Embedding Models

Embedding models convert text into dense numerical vectors that capture semantic meaning. These vectors enable similarity search, retrieval-augmented generation (RAG), clustering, and classification. Embedding models are lightweight and fast, making them ideal for running locally alongside your main language model. They are essential building blocks for search engines, recommendation systems, and knowledge bases.