AI Definitions: Vector databases
/Vector databases - The storage and search engine for vector embeddings. Language models use vectors (lists of numbers) with hundreds or even thousands of dimensions (characteristics of the data), allowing it to remember previous inputs, draw comparisons, identify relationships, and understand context. The vectors are grouped together if they relate to one another. For instance, the word "king" would relate to a man, while "queen" would relate to a woman. A deep learning model (typically through a transformer) will use these vectors to "understand" the meaning of words and their relationships. More than 1,000 numbers can be used to represent a single word. If there are many numbers, then the word vector has a high dimension, making it nuanced. A low dimension for a word vector means the list of numbers is low. While not as nuanced, a low-dimensional vector is easier to work with. Vector data bases is what allows a language model to “recall” previous inputs, draw comparisons, identify relationships, and understand context.