Embeddings are vectors of chunks of documents, lists of 1024 (depending on a model) float numbers that represent that short snippet of text. This kind of search works by finding the most similar vectors, calculating them cost fractions of the cent, but when you need to do it billions to trillions of times, it adds up.
