Considerations for the Embedding Service

• Available Embedding Models
  A number of embedding models are available to be used with the Private AI Services Container, including some that are shipped with the container. There are also pre-built models available for download as well as models that are known to work with the container but must be built using Oracle Machine Learning for Python (OML4Py) Client 2.1.
• Private AI Environment Variables
  Environment variables can be used to set configuration properties, such as file locations, logging settings, and others. They can be passed to the container when running the container using the standard -e flag.
• Container Input Validation
  Inputs to the Private AI Services Container can come from a variety of sources, each of which are validated using different methods.
• Container Automatic Image Conversion
  The request header x-convert-images can be used to instruct the container to examine the format of input images and convert them as necessary. The JPEG format is supported by default.
• Transport Layer Security
  The container admin can manage transport layer security using environment variables and a keystore file, along with a password file to access the keystore.
• Multi-threaded Scaling
  The ONNX Runtime enables multi-threading and can benefit from multiple CPU cores.