Vector databases organize data in the form of vectors, enabling efficient storage, indexing, and retrieval of unstructured data. They are essential in AI applications like natural language processing, computer vision, and automated speech recognition due to their ability to handle high-dimensional data and perform similarity searches. Using techniques such as approximate neighbour search and locality-sensitive hashing, vector databases offer high speed, scalability, and cost efficiency compared to traditional relational databases. They are particularly useful for applications like semantic search and personalized content recommendations.
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