## About security, APIs, and docs

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.

    Uvicorn and others support a `--reload` option that is useful during development.

    The `--reload` option consumes much more resources, is more unstable, etc.

    It helps a lot during **development**, but you **shouldn't** use it in **production**.

## Hypercorn with Trio

        cls, source: Type[Any], handler: Callable[[Any], CoreSchema]
    ) -> CoreSchema:
        return with_info_plain_validator_function(cls._validate)


class DefaultPlaceholder:
    """
    You shouldn't use this class directly.

    It's used internally to recognize when a default value has been overwritten, even
    if the overridden default value was truthy.
    """

    def __init__(self, value: Any):

            if it should exclude from the output the fields that had the same default
            value, even when they were explicitly set.
            """
        ),
    ] = False,
    exclude_none: Annotated[
        bool,
        Doc(
            """
            Pydantic's `exclude_none` parameter, passed to Pydantic models to define

    ```

=== "Python 3.8+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set types

But then we think about it, and realize that tags shouldn't repeat, they would probably be unique strings.

And Python has a special data type for sets of unique items, the `set`.

Then we can declare `tags` as a set of strings:

=== "Python 3.10+"

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

These features are what Marshmallow was built to provide. It is a great library, and I have used it a lot before.

But it was created before there existed Python type hints. So, to define every <abbr title="the definition of how data should be formed">schema</abbr> you need to use specific utils and classes provided by Marshmallow.

!!! check "Inspired **FastAPI** to"
    Use code to define "schemas" that provide data types and validation, automatically.

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["query", "query"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "42.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1