Embedding Model

An embedding model is a neural network trained to convert raw inputs into fixed-dimensional vectors. Sentence transformers, CLIP, and text-embedding-3-large are common examples. The model's training objective — typically contrastive loss — ensures that semantically related inputs cluster together in the output space. Choosing the right embedding model matters significantly: its output dimensionality, vocabulary coverage, and domain alignment all affect downstream retrieval accuracy.