How to Create PDF File in Laravel 11

 

Building an AI-Powered Laravel + MongoDB App

A Walkthrough Using Laravel Boost

This tutorial shows how to build a modern AI-enabled app using:

• ⚡ Laravel 11

• 🍃 MongoDB

• 🤖 OpenAI API (for AI features)

• 🚀 Laravel Boost (for AI-assisted development)

We’ll build a simple AI Knowledge Base App where:

• Users submit content

• AI generates summaries

• Data is stored in MongoDB

• AI responses are cached and reusable

Step 1: Install Laravel 11

composer create-project laravel/laravel ai-mongo-app
cd ai-mongo-app

Step 2: Install MongoDB in Laravel

Laravel doesn’t support MongoDB natively, so we install the MongoDB driver:

composer require mongodb/laravel-mongodb

Update .env

DB_CONNECTION=mongodb
DB_HOST=127.0.0.1
DB_PORT=27017
DB_DATABASE=ai_app
DB_USERNAME=
DB_PASSWORD=

Update config/database.php

'default' => env('DB_CONNECTION', 'mongodb'),

Step 3: Create MongoDB Model

Unlike SQL, MongoDB uses collections instead of tables.

php artisan make:model Article -m

Update Article.php:

use MongoDB\Laravel\Eloquent\Model;

class Article extends Model
{
    protected $connection = 'mongodb';
    protected $collection = 'articles';

    protected $fillable = [
        'title',
        'content',
        'summary',
        'ai_embedding'
    ];
}

 No traditional migrations needed unless you want validation structure.

---

Step 4: Add OpenAI Integration

Install HTTP client if needed:

composer require guzzlehttp/guzzle

Create a service:

php artisan make:service OpenAIService

app/Services/OpenAIService.php

namespace App\Services;

use Illuminate\Support\Facades\Http;

class OpenAIService
{
    public function summarize($text)
    {
        $response = Http::withToken(env('OPENAI_API_KEY'))
            ->post('https://api.openai.com/v1/chat/completions', [
                'model' => 'gpt-4o-mini',
                'messages' => [
                    ['role' => 'user', 'content' => "Summarize this: ".$text]
                ],
            ]);

        return $response['choices'][0]['message']['content'] ?? null;
    }
}

Add in .env:

OPENAI_API_KEY=your_key_here

---

Step 5: Store AI Summary in MongoDB

Create controller:

php artisan make:controller ArticleController

use App\Models\Article;
use App\Services\OpenAIService;

public function store(Request $request, OpenAIService $ai)
{
    $summary = $ai->summarize($request->content);

    Article::create([
        'title' => $request->title,
        'content' => $request->content,
        'summary' => $summary,
    ]);

    return back()->with('success', 'AI summary generated!');
}

Now every article automatically gets an AI-generated summary.

Step 6: Using Laravel Boost for Faster Development

Laravel Boost helps you:

• Generate models instantly

• Create controllers with AI prompts

• Generate test cases

• Refactor code intelligently

• Create API resources quickly

Example AI Prompt inside Boost:

Generate a RESTful API for Articles with pagination, filtering, and search.

Boost will auto-generate:

• Controller

• Resource class

• API routes

• Validation rules

• Feature tests

Huge productivity gain 🚀

---

Step 7: Add AI Embeddings (Advanced Feature)

For semantic search:

1. Generate embeddings from OpenAI

2. Store vector inside MongoDB

3. Use vector search (MongoDB Atlas)

Modify OpenAI service:

public function embedding($text)
{
    $response = Http::withToken(env('OPENAI_API_KEY'))
        ->post('https://api.openai.com/v1/embeddings', [
            'model' => 'text-embedding-3-small',
            'input' => $text,
        ]);

    return $response['data'][0]['embedding'] ?? [];
}

Store in MongoDB:

'ai_embedding' => $embeddingArray

Now you can implement:

• AI-powered search

• Recommendation system

• Similar content suggestions

---

Recommended Architecture

User Input →
Controller →
AI Service →
MongoDB Store →
Vector Index →
AI Search →
Response

---

Best Practices

✔ Cache AI responses
✔ Queue AI requests (don’t block request lifecycle)
✔ Use Laravel Queues
✔ Rate-limit API calls
✔ Validate input length
✔ Log AI failures

---

Real Use Cases

• AI Blog Platform

• Smart CRM Notes

• AI Resume Analyzer

• AI SaaS Dashboard

• Knowledge Base Search

• AI-powered Chat Support

---

When to Use MongoDB Instead of MySQL?

Use MongoDB when:

✔ You need flexible schema
✔ You store AI vectors
✔ You handle JSON-heavy data
✔ Rapid prototyping
✔ Large text content

Use MySQL when:

✔ Strong relational data
✔ Complex joins
✔ Traditional business systems

---

Final Thoughts

Laravel + MongoDB + AI is powerful for:

• SaaS apps

• AI startups

• Automation tools

• Smart dashboards

Using Laravel Boost dramatically speeds up development and testing.