Vector embeddings are generative AI applications often require input data to be transformed into a format that the model can interpret—commonly vector embeddings. These are numerical representations of data that capture its semantic meaning. Efficient handling of these high-dimensional vectors may require a vector database designed to store, retrieve, and manage vector representations of data. They enable quick access to relevant information by comparing vector similarities.