Supporting database queries using unsupervised vector embedding approaches over unseen data

Abstract

A computer-implemented method of performing queries using Artificial Intelligence (AI) database embeddings includes the operations of generating a plurality of vector embeddings describing a training data from a database for training a machine learning model. A test vector embedding is generated from the plurality of vector embeddings based on training data for unseen data from one or more rows of the database. One or more vectors from the plurality of vector embeddings describing the training data that are a closest match to the test vector embedding are identified. A task is determined based upon the unseen data. The determined task is performed using the trained machine learning model.