Document embeddings

In a Retrieval-Augmented Generation (RAG) architecture, an embedding is a numerical vector that represents the semantic content of a document or a text passage. Document embeddings transform text data into fixed-length vectors that capture meaning, context, and relationships within the content. These embeddings are crucial for RAG, as they enable the retrieval model to efficiently search through large document collections by comparing embeddings, finding relevant passages, and supplying them to the generation model. In this way, embeddings help RAG systems retrieve contextually similar documents and produce accurate, context-aware responses.