pydantic-async-validation

Add async validation to your pydantic models 🥳. This allows you to add validation that actually checks the database or makes an API call or just use any code you did write async.

Note that validation cannot happen during model creation, so you have to call await obj.model_async_validate() yourself. This is due to the fact that __init__() will always be a sync method and you cannot sanely call async methods from sync methods.

Note: pydantic-async-validation is compatible with pydantic versions 2.x only. It supports Python 3.8, 3.9, 3.10, 3.11 and 3.12. This is also ensured running all tests on all those versions using tox.

Example usage

import pydantic
from pydantic_async_validation import async_field_validator, AsyncValidationModelMixin


class SomethingModel(AsyncValidationModelMixin, pydantic.BaseModel):
    name: str

    @async_field_validator('name')
    async def validate_name(self, value: str) -> None:
        if value == "invalid":
            raise ValueError("Invalid name")


valid_instance = SomethingModel(name="valid")
await valid_instance.model_async_validate()

invalid_instance = SomethingModel(name="invalid")
await invalid_instance.model_async_validate()  # will raise normal pydantic ValidationError

Field validators

You can use async_field_validator to add async validators to your model. The first argument is the name of the field to validate. You may also pass additional field names, the validator will then be called for all fields. As validation is happening after the instance was created, you can access all fields of the model and the validator should just be a normal instance method (accepting self as its first parameter).

Field validators may use any combination of the following arguments:

• value: The value of the field to validate (same as getattr(self, field))
• field: The name of the field being validated, can be useful if you use the same validator for multiple fields
• config: The config of the validator, see ValidationInfo for details

You may also pass additional keyword arguments to async_field_validator, they will be passed to the validator config (ValidationInfo instance) and be available in the validator config as config.extra.

Example:

import pydantic
from pydantic_async_validation import async_field_validator, AsyncValidationModelMixin, ValidationInfo


class SomethingModel(AsyncValidationModelMixin, pydantic.BaseModel):
    name: str
    other_name: str

    @async_field_validator('name', 'other_name', some_extra='value')
    async def validate_name(self, value: str, field: str, config: ValidationInfo) -> None:
        if value == "invalid":
            # Using ValueError 
            raise ValueError(f"Invalid {field} with extra {config.extra['some_extra']}")

Model validators

You can use async_model_validator to add async validators to your model. The validator will be called after all field validators have been called. The validator should be a normal instance method (accepting self as its first parameter).

Model validators may use any combination of the following arguments:

• config: The config of the validator, see ValidationInfo for details

Example:

import pydantic
from pydantic_async_validation import async_model_validator, AsyncValidationModelMixin, ValidationInfo


class SomethingModel(AsyncValidationModelMixin, pydantic.BaseModel):
    name: str
    other_name: str

    @async_model_validator(some_extra='value')
    async def validate_names(self, config: ValidationInfo) -> None:
        # Using assertion 
        assert self.name != self.other_name, f"Names are equal with extra {config.extra['some_extra']}"

When to use field vs. model validators

As validation happens after the model instance was created, you can access all fields just using self anyways. So field vs. model validation is kind of the same thing. However field validators allow you to get the value of the field as its parameter, so this is perfect when you reuse validators or want to validate multiple fields with the same validator. Also field validators will tie the ValidationError to the field, so it will contain the detail about which field failed to validate. In general you should use field validators when you want to validate a single field. I also suggest using the value parameter to have a clean and consistent interface for your validators.

Model validators on the other hand should be used when you need to validate multiple fields at once. This is especially useful when you want to validate that multiple fields are consistent with each other. For example you might want to validate that a start date is before an end date. In this case you would use a model validator and access both fields using self. Note that model validators will be tied to "__root__" in the ValidationError as there is no specific field to tie it to.

Handling validation errors

Like with normal pydantic validation, you can catch ValidationError and access the errors() method to get a list of all errors. Like with pydantic errors will be collected and be raised as one ValidationError at the end of validation, including all errors that occurred.

model_async_validate() will also try to validate child model instances, that are also using the AsyncValidationModelMixin. This means the following example code will work as expected:

import pydantic
from pydantic_async_validation import async_field_validator, AsyncValidationModelMixin, ValidationInfo


class SomethingModel(AsyncValidationModelMixin, pydantic.BaseModel):
    name: str

    @async_field_validator('name')
    async def validate_name(self, value: str, field: str, config: ValidationInfo) -> None:
        if value == "invalid":
            raise ValueError(f"Value may not be 'invalid'")


class ParentModel(AsyncValidationModelMixin, pydantic.BaseModel):
    child: SomethingModel


invalid_instance = ParentModel(child=SomethingModel(name="invalid"))
await invalid_instance.model_async_validate()  # will raise normal pydantic ValidationError

Note the ValidationError will now have the location of the error set to "child.name".

Recursive validation will happen in those cases:

• Child models as direct instance variables (as in example above)
• Child models in list items (like child: List[SomethingModel])
• Child models in dict values (like child: Dict[str, SomethingModel])

FastAPI support

When using FastAPI you also can use the AsyncValidationModelMixin, note however that FastAPI will see any ValidationError risen in endpoint methods as unhandled exceptions and thus will return a HTTP 500 error. FastAPI will only handle the validation errors happening during handling the endpoint parameters in a special way and convert those to RequestValidationError - which will then be handled by the default exception handler for RequestValidationError FastAPI provides. This will then result in a HTTP 422 return code.

When using pydantic_async_validation this would be a major drawback, as using model_async_validate for validating input (/request) data is a totally fine use case and you cannot push this into the normal request validation step FastAPI does. To solve this issue you can use the ensure_request_validation_errors context manager provided in pydantic_async_validation.fastapi. This will ensure that any ValidationError risen inside the context manager will be converted to a RequestValidationError. Those RequestValidationErrors will then be handled by the default exception handler for RequestValidationError which FastAPI provides. This will then again result in a HTTP 422 return code.

Example for usage with FastAPI:

import fastapi
import pydantic
from pydantic_async_validation import AsyncValidationModelMixin
from pydantic_async_validation.fastapi import ensure_request_validation_errors


class SomethingModel(AsyncValidationModelMixin, pydantic.BaseModel): ...


app = fastapi.FastAPI()

@app.get("/return-http-422-on-async-validation-error")
async def return_http_422_on_async_validation_error():
    instance = SomethingModel(...)
    with ensure_request_validation_errors():
        await instance.model_async_validate()

You may also use ensure_request_validation_errors to do additional validation on the request data using normal pydantic validation and converting those ValidationErrors to RequestValidationErrors. 😉

Note: When using FastAPI you should install pydantic-async-validation using pip install pydantic-async-validation[fastapi] to ensure FastAPI is installed in a compatible version.

Contributing

If you want to contribute to this project, feel free to just fork the project, create a dev branch in your fork and then create a pull request (PR). If you are unsure about whether your changes really suit the project please create an issue first, to talk about this.