{!> ../../docs_src/body/tutorial004.py!}
```

////

函数参数按如下规则进行识别：

- **路径**中声明了相同参数的参数，是路径参数
- 类型是（`int`、`float`、`str`、`bool` 等）**单类型**的参数，是**查询**参数
- 类型是 **Pydantic 模型**的参数，是**请求体**

/// note | "笔记"

因为默认值是 `None`， FastAPI 会把 `q` 当作可选参数。

FastAPI 不使用 `Optional[str]` 中的 `Optional`， 但 `Optional` 可以让编辑器提供更好的支持，并检测错误。

///

## 不使用 Pydantic

* Se o parâmetro é de um **tipo único** (como `int`, `float`, `str`, `bool`, etc) será interpretado como um parâmetro de **consulta**.
* Se o parâmetro é declarado como um **modelo Pydantic**, será interpretado como o **corpo** da requisição.

/// note | "Observação"

O FastAPI saberá que o valor de `q` não é obrigatório por causa do valor padrão `= None`.

	for _, opt := range opts {
		str := stmt.Quote(opt.DBName)
		if opt.Expression != "" {
			str = opt.Expression
		} else if opt.Length > 0 {
			str += fmt.Sprintf("(%d)", opt.Length)
		}

		if opt.Collate != "" {
			str += " COLLATE " + opt.Collate
		}

		if opt.Sort != "" {
			str += " " + opt.Sort
		}
		results = append(results, clause.Expr{SQL: str})
	}
	return
}

from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@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}


@app.put("/items/{item_id}")

from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@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}


@app.put("/items/{item_id}")

// splitEqual splits key=value string into key,value. if no = is found
// the whole string is the key and value is empty.
func splitEqual(str string) (string, string) {
	idx := strings.Index(str, "=")
	var k string
	var v string
	if idx >= 0 {
		k = str[:idx]
		v = str[idx+1:]
	} else {
		k = str
	}
	return k, v
}

                    "ctx": {"pattern": "^fixedquery$"},
                    "loc": ["query", "item-query"],
                    "msg": 'string does not match regex "^fixedquery$"',
                    "type": "value_error.str.regex",
                }
            ]
        }
    )


def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text

                    "ctx": {"pattern": "^fixedquery$"},
                    "loc": ["query", "item-query"],
                    "msg": 'string does not match regex "^fixedquery$"',
                    "type": "value_error.str.regex",
                }
            ]
        }
    )


@needs_py310
def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")

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.

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sau đó có thể được sử dụng:

```Python