A vector embedding (or just an embedding) is a list of numbers that represents the meaning of a piece of data (such as text, image, or audio) as a point in a high-dimensional space. An embedding model produces these vectors so that items with similar meaning land close together, which lets software compare meaning by measuring distance between vectors rather than matching words.
In short: an embedding is a numeric representation of meaning. Essentially, a vector where similar things sit close together, so machines can compare by meaning instead of by exact words.
How vector embeddings work
The point is to convert meaning into coordinates that can be compared:
Train a model: An embedding model learns, from large amounts of data, to map inputs to vectors so that related inputs produce nearby vectors.
