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Vector databases are a groundbreaking development that is reshaping how we handle complex data. Unlike traditional relational databases, vector databases are uniquely equipped to manage and process high-dimensional vector data which is intrinsic to many AI and machine learning applications. This article explores the pivotal role of vector databases in the world of generative AI, highlighting their functionality, how they work, use cases, and a hands-on tutorial. Vector databases utilize indexing and search algorithms to conduct similarity searches, enabling them to rapidly identify the most similar vectors within a dataset. Vector embeddings are also discussed, which are how data eventually gets stored in a vector database.
