An embedding converts text into a vector of hundreds or thousands of dimensions. What the model learns is a space in which position encodes meaning: text used in similar ways ends up nearby, and directions in the space correspond to semantic relationships.
Because similarity becomes distance, you can compare meaning rather than keywords. This is the foundation of semantic search and the retrieval half of RAG.