Oracle Database 23ai: Creating Vectors and Understanding Distance Metrics for Similarity Search

Oracle Database 23ai introduces native vector capabilities that enable semantic search directly within SQL. Understanding how to create vectors, calculate distances, and choose appropriate metrics is fundamental to building effective AI-powered applications. This comprehensive guide explores vector operations in Oracle 23ai with practical examples and best practices. The VECTOR Data Type Oracle 23ai introduces a native VECTOR data type designed specifically to store and manage vector embeddings efficiently within the database. Declaration Syntax: -- Flexible: any dimensions and format embedding VECTOR -- Specific dimensions, flexible format embedding VECTOR ( 512 ) -- Fully specified: dimensions and format embedding VECTOR ( 512 , FLOAT32 ) Format Options: INT8 : 8-bit integers FLOAT32 : 32-bit floating-point (IEEE standard, most common) FLOAT64 : 64-bit floating-point (IEEE standard, higher precision) BINARY : Binary vectors for specialized use cases Oracle Database automatically cast