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Informations about the package embedding-mysql-driver
x-laravel/embedding — MySQL 9 Driver
MySQL HeatWave native vector driver for x-laravel/embedding.
How It Works
- Implements
SimilarityDriver— registers as themysqldriver, similarity search runs entirely in MySQL usingVEC_DISTANCE_COSINE - Implements
VectorStore— writes embeddings viaINSERT ... ON DUPLICATE KEY UPDATEwithSTRING_TO_VECTOR(), reads via aVECTOR_TO_STRINGglobal scope
Requirements
- PHP ^8.3
- Laravel ^12.0 | ^13.0
x-laravel/embedding ^1.0- MySQL HeatWave —
VEC_DISTANCE_COSINEis not available in MySQL Community Edition; this driver requires MySQL HeatWave
Installation
The MysqlEmbeddingServiceProvider is auto-discovered and registers the mysql driver automatically.
Setup
1. Configure x-laravel/embedding
Publish the config if you haven't already:
Set the similarity driver and database connection in config/embedding.php:
2. Create the embeddings table
This driver ships its own MySQL-native migration that replaces the default one from x-laravel/embedding. It creates a VECTOR(1536) column.
Run the migration:
If you need to customise the DDL, publish the migration first:
Note:
VEC_DISTANCE_COSINEis only available on MySQL HeatWave, not MySQL Community Edition. For production, add aVECTOR INDEXon thevectorcolumn after publishing the migration.
3. Model
Follow the standard x-laravel/embedding setup. No MySQL-specific changes are needed on your models.
Usage
The driver is transparent — use the standard x-laravel/embedding API:
All methods set a similarity_score float attribute on each returned model.
Testing
Tests require a MySQL HeatWave instance. The docker-compose.yml uses mysql:9.1.0 for CI convenience but VEC_DISTANCE_COSINE tests will fail against Community Edition — run them against a real HeatWave instance.
License
This package is open-sourced software licensed under the MIT license.