Handle invalid model data
A model receives malformed or incorrectly typed input.
Pydantic · Class
ValidationError
ValidationError is raised when Pydantic rejects input during validation. Catch it to access a readable report describing the model, location, expected value, error type, and received input.
How it works
ValidationError is raised when Pydantic cannot validate input against a model or other declared schema. Catch it to inspect human-readable output or structured details such as the failing location, message, error type, and supplied input.
Syntax
class ValidationError(Exception)Returns
Raises ValidationError when validation fails; the documented example shows the exception being caught and printed.
Key points
- Catch the exception with a normal Python try/except block to handle invalid model input.
- The formatted report identifies the number of validation failures and the model involved.
- Each failure includes a human-readable explanation of what the input should have been.
- Diagnostics can include the machine-oriented error type, received value, and received Python type.
Common mistakes
- When a model is configured to forbid extra fields, supplying one raises ValidationError rather than silently accepting it.
Example: Inspect validation errors
from pydantic import BaseModel, ValidationError
class User(BaseModel):
age: int
try:
User(age="not an integer")
except ValidationError as exc:
print(exc.errors())
Catch the exception and inspect structured error details.
When to use it
Build validation feedback
An application needs field-level diagnostics for a response.
Inspect validation failures
Debugging reveals unexpected values entering a model.
Interview questions
QWhat does ValidationError represent?
ValidationError reports that input failed Pydantic validation. It can be caught and printed or inspected programmatically; the documentation example shows its string form identifying the model, field, message, error type, and input details.
Follow-up: How would you handle several invalid fields? Inspect the structured error collection rather than parsing the formatted string, then map each location to an appropriate application response.
QWhen can ValidationError be raised by model configuration?
It is raised when a configured validation rule rejects input, such as forbidding an extra field. The resulting diagnostic identifies where validation failed and why.
Follow-up: What should an API return? Convert the structured diagnostics into your API's error format while avoiding exposure of sensitive input values.
QWhat information does a validation error provide?
The displayed diagnostic includes the field location, a validation message, an error type, and information about the received input. This makes the exception useful both for debugging and for building field-level error responses.
Follow-up: Why prefer structured diagnostics? They remain machine-readable when wording changes and can be grouped by location or error type.
Related
BaseModelConfigDictmodel_validate