Right before deciding to build **FastAPI** I found **APIStar** server. It had almost everything I was looking for and had a great design.

It was one of the first implementations of a framework using Python type hints to declare parameters and requests that I ever saw (before NestJS and Molten). I found it more or less at the same time as Hug. But APIStar used the OpenAPI standard.

We want FastAPI to keep **filtering** the data using the response model.

In the previous example, because the classes were different, we had to use the `response_model` parameter. But that also means that we don't get the support from the editor and tools checking the function return type.

      "input": "foo",
      "url": "https://errors.pydantic.dev/2.1/v/int_parsing"
    }
  ]
}
```

because the path parameter `item_id` had a value of `"foo"`, which is not an `int`.

* and in there, find the module `dependencies` (the file at `app/dependencies.py`)...
* and from it, import the function `get_token_header`.

That works correctly! 🎉

---

The same way, if we had used three dots `...`, like in:

```Python
from ...dependencies import get_token_header
```

that would mean:

{!../../../docs_src/python_types/tutorial001.py!}
```

### Edit it

It's a very simple program.

But now imagine that you were writing it from scratch.

At some point you would have started the definition of the function, you had the parameters ready...

But then you have to call "that method that converts the first letter to upper case".

Was it `upper`? Was it `uppercase`? `first_uppercase`? `capitalize`?

    * Read the new docs: [Dependencies with `yield` and `except`](https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-with-yield/#dependencies-with-yield-and-except).

In short, if you had dependencies that looked like:

```Python
def my_dep():

</blockquote>

## Investigation

By using all the previous alternatives I had the chance to learn from all of them, take ideas, and combine them in the best way I could find for myself and the teams of developers I have worked with.

    ```

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models

Each attribute of a Pydantic model has a type.

Now it's the time to use it with FastAPI. 🚀

We had this type annotation:

=== "Python 3.10+"

    ```Python
    q: str | None = None
    ```

=== "Python 3.8+"

    ```Python
    q: Union[str, None] = None
    ```

Next, it 🤖 takes the first task to finish (let's say, our "slow-file" 📝) and continues whatever it had to do with it.