How does Oracle Database 23ai use pretrained AI models for vector search?

Oracle Database 23c, particularly with its integration of AI features, enhances its capabilities by leveraging pretrained AI models for vector search through the use of ONNX (Open Neural Network Exchange) models. ONNX is an open format designed to facilitate the interoperability of machine learning models across various platforms and environments. By allowing the direct loading of ONNX models into the database, Oracle Database 23c effectively enables users to perform complex operations, including vector searches, leveraging the learning from these pretrained models.

This approach brings significant advantages, such as reduced latency and improved efficiency, as the models can be executed directly within the database environment, eliminating the need for external model hosting or service calls. This seamless integration supports a wide range of use cases, especially in scenarios requiring quick retrieval of information based on similarity measures in high-dimensional data, often essential for applications like recommendation systems and natural language processing.

The other options do not accurately describe how Oracle Database 23c incorporates pretrained AI models. For instance, interfacing with third-party AI services would involve additional overhead and possibly increased latency compared to utilizing models natively. Accessing cloud services via APIs is a distinct process relevant to broader cloud functionalities but does not specifically outline the mechanism of vector search in connection with pretrained AI models.