Vector databases are specialized data storage and retrieval systems optimized for handling high-dimensional vector representations of data. Unlike traditional databases that store structured or relational data, vector databases efficiently manage embeddings—numerical representations of unstructured data such as text, images, and audio. These embeddings, generated using machine learning models, capture semantic similarities and allow for fast similarity searches.