Build smarter with PHP — ship faster, learn deeper

Plugins for SEO tell you what needs to be fixed, but not why.

They look at readability scores, keyword density, and meta length, but they totally ignore search intent, semantic depth, and content quality.

In this tutorial, we'll use PHP and OpenAI to create an AI-powered SEO Content Quality Analyzer for WordPress.

AI will assess content in the same manner as a search engine rather than according to strict guidelines.

Real-time post analysis and actionable SEO feedback are provided by this tool, which operates within WordPress Admin.

For every post, the system will:

• Analyze the alignment of search intent
• Verify the content's completeness
• Identify generic or thin sections
• Examine the quality of semantic SEO
• Make suggestions for headings, FAQs, and enhancements
• Give an AI SEO score between 0 and 100.

This is much more advanced than conventional SEO plugins.

Requirements

• WordPress 6.x
• PHP 8.1+
• Composer (optional)
• An OpenAI API key
• Basic WordPress plugin development knowledge

Step 1: Create the WordPress Plugin

Create a new plugin folder:

    
        wp-content/plugins/ai-seo-analyzer/
    

Create ai-seo-analyzer.php

    
        /**
        * Plugin Name: AI SEO Content Quality Analyzer
        * Description: Analyzes WordPress content quality using AI and provides SEO recommendations.
        * Version: 1.0.0
        * Author: phpcmsframework.com
        */

        if (!defined('ABSPATH')) exit;
    

Activate the plugin from WP Admin → Plugins.

Step 2: Add Meta Box in Post Editor

    
        add_action('add_meta_boxes', function () {
            add_meta_box(
                'ai_seo_box',
                'AI SEO Content Analyzer',
                'ai_seo_meta_box',
                ['post', 'page'],
                'side',
                'high'
            );
        });

        function ai_seo_meta_box($post)
        {
            echo '<button class="button button-primary" id="ai-seo-analyze">Analyze Content</button>';
            echo '<div id="ai-seo-result" style="margin-top:10px;"></div>';
        }
    

Step 3: AJAX Handler (PHP Only)

    
        add_action('wp_ajax_ai_seo_analyze', 'ai_seo_analyze');

        function ai_seo_analyze()
        {
            $postId = intval($_POST['post_id']);
            $post = get_post($postId);

            if (!$post) {
                wp_send_json_error('Post not found');
            }

            $analysis = ai_seo_analyze_content($post->post_title, $post->post_content);

            wp_send_json_success($analysis);
        }
    

Step 4: AI Content Analysis Function

    
        function ai_seo_analyze_content($title, $content)
        {
            $prompt = "
        Analyze the SEO quality of the following content.

        Return JSON with:
        - seo_score (0-100)
        - intent_match (Good/Average/Poor)
        - strengths (list)
        - weaknesses (list)
        - improvement_suggestions (list)
        - suggested_headings
        - suggested_faqs

        Title:
        $title

        Content:
        " . strip_tags($content);

            $payload = [
                'model' => 'gpt-4o-mini',
                'messages' => [
                    ['role' => 'system', 'content' => 'You are an expert SEO auditor.'],
                    ['role' => 'user', 'content' => $prompt]
                ],
                'temperature' => 0.2
            ];

            $ch = curl_init('https://api.openai.com/v1/chat/completions');
            curl_setopt_array($ch, [
                CURLOPT_RETURNTRANSFER => true,
                CURLOPT_HTTPHEADER => [
                    'Content-Type: application/json',
                    'Authorization: Bearer ' . getenv('OPENAI_API_KEY')
                ],
                CURLOPT_POST => true,
                CURLOPT_POSTFIELDS => json_encode($payload)
            ]);

            $response = json_decode(curl_exec($ch), true);
            curl_close($ch);

            return json_decode($response['choices'][0]['message']['content'], true);
        }
    

Step 5: JavaScript for Admin UI

    
        add_action('admin_footer', function () {
        ?>
        <script>
        jQuery(function ($) {
            $('#ai-seo-analyze').on('click', function () {
                $('#ai-seo-result').html('Analyzing...');
                $.post(ajaxurl, {
                    action: 'ai_seo_analyze',
                    post_id: $('#post_ID').val()
                }, function (res) {
                    if (res.success) {
                        let r = res.data;
                        $('#ai-seo-result').html(`
                            <strong>SEO Score:</strong> ${r.seo_score}/100<br><br>
                            <strong>Strengths:</strong><ul>${r.strengths.map(i => `<li>${i}</li>`).join('')}</ul>
                            <strong>Weaknesses:</strong><ul>${r.weaknesses.map(i => `<li>${i}</li>`).join('')}</ul>
                        `);
                    } else {
                        $('#ai-seo-result').html('Error analyzing content.');
                    }
                });
            });
        });
        
        <?php
        });
    

How It Works (Editor View)

• Open any post or page
• Click “Analyze Content”
• AI reviews search intent, depth, structure
• You get a quality score + fixes
• Update content → re-analyze

Smart Enhancements You Can Add

• Compare against top-ranking competitor URLs
• Detect keyword stuffing vs natural language
• Analyze internal linking opportunities
• Auto-generate missing sections
• Save score history per post
• Bulk audit via WP-CLI

Security & Performance Notes

• Store API key in wp-config.php
• Limit analysis frequency
• Strip shortcodes before sending content
• Cache analysis results
• Use nonces for AJAX calls in production

Next up, we’ll build a AI Content Pruning Tool for Symfony.

In many Symfony-based CMS and blog applications, duplicate content is a silent issue. Similar articles are rewritten by editors, documentation develops naturally, and eventually you have several pages expressing the same idea in different ways.

The exact text matching used in traditional duplicate detection fails as soon as the wording is altered.

In this tutorial, we'll use OpenAI embeddings to create an AI-powered duplicate content detector in Symfony. We'll compare semantic meaning instead of matching keywords, which is the same method that contemporary search engines employ.

What We're Constructing

After completing this guide, you will have:

• AI-produced embeddings for every article
• A cosine similarity-based semantic similarity checker
• A command for the console to find duplicates
• A threshold for similarity (e.g., 85%+) to mark content
• Any Symfony CMS can be integrated with this foundation.

This is effective for:

• Blogs
• Knowledge bases
• Portals for documentation
• Pages with e-commerce content

Requirements

• Symfony 6 or 7
• PHP 8.1+
• Doctrine ORM
• MySQL / PostgreSQL
• An OpenAI API key

Step 1: Add an Embedding Column to Your Entity

Assume an Article entity.

src/Entity/Article.php

    
        #[ORM\Column(type: 'json', nullable: true)]
        private ?array $embedding = null;

        public function getEmbedding(): ?array
        {
            return $this->embedding;
        }

        public function setEmbedding(?array $embedding): self
        {
            $this->embedding = $embedding;
            return $this;
        }
    

Create and run migration:

    
        php bin/console make:migration
        php bin/console doctrine:migrations:migrate
    

Step 2: Generate Embeddings for Articles

Create a Symfony command:

    
        php bin/console make:command app:generate-article-embeddings
    

GenerateArticleEmbeddingsCommand.php

    
        namespace App\Command;

        use App\Entity\Article;
        use Doctrine\ORM\EntityManagerInterface;
        use Symfony\Component\Console\Command\Command;
        use Symfony\Component\Console\Input\InputInterface;
        use Symfony\Component\Console\Output\OutputInterface;

        class GenerateArticleEmbeddingsCommand extends Command
        {
            protected static $defaultName = 'app:generate-article-embeddings';

            public function __construct(
                private EntityManagerInterface $em,
                private string $apiKey
            ) {
                parent::__construct();
            }

            protected function execute(InputInterface $input, OutputInterface $output): int
            {
                $articles = $this->em->getRepository(Article::class)->findAll();

                foreach ($articles as $article) {
                    if ($article->getEmbedding()) {
                        continue;
                    }

                    $embedding = $this->getEmbedding(
                        strip_tags($article->getContent())
                    );

                    $article->setEmbedding($embedding);
                    $this->em->persist($article);

                    $output->writeln("Embedding generated for article ID {$article->getId()}");
                }

                $this->em->flush();
                return Command::SUCCESS;
            }

            private function getEmbedding(string $text): array
            {
                $payload = [
                    'model' => 'text-embedding-3-small',
                    'input' => mb_substr($text, 0, 4000)
                ];

                $ch = curl_init('https://api.openai.com/v1/embeddings');
                curl_setopt_array($ch, [
                    CURLOPT_RETURNTRANSFER => true,
                    CURLOPT_HTTPHEADER => [
                        "Content-Type: application/json",
                        "Authorization: Bearer {$this->apiKey}"
                    ],
                    CURLOPT_POST => true,
                    CURLOPT_POSTFIELDS => json_encode($payload)
                ]);

                $response = curl_exec($ch);
                curl_close($ch);

                return json_decode($response, true)['data'][0]['embedding'] ?? [];
            }
        }
    

Store the API key in .env.local

    
        OPENAI_API_KEY=your_key_here
    

Step 3: Cosine Similarity Helper

Create a reusable helper.

src/Service/SimilarityService.php

    
        namespace App\Service;

        class SimilarityService
        {
            public function cosine(array $a, array $b): float
            {
                $dot = 0;
                $magA = 0;
                $magB = 0;

                foreach ($a as $i => $val) {
                    $dot += $val * $b[$i];
                    $magA += $val ** 2;
                    $magB += $b[$i] ** 2;
                }

                return $dot / (sqrt($magA) * sqrt($magB));
            }
        }
    

Step 4: Detect Duplicate Articles

Create another command:

    
        php bin/console make:command app:detect-duplicates
    

DetectDuplicateContentCommand.php

    
        namespace App\Command;

        use App\Entity\Article;
        use App\Service\SimilarityService;
        use Doctrine\ORM\EntityManagerInterface;
        use Symfony\Component\Console\Command\Command;
        use Symfony\Component\Console\Input\InputInterface;
        use Symfony\Component\Console\Output\OutputInterface;

        class DetectDuplicateContentCommand extends Command
        {
            protected static $defaultName = 'app:detect-duplicates';

            public function __construct(
                private EntityManagerInterface $em,
                private SimilarityService $similarity
            ) {
                parent::__construct();
            }

            protected function execute(InputInterface $input, OutputInterface $output): int
            {
                $articles = $this->em->getRepository(Article::class)->findAll();
                $threshold = 0.85;

                foreach ($articles as $i => $a) {
                    foreach ($articles as $j => $b) {
                        if ($j <= $i) continue;
                        if (!$a->getEmbedding() || !$b->getEmbedding()) continue;

                        $score = $this->similarity->cosine(
                            $a->getEmbedding(),
                            $b->getEmbedding()
                        );

                        if ($score >= $threshold) {
                            $output->writeln(
                                sprintf(
                                    "⚠ Duplicate detected (%.2f): Article %d and %d",
                                    $score,
                                    $a->getId(),
                                    $b->getId()
                                )
                            );
                        }
                    }
                }

                return Command::SUCCESS;
            }
        }
    

Step 5: Run via Cron (Optional)

To scan regularly, add a cron job:

    
        0 2 * * * php /path/to/project/bin/console app:detect-duplicates
    

You can store results in a table or send email notifications.

Example Output

    
        Duplicate detected (0.91): Article 12 and 37
        Duplicate detected (0.88): Article 18 and 44
    

Useful Improvements

This system can be expanded with:

• Admin UI for reviewing duplicates
• Canonical page suggestions automatically
• Weighting of the title and excerpt
• Similarity detection at the section level
• Using Messenger for batch processing
• Large-scale vector databases

Cost & Performance Advice

• Create embeddings for each article only once.
• Before embedding, limit the length of the content.
• Ignore the draft content
• Cache similarity findings
• For big datasets, use queues.

Next up, we’ll build a AI SEO Content Quality Analyzer for WordPress.

In Drupal, choosing the right category for a page or article is very important. However, in real life, people can make mistakes. Editors may pick the wrong category, use different categories for similar content, or publish posts quickly without giving much thought to categorization.

AI-powered Drupal 11 can now recommend the best category for a node. Instead of depending only on keywords, this recommendation is based on the node's actual content.

You will learn how to create a custom Drupal 11 module by following this tutorial. After using OpenAI's capabilities to analyze node content, the module will intelligently suggest or assign the best taxonomy term.

This fits in perfectly with our AI + PHP CMS series, where we're adding modern AI features to improve traditional CMS workflows.

What This Module Will Do

Our AI category system will:

• Analyze node body content on save
• Compare it against existing taxonomy terms
• Recommend the most relevant category
• Automatically assign it (or display it to editors)

Use cases include:

• Blog posts
• Documentation pages
• News articles
• Knowledge bases

Prerequisites

Make sure you have:

• Drupal 11
• PHP 8.1+
• Composer
• A taxonomy vocabulary (example: categories)
• An OpenAI API key

Step 1: Create the Custom Module

Create a new folder:

    
        /modules/custom/ai_category/
    

Inside it, create the below files:

• ai_category.info.yml
• ai_category.module

ai_category.info.yml

    
        name: AI Category Recommendation
        type: module
        description: Automatically recommend and assign taxonomy categories using AI.
        core_version_requirement: ^11
        package: Custom
        version: 1.0.0
    

Step 2: Hook Into Node Save

We’ll use hook_entity_presave() to analyze content before it’s stored.

ai_category.module

    
        use Drupal\Core\Entity\EntityInterface;
        use Drupal\taxonomy\Entity\Term;

        /**
        * Implements hook_entity_presave().
        */
        function ai_category_entity_presave(EntityInterface $entity) {
            if ($entity->getEntityTypeId() !== 'node') {
                return;
            }

            // Only apply to articles (adjust as needed)
            if ($entity->bundle() !== 'article') {
                return;
            }

            $body = $entity->get('body')->value ?? '';
            if (empty($body)) {
                return;
            }

            $category = ai_category_recommend_term($body);
            if ($category) {
                $entity->set('field_category', ['target_id' => $category]);
            }
        }
    

This ensures our logic runs only for specific content types and avoids unnecessary processing.

Step 3: Ask AI for Category Recommendation

We’ll send the node content plus a list of available categories to OpenAI and ask it to pick the best one.

    
        function ai_category_recommend_term(string $text): ?int {
            $apiKey = 'YOUR_OPENAI_API_KEY';
            $endpoint = 'https://api.openai.com/v1/chat/completions';

            $terms = \Drupal::entityTypeManager()
                ->getStorage('taxonomy_term')
                ->loadTree('categories');

            $categoryNames = array_map(fn($t) => $t->name, $terms);

            $prompt = "Choose the best category from this list:\n"
                    . implode(', ', $categoryNames)
                    . "\n\nContent:\n"
                    . strip_tags($text)
                    . "\n\nReturn only the category name.";

            $payload = [
                "model" => "gpt-4o-mini",
                "messages" => [
                ["role" => "system", "content" => "You are a content classification assistant."],
                ["role" => "user", "content" => $prompt]
                ],
                "temperature" => 0
            ];

            $ch = curl_init($endpoint);
            curl_setopt_array($ch, [
                CURLOPT_RETURNTRANSFER => true,
                CURLOPT_HTTPHEADER => [
                "Content-Type: application/json",
                "Authorization: Bearer {$apiKey}"
                ],
                CURLOPT_POST => true,
                CURLOPT_POSTFIELDS => json_encode($payload),
                CURLOPT_TIMEOUT => 15
            ]);

            $response = curl_exec($ch);
            curl_close($ch);

            $data = json_decode($response, true);
            $chosen = trim($data['choices'][0]['message']['content'] ?? '');

            foreach ($terms as $term) {
                if (strcasecmp($term->name, $chosen) === 0) {
                return $term->tid;
                }
            }

            return null;
        }
    

What’s happening here:

• Drupal loads all available categories
• AI receives both content + allowed categories
• AI returns one matching category name
• Drupal maps it back to a taxonomy term ID

Step 4: Enable the Module

• Place the module in /modules/custom/ai_category
• Go to Extend → Enable module
• Enable AI Category Recommendation
• That’s it — no UI needed yet.

Step 5: Test It

• Create a new Article
• Write content related to PHP, Drupal, AI, or CMS topics
• Click Save
• The Category field is auto-filled

Example:

Article content:

        “This tutorial explains how to build a custom Drupal 11 module using PHP hooks…”
    

AI-selected category:

    
        Drupal
    

Optional Enhancements

Once the basics work, you can extend this system:

• Show AI recommendation as a suggestion, not auto-assignment
• Add admin settings (API key, confidence threshold)
• Use Queue API for bulk classification
• Switch to embeddings for higher accuracy
• Log category confidence scores
• Support multi-term assignment

Security & Performance Tips

• Never hard-code API keys (use settings.php or environment variables)
• Limit text length before sending to AI
• Cache recommendations to reduce API calls
• Add fallbacks if the AI response is invalid

Next up, we’ll build a AI Duplicate Content Detector for Symfony.

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 \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

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

Next up, we’ll build a AI Category Recommendation for Drupal 11

Writing product descriptions manually is slow, repetitive, and expensive — especially if you manage a large Magento store. But what if Magento could auto-generate SEO-friendly product descriptions using AI?

In this guide, you’ll learn how to integrate OpenAI’s API into Magento 2 to automatically generate product descriptions, short descriptions, and meta tags — directly from the Admin panel.

This is the next part of our AI + PHP CMS frameworks series.

What we are going to build

• A button in Magento 2 for the admin that says "Generate AI Description"
• An AJAX controller that sends product attributes to OpenAI
• A description, short description, and meta content made by AI
• Automatic insertion into Magento product fields
• Optional: button to regenerate to get better results

Requirements

• Magento 2.4+
• PHP 8.1+
• Composer
• An OpenAI API key
• Basic module development skills

Step 1: Create a Magento Module Skeleton

Create your module folders:

    app/code/AlbertAI/ProductDescription/

Inside it, create registration.php

    use Magento\Framework\Component\ComponentRegistrar;

    ComponentRegistrar::register(
        ComponentRegistrar::MODULE,
        'AlbertAI_ProductDescription',
        __DIR__
    );

Then create etc/module.xml

    <?xml version="1.0"?>
    <config xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
            xsi:noNamespaceSchemaLocation="urn:magento:framework:Module/etc/module.xsd">
        <module name="AlbertAI_ProductDescription" setup_version="1.0.0"/>
    </config>

Enable the module:

    php bin/magento setup:upgrade

Step 2: On the Product Edit Page, add a button that says "Generate Description."

Create a file: view/adminhtml/layout/catalog_product_edit.xml

    <?xml version="1.0"?>
    <page xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
        xsi:noNamespaceSchemaLocation="urn:magento:framework:View/Layout/etc/page_configuration.xsd">

        <body>
            <referenceBlock name="product_form">
                <block class="AlbertAI\ProductDescription\Block\Adminhtml\GenerateButton"
                    name="ai_description_button"/>
            </referenceBlock>
        </body>
    </page>

Step 3: Create the Admin Button Block

File: Block/Adminhtml/GenerateButton.php

    namespace AlbertAI\ProductDescription\Block\Adminhtml;

    use Magento\Backend\Block\Template;

    class GenerateButton extends Template
    {
        protected $_template = 'AlbertAI_ProductDescription::button.phtml';
    }

Step 4: The Button Markup

File: view/adminhtml/templates/button.phtml

    <button id="ai-generate-btn" class="action-default scalable primary">
        Generate AI Description
    </button>

    <script>
    require(['jquery'], function ($) {
        $('#ai-generate-btn').click(function () {
            const productId = $('#product_id').val();

            $.ajax({
                url: 'getUrl("ai/generator/description") ?>',
                type: 'POST',
                data: { product_id: productId },
                success: function (res) {
                    if (res.success) {
                        $('#description').val(res.description);
                        $('#short_description').val(res.short_description);
                        $('#meta_description').val(res.meta_description);
                        alert("AI description ready!");
                    } else {
                        alert("Error: " + res.error);
                    }
                }
            });
        });
    });
    </script>

Step 5: Create an Admin Route

File: etc/adminhtml/routes.xml

    <?xml version="1.0"?>
    <config xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
            xsi:noNamespaceSchemaLocation="urn:magento:framework:App/etc/routes.xsd">

        <router id="admin">
            <route id="ai" frontName="ai">
                <module name="AlbertAI_ProductDescription"/>
            </route>
        </router>
    </config>

Step 6: Build the AI Controller That Calls OpenAI

File: Controller/Adminhtml/Generator/Description.php

    namespace AlbertAI\ProductDescription\Controller\Adminhtml\Generator;

    use Magento\Backend\App\Action;
    use Magento\Catalog\Api\ProductRepositoryInterface;
    use Magento\Framework\Controller\Result\JsonFactory;

    class Description extends Action
    {
        protected $jsonFactory;
        protected $productRepo;
        private $apiKey = "YOUR_OPENAI_API_KEY";

        public function __construct(
            Action\Context $context,
            ProductRepositoryInterface $productRepo,
            JsonFactory $jsonFactory
        ) {
            parent::__construct($context);
            $this->productRepo = $productRepo;
            $this->jsonFactory = $jsonFactory;
        }

        public function execute()
        {
            $result = $this->jsonFactory->create();

            $id = $this->getRequest()->getParam('product_id');
            if (!$id) {
                return $result->setData(['success' => false, 'error' => 'Product not found']);
            }

            $product = $this->productRepo->getById($id);

            $prompt = sprintf(
                "Write an SEO-friendly product description.\nProduct Name: %s\nBrand: %s\nFeatures: %s\nOutput: Long description, short description, and meta description.",
                $product->getName(),
                $product->getAttributeText('manufacturer'),
                implode(', ', $product->getAttributes())
            );

            $generated = $this->generateText($prompt);

            return $result->setData([
                'success' => true,
                'description' => $generated['long'],
                'short_description' => $generated['short'],
                'meta_description' => $generated['meta']
            ]);
        }

        private function generateText($prompt)
        {
            $body = [
                "model" => "gpt-4.1-mini",
                "messages" => [
                    ["role" => "user", "content" => $prompt]
                ]
            ];

            $ch = curl_init("https://api.openai.com/v1/chat/completions");
            curl_setopt_array($ch, [
                CURLOPT_RETURNTRANSFER => true,
                CURLOPT_HTTPHEADER => [
                    "Content-Type: application/json",
                    "Authorization: Bearer " . $this->apiKey
                ],
                CURLOPT_POST => true,
                CURLOPT_POSTFIELDS => json_encode($body)
            ]);
            $response = json_decode(curl_exec($ch), true);
            curl_close($ch);

            $text = $response['choices'][0]['message']['content'] ?? "No response";

            // Split via sections
            return [
                'long' => $this->extract($text, 'Long'),
                'short' => $this->extract($text, 'Short'),
                'meta' => $this->extract($text, 'Meta')
            ];
        }

        private function extract($text, $type)
        {
            preg_match("/$type Description:\s*(.+)/i", $text, $m);
            return $m[1] ?? $text;
        }
    }

Step 7: Test It

• Go to Magento Admin → Catalog → Products
• Edit any product
• Click “Generate AI Description”
• Descriptions fields will auto-fill in seconds

Bonus Tips

You can extend the module to generate:

• Product titles
• Bullet points
• FAQ sections
• Meta keywords
• Category descriptions

Next up, we’ll build a AI Auto-Tagging System for Laravel Blogs Using Embeddings + Cron Jobs.

One of the strongest functions in any web application is search, however, the conventional search is restricted. It will only get an exact match of the key words and this fails to get the exact results when the user enters the query in different wordings.

What will happen when your Symfony application can comprehend the content of the query, rather than match text?

In that case, semantic search with AI can be introduced.

In this tutorial, we will create a semantic search functionality in the Symfony application with the help of the embeddings API of OpenAI and PHP. You will be able to transform text into vector embeddings, store them and compare them to be relevant, making your search results much smarter.

The present post is the continuation of our AI + PHP CMS Framework Series.

Prerequisites

Before we start, make sure you have:

• Symfony 6 or 7
• PHP 8.1+
• Composer
• A MySQL or SQLite database
• An OpenAI API key

Step 1: Create a New Symfony Command

We’ll use a console command to generate embeddings for your existing content (articles, pages, etc.).

Inside your Symfony project, run:

php bin/console make:command app:generate-embeddings

This will create a new file in src/Command/GenerateEmbeddingsCommand.php.

Replace its contents with the following:

src/Command/GenerateEmbeddingsCommand.php

namespace App\Command;

use Symfony\Component\Console\Attribute\AsCommand;
use Symfony\Component\Console\Command\Command;
use Symfony\Component\Console\Input\InputInterface;
use Symfony\Component\Console\Output\OutputInterface;
use Doctrine\ORM\EntityManagerInterface;
use App\Entity\Article;

#[AsCommand(
    name: 'app:generate-embeddings',
    description: 'Generate AI embeddings for all articles'
)]
class GenerateEmbeddingsCommand extends Command
{
    private $em;
    private $apiKey = 'YOUR_OPENAI_API_KEY';
    private $endpoint = 'https://api.openai.com/v1/embeddings';

    public function __construct(EntityManagerInterface $em)
    {
        $this->em = $em;
        parent::__construct();
    }

    protected function execute(InputInterface $input, OutputInterface $output): int
    {
        $articles = $this->em->getRepository(Article::class)->findAll();
        foreach ($articles as $article) {
            $embedding = $this->getEmbedding($article->getContent());
            if ($embedding) {
                $article->setEmbedding(json_encode($embedding));
                $this->em->persist($article);
                $output->writeln("✅ Generated embedding for article ID {$article->getId()}");
            }
        }

        $this->em->flush();
        return Command::SUCCESS;
    }

    private function getEmbedding(string $text): ?array
    {
        $payload = [
            'model' => 'text-embedding-3-small',
            'input' => $text,
        ];

        $ch = curl_init($this->endpoint);
        curl_setopt_array($ch, [
            CURLOPT_RETURNTRANSFER => true,
            CURLOPT_HTTPHEADER => [
                "Content-Type: application/json",
                "Authorization: Bearer {$this->apiKey}"
            ],
            CURLOPT_POST => true,
            CURLOPT_POSTFIELDS => json_encode($payload)
        ]);

        $response = curl_exec($ch);
        curl_close($ch);

        $data = json_decode($response, true);
        return $data['data'][0]['embedding'] ?? null;
    }
}

This command takes every article from the database, sends its content to OpenAI’s Embedding API, and saves the resulting vector in a database field.

Step 2: Update the Entity

Assume your entity is App\Entity\Article.

We’ll add a new column called embedding to store the vector data.

src/Entity/Article.php

    #[ORM\Column(type: 'text', nullable: true)]
    private ?string $embedding = null;

    public function getEmbedding(): ?string
    {
        return $this->embedding;
    }

    public function setEmbedding(?string $embedding): self
    {
        $this->embedding = $embedding;
        return $this;
    }

Then update your database:

    php bin/console make:migration
    php bin/console doctrine:migrations:migrate

Step 3: Create a Search Endpoint

We'll now include a basic controller that takes a search query, turns it into an embedding, and determines which article is the most semantically similar.

src/Controller/SearchController.php

    namespace App\Controller;

    use Symfony\Bundle\FrameworkBundle\Controller\AbstractController;
    use Symfony\Component\HttpFoundation\Request;
    use Symfony\Component\HttpFoundation\Response;
    use Symfony\Component\Routing\Annotation\Route;
    use Doctrine\ORM\EntityManagerInterface;
    use App\Entity\Article;

    class SearchController extends AbstractController
    {
        private $apiKey = 'YOUR_OPENAI_API_KEY';
        private $endpoint = 'https://api.openai.com/v1/embeddings';

        #[Route('/search', name: 'ai_search')]
        public function search(Request $request, EntityManagerInterface $em): Response
        {
            $query = $request->query->get('q');
            if (!$query) {
                return $this->json(['error' => 'Please provide a search query']);
            }

            $queryVector = $this->getEmbedding($query);
            $articles = $em->getRepository(Article::class)->findAll();

            $results = [];
            foreach ($articles as $article) {
                if ($article->getEmbedding()) {
                    $score = $this->cosineSimilarity(
                        $queryVector,
                        json_decode($article->getEmbedding(), true)
                    );
                    $results[] = [
                        'id' => $article->getId(),
                        'title' => $article->getTitle(),
                        'similarity' => $score,
                    ];
                }
            }

            usort($results, fn($a, $b) => $b['similarity'] <=> $a['similarity']);
            return $this->json(array_slice($results, 0, 5)); // top 5 results
        }

        private function getEmbedding(string $text): array
        {
            $payload = [
                'model' => 'text-embedding-3-small',
                'input' => $text,
            ];

            $ch = curl_init($this->endpoint);
            curl_setopt_array($ch, [
                CURLOPT_RETURNTRANSFER => true,
                CURLOPT_HTTPHEADER => [
                    "Content-Type: application/json",
                    "Authorization: Bearer {$this->apiKey}"
                ],
                CURLOPT_POST => true,
                CURLOPT_POSTFIELDS => json_encode($payload)
            ]);

            $response = curl_exec($ch);
            curl_close($ch);

            $data = json_decode($response, true);
            return $data['data'][0]['embedding'] ?? [];
        }

        private function cosineSimilarity(array $a, array $b): float
        {
            $dot = 0; $magA = 0; $magB = 0;
            for ($i = 0; $i < count($a); $i++) {
                $dot += $a[$i] * $b[$i];
                $magA += $a[$i] ** 2;
                $magB += $b[$i] ** 2;
            }
            return $dot / (sqrt($magA) * sqrt($magB));
        }
    }

Now, even if the articles don't contain the exact keywords, your /search?q=php framework tutorial endpoint will return those that are most semantically similar to the query.

Step 4: Try It Out

Run the below command.

php bin/console app:generate-embeddings

This generates embeddings for all articles.

Now visit the following URL.

http://your-symfony-app.local/search?q=learn symfony mvc

The top five most pertinent articles will be listed in a JSON response, arranged by meaning rather than keyword.

Real-World Applications

• A more intelligent search within a CMS or knowledge base
• AI-supported matching of FAQs
• Semantic suggestions ("you might also like..."
• Clustering of topics or duplicates in admin panels

Tips for Security and Performance

• Reuse and cache embeddings (avoid making repeated API calls for the same content).
• Keep your API key in.env.local (OPENAI_API_KEY=your_key).
• For better performance, think about using a vector database such as Pinecone, Weaviate, or Qdrant if you have thousands of records.

Next up, we’ll build a Product Description Generator in Magento — powered by AI — to automate product content creation.

The long-form content is easy to control in Drupal, which includes articles, documentation pages, blog posts, and so on. Writing summaries on each work may be time consuming.

Here we are going to use the API of OpenAI to automatically create short summaries whenever a node (content item) is saved in Drupal 11.

Step 1: Create a Custom Module

Create a new module called ai_summary.

Inside that folder, create two files:

• ai_summary.info.yml
• ai_summary.module

ai_summary.info.yml

Add the below code in the info.yml file.

   name: AI Summary
   type: module
   description: Automatically generate summaries for Drupal nodes using OpenAI API.
   core_version_requirement: ^11
   package: Custom
   version: 1.0.0

ai_summary.module

This is where the logic lives.

To run our code just before a node is saved we will use hook_entity_presave of Drupal.

use Drupal\node\Entity\Node;
use Drupal\Core\Entity\EntityInterface;

/**
* Implements hook_entity_presave().
*/
function ai_summary_entity_presave(EntityInterface $entity) {
    if ($entity->getEntityTypeId() !== 'node') {
        return;
    }

    // Only summarize articles (you can change this as needed)
    if ($entity->bundle() !== 'article') {
        return;
    }

    $body = $entity->get('body')->value ?? '';
    if (empty($body)) {
        return;
    }

    // Generate AI summary
    $summary = ai_summary_generate_summary($body);

    if ($summary) {
        // Save it in the summary field
        $entity->get('body')->summary = $summary;
    }
}

/**
* Generate summary using OpenAI API.
*/
function ai_summary_generate_summary($text) {
    $api_key = 'YOUR_OPENAI_API_KEY';
    $endpoint = 'https://api.openai.com/v1/chat/completions';

    $payload = [
        "model" => "gpt-4o-mini",
        "messages" => [
        ["role" => "system", "content" => "Summarize the following text in 2-3 sentences. Keep it concise and human-readable."],
        ["role" => "user", "content" => $text]
        ],
        "temperature" => 0.7
    ];

    $ch = curl_init($endpoint);
    curl_setopt_array($ch, [
        CURLOPT_RETURNTRANSFER => true,
        CURLOPT_HTTPHEADER => [
        "Content-Type: application/json",
        "Authorization: Bearer {$api_key}"
        ],
        CURLOPT_POST => true,
        CURLOPT_POSTFIELDS => json_encode($payload),
        CURLOPT_TIMEOUT => 15
    ]);

    $response = curl_exec($ch);
    curl_close($ch);

    $data = json_decode($response, true);
    return trim($data['choices'][0]['message']['content'] ?? '');
}

This functionality performs three primary functions:

• Identifies article saving in Drupal.
• Sends the content to OpenAI to be summarized.
• The summary is stored in the body summary field of the article.

Step 2: Enable the Module

1. Place the new module folder directly in /modules/custom/.
2. In Drupal Admin panel, go to: Extend → Install new module (or Enable module).
3. Check AI Summary and turn it on.

Step 3: Test the AI Summary

1. Select Content -> Add content -> Article.
2. Enter the long paragraph in the body field.
3. Save the article.
4. On reloading the page, open it one more time — the summary field will be already filled automatically.

Example:

Input Body:

Artificial Intelligence has been changing how developers build and deploy applications...

Generated Summary:

AI is reshaping software development by automating repetitive tasks and improving decision-making through data-driven insights.

Step 4: Extend It Further

The following are some of the ideas that can be used to improve the module:

• Add settings: Add a form to enable the user to add the API key and the select the type of model.
• Queue processing: Queue processing Use the drugndrup queue API to process the existing content in batches.
• Custom field storage: Store summaries in object now: field_ai_summary.
• Views integration: Show or hide articles in terms of length of summary or its presence.

Security & Performance Tips

• Never hardcode your API key but keep it in the configuration or in the.env file of Drupal.
• Shorten long text in order to send (OpenAI token limit = cost).
• Gracefully manage API timeouts.
• Watchdoging errors to log API.

Next, we will dive into the Symfony, and we will create an AI-Powered Semantic Search System that uses text embeddings to discover the content in a smarter way.

Artificial intelligence is changing the way we deal with user generated content. Sentiment analysis, or the automatic recognition of whether a comment or review or article is positive, negative, or neutral can be considered one of the most powerful applications.

In this tutorial, we are going to develop a basic Joomla content plug-in that is linked to the Open AI API. Each time an article or comment is saved the text will be processed by our plugin and the sentiment will be shown in Joomla where it belongs in the admin.

This can be considered an excellent representation of a merge between AI and the classic PHP CMS systems, such as Joomla.

What You’ll Need

Before we start, make sure you have:

• Joomla 5.x installed
• PHP 8.1 or newer
• cURL enabled on your server
• An OpenAI API key

Once that’s ready, let’s code.

Step 1: Creation of the Plugin

In your Joomala system, make a new folder within the system under the name of the plugin:

/plugins/content/aisentiment/

Thereupon in that folder generate two files:

• aisentiment.php
• aisentiment.xml

aisentiment.xml

This is the manifest file that the Joomla plugin identifies the identity of this particular plugin and the files that should be loaded into it.

Step 2: Add the PHP Logic

Now let’s write the plugin code.

aisentiment.php

Bonus Tips:

1. Enhanced Accessibility

Accessibility is no longer an option; it's a necessity. Drupal 10 understands this, and its commitment to making the web inclusive for all is evident through its enhanced accessibility features. The platform comes with improved semantic HTML, ARIA enhancements, and focus management, ensuring that websites built on Drupal 10 are accessible to people with disabilities. By prioritizing accessibility, you can reach a wider audience and demonstrate your commitment to providing a positive user experience for everyone.

2. API-First Approach

Drupal 10 embraces the API-first approach, a trend that's gaining momentum in the web development community. This feature allows developers to separate the frontend and backend of their websites, giving them the freedom to choose the best technology stack for each aspect. With Drupal's robust APIs, you can build headless or decoupled applications, providing seamless user experiences across various devices and platforms. This versatility is ideal for creating dynamic applications, progressive web apps (PWAs), and mobile experiences.

3. Streamlined Content Editing with Claro

Drupal 10 introduces the Claro administration theme, a significant improvement to the user interface for content editors and site administrators. Claro is designed to be visually appealing, intuitive, and user-friendly, making content management a breeze. With a modern and clean interface, content editors can focus on creating engaging content without getting bogged down by complicated backend systems. Claro streamlines the content editing process, increasing efficiency and productivity for website maintenance tasks.

4. Effortless Migration from Drupal 9

For organizations currently using Drupal 9, upgrading to Drupal 10 is a smooth and hassle-free process. The Drupal community has worked diligently to ensure backward compatibility between the versions, making it easier to migrate your existing projects seamlessly. This allows you to take advantage of Drupal 10's new features while leveraging your existing codebase and modules.

5. Improved Performance and Scalability

Performance is critical for any website, and Drupal 10 comes packed with performance improvements that enhance the overall speed and efficiency of your site. The platform has undergone codebase optimizations, caching enhancements, and database query optimizations, resulting in faster page load times and improved responsiveness. Whether you run a small blog or a large enterprise platform, Drupal 10 can handle the scale, ensuring a smooth user experience for all visitors.

6. Robust Security Features

Security is a paramount concern for any CMS, and Drupal has a long-standing reputation for its robust security architecture. Drupal 10 continues this tradition, providing a secure foundation for your web projects. Regular security updates, improved password hashing algorithms, and adherence to industry best practices make Drupal 10 a secure choice for building websites and applications.

Conclusion

Drupal 10 is a game-changer in the world of web development, offering a wide array of advantages and features that cater to the needs of modern websites and applications. With enhanced accessibility, an API-first approach, streamlined content editing, easy migration, improved performance, and robust security features, Drupal 10 empowers developers and site owners to build cutting-edge digital experiences.

Whether you're starting a new project or upgrading an existing one, Drupal 10 provides the flexibility, scalability, and security required to create exceptional websites that engage and delight users. Embrace the power of Drupal 10 and elevate your web development endeavors to new heights. Happy building!

Introduction

Welcome to the future of web development! Drupal, the powerful open-source content management system (CMS), has reached a new milestone with the release of Drupal 10. As we bid adieu to Drupal 9 and embrace the latest iteration, developers and site builders can look forward to a plethora of exciting features, enhancements, and optimizations that make building robust, secure, and scalable websites easier than ever. In this blog post, we'll delve into the highlights of Drupal 10 and explore the reasons why it's the ultimate choice for your web projects.

1. Accessibility First

Accessibility has always been a priority for Drupal, and with Drupal 10, this commitment is taken to new heights. The core development team has worked tirelessly to ensure that Drupal 10 adheres to the latest web accessibility standards, making it possible for everyone to access and interact with websites built on the platform, regardless of their physical abilities. Improved semantic HTML, ARIA enhancements, and focus management are some of the accessibility-focused improvements that will empower developers to create inclusive digital experiences effortlessly.

2. API-First Approach

Drupal has been at the forefront of embracing API-first design principles, and this trend continues with Drupal 10. The new version comes with enhanced support for headless and decoupled architectures, allowing developers to use Drupal as a content repository while building frontend experiences using the technologies of their choice, such as React, Angular, or Vue.js. This flexibility opens up a world of possibilities for creating seamless omnichannel experiences and empowers organizations to deliver content to various devices and platforms.

3. Easy Migration from Drupal 9

If you're already using Drupal 9 for your website, transitioning to Drupal 10 is a breeze. The development team has streamlined the upgrade process, making it easier and faster to move your existing projects to the latest version. Additionally, Drupal 10's backward compatibility ensures that modules and themes written for Drupal 9 will continue to function smoothly, allowing you to leverage your existing codebase and investments.

4. Enhanced Performance and Scalability

Drupal 10 brings significant performance improvements, optimizing the CMS to handle larger and more complex websites with ease. Whether you're running a small business site or a high-traffic enterprise platform, Drupal 10's enhancements in caching, database queries, and overall codebase optimization will ensure your website performs at its best, providing a seamless user experience even during peak traffic.

5. Streamlined Content Editing

Content editors and site administrators will love the enhanced content editing experience in Drupal 10. With the introduction of the Claro administration theme in Drupal 8 and further improvements in Drupal 9, Drupal 10 takes the user interface to the next level. The admin interface is now more intuitive, user-friendly, and visually appealing, reducing the learning curve and making content management a joyous task.

6. Robust Security Features

Security remains a top priority for Drupal, and Drupal 10 continues to build upon the platform's already robust security architecture. With regular security updates and features like improved password hashing algorithms, security-conscious organizations can trust Drupal 10 to safeguard their digital assets effectively.

Conclusion

Drupal 10 marks a significant milestone in the evolution of the CMS, presenting an impressive array of features that cater to the needs of modern web development. From enhanced accessibility to API-first capabilities, easy migration, improved performance, streamlined content editing, and robust security features, Drupal 10 promises to revolutionize the way websites are built and managed.

So, whether you're a developer, site builder, or organization looking to create dynamic and innovative digital experiences, Drupal 10 provides the ideal platform to bring your vision to life. Embrace the power of Drupal 10 and join the ever-growing community of web professionals shaping the future of the internet. Happy building!