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MySQL 9 Vector Store Integration for LLM Chain

This directory contains example files for using MySQL 9's native vector capabilities with LLM Chain.

Overview

MySQL 9 introduces built-in vector support through the VECTOR data type and functions like VECTOR_COSINE_DISTANCE for similarity operations. This allows using MySQL as a vector store for RAG (Retrieval-Augmented Generation) applications without requiring a separate vector database service.

Requirements

  • PHP 8.2 or higher
  • MySQL 9.0.0 or higher
  • PDO PHP extension with MySQL driver
  • llm-chain library

Quick Start with Docker

  1. Ensure you have Docker and Docker Compose installed
  2. Start MySQL 9: 
    docker-compose up -d
  3. Wait for the MySQL server to be ready (check with docker-compose logs -f)
  4. Configure your .env file: 
    MYSQL_DSN=mysql:host=localhost;port=3306;dbname=llm_chain;charset=utf8mb4
MYSQL_USERNAME=root
MYSQL_PASSWORD=password
OPENAI_API_KEY=sk-your-openai-key
  5. Run the example: 
    php ../store-mysql-similarity-search.php

Manual Setup

If you're not using Docker, you'll need to:

  1. Install MySQL 9
  2. Create a database: 
    CREATE DATABASE llm_chain;
  3. The example will automatically create the necessary table with a VECTOR column

How It Works

The MySQL 9 Store implementation:

  1. Automatically creates a table with the necessary structure when first used
  2. Converts vector data from JSON to MySQL's native VECTOR type during storage
  3. Uses MySQL's VECTOR_COSINE_DISTANCE function for similarity search
  4. Converts distance scores to similarity scores (1 - distance) for compatibility with other vector stores

Vector Table Schema

The automatically created table has the following structure:

CREATE TABLE vector_documents (
    id VARCHAR(36) PRIMARY KEY,
    vector_data JSON NOT NULL,
    metadata JSON,
    VECTOR USING vector_data(1536) -- dimensions is configurable
);

Advanced Configuration

You can customize the Store behavior through constructor parameters:

$store = new Store(
    $pdo,                   // PDO connection
    'custom_table_name',    // Custom table name (default: vector_documents)
    'embedding_vector',     // Custom vector column name (default: vector_data)
    'document_metadata',    // Custom metadata column name (default: metadata)
    [],                     // Additional options
    768,                    // Vector dimensions (default: 1536 for OpenAI)
    5                       // Default query result limit (default: 3)
);

Performance Considerations

For production use:

  • Consider adding indexes based on your specific query patterns
  • Monitor memory usage, especially with large vector collections
  • Adjust MySQL server configuration for vector operations

Further Reading