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  Here are the details for both Approximate Nearest Neighbors (ANN) and K-Nearest Neighbors (KNN) algorithms, including their usage in vector databases: Approximate Nearest Neighbors (ANN) Overview Approximate Nearest Neighbors (ANN) is an algorithm used for efficient similarity search in high-dimensional vector spaces. It quickly finds the closest points (nearest neighbors) to a query vector. How ANN Works Indexing: The ANN algorithm builds an index of the vector database, which enables efficient querying. Querying: When a query vector is provided, the algorithm searches the index for the closest vectors. Approximation: ANN sacrifices some accuracy to achieve efficiency, hence "approximate" nearest neighbors. Advantages Speed: ANN is significantly faster than exact nearest neighbor searches, especially in high-dimensional spaces. Scalability: Suitable for large vector databases. Disadvantages Accuracy: May not always find the exact nearest neighbors due to approximations. Us...