A vector database keeps those meaning-based number lists called embeddings and is very fast at finding the ones most similar to a given query. Unlike a regular search that matches exact keywords, it can surface content that is related in meaning even if the wording is different. It is the workhorse behind letting AI find relevant material quickly.
For example, Search your notes for 'money worries' and a vector database also finds entries about 'financial stress.'