from fastapi import FastAPI, Path, Query

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    *,
    item_id: int = Path(title="The ID of the item to get", ge=0, le=1000),
    q: str,
    size: float = Query(gt=0, lt=10.5),
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})
    if size:
        results.update({"size": size})

class FilterParams(BaseModel):
    model_config = {"extra": "forbid"}

    limit: int = Field(100, gt=0, le=100)
    offset: int = Field(0, ge=0)
    order_by: Literal["created_at", "updated_at"] = "created_at"
    tags: list[str] = []


@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):

from typing import Annotated, Union

from fastapi import Depends, FastAPI

app = FastAPI()


async def common_parameters(
    q: Union[str, None] = None, skip: int = 0, limit: int = 100
):
    return {"q": q, "skip": skip, "limit": limit}


@app.get("/items/")
async def read_items(commons: Annotated[dict, Depends(common_parameters)]):
    return commons


@app.get("/users/")

Dazu aktualisieren wir das Pydantic-Modell `TokenData` mit einem neuen Attribut `scopes`.

Durch die Validierung der Daten mit Pydantic können wir sicherstellen, dass wir beispielsweise präzise eine `list`e von `str`s mit den Scopes und einen `str` mit dem `username` haben.

Anstelle beispielsweise eines `dict`s oder etwas anderem, was später in der Anwendung zu Fehlern führen könnte und darum ein Sicherheitsrisiko darstellt.

    return JSONResponse(
        status_code=422,
        content=jsonable_encoder({"detail": exc.errors(), "body": exc.body}),
    )


class Item(BaseModel):
    title: str
    size: int


@app.post("/items/")
async def create_item(item: Item):

from typing import Annotated

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Annotated[
        str | None,
        Query(
            alias="item-query",
            title="Query string",
            description="Query string for the items to search in the database that have a good match",
            min_length=3,
            max_length=50,
            pattern="^fixedquery$",

	writer.WriteByte('?')
}

func (DummyDialector) QuoteTo(writer clause.Writer, str string) {
	var (
		underQuoted, selfQuoted bool
		continuousBacktick      int8
		shiftDelimiter          int8
	)

	for _, v := range []byte(str) {
		switch v {
		case '`':
			continuousBacktick++
			if continuousBacktick == 2 {
				writer.WriteString("``")

@pytest.mark.parametrize(
    ("user_id", "expected_response"),
    [
        ("me", {"user_id": "the current user"}),
        ("alice", {"user_id": "alice"}),
    ],
)
def test_get_users(user_id: str, expected_response: dict):
    response = client.get(f"/users/{user_id}")
    assert response.status_code == 200, response.text
    assert response.json() == expected_response


def test_openapi_schema():

  @Override
  public void writeShort(int v) throws IOException {
    out.write(0xFF & v);
    out.write(0xFF & (v >> 8));
  }

  @Override
  public void writeUTF(String str) throws IOException {
    ((DataOutputStream) out).writeUTF(str);
  }

  // Overriding close() because FilterOutputStream's close() method pre-JDK8 has bad behavior:

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel, ConfigDict, Field

app = FastAPI()


class Model(BaseModel):
    name: str = Field(alias="alias")


class ModelNoAlias(BaseModel):
    name: str

    model_config = ConfigDict(
        json_schema_extra={
            "description": (
                "response_model_by_alias=False is basically a quick hack, to support "