How can vector embeddings be generated inside Oracle Database 23ai?

Generating vector embeddings within Oracle Database 23ai can effectively be accomplished by downloading and importing pre-trained ONNX models. ONNX, which stands for Open Neural Network Exchange, provides a format for representing machine learning models that can be executed across various frameworks. By importing these models into Oracle Database, users can leverage sophisticated algorithms and pre-trained weights, allowing for efficient generation of vector embeddings directly within the database environment.

This approach is advantageous because it minimizes the need for deep machine learning expertise and extensive training data, enabling users to quickly implement advanced vector-based applications, such as similarity searches and recommendation systems. Utilizing pre-trained models ensures that the embeddings generated are based on robust, well-established methodologies that can enhance the application performance and accuracy.

In contrast, while built-in functions might offer some basic capabilities, they may not equate to the complexity and richness that pre-trained models can bring. Converting vectors to CLOBs does not generate embeddings; rather, it merely changes the data type, losing the semantic depth needed for vector analysis. Finally, relying on third-party databases suggests an external dependency that could complicate the workflow and data integration processes, which is counter to the streamlined abilities offered by native ONNX model support in Oracle Database.