The AI-on-Demand Metadata Schema

The conceptual AI-on-Demand metadata schema is defined in its own dedicated repository aiondemand/metadata-schema. Questions about the conceptual metadata schema and requests for changes should be directed at that repository instead.

In the REST API, we have an implementation of the schema defined in our src/database/model directory. For the model implementation we make use of SQLModel, a layer on top of the ORM framework SQLAlchemy and the serialization, validation and documentation (creating Swagger) framework pydantic, created by the developer of FASTApi, the framework we use for routing.

SQLModel makes it possible to define only a single model instead of defining the database-layer (SQLAlchemy) and the logic-layer (Pydantic) separately. Our implementation relies on inheritance to follow the same class hierarchy as defined in the metadata schema, this makes sure that generic fields, such as name and description, are present and consistent over all resources, and changes to the conceptual model and the model implementation should be similar.

A partial overview of the metadata model can be found in the following figure:

In addition to the implementation of the metadata schema, we maintain some metadata about that metadata. Specifically, a system which stores users, permissions, and reviews that relate to the metadata assets uploaded to AIoD. More on this can be found in the "User Model" documentation.

Reading the Conceptual Metadata Schema

Tools and documentation on how to read the conceptual metadata model are currently being written. This section will be updated at a later date (as of 16-12-2024).

Reading the Metadata Schema Implementation

This section will be updated at a later date (as of 16-12-2024) and will describe: - The use various class variants, such as XBase, XORM, XCreate, with a link to the "objects" page. - A brief discussion on how to read an attribute definition, with a link to the "attributes" page. - A brief discussion on how to relationships an attribute definition, with a link to the "relationships" page.

Changing the Metadata Schema Implementation

On a high level, changes to the metadata schema implementation consist of three steps:

• updating the schema implementation in src/database/model,
• updating or adding tests which test those changes, and
• adding a database migration script which updates the database accordingly.

This last step isn't needed during development, where you may recreate a database anytime to model changes. However, to deploy the changed schema in production we need to be able to change the database, both its schema and its content, to match the schema defined by the Python classes. For this reason, a migration script is also required when making changes to the metadata schema implementation.

The subsections in the sidebar document how to execute these steps depending on the type of change you want to make (work in progress):

• Attributes explains how to work with attributes that do not refer to any external tables. For example, adding a field which stores a URL.
• Relationships explains how to work with attributes which define relationships between objects. For example, an asset's creator which is represented with a link to an Agent.
• Objects explains how work with objects as a whole. For example, adding an entirely new entity to the schema.