What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

The VECTOR_DISTANCE function in Oracle Database 23ai is designed specifically to calculate the distance between vectors using various specified metrics. This capability is essential for similarity search applications, where the goal is to determine how similar or different two vectors are based on their spatial relationships in a multi-dimensional space.

By employing different distance metrics, such as Euclidean distance, cosine similarity, or others, users can assess proximity in a more nuanced manner. This allows for more effective querying when looking for similar items in large datasets, such as images, text, or other data forms represented as vectors. Calculating distances accurately is crucial for applications like recommendation systems, image retrieval, and natural language processing, where determining similarity plays a pivotal role in user experience and search relevance.

Other choices, while related to vector operations, do not directly align with the primary purpose of the VECTOR_DISTANCE function. Fetching rows that match exact vector embeddings and creating vector indexes pertain to different aspects of vector handling and optimization in databases, while grouping vectors by their scores relates to how results may be organized post-query execution rather than the calculation of distances itself.