Embedding models convert language, images, or other data into numerical vectors. These vectors are the foundation of semantic search, retrieval systems, and similarity matching.
The quality of an embedding model directly affects how well a retrieval or search system performs. A good embedding model captures meaningful distinctions between concepts and places semantically similar content close together in vector space. A poor one treats unrelated concepts as similar and vice versa — which cascades into worse retrieval, worse AI responses, and more hallucination risk.
