ValidationError.from_exception_data doesn't work with error type: 'ip_v4_address'"

[mikedavidson-evertz]

Describe the bug

When constructing validation errors using ValidationError.from_exception_data, this fails when the error type is ip_v4_address.It will raise:

KeyError: "Invalid error type: 'ip_v4_address'

To Reproduce
Run the code below. It uses a custom model validator that manually constructs the validation error using ValidationError.from_exception_data

from pydantic import BaseModel, Field, model_validator, ValidationError
from ipaddress import IPv4Address
from pydantic_core import InitErrorDetails


class OutputStream(BaseModel):
    destination_ip: IPv4Address = Field(
        ...,
    )


class StreamingDevice(BaseModel):
    output_stream: OutputStream = Field(
        ...,
    )

    @model_validator(mode="before")
    @classmethod
    def validate_model(cls, data: dict) -> dict:
        validation_errors: list[InitErrorDetails] = []

        if data.get("output_stream") is not None:
            try:
                OutputStream.model_validate(data["output_stream"])
            except ValidationError as e:
                validation_errors.extend(e.errors())

        if validation_errors:
            raise ValidationError.from_exception_data(title=cls.__name__, line_errors=validation_errors)

        return data



streaming_device_payload = {"output_stream": {"destination_ip": "123"}}
streaming_device = StreamingDevice.model_validate(streaming_device_payload)

Expected behavior
Pydantic is capable of generating 'ip_v4_address' errors when using model validation without the custom model validator. Therefore, we should be able to manually construct the validation error using the error dictionary that contains the ip_v4_address error type as input into ValidationError.from_exception_data.

If you were to remove the custom model validator in the example above, Pydantic would successfully throw that error type:

pydantic_core._pydantic_core.ValidationError: 1 validation error for StreamingDevice
output_stream.destination_ip
  Input is not a valid IPv4 address [type=ip_v4_address, input_value='123', input_type=str

Haven't tested it, but should ip_v4_address be in this error list here?
https://github.com/pydantic/pydantic-core/blob/main/python/pydantic_core/core_schema.py#L3900

Version:

• OS: Ubuntu 20.04.4 LTS
• Python version: 3.9.16
• pydantic version: 2.3.0

[westrachel]

I'm running into a similar issue where ValidationError.from_exception_data doesn't work with other error types, specifically value_error and enum.

Reproducing the Errors:
(1) enum

from pydantic_core import InitErrorDetails
from pydantic import ValidationError

validation_errors = []

validation_errors.append(InitErrorDetails(
    type="enum",
    loc=("direction",),
    input="Z",
))

raise ValidationError.from_exception_data(
    title="example",
    line_errors=validation_errors
)

# Result:
Traceback (most recent call last):
  File ".../example.py", line 12, in <module>
    raise ValidationError.from_exception_data(
TypeError: Enum: 'expected' required in context

(2) value_error

from pydantic_core import InitErrorDetails
from pydantic import ValidationError

validation_errors = []

validation_errors.append(InitErrorDetails(
    type="value_error",
    loc=("direction",),
    input="Z",
))

raise ValidationError.from_exception_data(
    title="example",
    line_errors=validation_errors
)

# Result:
Traceback (most recent call last):
  File ".../example.py", line 12, in <module>
    raise ValidationError.from_exception_data(
TypeError: ValueError: 'error' required in context

Expected Behavior:

Instead of the TypeErrors raised above, I expect a pydantic ValidationError to be raised that shows relevant info for the respective enum and value_error types. For example, If I assign type in InitErrorDetails in the above code to an error that does not fully fit my use case (string_type), it does work and does produce the validation error I want to raise (but with the wrong message; I don't want the invalid string message for my use case):

from pydantic_core import InitErrorDetails
from pydantic import ValidationError

validation_errors = []

validation_errors.append(InitErrorDetails(
    type="string_type",
    loc=("direction",),
    input="Z",
))

raise ValidationError.from_exception_data(
    title="example",
    line_errors=validation_errors
)

# Result:
Traceback (most recent call last):
  File ".../example.py", line 12, in <module>
    raise ValidationError.from_exception_data(
pydantic_core._pydantic_core.ValidationError: 1 validation error for example
direction
  Input should be a valid string [type=string_type, input_value='Z', input_type=str]
    For further information visit https://errors.pydantic.dev/2.5/v/string_type

Versions:
OS: macOS Sonoma 14.1.1
Python version: 3.10.12
pydantic version: 2.5.2

[KeynesYouDigIt]

I am having the same issues. The new things like @model_validator are an exciting direction, but we really need support on how to properly aggregate custom errors in this case.

When trying to aggregate errors, I am really struggling. Is this a sign I am using pydantic not as intended? if not, are there more complex cases in the docs?

If I am using this as intended, here were debug steps
specific error I am getting - TypeError: ValueError: 'error' required in context.

I looked at the Init error typed dict..... nothing jumps out as to what was wrong in my case.

pydantic-core/python/pydantic_core/__init__.py

Line 94 in 47aff70

class InitErrorDetails(_TypedDict):

[dsayling]

@KeynesYouDigIt

It's complaining about there being no error in the ctx dict

e.g.

...
            InitErrorDetails(
                {
                    "type": "value_error",
                    "loc": (info.field_name,),
                    "input": v,
                    "ctx": {
                        "error": f"your_message {v}",
                    },
                }
            )

---

edit

Also, depending on the "type" here, the ctx might need different key values. See the linked doc page and scroll down to ctx

As far as I know, you can inspect what needs to be in the ctx to render the error by doing

from pydantic_core._pydantic_core import list_all_errors
list_all_errors()