/laravel-vector-search

name: laravel-vector-search
description: Use when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.
versions:
  laravel: "13.0"
  php: "8.3"
  postgresql: "16+"
  pgvector: "0.7+"
user-invocable: true
references: references/pgvector-setup.md, references/embeddings-workflow.md, references/queries.md, references/templates/Document-model.php.md, references/templates/VectorSearchService.php.md
related-skills: laravel-ai-sdk, laravel-eloquent, laravel-migrations

<objective> Covers Laravel 13 vector/semantic search on PostgreSQL with the pgvector extension: enabling the extension via Schema::ensureVectorExtensionExists(), the vector column type and HNSW/IVFFlat indexing, the embedding generation and persistence workflow, and the query builder's vector methods (whereVectorSimilarTo, selectVectorDistance, whereVectorDistanceLessThan, orderByVectorDistance). PostgreSQL (pgvector) or MariaDB 11.7+ (13.27+) — no MySQL/SQLite fallback. For full-text/keyword search, see laravel-scout instead (the two can be combined for hybrid search). </objective>

Laravel 13 Vector Search (pgvector)

Agent Workflow (MANDATORY)

Before ANY implementation, spawn 3 agents in parallel, one `Agent` call each with a `name`:

1. **fuse-ai-pilot:explore-codebase** - Check current DB driver (must be PostgreSQL) and existing embedding columns 2. **fuse-ai-pilot:research-expert** - Verify pgvector extension version and HNSW vs IVFFlat tradeoffs 3. **mcp__context7__query-docs** - Pull `laravel.com/docs/13.x/search` + `queries` examples

After implementation, run **fuse-ai-pilot:sniper** for validation.

---

Overview

| Feature | Description | |---------|-------------| | **PostgreSQL / MariaDB** | PostgreSQL + `pgvector`, or MariaDB 11.7+ (vector queries since 13.27); not available on MySQL/SQLite | | **Schema helper** | `Schema::ensureVectorExtensionExists()` enables the extension | | **Query builder** | `whereVectorSimilarTo()`, `selectVectorDistance()`, `whereVectorDistanceLessThan()`, `orderByVectorDistance()` | | **Auto-embedding** | Pass a raw string and Laravel generates the embedding via AI SDK | | **Cosine similarity** | Default distance; threshold via `minSimilarity` (0.0 - 1.0) |

---

Critical Rules

1. **Use PostgreSQL (pgvector) or MariaDB 11.7+** - Vector clauses do not work on MySQL/SQLite - no fallback 2. **Create an HNSW index** - Without an index, queries do full table scans; > 10k rows means seconds-to-minutes latency 3. **Match dimensions exactly** - Insert-time and query-time embedding models MUST share the same dimensions 4. **Cache embeddings** - Regenerating embeddings on every request is the #1 cost driver; persist them 5. **Lock the embedding model** - Changing the model invalidates ALL stored embeddings; treat the model as a schema field

---

Architecture

database/migrations/
└── XXXX_create_documents_table.php   # Schema::ensureVectorExtensionExists(), vector(1536) col, HNSW index

app/Models/
└── Document.php                       # casts embedding to array, uses whereVectorSimilarTo

app/Ai/Services/
└── VectorSearchService.php            # encapsulates query + threshold logic

→ See [Document-model.php.md](references/templates/Document-model.php.md) for full example

---

Reference Guide

| Topic | Reference | When to Consult | |-------|-----------|-----------------| | **pgvector setup** | [pgvector-setup.md](references/pgvector-setup.md) | Migrations + index creation | | **Embedding workflow** | [embeddings-workflow.md](references/embeddings-workflow.md) | Generating + persisting vectors | | **Query patterns** | [queries.md](references/queries.md) | `whereVectorSimilarTo` and friends |

Templates

| Template | When to Use | |----------|-------------| | [Document-model.php.md](references/templates/Document-model.php.md) | Eloquent model with vector column | | [VectorSearchService.php.md](references/templates/VectorSearchService.php.md) | Reusable service |

---

Quick Reference

Migration

Schema::ensureVectorExtensionExists();

Schema::create('documents', function (Blueprint $table) {
    $table->id();
    $table->text('content');
    $table->vector('embedding', dimensions: 1536)->index(); // HNSW cosine index
    $table->timestamps();
});

Query

$documents = Document::query()
    ->whereVectorSimilarTo('embedding', 'best wineries in Napa Valley', minSimilarity: 0.4)
    ->limit(10)
    ->get();

→ See [VectorSearchService.php.md](references/templates/VectorSearchService.php.md) for complete example

---

Best Practices

DO

• Create an HNSW index BEFORE inserting bulk data - faster total ingest
• Store the embedding model name alongside the vector to detect drift
• Use `minSimilarity` 0.3-0.5 as a starting threshold; tune empirically
• Combine vector search with classic `where()` for hybrid filtering (date ranges, tenancy)

DON'T

• Don't run vector queries without an index past a few thousand rows - it becomes a full table scan
• Don't mix embedding models in the same column - distances become meaningless
• Don't generate query embeddings inside loops - batch them via `Embeddings::for([...])`
• Don't store embeddings as JSON strings - use the native `vector` column type for index support