. With AI embeddings, Laravel can automatically determine the topic of a blog post and assign the appropriate tags without the need for human intervention.
This guide demonstrates how to create a complete Laravel AI auto-tagging system using:
- The OpenAI Embeddings API
- Laravel Jobs & Queues
- Cron Scheduler
- Tag → Vector Matching
- Automatic Tag Assignment
This is one of the most useful AI enhancements for any Laravel-based CMS or blog system.
What We’re Constructing
You’ll construct:
- Table of Tag Vector – The meaning of each tag (such as “PHP”, “Laravel”, “Security”, and “AI”) will be represented by an embedding vector created by AI.
- A Generator for Post Embedding – We generate an embedding for the post content whenever a new post is saved.
- A Matching Algorithm – The system determines which post embeddings are closest by comparing them with tag embeddings.
- A Cron Job -The system automatically assigns AI-recommended tags every hour (or on any schedule).
This is ideal for:
- Custom blogs made with Laravel
- Headless CMS configurations
- Tagging categories in e-commerce
- Auto-classification of knowledge bases
- Websites for documentation
Now let’s get started.
Step 1: Create Migration for Tag Embeddings
Run:
php artisan make:migration create_tag_embeddings_table
Migration:
public function up()
{
Schema::create('tag_embeddings', function (Blueprint $table) {
$table->id();
$table->unsignedBigInteger('tag_id')->unique();
$table->json('embedding'); // store vector
$table->timestamps();
});
}
Run:
php artisan migrate
Step 2: Generate Embeddings for Tags
Create a command:
php artisan make:command GenerateTagEmbeddings
Add logic:
`public function handle() { $tags = Tag::all(); foreach ($tags as $tag) { $vector = $this->embed($tag->name); TagEmbedding::updateOrCreate( ['tag_id' => $tag->id], ['embedding' => json_encode($vector)] ); $this->info("Embedding created for tag: {$tag->name}"); } } private function embed($text) { $client = new GuzzleHttpClient(); $response = $client->post("https://api.openai.com/v1/embeddings", [ "headers" => [ "Authorization" => "Bearer " . env('OPENAI_API_KEY'), "Content-Type" => "application/json", ], "json" => [ "model" => "text-embedding-3-large", "input" => $text ] ]); $data = json_decode($response->getBody(), true); return $data['data'][0]['embedding'] ?? []; }`
Run once:
php artisan generate:tag-embeddings
Now all tags have AI meaning vectors.
Step 3: Save Embeddings for Each Post
Add to your Post model observer or event.
`$post->embedding = $this->embed($post->content); $post->save();`
Migration for posts:
`$table->json('embedding')->nullable();`
Step 4: Matching Algorithm (Post → Tags)
Create a helper class:
`class EmbeddingHelper { public static function cosineSimilarity($a, $b) { $dot = array_sum(array_map(fn($i, $j) => $i * $j, $a, $b)); $magnitudeA = sqrt(array_sum(array_map(fn($i) => $i * $i, $a))); $magnitudeB = sqrt(array_sum(array_map(fn($i) => $i * $i, $b))); return $dot / ($magnitudeA * $magnitudeB); } }`
Step 5: Assign Tags Automatically (Queue Job)
Create job:
php artisan make:job AutoTagPost
Job logic:
`public function handle() { $postEmbedding = json_decode($this->post->embedding, true); $tags = TagEmbedding::with('tag')->get(); $scores = []; foreach ($tags as $te) { $sim = EmbeddingHelper::cosineSimilarity( $postEmbedding, json_decode($te->embedding, true) ); $scores[$te->tag->id] = $sim; } arsort($scores); // highest similarity first $best = array_slice($scores, 0, 5, true); // top 5 matches $this->post->tags()->sync(array_keys($best)); }`
Step 6: Cron Job to Process New Posts
Add to app/Console/Kernel.php:
`protected function schedule(Schedule $schedule) { $schedule->command('ai:autotag-posts')->hourly(); }`
Create command:
php artisan make:command AutoTagPosts
Command logic:
`public function handle() { $posts = Post::whereNull('tags_assigned_at')->get(); foreach ($posts as $post) { AutoTagPost::dispatch($post); $post->update(['tags_assigned_at' => now()]); } }`
Now, every hour, Laravel processes all new posts and assigns AI-selected tags.
Step 7: Test the Full Flow
- Create tags in admin
- Run: php artisan generate:tag-embeddings
- Create a new blog post
- Cron or queue runs
- Post automatically gets AI-selected tags
Useful enhancements
- Weight tags by frequency
- Use title + excerpt, not full content
- Add confidence scores to DB
- Auto-create new tags using AI
- Add a manual override UI
- Cache embeddings for performance
- Batch process 1,000+ posts