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 }

Теперь в той же директории проекта создайте файл `Dockerfile`:

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

    System.out.println(response.getContentAsString());
}
```

### Asynchronous request

```java
import org.codelibs.curl.Curl;

Curl.post("https://api.example.com/items")
    .body("{\"name\":\"item1\"}")
    .header("Content-Type", "application/json")
    .execute(
        response -> System.out.println("Async status: " + response.getHttpStatusCode()),
        error -> error.printStackTrace());
```

```Python hl_lines="3  5"
{
    "name": "Bar",
    "description": "The bartenders",
    "price": 62,
    "tax": 20.2
}
```
вони будуть включені у відповідь.

#### Дані з тими самими значеннями, що й типові

Якщо дані мають ті самі значення, що й типові, як у елемента з item_id `baz`:

```Python hl_lines="3  5-6"
{

            "type": "value_error.missing"
        }
    ]
}
```

`needy` は必須のパラメータなので、URLにセットする必要があります:

```
http://127.0.0.1:8000/items/foo-item?needy=sooooneedy
```

...これはうまくいくでしょう:

```JSON
{
    "item_id": "foo-item",
    "needy": "sooooneedy"
}
```

そして当然、あるパラメータを必須に、別のパラメータにデフォルト値を設定し、また別のパラメータをオプショナルにできます:

{* ../../docs_src/query_params/tutorial006.py hl[10] *}

                    readings[0] = l.toArray(String[]::new);

                    if (items == null) {
                        items = new ArrayList<>(text.length() * fields.length / field.length());
                    }

                    final String[] langs = lang == null ? new String[] {} : new String[] { lang };
                    items.add(new SuggestItem(words, readings, new String[] { field }, 1L, 0, -1, tags, roles, langs,

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 }

Erstellen Sie nun im selben Projektverzeichnis eine Datei `Dockerfile` mit:

```{ .dockerfile .annotate }

async def read_users_me(current_user: User = Depends(get_current_active_user)):
    return current_user


@app.get("/users/me/items/")
async def read_own_items(
    current_user: User = Security(get_current_active_user, scopes=["items"]),
):
    return [{"item_id": "Foo", "owner": current_user.username}]


@app.get("/status/")
async def read_system_status(current_user: User = Depends(get_current_user)):

And Python has a special data type for sets of unique items, the `set`.

Then we can declare `tags` as a set of strings:

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

        }
        RenderDataUtil.register(data, "fileConfigItems", itemList);
    }

    /**
     * Creates a dropdown item with label and value.
     *
     * @param label the item label
     * @param value the item value
     * @return map containing the item
     */
    protected Map<String, String> createItem(final String label, final String value) {
        final Map<String, String> map = new HashMap<>(2);

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)!