Embeddings are numerical representations of text, images, or other data that capture semantic similarity. Vector search uses those embeddings to retrieve items that are close in vector space to a query embedding.
In RAG, embeddings and vector search are often the retrieval layer. Evaluation should check whether the embedding model, indexing strategy, metadata filters, and ranking logic actually return useful context for the tasks users perform.