fastapi/docs/tutorial/body-schema.md

The same way you can declare additional validation and metadata in path operation function parameters with Query, Path and Body, you can declare validation and metadata inside of Pydantic models using Schema.

Import Schema

First, you have to import it:

{!./tutorial/src/body_schema/tutorial001.py!}

!!! warning Notice that Schema is imported directly from pydantic, not form fastapi as are all the rest (Query, Path, Body, etc).

Declare model attributes

You can then use Schema with model attributes:

{!./tutorial/src/body_schema/tutorial001.py!}

Schema works the same way as Query, Path and Body, it has all the same parameters, etc.

!!! info Actually, Query, Path and others you'll see next are subclasses of a common Param which is itself a subclass of Pydantic's Schema.

`Body` is also a subclass of `Schema` directly. And there are others you will see later that are subclasses of `Body`.

!!! tip Notice how each model's attribute with a type, default value and Schema has the same structure as a path operation function's parameter, with Schema instead of Path, Query and Body.

Schema extras

In Schema, Path, Query, Body and others you'll see later, you can declare extra parameters apart from those described before.

Those parameters will be added as-is to the output JSON Schema.

If you know JSON Schema and want to add extra information appart from what we have discussed here, you can pass that as extra keyword arguments.

!!! warning Have in mind that extra parameters passed won't add any validation, only annotation, for documentation purposes.

For example, you can use that functionality to pass a JSON Schema example field to a body request JSON Schema:

{!./tutorial/src/body_schema/tutorial002.py!}

Recap

You can use Pydantic's Schema to declare extra validations and metadata for model attributes.

You can also use the extra keyword arguments to pass additional JSON Schema metadata.