请求体 - 字段
字段与使用 Query、Path 和 Body 在路径操作函数中声明额外的校验和元数据的方式相同，可以使用 Pydantic 的 Field 在 Pydantic 模型内部声明校验和元数据。
注意点：Field 是直接从 pydantic 导入的，而不是像其他的（Query，Path，Body 等）都从 fastapi 导入。
首先导入Field:
from pydantic import Field
其次开始声明模型定义字段:
from pydantic import Field, baseModel class Items(baseModel): name: str full_name: Optional[str] = Field(None, min_length=2) description: Optional[str] = Field(None, max_length=100) price: float = Field(..., gt=0) gender: str = Field(..., alias="g") tax: int = Field(None, title="用于文档中", description="在文档中显示")
注释信息：
min_length=2 是设置字段的最小长度max_length=100 是设置字段最大长度gt=0 是大于0 [ ge大于等于、lt小于、le小于等于]alias="g" 是取别名title="用于文档中" 显示在生成的docs文档中description="在文档中显示" 显示在生成的docs文档中
然后开始定义接口:
from typing import Optional
from fastapi import FastAPI
from fastapi import Body
from pydantic import Field, baseModel
class Items(baseModel):
name: str
full_name: Optional[str] = Field(None, min_length=2)
description: Optional[str] = Field(None, max_length=100)
price: float = Field(..., gt=0)
gender: str = Field(..., alias="g")
tax: int = Field(None, title="用于文档中", description="在文档中显示")
app = FastAPI()
@app.put("/items/{item_id}")
def read_field(item_id: int, item: Items = Body(..., embed=True)):
return {"item_id": item_id, "item": item}
然后启动服务:
lifeng@192 fastapiProject % uvicorn field_main:app --reload INFO: Will watch for changes in these directories: ['/Users/lifeng/python-projects/python-code/fastapiProject'] INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit) INFO: Started reloader process [45277] using statreload INFO: Started server process [45279] INFO: Waiting for application startup. INFO: Application startup complete. WARNING: StatReload detected file change in 'field_main.py'. Reloading... INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. INFO: Finished server process [45279] INFO: Started server process [45376] INFO: Waiting for application startup. INFO: Application startup complete. INFO: 127.0.0.1:55549 - "PUT /items/1 HTTP/1.1" 200 OK
最后请求接口：
PUT ：http://127.0.0.1:8000/items/1
传入请求参数：
{
"item": {
"name": "haha",
"price": 2.8,
"g": "eee"
}
}
返回响应结果：
{
"item_id": 1,
"item": {
"name": "haha",
"full_name": null,
"description": null,
"price": 2.8,
"g": "eee",
"tax": null
}
}
请求体 - 嵌套模型
就是给属性（字段）定义为拥有子元素的类型，如：name: list 或 name: List)，还有就是嵌套子模型（可以直接理解为字段又嵌套一个子字段），如：image: Optional[Image] = None等。
from fastapi import FastAPI
from typing import Optional, List, Set
from pydantic import baseModel, Field
class Image(baseModel):
url: str
name: str
class User(baseModel):
name: str
full_name: Optional[str] = Field(None)
age: int = Field(..., ge=0)
tags: List[str] = []
email: Set[str] = set()
image: Image
app = FastAPI()
@app.put("/items/{item_id}")
def read_items(item_id: int, user: User):
results = {"item_id": item_id, "user": user}
return results
上述例子User就是声明的模型，Image也是声明的模型，只是Image用在User中，故被称为子模型(嵌套模型)，是image属性（字段）拥有的子元素的类型
tags和email是属性含子类型的类型image属于子模型用作类型email属性定义为集合的原因，是可以过滤重复值，保证唯一值，但是在传参时，按列表类型传即可
请求接口：
PUT ：http://127.0.0.1:8000/items/1
请求参数：
{
"name": "haha",
"age": 11,
"tags": [
"1",
"2"
],
"email": [
"1",
"2"
],
"image": {
"url": "http://example.com/baz.jpg",
"name": "heihei"
}
}
请求结果：
{
"item_id": 1,
"user": {
"name": "haha",
"full_name": null,
"age": 11,
"tags": [
"1",
"2"
],
"email": [
"1",
"2"
],
"image": {
"url": "http://example.com/baz.jpg",
"name": "heihei"
}
}
}
如果你遇到字段需要定义为url，就像这样的：
from pydantic import baseModel class Image(baseModel): url: str name: str
那就直接把url声明为HttpUrl，而不是声明为str：
from pydantic import baseModel, HttpUrl class Image(baseModel): url: HttpUrl name: str
因为声明为HttpUrl，该字符串将被检查是否为有效的 URL，若检查出不是有效的URL，则抛出异常：
{
"detail": [
{
"loc": [
"body",
"image",
"url"
],
"msg": "invalid or missing URL scheme",
"type": "value_error.url.scheme"
}
]
}
有些时候可能会遇到要在子参数中传数组，数组中要传多个参数，那这个时候就需要给子模型定义类型，也称子模型的属性：
from fastapi import FastAPI
from typing import List
from pydantic import baseModel, Field, HttpUrl
class Image(baseModel):
url: HttpUrl
name: str
class User(baseModel):
name: str
age: int = Field(..., ge=0)
images: List[Image]
app = FastAPI()
@app.put("/items/{item_id}")
def read_items(item_id: int, user: User):
results = {"item_id": item_id, "user": user}
return results
请求接口：
PUT ：http://127.0.0.1:8000/items/1
请求参数：
{
"name": "haha",
"age": 11,
"images": [
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
},
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
},
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
}
]
}
请求结果：
{
"item_id": 1,
"user": {
"name": "haha",
"age": 11,
"images": [
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
},
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
},
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
}
]
}
}
你还可以自定义任意深度的嵌套模型：
from fastapi import FastAPI
from typing import List, Optional, Set
from pydantic import baseModel, HttpUrl
app = FastAPI()
class Image(baseModel):
url: HttpUrl
name: str
class Item(baseModel):
name: str
description: Optional[str] = None
price: float
tax: Optional[float] = None
tags: Set[str] = set()
images: List[Image]
class Offer(baseModel):
name: str
description: Optional[str] = None
price: float
items: List[Item]
@app.post("/items/")
async def read_items(offer: Offer):
return offer
请求接口：
POST ：http://127.0.0.1:8000/items
请求参数：
{
"name": "haha",
"price": 3.9,
"items": [
{
"name": "heihei",
"price": 4.99,
"tags": [
1,
2,
3,
3,
3,
3
],
"images": [
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
}
]
}
]
}
请求结果：
{
"name": "haha",
"description": null,
"price": 3.9,
"items": [
{
"name": "heihei",
"description": null,
"price": 4.99,
"tax": null,
"tags": [
"1",
"3",
"2"
],
"images": [
{
"url": "http://example.com/baz.jpg",
"name": "heihei"
}
]
}
]
}
从请求结果可以看到，请求参数中的tags是一个数组，其实在接口中是一个集合，集合有去重功能，所以返回的结果自然就只有一个3了。
今天先聊到这里吧，以上总结或许能帮助到你，或许帮助不到你，但还是希望能帮助到你，如有疑问、歧义，直接私信留言会及时修正发布；非常期待你的一键 3 连【 点赞、收藏、分享 】哟，谢谢！
未完成，待续……
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