"title": "JsonApiError",
                    "required": ["errors"],
                    "type": "object",
                    "properties": {
                        "errors": {
                            "title": "Errors",
                            "type": "array",
                            "items": {"$ref": "#/components/schemas/Error"},
                        }
                    },
                },

This also means that if something changed it will be **reflected** on the client code automatically. And if you **build** the client it will error out if you have any **mismatch** in the data used.

        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.model_validate(data)
    except ValidationError as e:
        raise HTTPException(status_code=422, detail=e.errors(include_url=False))

                    "title": "JsonApiError",
                    "required": ["errors"],
                    "type": "object",
                    "properties": {
                        "errors": {
                            "title": "Errors",
                            "type": "array",
                            "items": {"$ref": "#/components/schemas/Error"},
                        }
                    },
                },

from dirty_equals import IsDict
from fastapi.testclient import TestClient

from docs_src.handling_errors.tutorial005 import app

client = TestClient(app)


def test_post_validation_error():
    response = client.post("/items/", json={"title": "towel", "size": "XL"})
    assert response.status_code == 422, response.text
    assert response.json() == IsDict(
        {
            "detail": [
                {

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

app = SomeASGIApp()

new_app = UnicornMiddleware(app, some_config="rainbow")
```

But FastAPI (actually Starlette) provides a simpler way to do it that makes sure that the internal middlewares to handle server errors and custom exception handlers work properly.

For that, you use `app.add_middleware()` (as in the example for CORS).

```Python
from fastapi import FastAPI
from unicorn import UnicornMiddleware

app = FastAPI()

之后，所有处理逻辑都一样。

但因为改变了 `GzipRequest.body`，**FastAPI** 加载请求体时会自动解压。

## 在异常处理器中访问请求体

!!! tip "提示"

    为了解决同样的问题，在 `RequestValidationError` 的自定义处理器使用 `body`  （[处理错误](../tutorial/handling-errors.md#use-the-requestvalidationerror-body){.internal-link target=_blank}）可能会更容易。

    但本例仍然可行，而且本例展示了如何与内部组件进行交互。

同样也可以在异常处理器中访问请求体。

此时要做的只是处理 `try`/`except` 中的请求：

```Python hl_lines="13  15"

│   └── test_main.py
```

Let's say that now the file `main.py` with your **FastAPI** app has some other **path operations**.

It has a `GET` operation that could return an error.

It has a `POST` operation that could return several errors.

Both *path operations* require an `X-Token` header.

=== "Python 3.10+"

    ```Python
    {!> ../../../docs_src/app_testing/app_b_an_py310/main.py!}
    ```

## Accessing the request body in an exception handler

!!! tip
    To solve this same problem, it's probably a lot easier to use the `body` in a custom handler for `RequestValidationError` ([Handling Errors](../tutorial/handling-errors.md#use-the-requestvalidationerror-body){.internal-link target=_blank}).

    But this example is still valid and it shows how to interact with the internal components.