What model framework does the VECTOR_EMBEDDING function support?

The VECTOR_EMBEDDING function supports the Open Neural Net Exchange (ONNX) model framework. ONNX serves as an intermediary format for deep learning models, allowing interoperability between different frameworks. This means that models built in various environments can be converted to the ONNX format, enabling use in other platforms that support ONNX.

Using ONNX ensures that users can leverage the strengths of the Oracle database's capabilities for vector search functionality while working with models developed in diverse environments. The adoption of ONNX simplifies the deployment and use of machine learning models, creating a seamless workflow for data scientists and engineers.

Other frameworks like TensorFlow, Caffe, and Pytorch are popular but do not align with the support that is specifically provided for the VECTOR_EMBEDDING function in this context. Each of those frameworks has its own distinct tooling and formats, which do not allow the straightforward integration offered by ONNX in this instance. This makes ONNX a valuable choice for users needing flexibility in model deployment and execution in Oracle's vector search infrastructure.