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
无论任意嵌套深度的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、模型的属性类型可以包含子类型
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，通常我们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，这个类型本身也可以是另一个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。
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