def test_extra_types(client: TestClient):
    item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",

        )
    try:
        data = dict(obj)
    except Exception as e:
        errors: list[Exception] = []
        errors.append(e)
        try:
            data = vars(obj)
        except Exception as e:
            errors.append(e)
            raise ValueError(errors) from e
    return jsonable_encoder(
        data,
        include=include,
        exclude=exclude,

Y por supuesto, soporta lo mismo:

* validación de datos
* serialización de datos
* documentación de datos, etc.

Esto funciona de la misma manera que con los modelos de Pydantic. Y en realidad se logra de la misma manera internamente, utilizando Pydantic.

/// info | Información

        @app.post("/example/")
        def example(item: Item = Body(example={"data": "Data in Body example"})):
            return item

    @app.post("/examples/")
    def examples(
        item: Item = Body(
            examples=[
                {"data": "Data in Body examples, example1"},
                {"data": "Data in Body examples, example2"},
            ],
        ),
    ):
        return item

def test_sub_duplicates():
    response = client.post("/with-duplicates-sub", json={"data": "myitem"})
    assert response.status_code == 200, response.text
    assert response.json() == [
        {"data": "myitem"},
        [{"data": "myitem"}, {"data": "myitem"}],
    ]


def test_openapi_schema():
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text

                "value": {"data": "Data in Body examples, example1"},
            },
            "Example Two": {
                "value": {"data": "Data in Body examples, example2"},
            },
        },
    ),
):
    return item


@app.get("/path_examples/{item_id}")
def path_examples(
    item_id: str = Path(
        examples=[
            "json_schema_item_1",

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

def test_post_body(client: TestClient):
    data = [
        {"url": "http://example.com/", "name": "Example"},
        {"url": "http://fastapi.tiangolo.com/", "name": "FastAPI"},
    ]
    response = client.post("/images/multiple", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


def test_post_invalid_list_item(client: TestClient):
    data = [{"url": "not a valid url", "name": "Example"}]

    assert response.status_code == 422, response.text

    data = {"items": [["foo", "bar"], ["baz"]]}
    response = client.post("/model-with-tuple/", json=data)
    assert response.status_code == 422, response.text


def test_tuple_with_model_valid():
    data = [{"x": 1, "y": 2}, {"x": 3, "y": 4}]
    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 200, response.text

                            },
                            {
                                "name": "save-data",
                                "in": "header",
                                "required": True,
                                "schema": {"type": "boolean", "title": "Save Data"},
                            },
                            {
                                "name": "if-modified-since",