AI Definitions: Vector databases
/Vector databases – Raw data is converted into lists of numbers (word vectors) so that machine learning models can use them. 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 a transformer model) 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.