Which vector index in Oracle Database 23ai is preferred for its speed and accuracy in vector search?

The Hierarchical Navigable Small World (HNSW) index is preferred for its speed and accuracy in vector search within Oracle Database 23ai due to its highly efficient construction and query optimization mechanisms. HNSW is designed specifically for approximate nearest neighbor (ANN) search, which is critical when dealing with high-dimensional data commonly encountered in AI vector searches.

This index structure allows for rapid traversal and retrieval of nearest neighbors by leveraging a multilevel graph-based approach. The hierarchical aspect of HNSW means that it can quickly navigate through layers to find approximate results, which significantly enhances search times while maintaining a high level of accuracy. This makes HNSW particularly suitable for applications where both performance and precision are crucial, such as AI and machine learning tasks involving large datasets.

In contrast, other indexing strategies like the Binary Tree index, Inverted File System index, and Full-Text index are not primarily optimized for vector similarity searches. Instead, they are more tailored for different types of querying and data retrieval operations, making them less effective for the specific requirements of AI vector searching that HNSW addresses.