📺 Video Tutorials

Why LaravelAI?

Building AI features in Laravel normally means separate SDKs, different formats, and custom error handling for every provider. LaravelAI eliminates all of that.

// Same code. Any provider. Just change the name.
$response = AI::provider('ollama')->chat($messages);    // Self-hosted, free
$response = AI::provider('openai')->chat($messages);    // ChatGPT
$response = AI::provider('anthropic')->chat($messages); // Claude
$response = AI::provider('deepseek')->chat($messages);  // DeepSeek

Built on Laravel's driver pattern — same architecture as Mail, Cache, and Queue.

📦 Installation

Step 1: Install via Composer

composer require easybdit/laraveleasyai

Step 2: Publish config and assets

php artisan vendor:publish --tag=ai-config
php artisan vendor:publish --tag=ai-chat-assets

Step 3: Run migrations

php artisan migrate

Step 4: Add to .env

AI_PROVIDER=ollama
AI_OLLAMA_URL=http://127.0.0.1:11434
AI_OLLAMA_MODEL=qwen2:1.5b

Step 5: Visit /ai-chat in your browser ✅

Requirements

🚀 Quick Start

use EasyAI\LaravelAI\Facades\AI;

$response = AI::chat([['role' => 'user', 'content' => 'What is Laravel?']]);
echo $response->content;

One-Liner Helper

$answer = ai('What is Laravel?');

Test in Tinker

php artisan tinker
>>> AI::provider('ollama')->health()
=> true
>>> ai('Say hello in 3 words')
=> "Hello there, friend!"

💬 Built-in Chat UI

New in v1.3.0 — A full ChatGPT-like chat app included. Zero setup required.

What you get out of the box

Customize the view

php artisan vendor:publish --tag=ai-chat-views
# → resources/views/vendor/laravelai/chat.blade.php

Routes registered automatically

🗂️ Projects & Knowledge Bases

New in v1.4.0 — Self-hosted Claude-like Projects. Create knowledge bases, upload documents, and get RAG-powered answers scoped per project.

How it works

Create Project → Upload Files → Chat Inside Project → RAG answers from your docs

1. Click ＋ next to Projects in the sidebar
2. Upload .txt, .md, or .pdf files — auto-ingested into RAG on upload
3. Click the project to start a new RAG-powered chat session
4. Every message retrieves relevant context from that project's documents only
5. Normal chats outside projects are completely unaffected

What you see in the UI

• 📁 Projects section in sidebar with file count badge
• 🧠 RAG ON badge in chat header when inside a project session
• 📎 Manage Files button — upload, view ingestion status, delete files
• 🟢 Status per file: pending → ingested → failed
• Project context active indicator in the input footer

PDF support (optional)

composer require smalot/pdfparser

RAG Scoping API

$results = AI::rag()->source('project_5')->search('your query');
$answer  = AI::rag()->source('project_5')->ask('your question');
AI::rag()->flush('project_5');

🧠 RAG (Built-in)

No external vector database required — uses your existing SQL database.

Setup

ollama pull nomic-embed-text
php artisan migrate

AI_RAG_PROVIDER=ollama
AI_RAG_EMBED_MODEL=nomic-embed-text

Usage

// Store
AI::rag()->ingest('Laravel is a PHP framework using MVC.', 'docs');

// Ask
$answer = AI::rag()->ask('What is Laravel?');

// Search
$results = AI::rag()->search('MVC pattern');
// [['content' => '...', 'source' => 'docs', 'score' => 0.91]]

// Scoped
$results = AI::rag()->source('project_5')->search('your query');

// Flush
AI::rag()->flush();
AI::rag()->flush('project_5');

Artisan

php artisan ai:rag:ingest storage/docs/manual.txt --source=manual
php artisan ai:rag:ingest storage/docs/ --flush

RAG Configuration

🤖 Providers

Ollama — Self-Hosted & Free

AI_PROVIDER=ollama
AI_OLLAMA_URL=http://127.0.0.1:11434
AI_OLLAMA_MODEL=qwen2:1.5b
AI_OLLAMA_TIMEOUT=120

Note for small models (qwen2, qwen2.5): If you get 400 errors with RAG context, set num_ctx to match your model's context window:

ollama show qwen2:1.5b --modelfile > /tmp/modelfile
echo "PARAMETER num_ctx 2048" >> /tmp/modelfile
ollama create qwen2-fixed -f /tmp/modelfile

Then use AI_OLLAMA_MODEL=qwen2-fixed in .env.

OpenAI (ChatGPT)

AI_OPENAI_KEY=sk-your-api-key
AI_OPENAI_MODEL=gpt-4o-mini

Anthropic (Claude)

AI_ANTHROPIC_KEY=sk-ant-your-api-key
AI_ANTHROPIC_MODEL=claude-sonnet-4-20250514

DeepSeek

AI_DEEPSEEK_KEY=sk-your-api-key
AI_DEEPSEEK_MODEL=deepseek-chat

✨ Features

Fluent Builder API

$response = AI::provider('ollama')
    ->model('qwen2:1.5b')
    ->temperature(0.9)
    ->maxTokens(500)
    ->systemPrompt('You are a helpful Laravel expert.')
    ->chat([['role' => 'user', 'content' => 'Explain middleware']]);

Streaming

AI::provider('ollama')->stream(
    [['role' => 'user', 'content' => 'Write a poem']],
    function (string $chunk) { echo $chunk; }
);

Health Check + Fallback

foreach (['ollama', 'deepseek', 'openai'] as $provider) {
    try {
        if (!AI::provider($provider)->health()) continue;
        return AI::provider($provider)->chat($messages)->content;
    } catch (\Throwable $e) {
        Log::warning("{$provider} failed: {$e->getMessage()}");
    }
}

Token Estimation

$tokens = AI::estimateTokens('Hello world');
$tokens = AI::estimateTokens($messagesArray);

Ollama Advanced Features

AI::provider('ollama')->format('json')->chat($messages);
AI::provider('ollama')->embed('Hello world');
AI::provider('ollama')->keepAlive('10m')->chat($messages);
AI::provider('ollama')->options(['num_ctx' => 2048])->chat($messages);
AI::provider('ollama')->pullModel('llama3.1:8b');
AI::provider('ollama')->runningModels();
AI::provider('ollama')->deleteModel('old-model');

Error Handling

use EasyAI\LaravelAI\Exceptions\ConnectionException;
use EasyAI\LaravelAI\Exceptions\ProviderException;

try {
    $response = AI::provider('openai')->chat($messages);
} catch (ConnectionException $e) {
    Log::error("Connection failed: " . $e->getMessage());
} catch (ProviderException $e) {
    Log::error("Provider [{$e->getProvider()}]: " . $e->getMessage());
}

📖 API Reference

Facade Methods

Provider Methods (Chainable)

RAG Methods

Ollama-Only Methods

AIResponse Object

Helper Function

ai('Your question')
ai('Your question', 'openai')
ai('Your question', 'anthropic', 'claude-haiku-...')

⚙️ Configuration

// config/ai.php
return [
    'default' => env('AI_PROVIDER', 'ollama'),
    'providers' => [
        'ollama'    => ['driver' => 'ollama',    'url'     => env('AI_OLLAMA_URL'),    'model' => env('AI_OLLAMA_MODEL', 'qwen2:1.5b'),      'timeout' => env('AI_OLLAMA_TIMEOUT', 120)],
        'openai'    => ['driver' => 'openai',    'api_key' => env('AI_OPENAI_KEY'),    'model' => env('AI_OPENAI_MODEL', 'gpt-4o-mini'),      'timeout' => 60],
        'anthropic' => ['driver' => 'anthropic', 'api_key' => env('AI_ANTHROPIC_KEY'), 'model' => env('AI_ANTHROPIC_MODEL'),                  'timeout' => 60],
        'deepseek'  => ['driver' => 'deepseek',  'api_key' => env('AI_DEEPSEEK_KEY'),  'model' => env('AI_DEEPSEEK_MODEL', 'deepseek-chat'),  'timeout' => 60],
    ],
    'rag' => [
        'embed_provider' => env('AI_RAG_PROVIDER', 'ollama'),
        'embed_model'    => env('AI_RAG_EMBED_MODEL', 'nomic-embed-text'),
        'chat_provider'  => env('AI_RAG_CHAT_PROVIDER', null),
        'chunk_size'     => (int) env('AI_RAG_CHUNK_SIZE', 2000),
        'top_k'          => (int) env('AI_RAG_TOP_K', 3),
        'table'          => env('AI_RAG_TABLE', 'ai_documents'),
        'system_prompt'  => env('AI_RAG_SYSTEM_PROMPT', 'Answer using ONLY the context below. If unsure, say so.'),
    ],
];

Complete .env Reference

# Provider
AI_PROVIDER=ollama

# Ollama (self-hosted, free)
AI_OLLAMA_URL=http://127.0.0.1:11434
AI_OLLAMA_MODEL=qwen2:1.5b
AI_OLLAMA_TIMEOUT=120

# OpenAI
AI_OPENAI_KEY=sk-proj-xxxx
AI_OPENAI_MODEL=gpt-4o-mini

# Anthropic (Claude)
AI_ANTHROPIC_KEY=sk-ant-xxxx
AI_ANTHROPIC_MODEL=claude-sonnet-4-20250514

# DeepSeek
AI_DEEPSEEK_KEY=sk-xxxx
AI_DEEPSEEK_MODEL=deepseek-chat

# RAG
AI_RAG_PROVIDER=ollama
AI_RAG_EMBED_MODEL=nomic-embed-text
AI_RAG_CHUNK_SIZE=500
AI_RAG_TOP_K=1
AI_RAG_TABLE=ai_documents

# RAG for small models — reduce chunk size and limit context
# AI_OLLAMA_NUM_CTX=2048

🧪 Testing

vendor/bin/phpunit
vendor/bin/phpunit --filter=test_ollama_chat

Uses Http::fake() — no real API calls needed.

🗺️ Roadmap

❤️ Support

• ⭐ Star this repo on GitHub
• 🐛 Report bugs via Issues
• 🔀 Submit a PR — contributions welcome
• 📢 Share with your developer friends

👤 Credits

Md Murad Hosen — Full-Stack Laravel Vue Developer and DevOps Engineer from Chittagong, Bangladesh 🇧🇩

📄 License

MIT License — free to use in personal and commercial projects. See LICENSE for details.