What is a text embedding and how does it represent meaning?

A text embedding is a fixed-length vector of numbers that represents the meaning of text in a high-dimensional space. Texts with similar meaning produce vectors close together, measured by cosine similarity or dot product. Modern embeddings come from neural models trained on massive corpora, typically transformer encoders that learn to map semantically related sentences near each other. A typical embedding has between 384 and 3072 dimensions, with each dimension capturing some abstract feature of meaning learned during training.