[files]
extend-exclude = [
    ".git/",
    "docs/",
]
ignore-hidden = false

[default]
extend-ignore-re = [
    "Patrick Collison",
    "Copyright 2014 Unknwon",
    "[0-9A-Za-z/+=]{64}",
    "ZXJuZXQxDjAMBgNVBA-some-junk-Q4wDAYDVQQLEwVNaW5pbzEOMAwGA1UEAxMF",
    "eyJmb28iOiJiYXIifQ",
    "eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.*",
    "MIIDBTCCAe2gAwIBAgIQWHw7h.*",
    'http\.Header\{"X-Amz-Server-Side-Encryptio":',

    author_link: https://www.linkedin.com/in/navule/
    link: https://www.tutlinks.com/deploy-fastapi-on-ubuntu-gunicorn-caddy-2/
    title: Deploy FastAPI on Ubuntu and Serve using Caddy 2 Web Server
  - author: Patrick Ladon
    author_link: https://dev.to/factorlive
    link: https://dev.to/factorlive/python-facebook-messenger-webhook-with-fastapi-on-glitch-4n90
    title: Python Facebook messenger webhook with FastAPI on Glitch

musikisomorphie, Nagy Mostafa, Nayana Thorat, Neil, Niels Ole Salscheider,
Niklas SilfverströM, Niranjan Hasabnis, ocjosen, olramde, Pariksheet Pinjari,
Patrick J. Lopresti, Patrik Gustavsson, per1234, PeterLee, Phan Van Nguyen Duc,
Phillip Kravtsov, Pooya Davoodi, Pranav Marathe, Putra Manggala, Qingqing Cao,
Rajeshwar Reddy T, Ramon ViñAs, Rasmus Diederichsen, Reuben Morais, richardbrks,

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"

val startScripts = tasks.register<GradleStartScriptGenerator>("startScripts") {
    startScriptsDir = layout.buildDirectory.dir("startScripts")
    launcherJar.from(tasks.jar)
    agentJars.from(agentsClasspath)
    // The trick below is to use the templates from the current code instead of the wrapper. It does not cover the case where the generation logic is updated though.

    Daher müssen Sie möglicherweise weiterhin Pydantic-Modelle verwenden.

    Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

## Datenklassen als `response_model`

Sie können `dataclasses` auch im Parameter `response_model` verwenden:

```Python hl_lines="1  7-13  19"

Nevertheless, we can declare the expected schema for the request body.

### Custom OpenAPI content type

Using this same trick, you could use a Pydantic model to define the JSON Schema that is then included in the custom OpenAPI schema section for the *path operation*.

And you could do this even if the data type in the request is not JSON.

Dennoch können wir das zu erwartende Schema für den Requestbody deklarieren.

### Benutzerdefinierter OpenAPI-Content-Type

Mit demselben Trick könnten Sie ein Pydantic-Modell verwenden, um das JSON-Schema zu definieren, das dann im benutzerdefinierten Abschnitt des OpenAPI-Schemas für die *Pfadoperation* enthalten ist.

      sort(
          insertionOrder,
          new Comparator<V>() {
            @Override
            public int compare(V left, V right) {
              // The indexes are small enough for the subtraction trick to be safe.
              return indexOfEntryWithValue(left) - indexOfEntryWithValue(right);
            }

            int indexOfEntryWithValue(V value) {
              for (int i = 0; i < orderedEntries.size(); i++) {

!!! tip
    This is just standard Python, it's called a "type alias", it's actually not specific to **FastAPI**.

    But because **FastAPI** is based on the Python standards, including `Annotated`, you can use this trick in your code. 😎