4 Use the Vector Embedding Service

The Private AI Services Container embedding service supports generation of vector embeddings outside of the database. The container provides an interface for performing inference on Oracle ONNX Pipeline format models using REST.

The management of the AI Services Container is handled by certain logical roles including the Container Admin, Model Creator, and Inference Client. These roles may be assumed by the same Linux user simultaneously. The Model Creator and Inference Client roles are specific to the vector embedding service.
  • Container Admin: Acting as container admin, a user can configure, stop, start, and perform general management of a container.
  • Model Creator: As model creator, a user performs actions that are related to the provision of model files. This includes tasks such as creating an ONNX pipeline and providing metadata and model specific configurations when necessary.
  • Inference Client: As inference client, a user performs inference on data and queries available models using REST APIs.

The container enables multiple concurrent users. The effective number of concurrent users is determined by the number of CPU cores and the embedding model.

The AI Services Container supports a fixed set of Oracle Machine Learning (OML) model types and mining functions. The container automatically determines the model type based on the data type and shape of the model inputs. The supported model types are as follows:

Model Type Model Function Goal
ONNX_TXT EMBEDDING

Generate text embeddings.

The model takes text as input and produces embeddings as output. Example models include sentence transformers and CLIP (text).
ONNX_IMG EMBEDDING

Generate image embeddings.

The model takes an image as input and produces embeddings as output. Example models include Vision Transformers and CLIP (image).

Only Oracle ONNX Pipeline formatted models that are produced by OML4Py 2.1.1 are supported for deployment in the Private AI Services Container and in the database, as of Oracle AI Database 26ai. Both text and image embedding pipelines are supported. For more information about ONNX pipeline models, see Oracle Machine Learning for Python User’s Guide. For more information about importing pretrained models in ONNX format, see Oracle AI Database AI Vector Search User's Guide.

See Also:

For additional information that is helpful to understanding the use and implementation of the Private AI Services Container embedding service, see the following: