Oracle is adding a Vector Store and new generative AI features to its data analytics cloud service MySQL HeatWave, the company said at its annual CloudWorld conference.
The generative AI features added to the data analytics cloud service include a large language model-driven interface that allows enterprise users to interact with different aspects of the service — including searching for different files — in natural language.
The new Vector Store, which is also in private preview, can ingest documents in a variety of formats and store them as embeddings generated via an encoder model in order to process queries faster, the company said.
“For a given user query, the Vector Store identifies the most similar documents by performing a similarity search over the stored embeddings and the embedded query,” an Oracle spokesperson said. These documents can be later used to augment the prompt given to the LLM-driven interface so that it returns a more contextual answer.
AutoML support for MySQL HeatWave Lakehouse
Oracle’s MySQL HeatWave Lakehouse, which was released last year in October, has been updated to support AutoML.
HeatWave’s AutoML, which is a machine learning component or feature within the service, supports training, inference, and explanations on data in object storage in addition to data in the MySQL database, the company said.
Other updates to AutoML include support for text columns, an enhanced recommender system, and a training progress monitor.
Support for text columns, according to the company, will now allow enterprises to run various machine learning tasks — including anomaly detection, forecasting, classification, and regression — on data stored in these columns.
In March, Oracle added several new machine-learning features to MySQL HeatWave including AutoML and MySQL Autopilot.
Oracle’s recommender system — a recommendation engine within AutoML — has also been updated to support wider feedback, including implicit feedback, such as past purchases and browsing history, and explicit feedback, such as ratings and likes, in order to generate more accurate personalised recommendations.
A separate component, dubbed the Training Progress Monitor, has also been added to AutoML in order to allow enterprises to monitor the progress of their models being trained with HeatWave.
MySQL Autopilot to support automated indexing
Oracle has also updated its MySQL Autopilot component within HeatWave to support automatic indexing.
The new feature, which is currently in limited availability, is targeted at helping enterprises to eliminate the need to create optimal indexes for their OLTP workloads and maintain them as workloads evolve.
“MySQL Autopilot automatically determines the indexes customers should create or drop from their tables to optimise their OLTP throughput, using machine learning to make a prediction based on individual application workloads,” the company said in a statement.
Another feature, dubbed auto compression, has also been added to Autopilot. Auto compression helps enterprises determine the optimal compression algorithm for each column, which improves load, and query performance and reduces cost.
The other updates in Autopilot include adaptive query execution and auto load and unload.
Adaptive query execution, as the name suggests, helps enterprises optimise the execution plan of a query in order to improve performance by using information obtained from the partial execution of the query to adjust data structures and system resources.
Separately, auto load and unload improve performance by automatically loading columns that are in use to HeatWave and unloading columns that are never in use.
“This feature automatically unloads tables that were never or rarely queried. This helps free up memory and reduce costs for customers, without having to manually perform this task,” the company said.
Other MySQL HeatWave enhancements
Other updates include JSON acceleration, new analytic operators for migrating more workloads into HeatWave, and a bulk ingest feature into MySQL HeatWave.
The bulk ingest feature adds support for parallel building of index sub-tress while loading data from CSV files. This provides a performance increase in data ingestion, thereby allowing newly loaded data to be queried sooner, the company said.