FastAPI 基础学习(十) Pydantic复杂模型

作者：麦克煎蛋   出处：https://www.cnblogs.com/mazhiyong/ 转载请保留这段声明，谢谢！

一、Pydantic模型的附加信息

与前面讲过的Query、Path、Body类似，我们也可以为Pydantic模型添加附加信息，基于模块Field。

1、导入Field模块

from pydantic import BaseModel, Field

2、声明模型属性

class Item(BaseModel):
    name: str
    description: str = Field(None, title="The description of the item", max_length=300)
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: float = None

Field模块的参数与Query、Path、Body等相同。

完整示例：

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str = Field(None, title="The description of the item", max_length=300)
    price: float = Field(..., gt=0, description="The price must be greater than zero")
    tax: float = None


@app.put("/items/{item_id}")
async def update_item(*, item_id: int, item: Item = Body(..., embed=True)):
    results = {"item_id": item_id, "item": item}
    return results

二、Pydantic嵌套模型

任意嵌套深度的Pydantic模型，我们都可以进行定义、校验、自动文档化和灵活使用。

1、模型的属性可以是数据集合类型

比如list，dict,tuple,set等等。

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: list = []

from typing import List

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: List[str] = []

from typing import Set

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = set()

我们也可以直接定义一个字典类型的body，通常指定了键和值的数据类型。

from typing import Dict
from fastapi import FastAPI

app = FastAPI()


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

2、嵌套模型

每一个Pydantic模型的属性都有一个类型，这个类型也可以是另一个Pydantic模型。

from typing import Set

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Image(BaseModel):
    url: str
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    image: Image = None


@app.put("/items/{item_id}")
async def update_item(*, item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}
    return results

FastAPI期望的Request Body内容格式如下：

{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2,
    "tags": ["rock", "metal", "bar"],
    "image": {
        "url": "http://example.com/baz.jpg",
        "name": "The Foo live"
    }
}

我们也可以把Pydantic模型作为list、set等集合类型的元素类型。

from typing import List, Set
from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    images: List[Image] = None


@app.put("/items/{item_id}")
async def update_item(*, item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}
    return results

这里FastAPI期望的Request Body内容格式如下：

{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2,
    "tags": [
        "rock",
        "metal",
        "bar"
    ],
    "images": [
        {
            "url": "http://example.com/baz.jpg",
            "name": "The Foo live"
        },
        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}

3、深层嵌套模型

我们可以定义任意深度的嵌套模型，如下示例：

from typing import List, Set

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Image(BaseModel):
    url: HttpUrl
    name: str


class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None
    tags: Set[str] = []
    images: List[Image] = None


class Offer(BaseModel):
    name: str
    description: str = None
    price: float
    items: List[Item]


@app.post("/offers/")
async def create_offer(*, offer: Offer):
    return offer