fusengine Laravel Scout
Agent Workflow (MANDATORY)
Before ANY implementation, spawn 3 agents in parallel, one Agent call each with a name:
- fuse-ai-pilot:explore-codebase - Analyze existing model and search patterns
- fuse-ai-pilot:research-expert - Verify Scout docs via Context7
- mcp__context7__query-docs - Check search and indexing patterns
After implementation, run fuse-ai-pilot:sniper for validation.
Overview
| Component | Purpose |
|---|---|
| Searchable Trait | Makes Eloquent models searchable |
| Search Drivers | Meilisearch, Algolia, database, collection |
| Indexing | Automatic sync on model changes |
| Search Builder | Fluent search API with filters |
Decision Guide: Search Driver
Which driver?
├── Production (recommended) → Meilisearch (fast, self-hosted, free)
├── Managed service → Algolia (hosted, pay per search)
├── Small dataset → database (no extra infra)
└── Testing → collection (in-memory, no engine)
Quick Setup
composer require laravel/scout
composer require meilisearch/meilisearch-php http-interop/http-factory-guzzle
SCOUT_DRIVER=meilisearch
MEILISEARCH_HOST=http://127.0.0.1:7700
MEILISEARCH_KEY=masterKey
$results = Article::search('laravel tutorial')->paginate(15);
Critical Rules
- Use
toSearchableArray()to control indexed data - Queue indexing with
SCOUT_QUEUE=truefor performance - Use
searchable()for bulk import after setup - Pause indexing during seeders with
Scout::withoutSyncing()
Reference Guide
| Need | Reference |
|---|---|
| Searchable trait, indexing, conditions | searchable.md |
| Driver setup, Meilisearch, Algolia | drivers.md |
Best Practices
DO
- Use Meilisearch for production (fast, typo-tolerant)
- Queue indexing operations (
SCOUT_QUEUE=true) - Limit indexed fields with
toSearchableArray()
DON'T
- Index sensitive data (passwords, tokens)
- Forget to import existing records after setup
- Use collection driver in production
Laravel 13 Notes
Vector search natif pgvector
Pour la recherche sémantique (embeddings) sur PostgreSQL, Laravel 13 expose Schema::ensureVectorExtensionExists() et whereVectorSimilarTo() via la skill dédiée [[laravel-vector-search]]. Scout reste pertinent pour le full-text (Meilisearch/Algolia) ; pour la similarité vectorielle, utiliser pgvector directement sans driver Scout.
// Hybride : Scout pour full-text, pgvector pour similarité
$keyword = Post::search($query)->get();
$semantic = Post::whereVectorSimilarTo('embedding', $embedding, limit: 10)->get();