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

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}

To declare a **request** body, you use <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> models with all their power and benefits.

!!! info
    To send data, you should use one of: `POST` (the more common), `PUT`, `DELETE` or `PATCH`.

There you could buy me a coffee ☕️ to say thanks. 😄

And you can also become a Silver or Gold sponsor for FastAPI. 🏅🎉

## Sponsor the tools that power FastAPI

As you have seen in the documentation, FastAPI stands on the shoulders of giants, Starlette and Pydantic.

You can also sponsor:

## Top Translation Reviewers

These users are the **Top Translation Reviewers**. 🕵️

I only speak a few languages (and not very well 😅). So, the reviewers are the ones that have the [**power to approve translations**](contributing.md#translations){.internal-link target=_blank} of the documentation. Without them, there wouldn't be documentation in several other languages.

{% if people %}

If you are paying for 3 servers but you are using only a little bit of their RAM and CPU, you are probably **wasting money** 💸, and probably **wasting server electric power** 🌎, etc.

In that case, it could be better to have only 2 servers and use a higher percentage of their resources (CPU, memory, disk, network bandwidth, etc).

    },
)
async def b():
    return "b"


@router.get(
    "/c",
    responses={
        "400": {"description": "Error with str"},
        "5xx": {"description": "Error with range, lower"},
        "default": {"description": "A default response"},
    },
)
async def c():
    return "c"


@router.get(
    "/d",
    responses={
        "400": {"description": "Error with str"},

    class ModelC(ModelB):
        password: str

    class ModelA(BaseModel):
        name: str
        description: Optional[str] = None
        foo: ModelB

        @field_validator("name")
        def lower_username(cls, name: str, info: ValidationInfo):
            if not name.endswith("A"):
                raise ValueError("name must end in A")
            return name

    async def get_model_c() -> ModelC:

```

### Declare a `list` with a type parameter

To declare types that have type parameters (internal types), like `list`, `dict`, `tuple`:

* If you are in a Python version lower than 3.9, import their equivalent version from the `typing` module
* Pass the internal type(s) as "type parameters" using square brackets: `[` and `]`

In Python 3.9 it would be:

```Python
my_list: list[str]
```

    username: str


class ModelC(ModelB):
    password: str


class ModelA(BaseModel):
    name: str
    description: Optional[str] = None
    model_b: ModelB

    @validator("name")
    def lower_username(cls, name: str, values):
        if not name.endswith("A"):
            raise ValueError("name must end in A")
        return name


async def get_model_c() -> ModelC:

    Because of that, versions of FastAPI previous to 0.99.0 still used versions of OpenAPI lower than 3.1.0.

### Pydantic and FastAPI `examples`

When you add `examples` inside a Pydantic model, using `schema_extra` or `Field(examples=["something"])` that example is added to the **JSON Schema** for that Pydantic model.