Embeddings
Also called vector embeddings, text embeddings, embedding
Lists of numbers that represent the meaning of text, so a computer can find texts that mean similar things.
An embedding is a list of numbers, often hundreds or thousands long, that represents a piece of text in a way that captures its meaning. An embedding model turns text into these vectors. Texts with similar meanings end up with similar vectors, even if they share no words: "How do I reset my password?" and "I forgot my login" land close together.
Because closeness between vectors can be measured quickly, embeddings make it possible to search, group and compare large amounts of text by meaning. They power semantic search, recommendation systems, duplicate detection, clustering of feedback or keywords, and the retrieval step in RAG systems.
Embedding models are separate from chat models, cheaper to run, and produce a fixed-size vector rather than text. Different embedding models produce different, incompatible vectors, so a collection must be embedded with one model and searched with the same model. Images and audio can be embedded too, sometimes into the same space as text.
Example
A company embeds 5,000 customer reviews. Clustering the vectors reveals groups about delivery delays, sizing problems and packaging, without anyone reading all 5,000 reviews first or choosing keywords in advance.
Common mistake
Treating embedding similarity as agreement. "The drug is safe" and "The drug is not safe" can have very similar embeddings, because they are about the same topic. Embeddings find related text; they do not judge whether it says the same thing.