A vector database is literally a database of vectors, where vectors are data representations, such as embeddings, or features. Contrary to traditional databases, where search is done by matching keywords, in vector databases the search is by proximity -- this is called 'semantic search' since nearby vectors correspond to data points of similar semantic content. Vector databases are growing in popularity due to their use in extending LLM (Large Language Model) capabilities without re-training or fine-tuning them. For example: given a set of documents, one can create a corresponding set of vector embeddings (say one vector per paragraph), so that when a prompt is made, the embedding of the prompt is used to query the database for the closest vector(s), and the corresponding returned document(s) are then appended to the prompt (as 'context') before being passed to the LLM.