Laravel 13 introduces Vector Store Integrations, making it easier to build AI-powered applications that understand the meaning behind user queries rather than relying on exact keyword matches. By storing text as vector embeddings, your application can perform semantic search, recommend related content, power intelligent chatbots, and implement Retrieval-Augmented Generation (RAG) systems.
With support for popular AI providers and vector databases, Laravel 13 simplifies embedding generation, storage, and similarity search, allowing developers to create smarter search experiences with minimal code.
use Illuminate\Support\Facades\AI;
$embedding = AI::embeddings()->create([
'model' => 'text-embedding-3-small',
'input' => 'Laravel is a powerful PHP framework.',
]);
$vector = $embedding->embedding();
Store the Vector
Document::create([
'title' => 'Laravel Guide',
'content' => 'Laravel is a powerful PHP framework.',
'embedding' => $vector,
]);
Perform a Semantic Search
$results = Document::semanticSearch(
'How to build REST APIs in Laravel?'
)->take(5)->get();
Key Benefits
- 🚀 Fast semantic search
- 🤖 AI-powered recommendations
- 💬 Better chatbot context (RAG)
- 📚 Intelligent document retrieval
- 🔍 Finds related content beyond exact keywords
- ⚡ Easy integration with AI providers and vector databases
Laravel 13's Vector Store Integrations help developers build modern AI applications with clean, expressive APIs while reducing the complexity of managing embeddings and similarity search.