An embedding model maps text inputs to dense vectors in a high-dimensional space where semantically similar texts land close together. Unlike generative LLMs, embedding models are optimized with contrastive or ranking losses and produce fixed-size vectors suitable for indexing. Choosing the right embedding model — considering dimension size, training domain, and multilingual support — is one of the most impactful decisions in a retrieval pipeline.