from typing import Annotated

    from fastapi import FastAPI, Path

    app = FastAPI()


    @app.get("/items/{item_id}")
    async def read_items(
        item_id: Annotated[int, Path(title="The ID of the item to get")],
    ):
        return {"item_id": item_id}
    ```
    """
    return params.Path(
        default=default,
        default_factory=default_factory,
        alias=alias,

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

### Dockerfile { #dockerfile }

Now in the same project directory create a file `Dockerfile` with:

```{ .dockerfile .annotate }
# (1)!

    }

    @Test
    public void test_update() throws Exception {
        SuggestItem[] items = getItemSet1();
        suggester.indexer().index(items);
        suggester.refresh();

        SuggestItem[] items2 = getItemSet1();
        SuggestIndexResponse response = suggester.indexer().index(items2);

        assertFalse(response.hasError());

        suggester.refresh();

    assert response.status_code == 422
    assert response.json() == {
        "detail": [
            {
                "type": "int_parsing",
                "loc": ["path", "item_id"],
                "msg": "Input should be a valid integer, unable to parse string as an integer",
                "input": "foobar",
            }
        ]
    }


def test_path_int_True():

     * the item's current state.
     * </p>
     *
     * @param dictId the dictionary ID to store the character mapping item in
     * @param charMappingItem the character mapping item to store
     */
    public void store(final String dictId, final CharMappingItem charMappingItem) {
        getCharMappingFile(dictId).ifPresent(file -> {
            if (charMappingItem.getId() == 0) {

{* ../../docs_src/pydantic_v1_in_v2/tutorial003_an_py310.py hl[2:3,6,12,21:22] *}

Im obigen Beispiel ist das Eingabemodell ein Pydantic‑v1‑Modell, und das Ausgabemodell (definiert in `response_model=ItemV2`) ist ein Pydantic‑v2‑Modell.

### Pydantic v1 Parameter { #pydantic-v1-parameters }

            },
        ),
        (
            "/items?q=foo&skip=1",
            200,
            {"items": [{"item_name": "Bar"}, {"item_name": "Baz"}], "q": "foo"},
        ),
        (
            "/items?q=bar&limit=2",
            200,
            {"items": [{"item_name": "Foo"}, {"item_name": "Bar"}], "q": "bar"},
        ),
        (
            "/items?q=bar&skip=1&limit=1",
            200,

@app.get("/items/validlist", response_model=list[Item])
def get_validlist():
    return [
        Item(aliased_name="foo"),
        Item(aliased_name="bar", price=1.0),
        Item(aliased_name="baz", price=2.0, owner_ids=[1, 2, 3]),
    ]


@app.get("/items/validdict", response_model=dict[str, Item])
def get_validdict():
    return {
        "k1": Item(aliased_name="foo"),
        "k2": Item(aliased_name="bar", price=1.0),

app = FastAPI()


@dataclass
class Item:
    name: str
    date: datetime
    price: Optional[float] = None
    owner_ids: Optional[list[int]] = None


@app.get("/items/valid", response_model=Item)
def get_valid():
    return {"name": "valid", "date": datetime(2021, 7, 26), "price": 1.0}


@app.get("/items/object", response_model=Item)
def get_object():
    return Item(

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

### 도커파일

이제 같은 프로젝트 디렉터리에 다음과 같은 파일 `Dockerfile`을 생성합니다:

```{ .dockerfile .annotate }
# (1)
FROM python:3.9

# (2)