Oracle Database 23ai: Vector Similarity Search - Exact, Approximate, and Multi-Vector Strategies

Oracle Database 23ai's AI Vector Search provides multiple strategies for finding similar vectors, each with different trade-offs between accuracy, speed, and resource usage. Understanding when to use exact search, approximate search, or multi-vector search—and knowing the essential vector functions—is crucial for building high-performance semantic search applications. Understanding Similarity Search Types 1. Exact Similarity Search (Flat Search) Exact similarity search calculates a query vector's distance to all other vectors . It's also called flat search or exhaustive search because every vector in the dataset is compared. Characteristics: Gives the most accurate results Perfect search quality (100% recall) Involves potentially significant time as dataset grows No indexes required Suitable for small to medium datasets (thousands to hundreds of thousands of vectors) SQL Example: SELECT product_id , product_name , VECTOR_DISTANCE ( embedding , : query_vector , COSINE ) AS similarity FR