For Pycon 2025, I created a poster exploring vector embedding models, which you can download at full-size. In this post, I'll translate that poster into words. A vector embedding is a mapping from an input (like a word, list of words, or image) into a list of floating point numbers. That list of numbers represents that input in the multidimensional embedding space of the model. We refer to the length of the list as its dimensions, so a list with 1024 numbers would have 1024 dimensions. Embedding...