This lecture reviews database concepts, then explains how to add and retrieve vector embeddings from a database using SQL. It covers calculating dot product similarity and Euclidean distance for vector comparisons, demonstrates creating sentence embeddings by averaging word embeddings, and briefly introduces Transformers for more sophisticated embedding generation. The lecture concludes with a discussion of efficient nearest neighbor search algorithms.