"application/json": {
                                    "schema": {
                                        "title": "Response Response Model Union No Annotation Return Model2 Response Model Union No Annotation Return Model2 Get",
                                        "anyOf": [
                                            {"$ref": "#/components/schemas/User"},

    from pydantic import BaseModel, ValidationInfo, field_validator

    app = FastAPI()

    class ModelB(BaseModel):
        username: str

    class ModelC(ModelB):
        password: str

    class ModelA(BaseModel):
        name: str
        description: Optional[str] = None
        foo: ModelB
        tags: dict[str, str] = {}

        @field_validator("name")

Sebastián Ramírez <******@****.***> 1766840096 -0800

    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text

    data = [{"x": 1, "y": 2}]
    response = client.post("/tuple-of-models/", json=data)
    assert response.status_code == 422, response.text


def test_tuple_form_valid():
    response = client.post("/tuple-form/", data={"values": ("1", "2")})

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200

        Doc(
            """
            Pydantic's `include` parameter, passed to Pydantic models to set the
            fields to include.
            """
        ),
    ] = None,
    exclude: Annotated[
        Optional[IncEx],
        Doc(
            """
            Pydantic's `exclude` parameter, passed to Pydantic models to set the
            fields to exclude.
            """
        ),
    ] = None,

* 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

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

    version: Literal["1", "auto"] = "auto",
) -> ModelField:
    if annotation_is_pydantic_v1(type_):
        raise PydanticV1NotSupportedError(
            "pydantic.v1 models are no longer supported by FastAPI."
            f" Please update the response model {type_!r}."
        )
    class_validators = class_validators or {}

    field_info = field_info or FieldInfo(annotation=type_, default=default, alias=alias)

            field_info=FieldInfo(annotation=model),
            name=model.__name__,
            mode="validation",
        )
        for model in flat_validation_models
    ]
    flat_serialization_model_fields = [
        ModelField(
            field_info=FieldInfo(annotation=model),
            name=model.__name__,
            mode="serialization",
        )
        for model in flat_serialization_models
    ]

@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
    return filter_query
```

Read the new docs: [Query Parameter Models](https://fastapi.tiangolo.com/tutorial/query-param-models/).

#### `Header` Parameter Models

Use Pydantic models for `Header` parameters:

```python
from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel