Example Concept: Vector databases store data as high-dimensional vectors, which are numerical representations of semantic information. When a query is made, the database retrieves vectors that are closest in distance to the query vector, using algorithms like k-Nearest Neighbors (k-NN). This process allows for efficient semantic search by finding items that are semantically similar to the input query, rather than relying on exact keyword matches.