Cloud SQL for MySQL offers the robust performance, scalability, and reliability businesses need for a wide range of applications. Whether managing complex game data or powering smart home devices, companies like Chess.com and Nest are already leveraging Cloud SQL for MySQL to drive innovation and enhance user experiences, providing a solid foundation for data-driven solutions. With the growing demand of AI capabilities, organizations are looking to leverage AI for their business needs while using the database that already supports their applications.
To help companies transform their business, we’ve recently announced several new features for Cloud SQL for MySQL, available in Preview, that help companies power their database and applications with AI. We now offer integrated support for vector embedding search to help you build innovative generative AI applications and AI-assistive tools that simplify database management and take performance to the next level with Gemini. Let’s dig in to these new features!
1. Use Vector Search to build generative AI applications and integrate with MySQL
Cloud SQL for MySQL now offers storage and similarity search of vector embeddings, so you can use generative AI in your existing applications. It now provides K-nearest-neighbor (KNN) and approximate-nearest-neighbor (ANN) search between embeddings, all within the MySQL engine.
LangChain integration for generating vector embeddings
Embedding your data as vectors allows AI systems to interact with it more meaningfully. When embedded as vectors, information is stored efficiently while preserving complexities. This enables AI applications to systematically compare unique data to find similarities.
LangChain is a popular open-source framework for building applications using large language models (LLMs). The Cloud SQL team built a Vector LangChain package to help with processing data to generate vector embeddings and connect it with your MySQL instance. The integration offers a vector store, document loader, and chat message history.
We have a guide for using vector embeddings in MySQL with LangChain and an end-to-end example on how to generate embeddings of data such as chat histories or large documents, store the embeddings in MySQL, and also search them.
