The following Gemini in Databases features are now available in Public Preview

## Feature The following [Gemini in Databases](https://cloud.google.com/gemini/docs/databases/overview) features are now available in Public Preview: * [Database Center](https://cloud.google.com/database-center/docs/overview): an AI-assisted dashboard that gives you one centralized view across your entire database fleet. * [Cloud SQL Studio (GA)](https://cloud.google.com/sql/docs/mysql/manage-data-using-studio): lets authorized users interact directly with the SQL database and run SQL queries from the Google Cloud console to access and manipulate data. * [Monitor active queries](https://cloud.google.com/sql/docs/mysql/monitor-active-queries): monitor and troubleshoot the queries that are active in your database. * [Index advisor](https://cloud.google.com/sql/docs/mysql/index-advisor-overview): a fully managed index advisor that tracks the queries your database handles regularly. * 4-week query metric retention in the [Query Insights dashboard](https://cloud.google.com/sql/docs/mysql/using-query-insights). * 17 new database insight recommendations. To learn how to enable and activate Gemini in Databases, see [Set up Gemini in Databases](https://cloud.google.com/gemini/docs/databases/set-up-gemini). ## Feature Cloud SQL for MySQL now supports the storage of vector embeddings in MySQL 8.0.36 and later databases. To use this feature, update your instance to `MySQL 8.0.36.R20240401.03_00` or later. After you store vector embeddings in your database, you can then perform K-nearest neighbor (KNN) searches on the dataset along with the rest of your data. Cloud SQL for MySQL also supports the creation of vector search indexes for several different index types using approximate nearest neighbor (ANN) search. For more information, see [Working with vector embeddings using Cloud SQL for MySQL](https://cloud.google.com/sql/docs/mysql/work-with-vectors). This feature is in [Preview](https://cloud.google.com/products#product-launch-stages).