高级输入验证与错误处理

使用 FastAPI 在生产环境中部署 AI

Matt Eckerle

Software and Data Engineering Leader

为何需要高级输入

餐厅点单的 API
可变数量的菜品

class Order(BaseModel):
    item1: str
    item2: str
    item3: str

一家餐厅

嵌套的 Pydantic 模型

from pydantic import BaseModel

class Foo(BaseModel):
    count: int

class Bar(BaseModel):
    foo: Foo

>>> m = Bar(foo={'count': 4})
>>> print(m)
foo=Foo(count=4)

from pydantic import BaseModel
from typing import List


class OrderItem(BaseModel):
    name: str
    quantity: int


class RestaurantOrder(BaseModel):
    customer_name: str
    items: List[OrderItem]

自定义模型验证器

from fastapi import FastAPI
from fastapi.exceptions import (
    RequestValidationError
)
from pydantic import (
    BaseModel,
    model_validator,
)

from typing import List

class OrderItem(BaseModel):
    name: str
    quantity: int

class RestaurantOrder(BaseModel):
    customer_name: str
    items: List[OrderItem]


    @model_validator(mode="after")

    def validate_after(self):
        if len(self.items) == 0:
           raise RequestValidationError(
               "No items in order!"
           )
        return self

{"detail":"No items in order!"}

全局异常处理程序

from fastapi import FastAPI
from fastapi.exceptions import RequestValidationError
from fastapi.responses import PlainTextResponse


app = FastAPI()

@app.exception_handler(RequestValidationError)

async def validation_exception_handler(request, exc):
    msg = "Input validation error. See the documentation: http://127.0.0.1:8000/docs"
    return PlainTextResponse(msg, status_code=422)

Input validation error. See the documentation: http://127.0.0.1:8000/docs

让我们来练习！

使用 FastAPI 在生产环境中部署 AI