* Datenkonvertierung
* Datenvalidierung
* Automatische Dokumentation

## Spezielle Typen und Validierungen

Abgesehen von normalen einfachen Typen, wie `str`, `int`, `float`, usw. können Sie komplexere einfache Typen verwenden, die von `str` erben.

from datetime import datetime

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Subscription(BaseModel):
    username: str
    monthly_fee: float
    start_date: datetime


@app.webhooks.post("new-subscription")
def new_subscription(body: Subscription):
    """
    When a new user subscribes to your service we'll send you a POST request with this

                            <la:message key="labels.dict_stemmeroverride_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class ItemGroup(BaseModel):
    items: List[Tuple[str, str]]


class Coordinate(BaseModel):
    x: float
    y: float


@app.post("/model-with-tuple/")
def post_model_with_tuple(item_group: ItemGroup):
    return item_group


@app.post("/tuple-of-models/")

}

func (t ScanToken) String() string {
	switch t {
	case scanner.EOF:
		return "EOF"
	case scanner.Ident:
		return "identifier"
	case scanner.Int:
		return "integer constant"
	case scanner.Float:
		return "float constant"
	case scanner.Char:
		return "rune constant"
	case scanner.String:
		return "string constant"
	case scanner.RawString:
		return "raw string constant"
	case scanner.Comment:
		return "comment"

    "aliases" : { },
    "mappings" : {
      "data_config" : {
        "properties" : {
          "available" : {
            "type" : "boolean"
          },
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "description" : {
            "type" : "text"

from typing import Dict

from fastapi import FastAPI

app = FastAPI()


@app.post("/index-weights/")
async def create_index_weights(weights: Dict[int, float]):

      "suggestWord": {
        "type": "keyword"
      },
      "reading": {
        "type": "keyword"
      },
      "permissions": {
        "type": "keyword"
      },
      "boost": {
        "type": "float"
      },
      "createdBy": {
        "type": "keyword"
      },
      "createdTime": {
        "type": "long"
      },
      "updatedBy": {
        "type": "keyword"
      },
      "updatedTime": {

                    "msg": "value is not a valid float",
                    "type": "type_error.float",
                }
            ]
        }
    )


def test_path_float_42():
    response = client.get("/path/float/42")
    assert response.status_code == 200
    assert response.json() == 42


def test_path_float_42_5():
    response = client.get("/path/float/42.5")
    assert response.status_code == 200

from typing import Dict

from fastapi import FastAPI

app = FastAPI()


@app.get("/keyword-weights/", response_model=Dict[str, float])
async def read_keyword_weights():