lt: Annotated[
        Optional[float],
        Doc(
            """
            Less than. If set, value must be less than this. Only applicable to numbers.
            """
        ),
    ] = None,
    le: Annotated[
        Optional[float],
        Doc(
            """
            Less than or equal. If set, value must be less than or equal to this.
            Only applicable to numbers.
            """
        ),

* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.
    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

But still, FastAPI got quite some inspiration from Requests.

**Requests** is a library to *interact* with APIs (as a client), while **FastAPI** is a library to *build* APIs (as a server).

They are, more or less, at opposite ends, complementing each other.

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python
{**note.dict(), "id": last_record_id}
```

    assert response.status_code == 422
    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "less_than",
                    "loc": ["path", "item_id"],
                    "msg": "Input should be less than 3",
                    "input": "42",
                    "ctx": {"lt": 3.0},
                }
            ]
        }
    ) | IsDict(

Und Sie können auch Validierungen für Zahlen deklarieren:

* `gt`: `g`reater `t`han – größer als
* `ge`: `g`reater than or `e`qual – größer oder gleich
* `lt`: `l`ess `t`han – kleiner als
* `le`: `l`ess than or `e`qual – kleiner oder gleich

!!! info
    `Query`, `Path`, und andere Klassen, die Sie später kennenlernen, sind Unterklassen einer allgemeinen `Param`-Klasse.

{% endfor %}
{% endif %}

## About the data - technical details

The main intention of this page is to highlight the effort of the community to help others.

Especially including efforts that are normally less visible, and in many cases more arduous, like helping others with questions and reviewing Pull Requests with translations.

    * Normally, a token is set to expire after some time.
        * So, the user will have to log in again at some point later.
        * And if the token is stolen, the risk is less. It is not like a permanent key that will work forever (in most of the cases).
* The frontend stores that token temporarily somewhere.
* The user clicks in the frontend to go to another section of the frontend web app.

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

!!! note
    `PATCH` is less commonly used and known than `PUT`.

    And many teams use only `PUT`, even for partial updates.

    You are **free** to use them however you want, **FastAPI** doesn't impose any restrictions.

    But this guide shows you, more or less, how they are intended to be used.

### Using Pydantic's `exclude_unset` parameter