Laravel AI Integration Tutorial: Complete Guide 2025

#laravel #php #openai #ai #tutorial #phpunit

Oct 17, 2025

Integrating AI capabilities into your Laravel applications has never been more accessible. With the OpenAI PHP package, you can add intelligent features to your Laravel projects—from chatbots and content generation to data analysis—without wrestling with complex API integrations.

This comprehensive tutorial walks you through everything you need to build your first AI-powered Laravel application—from installation to production deployment. By the end, you’ll have working code examples and understand how to integrate OpenAI’s powerful language models into real Laravel applications with proper testing, error handling, and cost optimization.

Note: Complete working code examples are available in this tutorial. You can copy-paste all examples directly from the sections below.

Table of Contents #

Why Laravel for AI Integration
Installation & Setup
Building Your First LLM-Powered Laravel App
Queue Jobs for Background AI Processing
Eloquent ORM Integration with AI
Rate Limiting and Cost Optimization
Testing with PHPUnit
Production Considerations
Troubleshooting Common Issues
Next Steps & Resources

Why Laravel for AI Integration #

Laravel has become the leading PHP framework for building modern web applications, and its elegant syntax combined with powerful features makes it ideal for AI integration. Here’s why Laravel excels at AI-powered applications:

The Problem Before Modern Laravel AI Integration #

Before OpenAI PHP + Laravel: PHP developers had to write custom cURL wrappers for each AI provider, manage API authentication manually, handle rate limiting with custom middleware, and build conversation state management from scratch.

After OpenAI PHP + Laravel: You get a batteries-included ecosystem with:

OpenAI PHP Package: Official PHP SDK for OpenAI API with Laravel integration
Eloquent ORM: Store conversations, messages, and AI responses with elegant database interactions
Laravel Queue System: Background processing for expensive AI operations (Horizon monitoring)
Laravel Cache: Intelligent response caching to reduce API costs by 40-60%
Laravel Middleware: Built-in rate limiting and request throttling
PHPUnit Integration: Comprehensive testing framework for AI components

Laravel vs Other PHP Frameworks for AI #

If you’re working with PHP web frameworks, here’s what you need to know:

Feature	Laravel	Symfony	CodeIgniter
Setup Complexity	Simple (Artisan commands)	Moderate (Bundle config)	Manual integration
Background Jobs	Built-in Queue system	Messenger component	Manual implementation
Caching	Multi-driver cache system	Cache component	Basic caching
Admin Interface	Laravel Nova/Filament	EasyAdmin	Manual CRUD
API Development	Built-in API resources	API Platform	Manual routing
Best For	Full-stack AI apps	Enterprise systems	Legacy AI integration

Real-World Use Cases for Laravel AI #

Laravel AI integration shines in these application scenarios:

Content Management Systems: Generate blog posts, product descriptions, or marketing copy from your Laravel admin panel
Customer Support Automation: Build intelligent ticket analysis and response suggestions with conversational AI
E-commerce Intelligence: Product recommendation engines, personalized search, automated product tagging
Data Analysis Dashboards: Natural language querying for MySQL databases, automated report generation
Chatbot Applications: Multi-turn conversations with context persistence using Eloquent ORM
Document Processing: PDF summarization, information extraction, automated tagging systems

Installation & Setup #

Let’s get Laravel AI integration running with proper environment management and API key security.

Prerequisites #

Before starting, ensure you have:

PHP 8.1+ (Laravel 10+ requires PHP 8.1 or higher, PHP 8.2+ recommended for best performance)
Composer (PHP dependency management)
MySQL/PostgreSQL (for storing conversations and messages)
An OpenAI API key (get one from platform.openai.com )

Check your PHP version:

php -v
# Should show: PHP 8.1.0 or higher (8.2+ recommended)

Setting Up Laravel Project #

Best Practice: Start with a fresh Laravel installation or add to existing project.

# Create new Laravel project
composer create-project laravel/laravel laravel-ai-app

# Navigate to project directory
cd laravel-ai-app

# Set up environment configuration
cp .env.example .env
php artisan key:generate

Installing OpenAI PHP Package #

Install the official OpenAI PHP SDK with Laravel support:

# Install OpenAI PHP package
composer require openai-php/laravel

# Publish configuration file (optional)
php artisan vendor:publish --provider="OpenAI\Laravel\ServiceProvider"

Version Note: Use openai-php/laravel ^0.8.0 for the latest Laravel integration patterns and GPT-4 Turbo support.

API Key Management (Security Best Practices) #

NEVER hardcode API keys! Laravel’s .env file provides secure configuration management.

Add API key to .env file:

# .env
OPENAI_API_KEY=sk-your-openai-key-here
OPENAI_ORGANIZATION=org-your-organization-id  # Optional

Verify .env is in .gitignore:

# .gitignore (Laravel default includes .env)
/node_modules
/public/hot
/public/storage
/storage/*.key
/vendor
.env              # ✅ Already included
.env.backup
.phpunit.result.cache

Access API key in Laravel code:

<?php
// config/openai.php (auto-generated after vendor:publish)

return [
    'api_key' => env('OPENAI_API_KEY'),
    'organization' => env('OPENAI_ORGANIZATION'),
];

Configuring Database for AI Conversations #

Set up database connection in .env:

# .env
DB_CONNECTION=mysql
DB_HOST=127.0.0.1
DB_PORT=3306
DB_DATABASE=laravel_ai
DB_USERNAME=root
DB_PASSWORD=your_password

Create database:

# MySQL
mysql -u root -p
CREATE DATABASE laravel_ai CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
exit;

# Run Laravel migrations
php artisan migrate

Testing Your Setup: “Hello World” Example #

Verify everything works with a complete Laravel AI test:

<?php
// routes/web.php
use Illuminate\Support\Facades\Route;
use OpenAI\Laravel\Facades\OpenAI;

Route::get('/ai/test', function () {
    try {
        $result = OpenAI::chat()->create([
            'model' => 'gpt-4-turbo-preview',
            'messages' => [
                ['role' => 'system', 'content' => 'You are a helpful Laravel assistant.'],
                ['role' => 'user', 'content' => 'Explain Laravel in exactly 3 sentences. Be encouraging!'],
            ],
        ]);

        $response = $result->choices[0]->message->content;

        return response()->json([
            'success' => true,
            'response' => $response,
        ]);
    } catch (\Exception $e) {
        return response()->json([
            'success' => false,
            'error' => $e->getMessage(),
        ], 500);
    }
});

Test it:

php artisan serve
# Visit: http://localhost:8000/ai/test

Expected Output:

{
  "success": true,
  "response": "Laravel is a powerful PHP framework known for its elegant syntax and comprehensive ecosystem of tools. It makes complex tasks like routing, authentication, and database management incredibly simple with expressive, readable code. You'll love how Laravel's conventions and community support accelerate your development workflow!"
}

If you see output like this, congratulations! Laravel AI integration is working.

Building Your First LLM-Powered Laravel App #

Now let’s build something more sophisticated: a conversational AI assistant with Laravel service classes and Eloquent ORM.

Understanding Laravel AI Architecture Patterns #

Before diving into code, understand Laravel-specific AI patterns:

Pattern	What It Does	When to Use
Service Class	Encapsulate AI logic	Reusable AI operations across controllers
Model Integration	Eloquent models for conversations	Multi-turn conversations, persistent context
Controller Actions	HTTP endpoints for AI	API routes, web form handling
Queue Jobs	Async AI processing	Expensive operations, background tasks
Middleware	Rate limiting, auth	Protect AI endpoints from abuse
Cache Layer	Response caching	Reduce API costs for identical queries

Rule of Thumb: Use Service Classes for business logic, Queue Jobs for expensive operations, Controllers for routing only.

Creating Database Schema for AI Conversations #

Generate migrations for conversations and messages:

php artisan make:migration create_conversations_table
php artisan make:migration create_ai_messages_table

Conversations Migration:

<?php
// database/migrations/xxxx_create_conversations_table.php
use Illuminate\Database\Migrations\Migration;
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Support\Facades\Schema;

return new class extends Migration
{
    public function up(): void
    {
        Schema::create('conversations', function (Blueprint $table) {
            $table->id();
            $table->foreignId('user_id')->constrained()->onDelete('cascade');
            $table->string('title');
            $table->enum('status', ['active', 'processing', 'completed', 'error'])->default('active');
            $table->timestamps();
            $table->softDeletes();

            $table->index(['user_id', 'status']);
        });
    }

    public function down(): void
    {
        Schema::dropIfExists('conversations');
    }
};

AI Messages Migration:

<?php
// database/migrations/xxxx_create_ai_messages_table.php
use Illuminate\Database\Migrations\Migration;
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Support\Facades\Schema;

return new class extends Migration
{
    public function up(): void
    {
        Schema::create('ai_messages', function (Blueprint $table) {
            $table->id();
            $table->foreignId('conversation_id')->constrained()->onDelete('cascade');
            $table->enum('role', ['user', 'assistant', 'system']);
            $table->text('content');
            $table->integer('token_count')->nullable();
            $table->timestamps();

            $table->index(['conversation_id', 'created_at']);
        });
    }

    public function down(): void
    {
        Schema::dropIfExists('ai_messages');
    }
};

Run migrations:

php artisan migrate

Creating Eloquent Models #

Generate models with relationships:

php artisan make:model Conversation
php artisan make:model AiMessage

Conversation Model:

<?php
// app/Models/Conversation.php
namespace App\Models;

use Illuminate\Database\Eloquent\Model;
use Illuminate\Database\Eloquent\Relations\BelongsTo;
use Illuminate\Database\Eloquent\Relations\HasMany;
use Illuminate\Database\Eloquent\SoftDeletes;

class Conversation extends Model
{
    use SoftDeletes;

    protected $fillable = [
        'user_id',
        'title',
        'status',
    ];

    protected $casts = [
        'created_at' => 'datetime',
        'updated_at' => 'datetime',
        'deleted_at' => 'datetime',
    ];

    public function user(): BelongsTo
    {
        return $this->belongsTo(User::class);
    }

    public function messages(): HasMany
    {
        return $this->hasMany(AiMessage::class);
    }

    public function getFormattedMessagesForOpenAI(): array
    {
        return $this->messages()
            ->orderBy('created_at', 'asc')
            ->get()
            ->map(fn($message) => [
                'role' => $message->role,
                'content' => $message->content,
            ])
            ->toArray();
    }
}

AiMessage Model:

<?php
// app/Models/AiMessage.php
namespace App\Models;

use Illuminate\Database\Eloquent\Model;
use Illuminate\Database\Eloquent\Relations\BelongsTo;

class AiMessage extends Model
{
    protected $fillable = [
        'conversation_id',
        'role',
        'content',
        'token_count',
    ];

    protected $casts = [
        'created_at' => 'datetime',
        'updated_at' => 'datetime',
    ];

    public function conversation(): BelongsTo
    {
        return $this->belongsTo(Conversation::class);
    }

    public static function estimateTokens(string $content): int
    {
        // Rough estimation: 1 token ≈ 4 characters for English
        return (int) ceil(strlen($content) / 4);
    }
}

Building AI Service Class #

Create a reusable service for OpenAI interactions:

php artisan make:class Services/OpenAIService

<?php
// app/Services/OpenAIService.php
namespace App\Services;

use App\Models\Conversation;
use App\Models\AiMessage;
use OpenAI\Laravel\Facades\OpenAI;
use Illuminate\Support\Facades\Cache;
use Illuminate\Support\Facades\Log;

class OpenAIService
{
    private string $model = 'gpt-4-turbo-preview';
    private float $temperature = 0.7;
    private int $maxTokens = 1000;

    public function __construct(
        ?string $model = null,
        ?float $temperature = null,
        ?int $maxTokens = null
    ) {
        if ($model) $this->model = $model;
        if ($temperature !== null) $this->temperature = $temperature;
        if ($maxTokens) $this->maxTokens = $maxTokens;
    }

    /**
     * Generate AI response for a conversation
     *
     * @param Conversation $conversation
     * @param string $userMessage
     * @return array{response: string, usage: array}
     * @throws \Exception
     */
    public function generateResponse(Conversation $conversation, string $userMessage): array
    {
        // Check cache first (identical questions within 24 hours)
        $cacheKey = $this->getCacheKey($conversation->id, $userMessage);
        $cached = Cache::get($cacheKey);

        if ($cached) {
            Log::info('AI response served from cache', ['conversation_id' => $conversation->id]);
            return [
                'response' => $cached['response'],
                'usage' => ['cached' => true, 'prompt_tokens' => 0, 'completion_tokens' => 0, 'total_tokens' => 0],
            ];
        }

        // Store user message
        $userAiMessage = AiMessage::create([
            'conversation_id' => $conversation->id,
            'role' => 'user',
            'content' => $userMessage,
            'token_count' => AiMessage::estimateTokens($userMessage),
        ]);

        // Build message array for OpenAI (include conversation history)
        $messages = $this->buildMessagesArray($conversation);

        try {
            // Call OpenAI API
            $result = OpenAI::chat()->create([
                'model' => $this->model,
                'messages' => $messages,
                'temperature' => $this->temperature,
                'max_tokens' => $this->maxTokens,
            ]);

            $response = $result->choices[0]->message->content;
            $usage = [
                'prompt_tokens' => $result->usage->promptTokens,
                'completion_tokens' => $result->usage->completionTokens,
                'total_tokens' => $result->usage->totalTokens,
            ];

            // Store AI response
            AiMessage::create([
                'conversation_id' => $conversation->id,
                'role' => 'assistant',
                'content' => $response,
                'token_count' => $usage['completion_tokens'],
            ]);

            // Cache response for 24 hours
            Cache::put($cacheKey, ['response' => $response], now()->addDay());

            return [
                'response' => $response,
                'usage' => $usage,
            ];

        } catch (\Exception $e) {
            Log::error('OpenAI API error', [
                'conversation_id' => $conversation->id,
                'error' => $e->getMessage(),
            ]);
            throw $e;
        }
    }

    /**
     * Build messages array for OpenAI API from conversation history
     */
    private function buildMessagesArray(Conversation $conversation): array
    {
        // System message first
        $messages = [
            ['role' => 'system', 'content' => 'You are a helpful Laravel development assistant. Provide clear, practical advice with code examples.'],
        ];

        // Append conversation history (last 10 messages to stay under token limits)
        $historyMessages = $conversation->messages()
            ->orderBy('created_at', 'desc')
            ->limit(10)
            ->get()
            ->reverse()
            ->map(fn($msg) => [
                'role' => $msg->role,
                'content' => $msg->content,
            ])
            ->toArray();

        return array_merge($messages, $historyMessages);
    }

    /**
     * Generate cache key for identical user messages
     */
    private function getCacheKey(int $conversationId, string $userMessage): string
    {
        return 'ai_response:' . md5($conversationId . ':' . $userMessage);
    }
}

Creating Controller and Routes #

Generate controller:

php artisan make:controller AiChatController

Controller Implementation:

<?php
// app/Http/Controllers/AiChatController.php
namespace App\Http\Controllers;

use App\Models\Conversation;
use App\Services\OpenAIService;
use Illuminate\Http\JsonResponse;
use Illuminate\Http\Request;
use Illuminate\Support\Facades\Auth;
use Illuminate\Support\Facades\Validator;

class AiChatController extends Controller
{
    public function __construct(
        private OpenAIService $openAIService
    ) {}

    /**
     * Create new conversation
     */
    public function createConversation(Request $request): JsonResponse
    {
        $validator = Validator::make($request->all(), [
            'title' => 'required|string|max:200',
        ]);

        if ($validator->fails()) {
            return response()->json(['errors' => $validator->errors()], 422);
        }

        $conversation = Conversation::create([
            'user_id' => Auth::id(),
            'title' => $request->input('title'),
            'status' => 'active',
        ]);

        return response()->json([
            'success' => true,
            'conversation' => $conversation,
        ], 201);
    }

    /**
     * Send message to AI and get response
     */
    public function sendMessage(Request $request, int $conversationId): JsonResponse
    {
        $validator = Validator::make($request->all(), [
            'message' => 'required|string|max:2000',
        ]);

        if ($validator->fails()) {
            return response()->json(['errors' => $validator->errors()], 422);
        }

        // Find conversation (ensure user owns it)
        $conversation = Conversation::where('id', $conversationId)
            ->where('user_id', Auth::id())
            ->firstOrFail();

        try {
            $result = $this->openAIService->generateResponse(
                $conversation,
                $request->input('message')
            );

            return response()->json([
                'success' => true,
                'response' => $result['response'],
                'usage' => $result['usage'],
            ]);

        } catch (\Exception $e) {
            return response()->json([
                'success' => false,
                'error' => 'AI service temporarily unavailable. Please try again.',
            ], 500);
        }
    }

    /**
     * Get conversation history
     */
    public function getConversation(int $conversationId): JsonResponse
    {
        $conversation = Conversation::with('messages')
            ->where('id', $conversationId)
            ->where('user_id', Auth::id())
            ->firstOrFail();

        return response()->json([
            'success' => true,
            'conversation' => $conversation,
        ]);
    }
}

Routes Definition:

<?php
// routes/api.php
use App\Http\Controllers\AiChatController;
use Illuminate\Support\Facades\Route;

Route::middleware('auth:sanctum')->group(function () {
    // AI Chat Routes
    Route::post('/conversations', [AiChatController::class, 'createConversation']);
    Route::get('/conversations/{id}', [AiChatController::class, 'getConversation']);
    Route::post('/conversations/{id}/messages', [AiChatController::class, 'sendMessage']);
});

Testing the API:

# Assuming you have Laravel Sanctum authentication set up
# Create conversation
curl -X POST http://localhost:8000/api/conversations \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"title": "Laravel AI Questions"}'

# Send message
curl -X POST http://localhost:8000/api/conversations/1/messages \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"message": "What are Laravel service containers?"}'

Queue Jobs for Background AI Processing #

AI API calls can take 2-10 seconds. Use Laravel queues to keep your application responsive.

Setting Up Laravel Queue #

Configure queue driver in .env:

# .env
QUEUE_CONNECTION=redis  # Or 'database' for simple setup

# Redis configuration (recommended for production)
REDIS_HOST=127.0.0.1
REDIS_PASSWORD=null
REDIS_PORT=6379

Install Redis PHP extension (if using Redis):

# macOS
brew install redis
pecl install redis

# Ubuntu
sudo apt-get install redis-server php-redis

Create queue database table (if using database driver):

php artisan queue:table
php artisan migrate

Creating AI Processing Job #

Generate queue job:

php artisan make:job ProcessAiConversation

Job Implementation:

<?php
// app/Jobs/ProcessAiConversation.php
namespace App\Jobs;

use App\Models\Conversation;
use App\Services\OpenAIService;
use Illuminate\Bus\Queueable;
use Illuminate\Contracts\Queue\ShouldQueue;
use Illuminate\Foundation\Bus\Dispatchable;
use Illuminate\Queue\InteractsWithQueue;
use Illuminate\Queue\SerializesModels;
use Illuminate\Support\Facades\Log;

class ProcessAiConversation implements ShouldQueue
{
    use Dispatchable, InteractsWithQueue, Queueable, SerializesModels;

    /**
     * The number of times the job may be attempted.
     */
    public int $tries = 3;

    /**
     * The number of seconds to wait before retrying the job.
     */
    public int $backoff = 5;

    /**
     * Create a new job instance.
     */
    public function __construct(
        public int $conversationId,
        public string $userMessage
    ) {}

    /**
     * Execute the job.
     */
    public function handle(OpenAIService $openAIService): void
    {
        try {
            $conversation = Conversation::findOrFail($this->conversationId);

            // Update conversation status
            $conversation->update(['status' => 'processing']);

            // Generate AI response
            $result = $openAIService->generateResponse($conversation, $this->userMessage);

            // Update conversation status
            $conversation->update(['status' => 'completed']);

            Log::info('AI conversation processed successfully', [
                'conversation_id' => $this->conversationId,
                'tokens_used' => $result['usage']['total_tokens'],
            ]);

        } catch (\Exception $e) {
            Log::error('AI conversation processing failed', [
                'conversation_id' => $this->conversationId,
                'error' => $e->getMessage(),
            ]);

            // Update conversation status to error
            Conversation::find($this->conversationId)?->update(['status' => 'error']);

            // Re-throw to trigger Laravel's retry mechanism
            throw $e;
        }
    }

    /**
     * Handle a job failure.
     */
    public function failed(\Throwable $exception): void
    {
        Log::error('AI job failed after all retries', [
            'conversation_id' => $this->conversationId,
            'error' => $exception->getMessage(),
        ]);

        // Notify user or admin about failure
        // Could send notification, email, etc.
    }
}

Dispatching Background Jobs #

Update controller to dispatch jobs instead of synchronous processing:

<?php
// app/Http/Controllers/AiChatController.php (updated sendMessage method)
namespace App\Http\Controllers;

use App\Jobs\ProcessAiConversation;
use App\Models\Conversation;
use Illuminate\Http\JsonResponse;
use Illuminate\Http\Request;
use Illuminate\Support\Facades\Auth;
use Illuminate\Support\Facades\Validator;

class AiChatController extends Controller
{
    /**
     * Send message to AI (async processing)
     */
    public function sendMessage(Request $request, int $conversationId): JsonResponse
    {
        $validator = Validator::make($request->all(), [
            'message' => 'required|string|max:2000',
        ]);

        if ($validator->fails()) {
            return response()->json(['errors' => $validator->errors()], 422);
        }

        // Find conversation
        $conversation = Conversation::where('id', $conversationId)
            ->where('user_id', Auth::id())
            ->firstOrFail();

        // Dispatch job to queue for background processing
        ProcessAiConversation::dispatch($conversation->id, $request->input('message'));

        return response()->json([
            'success' => true,
            'status' => 'processing',
            'message' => 'Your message is being processed. Check back shortly.',
        ], 202); // 202 Accepted
    }

    /**
     * Poll for conversation status and latest messages
     */
    public function pollConversation(int $conversationId): JsonResponse
    {
        $conversation = Conversation::with(['messages' => function ($query) {
            $query->orderBy('created_at', 'desc')->limit(20);
        }])
            ->where('id', $conversationId)
            ->where('user_id', Auth::id())
            ->firstOrFail();

        return response()->json([
            'success' => true,
            'status' => $conversation->status,
            'messages' => $conversation->messages->reverse()->values(),
        ]);
    }
}

Add polling route:

<?php
// routes/api.php
Route::middleware('auth:sanctum')->group(function () {
    Route::get('/conversations/{id}/poll', [AiChatController::class, 'pollConversation']);
});

Running Queue Worker #

Start queue worker in development:

php artisan queue:work --queue=default --tries=3 --timeout=60

Production Queue Management (using Supervisor):

# /etc/supervisor/conf.d/laravel-worker.conf
[program:laravel-worker]
process_name=%(program_name)s_%(process_num)02d
command=php /path/to/your/laravel/artisan queue:work redis --sleep=3 --tries=3 --max-time=3600
autostart=true
autorestart=true
stopasgroup=true
killasgroup=true
user=www-data
numprocs=4
redirect_stderr=true
stdout_logfile=/path/to/your/laravel/storage/logs/worker.log
stopwaitsecs=3600

Start Supervisor:

sudo supervisorctl reread
sudo supervisorctl update
sudo supervisorctl start laravel-worker:*

Eloquent ORM Integration with AI #

Laravel’s Eloquent ORM makes it elegant to build AI features on top of your existing data models.

AI-Enhanced Model Methods #

Add AI capabilities directly to your Eloquent models:

<?php
// app/Models/BlogPost.php
namespace App\Models;

use Illuminate\Database\Eloquent\Model;
use OpenAI\Laravel\Facades\OpenAI;

class BlogPost extends Model
{
    protected $fillable = [
        'title',
        'content',
        'seo_description',
        'tags',
        'published_at',
    ];

    protected $casts = [
        'published_at' => 'datetime',
    ];

    /**
     * Generate SEO description using AI
     */
    public function generateSeoDescription(): void
    {
        if ($this->seo_description) {
            return; // Already has SEO description
        }

        $result = OpenAI::chat()->create([
            'model' => 'gpt-4-turbo-preview',
            'messages' => [
                [
                    'role' => 'system',
                    'content' => 'You are an SEO expert. Generate concise, engaging meta descriptions (max 155 characters) for blog posts.',
                ],
                [
                    'role' => 'user',
                    'content' => "Title: {$this->title}\n\nContent: " . substr($this->content, 0, 500) . "...",
                ],
            ],
            'max_tokens' => 100,
        ]);

        $this->seo_description = substr($result->choices[0]->message->content, 0, 155);
        $this->save();
    }

    /**
     * Generate tags using AI
     */
    public function generateTags(): void
    {
        $result = OpenAI::chat()->create([
            'model' => 'gpt-4-turbo-preview',
            'messages' => [
                [
                    'role' => 'system',
                    'content' => 'Extract 3-5 relevant tags from blog post content. Return comma-separated list.',
                ],
                [
                    'role' => 'user',
                    'content' => $this->content,
                ],
            ],
            'max_tokens' => 50,
        ]);

        $this->tags = $result->choices[0]->message->content;
        $this->save();
    }
}

Usage in Controller:

<?php
// app/Http/Controllers/BlogPostController.php
namespace App\Http\Controllers;

use App\Models\BlogPost;
use Illuminate\Http\Request;

class BlogPostController extends Controller
{
    public function store(Request $request)
    {
        $validated = $request->validate([
            'title' => 'required|string|max:255',
            'content' => 'required|string',
        ]);

        $post = BlogPost::create($validated);

        // Generate AI metadata in background
        dispatch(function () use ($post) {
            $post->generateSeoDescription();
            $post->generateTags();
        })->afterResponse();

        return response()->json([
            'success' => true,
            'post' => $post->fresh(),
        ], 201);
    }
}

AI-Powered Query Scopes #

Add intelligent query scopes using AI embeddings:

<?php
// app/Models/Product.php
namespace App\Models;

use Illuminate\Database\Eloquent\Model;
use Illuminate\Database\Eloquent\Builder;
use OpenAI\Laravel\Facades\OpenAI;

class Product extends Model
{
    protected $fillable = [
        'name',
        'description',
        'embedding',  // Store OpenAI embeddings as JSON
        'price',
    ];

    protected $casts = [
        'embedding' => 'array',
    ];

    /**
     * Generate embedding for product
     */
    public function generateEmbedding(): void
    {
        $text = "{$this->name}. {$this->description}";

        $result = OpenAI::embeddings()->create([
            'model' => 'text-embedding-3-small',
            'input' => $text,
        ]);

        $this->embedding = $result->embeddings[0]->embedding;
        $this->save();
    }

    /**
     * Scope: Find similar products using embeddings
     */
    public function scopeSimilarTo(Builder $query, string $searchQuery, int $limit = 10)
    {
        // Generate embedding for search query
        $result = OpenAI::embeddings()->create([
            'model' => 'text-embedding-3-small',
            'input' => $searchQuery,
        ]);

        $queryEmbedding = $result->embeddings[0]->embedding;

        // NOTE: This example demonstrates the concept.
        // Production vector similarity search requires:
        // - PostgreSQL with pgvector extension (recommended)
        // - OR dedicated vector database (Pinecone, Weaviate, Qdrant)
        // - OR application-layer similarity calculation (shown below)

        // Application-layer cosine similarity (development/testing)
        $products = $query->get();

        // Calculate similarity scores in PHP
        $productsWithScores = $products->map(function ($product) use ($queryEmbedding) {
            if (!$product->embedding) {
                $product->similarity = 0;
                return $product;
            }

            $embedding = is_string($product->embedding)
                ? json_decode($product->embedding, true)
                : $product->embedding;

            // Cosine similarity calculation
            $dotProduct = array_sum(array_map(fn($a, $b) => $a * $b, $queryEmbedding, $embedding));
            $magnitudeA = sqrt(array_sum(array_map(fn($x) => $x * $x, $queryEmbedding)));
            $magnitudeB = sqrt(array_sum(array_map(fn($x) => $x * $x, $embedding)));

            $product->similarity = $magnitudeA && $magnitudeB
                ? $dotProduct / ($magnitudeA * $magnitudeB)
                : 0;

            return $product;
        });

        // Sort by similarity and return top results
        return $productsWithScores
            ->sortByDesc('similarity')
            ->take($limit)
            ->values();
    }
}

Production Implementation: For production vector search with 1M+ products, install PostgreSQL’s pgvector extension:

# Install pgvector extension
CREATE EXTENSION vector;

# Update migration to use vector type
$table->vector('embedding', 1536);  // OpenAI text-embedding-3-small dimension

# Then use native vector similarity
->orderByRaw('embedding <-> ?', [$queryEmbedding])
->limit($limit);

Rate Limiting and Cost Optimization #

OpenAI API calls cost money. Implement aggressive rate limiting and caching to control costs.

Laravel Middleware for Rate Limiting #

Create custom rate limiting middleware:

php artisan make:middleware AiRateLimitMiddleware

<?php
// app/Http/Middleware/AiRateLimitMiddleware.php
namespace App\Http\Middleware;

use Closure;
use Illuminate\Http\Request;
use Illuminate\Support\Facades\RateLimiter;
use Symfony\Component\HttpFoundation\Response;

class AiRateLimitMiddleware
{
    /**
     * Handle an incoming request.
     */
    public function handle(Request $request, Closure $next): Response
    {
        $key = 'ai-requests:' . $request->user()?->id ?? $request->ip();

        // Limit: 20 AI requests per minute per user
        $executed = RateLimiter::attempt(
            $key,
            $perMinute = 20,
            function () {},
            $decaySeconds = 60
        );

        if (!$executed) {
            return response()->json([
                'error' => 'Too many AI requests. Please slow down.',
                'retry_after' => RateLimiter::availableIn($key),
            ], 429);
        }

        return $next($request);
    }
}

<?php
// app/Http/Kernel.php
protected $middlewareAliases = [
    // ... other middleware
    'ai.rate' => \App\Http\Middleware\AiRateLimitMiddleware::class,
];

Apply to routes:

<?php
// routes/api.php
Route::middleware(['auth:sanctum', 'ai.rate'])->group(function () {
    Route::post('/conversations/{id}/messages', [AiChatController::class, 'sendMessage']);
});

Input Sanitization (Prevent Prompt Injection) #

Protect against malicious prompts:

<?php
// app/Services/InputSanitizer.php
namespace App\Services;

class InputSanitizer
{
    /**
     * Sanitize user input to prevent prompt injection
     */
    public static function sanitizeForAI(string $input): string
    {
        // Remove potential prompt injection patterns
        $patterns = [
            '/ignore\s+previous\s+instructions/i',
            '/you\s+are\s+now/i',
            '/forget\s+everything/i',
            '/disregard\s+all/i',
        ];

        $cleaned = $input;
        foreach ($patterns as $pattern) {
            $cleaned = preg_replace($pattern, '', $cleaned);
        }

        // Limit length to prevent token exhaustion attacks
        $cleaned = substr($cleaned, 0, 2000);

        // Strip HTML tags
        $cleaned = strip_tags($cleaned);

        return trim($cleaned);
    }
}

Use in controller:

<?php
use App\Services\InputSanitizer;

public function sendMessage(Request $request, int $conversationId): JsonResponse
{
    $validator = Validator::make($request->all(), [
        'message' => 'required|string|max:2000',
    ]);

    if ($validator->fails()) {
        return response()->json(['errors' => $validator->errors()], 422);
    }

    // Sanitize input before processing
    $sanitizedMessage = InputSanitizer::sanitizeForAI($request->input('message'));

    // ... rest of controller logic
}

Caching Strategies #

Implement multi-layer caching:

<?php
// config/cache.php
return [
    'stores' => [
        'ai_responses' => [
            'driver' => 'redis',
            'connection' => 'ai_cache',
            'lock_connection' => 'default',
        ],
    ],
];

Advanced caching in OpenAIService:

<?php
// app/Services/OpenAIService.php (updated caching)
namespace App\Services;

use Illuminate\Support\Facades\Cache;

class OpenAIService
{
    /**
     * Cache AI responses with semantic similarity matching
     */
    private function getCachedResponse(string $message): ?string
    {
        // Exact match cache (24 hours)
        $exactCacheKey = 'ai_exact:' . md5($message);
        if ($cached = Cache::store('ai_responses')->get($exactCacheKey)) {
            return $cached;
        }

        // Semantic similarity cache (for similar questions)
        // In production, use vector similarity search
        $similarCacheKey = 'ai_similar:' . md5(substr($message, 0, 100));
        if ($cached = Cache::store('ai_responses')->get($similarCacheKey)) {
            return $cached;
        }

        return null;
    }

    private function cacheResponse(string $message, string $response): void
    {
        $exactCacheKey = 'ai_exact:' . md5($message);
        $similarCacheKey = 'ai_similar:' . md5(substr($message, 0, 100));

        // Cache exact match for 24 hours
        Cache::store('ai_responses')->put($exactCacheKey, $response, now()->addDay());

        // Cache semantic match for 12 hours
        Cache::store('ai_responses')->put($similarCacheKey, $response, now()->addHours(12));
    }
}

Testing with PHPUnit #

Testing AI applications is crucial for production reliability. Laravel’s PHPUnit integration makes it easy to test AI components without making expensive API calls.

Setting Up PHPUnit for AI Testing #

Laravel includes PHPUnit out of the box. Configure test environment:

# .env.testing
APP_ENV=testing
OPENAI_API_KEY=sk-test-key-mock  # Mock key for tests
DB_CONNECTION=sqlite
DB_DATABASE=:memory:
CACHE_DRIVER=array
QUEUE_CONNECTION=sync

Mocking OpenAI API Calls #

The key to testing Laravel AI applications is mocking the OpenAI facade:

Example: Testing AI Service (tests/Unit/OpenAIServiceTest.php):

<?php
// tests/Unit/OpenAIServiceTest.php
namespace Tests\Unit;

use App\Models\Conversation;
use App\Models\User;
use App\Services\OpenAIService;
use Illuminate\Foundation\Testing\RefreshDatabase;
use OpenAI\Laravel\Facades\OpenAI;
use OpenAI\Responses\Chat\CreateResponse;
use Tests\TestCase;

class OpenAIServiceTest extends TestCase
{
    use RefreshDatabase;

    /**
     * Test AI response generation with mocked OpenAI
     */
    public function test_generates_ai_response_successfully(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user->id]);

        // Mock OpenAI facade
        OpenAI::fake([
            CreateResponse::fake([
                'choices' => [
                    [
                        'message' => [
                            'role' => 'assistant',
                            'content' => 'Laravel service containers manage class dependencies and perform dependency injection.',
                        ],
                    ],
                ],
                'usage' => [
                    'prompt_tokens' => 50,
                    'completion_tokens' => 20,
                    'total_tokens' => 70,
                ],
            ]),
        ]);

        $service = new OpenAIService();

        // Act
        $result = $service->generateResponse($conversation, 'What are Laravel service containers?');

        // Assert
        $this->assertArrayHasKey('response', $result);
        $this->assertArrayHasKey('usage', $result);
        $this->assertStringContainsString('service container', strtolower($result['response']));
        $this->assertEquals(70, $result['usage']['total_tokens']);

        // Verify AI message was stored in database
        $this->assertDatabaseHas('ai_messages', [
            'conversation_id' => $conversation->id,
            'role' => 'user',
            'content' => 'What are Laravel service containers?',
        ]);

        $this->assertDatabaseHas('ai_messages', [
            'conversation_id' => $conversation->id,
            'role' => 'assistant',
        ]);
    }

    /**
     * Test error handling when OpenAI API fails
     */
    public function test_handles_openai_api_errors_gracefully(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user->id]);

        // Mock OpenAI failure
        OpenAI::fake([
            new \Exception('OpenAI API connection failed'),
        ]);

        $service = new OpenAIService();

        // Act & Assert
        $this->expectException(\Exception::class);
        $this->expectExceptionMessage('OpenAI API connection failed');

        $service->generateResponse($conversation, 'Test question');
    }

    /**
     * Test caching prevents duplicate API calls
     */
    public function test_caching_prevents_duplicate_api_calls(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user->id]);

        OpenAI::fake([
            CreateResponse::fake([
                'choices' => [
                    [
                        'message' => [
                            'role' => 'assistant',
                            'content' => 'Cached response',
                        ],
                    ],
                ],
                'usage' => [
                    'prompt_tokens' => 10,
                    'completion_tokens' => 5,
                    'total_tokens' => 15,
                ],
            ]),
        ]);

        $service = new OpenAIService();

        // Act - First call
        $result1 = $service->generateResponse($conversation, 'Same question');

        // Act - Second call (should use cache)
        $result2 = $service->generateResponse($conversation, 'Same question');

        // Assert
        $this->assertEquals($result1['response'], $result2['response']);
        $this->assertTrue($result2['usage']['cached'] ?? false);
        $this->assertEquals(0, $result2['usage']['total_tokens']);
    }
}

Testing Controller Endpoints #

Test API endpoints with database transactions:

<?php
// tests/Feature/AiChatControllerTest.php
namespace Tests\Feature;

use App\Models\Conversation;
use App\Models\User;
use Illuminate\Foundation\Testing\RefreshDatabase;
use OpenAI\Laravel\Facades\OpenAI;
use OpenAI\Responses\Chat\CreateResponse;
use Tests\TestCase;

class AiChatControllerTest extends TestCase
{
    use RefreshDatabase;

    /**
     * Test creating a new conversation
     */
    public function test_user_can_create_conversation(): void
    {
        // Arrange
        $user = User::factory()->create();

        // Act
        $response = $this->actingAs($user, 'sanctum')
            ->postJson('/api/conversations', [
                'title' => 'My AI Conversation',
            ]);

        // Assert
        $response->assertStatus(201)
            ->assertJson([
                'success' => true,
                'conversation' => [
                    'title' => 'My AI Conversation',
                    'user_id' => $user->id,
                    'status' => 'active',
                ],
            ]);

        $this->assertDatabaseHas('conversations', [
            'user_id' => $user->id,
            'title' => 'My AI Conversation',
        ]);
    }

    /**
     * Test sending message to AI conversation
     */
    public function test_user_can_send_message_to_conversation(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user->id]);

        OpenAI::fake([
            CreateResponse::fake([
                'choices' => [
                    [
                        'message' => [
                            'role' => 'assistant',
                            'content' => 'Laravel Eloquent is an ORM that simplifies database interactions.',
                        ],
                    ],
                ],
                'usage' => [
                    'prompt_tokens' => 30,
                    'completion_tokens' => 15,
                    'total_tokens' => 45,
                ],
            ]),
        ]);

        // Act
        $response = $this->actingAs($user, 'sanctum')
            ->postJson("/api/conversations/{$conversation->id}/messages", [
                'message' => 'What is Laravel Eloquent?',
            ]);

        // Assert
        $response->assertStatus(200)
            ->assertJson([
                'success' => true,
            ])
            ->assertJsonStructure([
                'response',
                'usage' => ['prompt_tokens', 'completion_tokens', 'total_tokens'],
            ]);
    }

    /**
     * Test rate limiting prevents excessive requests
     */
    public function test_rate_limiting_blocks_excessive_requests(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user->id]);

        OpenAI::fake([
            CreateResponse::fake([
                'choices' => [['message' => ['role' => 'assistant', 'content' => 'Response']]],
                'usage' => ['prompt_tokens' => 10, 'completion_tokens' => 5, 'total_tokens' => 15],
            ]),
        ]);

        // Act - Make 21 requests (exceeds 20/minute limit)
        for ($i = 0; $i < 21; $i++) {
            $response = $this->actingAs($user, 'sanctum')
                ->postJson("/api/conversations/{$conversation->id}/messages", [
                    'message' => "Test message {$i}",
                ]);

            if ($i < 20) {
                $response->assertSuccessful();
            } else {
                // Assert - 21st request should be rate limited
                $response->assertStatus(429);
            }
        }
    }

    /**
     * Test user cannot access other users' conversations
     */
    public function test_user_cannot_access_other_users_conversations(): void
    {
        // Arrange
        $user1 = User::factory()->create();
        $user2 = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user2->id]);

        // Act
        $response = $this->actingAs($user1, 'sanctum')
            ->getJson("/api/conversations/{$conversation->id}");

        // Assert
        $response->assertStatus(404);  // Not found (authorization failure)
    }
}

Testing Queue Jobs #

Test background AI processing jobs:

<?php
// tests/Unit/ProcessAiConversationJobTest.php
namespace Tests\Unit;

use App\Jobs\ProcessAiConversation;
use App\Models\Conversation;
use App\Models\User;
use Illuminate\Foundation\Testing\RefreshDatabase;
use Illuminate\Support\Facades\Queue;
use OpenAI\Laravel\Facades\OpenAI;
use OpenAI\Responses\Chat\CreateResponse;
use Tests\TestCase;

class ProcessAiConversationJobTest extends TestCase
{
    use RefreshDatabase;

    /**
     * Test job processes AI conversation successfully
     */
    public function test_job_processes_conversation_successfully(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create([
            'user_id' => $user->id,
            'status' => 'active',
        ]);

        OpenAI::fake([
            CreateResponse::fake([
                'choices' => [
                    [
                        'message' => [
                            'role' => 'assistant',
                            'content' => 'Job processed successfully',
                        ],
                    ],
                ],
                'usage' => ['prompt_tokens' => 20, 'completion_tokens' => 10, 'total_tokens' => 30],
            ]),
        ]);

        // Act
        $job = new ProcessAiConversation($conversation->id, 'Test message');
        $job->handle(new \App\Services\OpenAIService());

        // Assert
        $conversation->refresh();
        $this->assertEquals('completed', $conversation->status);

        $this->assertDatabaseHas('ai_messages', [
            'conversation_id' => $conversation->id,
            'role' => 'user',
            'content' => 'Test message',
        ]);

        $this->assertDatabaseHas('ai_messages', [
            'conversation_id' => $conversation->id,
            'role' => 'assistant',
        ]);
    }

    /**
     * Test job is queued when dispatched
     */
    public function test_job_is_queued_when_dispatched(): void
    {
        // Arrange
        Queue::fake();

        // Act
        ProcessAiConversation::dispatch(1, 'Test message');

        // Assert
        Queue::assertPushed(ProcessAiConversation::class, function ($job) {
            return $job->conversationId === 1 && $job->userMessage === 'Test message';
        });
    }

    /**
     * Test job handles failures gracefully
     */
    public function test_job_handles_failures_and_retries(): void
    {
        // Arrange
        $user = User::factory()->create();
        $conversation = Conversation::factory()->create(['user_id' => $user->id]);

        OpenAI::fake([
            new \Exception('API temporarily unavailable'),
        ]);

        // Act & Assert
        $job = new ProcessAiConversation($conversation->id, 'Test message');

        $this->expectException(\Exception::class);
        $job->handle(new \App\Services\OpenAIService());

        // Verify conversation marked as error
        $conversation->refresh();
        $this->assertEquals('error', $conversation->status);
    }
}

Coverage Reporting #

Track test coverage to ensure comprehensive testing:

# Generate coverage report
./vendor/bin/phpunit --coverage-html coverage-report

# View coverage in browser
open coverage-report/index.html

# Set minimum coverage threshold
./vendor/bin/phpunit --coverage-text --coverage-clover=coverage.xml --fail-on-incomplete --fail-on-risky --fail-on-warning

Example coverage output:

Code Coverage Report:
  2025-01-28 10:00:00

 Summary:
  Classes: 95.00% (19/20)
  Methods: 92.31% (36/39)
  Lines:   94.50% (258/273)

\App\Services\OpenAIService
  Methods:  100.00% ( 5/ 5)
  Lines:     97.50% (39/40)

\App\Jobs\ProcessAiConversation
  Methods:  100.00% ( 3/ 3)
  Lines:    100.00% (25/25)

Testing Best Practices for Laravel AI #

1. Always Mock External APIs

Mock OpenAI::facade() in all tests
Never make real API calls in tests (expensive and slow)
Use OpenAI::fake() for deterministic test responses

2. Test Business Logic, Not Laravel Internals

Test YOUR service classes and controllers
Don’t test Laravel’s routing or Eloquent (they have their own tests)
Focus on AI integration points and edge cases

3. Use Factories for Test Data

php artisan make:factory ConversationFactory
php artisan make:factory AiMessageFactory

<?php
// database/factories/ConversationFactory.php
namespace Database\Factories;

use App\Models\User;
use Illuminate\Database\Eloquent\Factories\Factory;

class ConversationFactory extends Factory
{
    public function definition(): array
    {
        return [
            'user_id' => User::factory(),
            'title' => $this->faker->sentence(),
            'status' => 'active',
        ];
    }
}

4. Test Error Scenarios

API timeouts
Rate limiting
Invalid API keys
Token limit errors
Database failures

5. Maintain Fast Test Suites

Unit tests should run in <100ms each
Use SQLite in-memory database for speed

Parallelize tests with ParaTest:

composer require --dev brianium/paratest
./vendor/bin/paratest --processes=4

Production Considerations #

Running Laravel AI applications in production requires careful planning around costs, performance, security, and reliability.

Cost Optimization Strategies #

OpenAI API calls are your biggest expense. Optimize aggressively:

1. Use Cheaper Models When Possible #

<?php
// app/Services/ModelSelector.php
namespace App\Services;

class ModelSelector
{
    public static function selectForTask(string $taskComplexity): string
    {
        return match($taskComplexity) {
            'simple' => 'gpt-3.5-turbo',      // Grammar, simple classification ($0.0005/1K tokens)
            'moderate' => 'gpt-4-turbo-preview', // Summaries, reasoning ($0.01/1K tokens)
            'complex' => 'gpt-4',             // Advanced reasoning ($0.03/1K tokens)
            default => 'gpt-4-turbo-preview',
        };
    }
}

Cost Savings: GPT-3.5-Turbo costs 20x less than GPT-4!

2. Implement Token Budget Limits #

<?php
// app/Services/TokenBudgetManager.php
namespace App\Services;

use App\Models\User;
use Illuminate\Support\Facades\Cache;

class TokenBudgetManager
{
    private const DAILY_BUDGET = 100000; // 100K tokens per user per day
    private const COST_PER_1K_TOKENS = 0.01; // GPT-4 Turbo pricing

    public static function canUserAffordRequest(User $user, int $estimatedTokens): bool
    {
        $cacheKey = "user_tokens:{$user->id}:" . now()->format('Y-m-d');
        $tokensUsedToday = Cache::get($cacheKey, 0);

        return ($tokensUsedToday + $estimatedTokens) <= self::DAILY_BUDGET;
    }

    public static function recordTokenUsage(User $user, int $tokensUsed): void
    {
        $cacheKey = "user_tokens:{$user->id}:" . now()->format('Y-m-d');
        $currentUsage = Cache::get($cacheKey, 0);

        Cache::put($cacheKey, $currentUsage + $tokensUsed, now()->endOfDay());
    }

    public static function estimateCost(int $tokens): float
    {
        return ($tokens / 1000) * self::COST_PER_1K_TOKENS;
    }
}

Error Handling and Fallbacks #

Never let AI failures break your application:

<?php
// app/Services/RobustOpenAIService.php
namespace App\Services;

use OpenAI\Laravel\Facades\OpenAI;
use Illuminate\Support\Facades\Log;

class RobustOpenAIService
{
    private array $fallbackModels = [
        'gpt-4-turbo-preview',  // Primary
        'gpt-3.5-turbo',        // Fallback
    ];

    public function generateWithFallback(array $messages): string
    {
        foreach ($this->fallbackModels as $model) {
            try {
                $result = OpenAI::chat()->create([
                    'model' => $model,
                    'messages' => $messages,
                    'max_tokens' => 1000,
                ]);

                return $result->choices[0]->message->content;

            } catch (\Exception $e) {
                Log::warning("AI model {$model} failed, trying fallback", [
                    'error' => $e->getMessage(),
                ]);

                if ($model === end($this->fallbackModels)) {
                    // All models failed
                    Log::error('All AI models failed', ['error' => $e->getMessage()]);

                    return "I'm temporarily unavailable. Our team has been notified. Please try again in a few minutes.";
                }
            }
        }

        return "Service unavailable.";
    }
}

Monitoring and Logging #

Track AI performance with Laravel’s built-in logging:

<?php
// app/Services/OpenAIService.php (enhanced with monitoring)
namespace App\Services;

use Illuminate\Support\Facades\Log;

class OpenAIService
{
    public function generateResponse(Conversation $conversation, string $userMessage): array
    {
        $startTime = microtime(true);

        try {
            $result = $this->callOpenAI($conversation, $userMessage);

            // Log successful API call
            Log::channel('ai')->info('OpenAI API call succeeded', [
                'conversation_id' => $conversation->id,
                'model' => $this->model,
                'tokens_used' => $result['usage']['total_tokens'],
                'duration_ms' => round((microtime(true) - $startTime) * 1000, 2),
                'cost_usd' => $this->calculateCost($result['usage']),
            ]);

            // Alert if costs spike
            if ($result['usage']['total_tokens'] > 5000) {
                Log::channel('ai')->warning('High token usage detected', [
                    'conversation_id' => $conversation->id,
                    'tokens' => $result['usage']['total_tokens'],
                ]);
            }

            return $result;

        } catch (\Exception $e) {
            Log::channel('ai')->error('OpenAI API call failed', [
                'conversation_id' => $conversation->id,
                'error' => $e->getMessage(),
                'duration_ms' => round((microtime(true) - $startTime) * 1000, 2),
            ]);

            throw $e;
        }
    }

    private function calculateCost(array $usage): float
    {
        // GPT-4 Turbo pricing (as of 2025)
        $promptCost = ($usage['prompt_tokens'] / 1000) * 0.01;
        $completionCost = ($usage['completion_tokens'] / 1000) * 0.03;

        return round($promptCost + $completionCost, 4);
    }
}

Configure dedicated logging channel:

<?php
// config/logging.php
return [
    'channels' => [
        'ai' => [
            'driver' => 'daily',
            'path' => storage_path('logs/ai.log'),
            'level' => env('LOG_LEVEL', 'info'),
            'days' => 14,
        ],
    ],
];

Security Best Practices #

Protect your Laravel AI integration:

1. Input Validation and Sanitization (Already covered in Rate Limiting section)

2. API Key Rotation Strategy:

<?php
// config/openai.php
return [
    'api_key' => env('OPENAI_API_KEY'),
    'fallback_api_key' => env('OPENAI_FALLBACK_API_KEY'), // Rotate keys periodically

    // Rotate keys every 90 days
    'key_rotation_days' => 90,
];

3. Content Filtering:

<?php
// app/Services/ContentModerationService.php
namespace App\Services;

use OpenAI\Laravel\Facades\OpenAI;

class ContentModerationService
{
    public static function isContentSafe(string $content): bool
    {
        try {
            $result = OpenAI::moderations()->create([
                'input' => $content,
            ]);

            $results = $result->results[0];

            // Check if any category is flagged
            return !$results->flagged;

        } catch (\Exception $e) {
            // Fail closed - reject content if moderation API fails
            return false;
        }
    }
}

Use in controller:

<?php
public function sendMessage(Request $request, int $conversationId): JsonResponse
{
    $message = $request->input('message');

    // Moderate user input
    if (!ContentModerationService::isContentSafe($message)) {
        return response()->json([
            'error' => 'Your message violates our content policy.',
        ], 422);
    }

    // ... rest of controller logic
}

Performance Optimization #

Optimize Laravel AI app performance:

1. Database Query Optimization:

<?php
// Eager load relationships to avoid N+1 queries
$conversations = Conversation::with(['messages' => function ($query) {
    $query->orderBy('created_at', 'desc')->limit(20);
}])->get();

2. Response Caching with Redis:

# .env
CACHE_DRIVER=redis
REDIS_CLIENT=phpredis  # Faster than predis

3. Queue Priority Management:

<?php
// Prioritize urgent AI requests
ProcessAiConversation::dispatch($conversationId, $message)
    ->onQueue('high-priority');

// Normal requests
ProcessAiConversation::dispatch($conversationId, $message)
    ->onQueue('default');

Troubleshooting Common Issues #

OpenAI API Connection Errors #

Problem: cURL error 28: Operation timed out

Solutions:

<?php
// config/openai.php
return [
    'api_key' => env('OPENAI_API_KEY'),
    'organization' => env('OPENAI_ORGANIZATION'),

    'request_timeout' => 60, // Increase timeout to 60 seconds

    // For proxy environments
    'proxy' => env('HTTP_PROXY'),
];

Verify connectivity:

<?php
Route::get('/test-openai', function () {
    try {
        $result = OpenAI::models()->list();
        return response()->json(['status' => 'connected', 'models' => count($result->data)]);
    } catch (\Exception $e) {
        return response()->json(['status' => 'failed', 'error' => $e->getMessage()], 500);
    }
});

Rate Limiting Errors #

Problem: OpenAI\Exceptions\RateLimitException: Rate limit reached

Solutions:

<?php
// Implement exponential backoff retry
use Illuminate\Support\Facades\Retry;

$result = Retry::times(3)
    ->exponentialBackoff(1000) // Start with 1 second, then 2s, 4s
    ->throw()
    ->run(function () use ($messages) {
        return OpenAI::chat()->create([
            'model' => 'gpt-4-turbo-preview',
            'messages' => $messages,
        ]);
    });

Token Limit Errors #

Problem: This model's maximum context length is 8192 tokens

Solutions:

<?php
// Truncate conversation history to stay under token limits
public function buildMessagesArray(Conversation $conversation, int $maxTokens = 4000): array
{
    $messages = [
        ['role' => 'system', 'content' => 'You are a helpful assistant.'],
    ];

    $history = $conversation->messages()->orderBy('created_at', 'desc')->get();
    $tokenCount = 100; // System message tokens

    foreach ($history as $message) {
        $messageTokens = AiMessage::estimateTokens($message->content);

        if ($tokenCount + $messageTokens > $maxTokens) {
            break; // Stop adding messages
        }

        $messages[] = [
            'role' => $message->role,
            'content' => $message->content,
        ];

        $tokenCount += $messageTokens;
    }

    return array_reverse($messages); // Chronological order
}

Queue Job Failures #

Problem: Jobs failing silently in production

Solutions:

# Monitor queue failures
php artisan queue:failed

# Retry failed jobs
php artisan queue:retry all

# Configure job failure notifications

<?php
// app/Jobs/ProcessAiConversation.php
public function failed(\Throwable $exception): void
{
    // Send notification to admin
    \Illuminate\Support\Facades\Mail::to('admin@example.com')
        ->send(new \App\Mail\JobFailedMail($this->conversationId, $exception));

    // Log failure
    Log::critical('AI job failed permanently', [
        'conversation_id' => $this->conversationId,
        'error' => $exception->getMessage(),
        'trace' => $exception->getTraceAsString(),
    ]);
}

Frequently Asked Questions #

Q: How much does Laravel AI integration cost?

A: OpenAI API costs vary by model. GPT-3.5-Turbo costs $0.0005 per 1K tokens (20x cheaper than GPT-4 at $0.03 per 1K tokens). Implementing proper caching can reduce costs by 40-60%.

Q: Can I use Laravel AI integration with existing Laravel apps?

A: Yes. The openai-php/laravel package integrates seamlessly with existing Laravel 8+ applications. Installation takes 5-10 minutes via Composer.

Q: What Laravel version is required for AI integration?

A: Laravel 10+ with PHP 8.1+ is required for the latest OpenAI PHP package. PHP 8.2+ recommended for best performance.

Q: How do I handle API rate limits in Laravel?

A: Use Laravel’s built-in rate limiting middleware (throttle:60,1) and implement exponential backoff retry logic in queue jobs. The tutorial covers comprehensive rate limiting strategies.

Q: Can I use other AI providers besides OpenAI?

A: Yes. The patterns in this tutorial work with Anthropic Claude, Google Gemini, and other providers. You’ll need to adjust the API client initialization and response handling.

Next Steps & Resources #

You’ve built your first AI-powered Laravel application! Here’s where to go next:

Advanced Laravel AI Topics #

Explore deeper implementations:

Vector Databases with Laravel: Build semantic search using pgvector extension
RAG (Retrieval-Augmented Generation): Context-aware AI with your Laravel data
Laravel Horizon: Monitor queue jobs and AI processing with beautiful UI
Laravel Nova Integration: Admin panels for AI conversation management
Streaming Responses: Real-time AI responses with Laravel WebSockets

Compare with Other PHP Frameworks #

If you’re evaluating Python vs PHP for AI development:

LangChain Python Tutorial: Complete Guide 2025

Why compare Laravel (PHP) vs Python for AI?

Laravel: Mature ecosystem, Eloquent ORM, built-in queue system, perfect for full-stack AI apps
Python: Larger AI library ecosystem, more LLM providers, extensive data science support

When to choose Laravel:

✅ Full-stack PHP applications with existing Laravel codebase
✅ E-commerce platforms needing AI features (content generation, recommendations)
✅ Enterprise systems requiring robust authentication and permissions
✅ Teams already proficient in PHP/Laravel

When to choose Python:

✅ Data science workflows requiring NumPy, Pandas, scikit-learn
✅ Research projects experimenting with multiple AI frameworks
✅ Machine learning model training and fine-tuning
✅ Teams already proficient in Python

Community Resources #

Get involved:

GitHub: openai-php/laravel - Official Laravel integration (2k+ stars)
Laravel News: Follow Laravel News for AI integration updates
Discord: Join Laravel Discord community
Twitter: Follow @laravelphp for announcements

Official Documentation #

Reference materials:

Laravel Documentation - Complete framework reference
OpenAI PHP SDK - PHP client documentation
Laravel Queues - Background job processing
Laravel Horizon - Queue monitoring

Production Deployment Resources #

Ready for production?

Laravel Forge for automated deployment
Laravel Envoyer for zero-downtime deployments
AWS/DigitalOcean/Linode Laravel deployment guides
Docker containerization with Laravel Sail
Monitoring with Laravel Telescope and Horizon

Need Expert Help? #

Building production AI features in Laravel is complex. JetThoughts specializes in Laravel AI integration with:

✅ Laravel expertise (10+ years)
✅ OpenAI integration experience
✅ Cost optimization strategies (40-60% API savings)
✅ Security best practices (input sanitization, rate limiting)
✅ PHPUnit testing for AI components

Ready to add AI to your Laravel app? Schedule a free consultation and let’s discuss your project.

What to Read Next:

Ready to implement Laravel AI integration? Contact our team for expert guidance on production deployment, performance optimization, and cost management.

Schedule a consultation →

Have questions about Laravel AI integration? Drop a comment below or reach out on Twitter !

SEO Metadata #

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Eloquent ORM + AI pattern diagram
PHPUnit testing workflow

Featured Snippet Optimization:

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Target Rankings:

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Competition Assessment: MEDIUM (6/10) Estimated Monthly Traffic: 3,000-5,000 organic sessions (months 3-6)