Embeddings are behind search, recommendations, and most of modern AI, and they are usually explained with intimidating diagrams. The core idea is simple and worth building yourself: an embedding turns a thing (a word, a document, a product) into a list of numbers (a vector) so that similar things end up close together in that number space.
Why turn things into vectors
Computers cannot compare meaning directly, but they can compare vectors. If "king" and "queen" are nearby points, and "king" and "banana" are far apart, then "closeness of vectors" becomes a usable stand-in for "similarity of meaning." Once your items are vectors, search and recommendation become geometry: find the nearest points.
Measuring closeness
The standard measure is cosine similarity, the angle between two vectors. Identical direction scores 1, unrelated scores near 0, opposite scores -1.
