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 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). 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. Table of Tag Vector A Generator for Post Embedding - We generate an embedding for the post content whenever a new post is saved. A Generator for Post Embedding A Matching Algorithm - The system determines which post embeddings are closest by comparing them with tag embeddings. A Matching Algorithm A Cron Job -The system automatically assigns AI-recommended tags every hour (or on any schedule). A Cron Job 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 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 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();
        });
    } 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 php artisan migrate Step 2: Generate Embeddings for Tags Create a command: php artisan make:command GenerateTagEmbeddings 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 \GuzzleHttp\Client();              $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'] ?? [];         }` `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 \GuzzleHttp\Client();              $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 $data['data'][0]['embedding'] ?? [];         }` Run once: php artisan generate:tag-embeddings php artisan generate:tag-embeddings Now all tags have AI meaning vectors. Step 3: Save Embeddings for Each Post Add to your Post model observer or event. `$post->embedding = $this->embed($post->content);         $post->save();` `$post->embedding = $this->embed($post->content);         $post->save();` Migration for posts: `$table->json('embedding')->nullable();` `$table->json('embedding')->nullable();` Step 4: Matching Algorithm (Post → Tags) Create a helper class: `class EmbeddingHelper         {             public static function cosineSimilarity($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 * $magnitudeB);             }         }` `class EmbeddingHelper         {             public static function cosineSimilarity($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 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));         }` `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: app/Console/Kernel.php app/Console/Kernel.php `protected function schedule(Schedule $schedule)         {             $schedule->command('ai:autotag-posts')->hourly();         }` `protected function schedule(Schedule $schedule)         {             $schedule->command('ai:autotag-posts')->hourly();         }` Create command: php artisan make:command AutoTagPosts 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()]);             }         }` `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 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 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

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something better.

Automating Content Tagging in Laravel Using OpenAI Embeddings and Cron Jobs

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

How to Build a Smart Documentation - Based on OpenAI Embeddings (Chunking, Indexing, and Searching)

How to Use RAG with LLMs: Vector Search

On Choosing the Right AI Embedding Platform: A Developer's Guide

Machine Learning Frameworks for PHP Developers

Medical Image Retrieval Needs a New Benchmark

Building a CBIR Benchmark with TotalSegmentator and FAISS

How to Build a Smart Documentation - Based on OpenAI Embeddings (Chunking, Indexing, and Searching)

How to Use RAG with LLMs: Vector Search

On Choosing the Right AI Embedding Platform: A Developer's Guide

Machine Learning Frameworks for PHP Developers

Medical Image Retrieval Needs a New Benchmark

Building a CBIR Benchmark with TotalSegmentator and FAISS

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps