A Vector Database is optimized for indexing and querying high-dimensional vectors, commonly used for RAG and semantic search.

Textual Inversion introduces new concept tokens by learning embeddings that map to visual ideas. It is lightweight compared to full training and connects closely with Embeddings and DreamBooth workflows.