MySQL AI 9.4
MySQL AI 9.5
-
MySQL AI AutoML now supports advanced recommendation capabilities with the introduction of Deep Recommendation models, a deep learning-based approach to building embedding models for personalized recommendations. This new feature leverages PyTorch to construct a deep learning pipeline, enabling faster similarity searches between you and products through the generation of user and item embeddings. With this update, MySQL AI AutoML lays the groundwork for delivering highly accurate and scalable personalized recommendations, enhancing the overall user experience.
For more information, see (WL #16656)
-
MySQL AI AutoML now supports enhanced log anomaly detection capabilities with the introduction of semantic feature embeddings. This update enriches the feature space by extracting semantic features from logs using embedding models and combining them with existing statistical TF-IDF features, ultimately improving the accuracy and effectiveness of log anomaly detection.
For more information, see Anomaly Detection for Logs. (WL #17097)
MySQL AI now lets you generate SQL queries from natural-language statements using the new
NL_SQLroutine, making it easier for you to interact with databases. This feature collects information on the schemas, tables, and columns that you have access to, and then uses a Large Language Model (LLM) to generate an SQL query for the question pertaining to your data. It also lets you run the generated query and view the result set. (Bug #38193173, WL #16315)-
MySQL AI now lets you run the
VECTOR_STORE_LOADroutine asynchronously, enabling you to load tables without blocking or waiting for the load to complete.For more information, see Ingesting Files into a Vector Store. (WL #17114)
-
MySQL AI now supports updating vector store tables, which lets you append specified files to the table and remove files from the table using a simple
DELETEstatement.For more information, see Updating a Vector Store. (WL #17126)