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:

{* ../../docs_src/dataclasses/tutorial003.py hl[1,5,8:11,14:17,23:25,28] *}

1. We still import `field` from standard `dataclasses`.

**FastAPI** is already being used in production in many applications and systems. And the test coverage is kept at 100%. But its development is still moving quickly.

New features are added frequently, bugs are fixed regularly, and the code is still continuously improving.

Hiding your documentation user interfaces in production *shouldn't* be the way to protect your API.

That doesn't add any extra security to your API, the *path operations* will still be available where they are.

If there's a security flaw in your code, it will still exist.

## Use case

For example, imagine that you want to return an HTTP status code of "OK" `200` by default.

But if the data didn't exist, you want to create it, and return an HTTP status code of "CREATED" `201`.

But you still want to be able to filter and convert the data you return with a `response_model`.

For those cases, you can use a `Response` parameter.

## Use a `Response` parameter

    assertEquals(
        "next() should still return first element after peeking", "A", peekingIterator.next());

    assertEquals("Should be able to peek() at middle element", "B", peekingIterator.peek());
    assertEquals(
        "Should be able to peek() middle element multiple times", "B", peekingIterator.peek());
    assertEquals(
        "next() should still return middle element after peeking", "B", peekingIterator.next());

Like `item.model_dump(exclude_unset=True)`.

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///

    assertEquals(
        "next() should still return first element after peeking", "A", peekingIterator.next());

    assertEquals("Should be able to peek() at middle element", "B", peekingIterator.peek());
    assertEquals(
        "Should be able to peek() middle element multiple times", "B", peekingIterator.peek());
    assertEquals(
        "next() should still return middle element after peeking", "B", peekingIterator.next());

        example: Annotated[
            Optional[Any],
            deprecated(
                "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
                "although still supported. Use examples instead."
            ),
        ] = _Unset,
        openapi_examples: Optional[Dict[str, Example]] = None,
        deprecated: Union[deprecated, str, bool, None] = None,

/// tip

But it is still recommended to use the ideas above, using multiple classes, instead of these parameters.

This is because the JSON Schema generated in your app's OpenAPI (and the docs) will still be the one for the complete model, even if you use `response_model_include` or `response_model_exclude` to omit some attributes.

When you return a `Response` directly its data is not validated, converted (serialized), nor documented automatically.

But you can still document it as described in [Additional Responses in OpenAPI](additional-responses.md){.internal-link target=_blank}.