What is a Vector database? The rise of Vector databases We have recently seen a surge in vector databases in this era of generative AI. The idea behind vector databases is to index the data with vectors that relate to that data Vector databases are often used for recommender engines where we learn vector representations of users and items we want to recommend

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Vector databases are used for recommender engines to find similar items using approximate nearest-neighbor search. They can be indexed using algorithms like Product Quantization, Locality-sensitive hashing, and Hierarchical Navigable Small World. Euclidean distance, dot product, and cosine similarity are common measures for comparing vectors. Vector databases also offer database operations, metadata and filtering capabilities, and scalability.

Understanding How Vector Databases Work!