检查pydantic中的输入数据类型

发布于 2025-01-29 08:00:18 字数 294 浏览 1 评论 0原文

是否有一种方法可以在Pydantic中检查输入变量的数据类型，例如：

class ModelParameters(BaseModel):
    str_val: str
    int_val: int
    wrong_val: int


test = ModelParameters(**dict({
    "str_val":"test",
    "int_val":1,
    "wrong_val":1.2}))

它应该为forgation_val丢弃错误。

原文

Is there a way to check the datatypes of the input variables natively in pydantic, like:

class ModelParameters(BaseModel):
    str_val: str
    int_val: int
    wrong_val: int


test = ModelParameters(**dict({
    "str_val":"test",
    "int_val":1,
    "wrong_val":1.2}))

Which should throw an error for wrong_val.

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还如梦归 2025-02-05 08:00:18

v2.0编辑：

随着 2.0版的发布，Pydantic的行为发生了变化。 现在给出的示例将为forgation_val 提出错误。

与下面公开的pydantic 1.x相比，v2.x也不会将int类似123放入“ 123” str中，如果属性类型为str。

对于pydantic 1.x （原始答案）

Pydantic进行了一些隐式转换，尤其是在INT，STR或Float等原始类型上。讨论了这种行为背后的原因在这里。

因此，确实，有了这样的课程：

class ModelParameters(BaseModel):
    str_val: str
    int_val: int
    wrong_val: int

您可以绝对实例化这样的对象：

test = ModelParameters(**dict({
    "str_val": 123,
    "int_val": "1",
    "wrong_val": 1.2}))

test
# ModelParameters(str_val='123', int_val=1, wrong_val=1)

，但是您 do 可以选择执行类型检查。您需要做的是要使用strictstr，strictfloat和strictInt作为STR，float和int的类型纯种替代品。您会在pydantic.types中找到它们。在您的情况下：

from pydantic.types import StrictStr, StrictInt

class ModelParameters(BaseModel):
    str_val: StrictStr
    int_val: StrictInt
    wrong_val: StrictInt

现在，如果您尝试相同的实例化，您会发现到处都有验证错误，就像您期望的那样：

test = ModelParameters(**dict({
    "str_val": 123,
    "int_val": "1",
    "wrong_val": 1.2}))

pydantic.error_wrappers.ValidationError: 3 validation errors for ModelParameters
str_val
  str type expected (type=type_error.str)
int_val
  value is not a valid integer (type=type_error.integer)
wrong_val
  value is not a valid integer (type=type_error.integer)

v2.0 Edit:

Pydantic's behaviour has changed with the release of version 2.0. Now the example given will raise an error for wrong_val, as expected.

Compared to pydantic 1.x as exposed below, v2.x also doesn't parse an int like 123 into a "123" str anymore if the attribute type is str.

For pydantic 1.x (original answer)

Pydantic does a handful of implicit conversion, particularly on primitive types like int, str, or float. The reason behind this behaviour is discussed here.

So indeed, with a class like this:

class ModelParameters(BaseModel):
    str_val: str
    int_val: int
    wrong_val: int

You can absolutely instantiate an object like that:

test = ModelParameters(**dict({
    "str_val": 123,
    "int_val": "1",
    "wrong_val": 1.2}))

test
# ModelParameters(str_val='123', int_val=1, wrong_val=1)

But you do have the option to enforce type checking. What you need to do, is to use StrictStr, StrictFloat and StrictInt as a type-hint replacement for str, float and int. You'll find them in pydantic.types. In your case:

from pydantic.types import StrictStr, StrictInt

class ModelParameters(BaseModel):
    str_val: StrictStr
    int_val: StrictInt
    wrong_val: StrictInt

Now, if you try the same instantiation, you'll see validation errors all around the place, like you'd expect:

test = ModelParameters(**dict({
    "str_val": 123,
    "int_val": "1",
    "wrong_val": 1.2}))

pydantic.error_wrappers.ValidationError: 3 validation errors for ModelParameters
str_val
  str type expected (type=type_error.str)
int_val
  value is not a valid integer (type=type_error.integer)
wrong_val
  value is not a valid integer (type=type_error.integer)

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