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Pydantic A number is greater than the specified maximum value.
Understanding Pydantic
Pydantic is a data validation and settings management library for Python, leveraging Python's type annotations. It is widely used for ensuring data integrity and correctness by validating input data against defined models. Pydantic is particularly popular in FastAPI for request validation and response modeling.
Identifying the Symptom
When working with Pydantic, you might encounter the error code value_error.number.not_le. This error typically manifests when a number provided as input exceeds the maximum value specified in your Pydantic model. The error message might look something like this:
pydantic.error_wrappers.ValidationError: 1 validation error for ModelName
field_name
ensure this value is less than or equal to 100 (type=value_error.number.not_le; limit_value=100)
Explaining the Issue
The error code value_error.number.not_le indicates a validation failure due to a number being greater than the allowed maximum value. In Pydantic, you can specify constraints on numerical fields using validators such as le (less than or equal to). When the input data does not meet these constraints, Pydantic raises a validation error.
Example Scenario
Consider a Pydantic model where a field age is expected to be less than or equal to 100:
from pydantic import BaseModel, conint
class Person(BaseModel):
age: conint(le=100)
If you attempt to create an instance of Person with an age of 101, Pydantic will raise the value_error.number.not_le error.
Steps to Fix the Issue
Review the Model Definition
First, ensure that the constraints defined in your Pydantic model are correct and reflect the intended business logic. If the maximum value is indeed correct, proceed to the next step.
Validate Input Data
Ensure that the input data being passed to the Pydantic model adheres to the defined constraints. For instance, if the maximum allowed value is 100, verify that the input value does not exceed this limit.
Adjust Input Values
If the input data is incorrect, adjust the values to comply with the model's constraints. For example, if the input age is 101, change it to a value less than or equal to 100.
Test the Solution
After making the necessary adjustments, test the solution by creating an instance of the Pydantic model with valid data. Ensure that no validation errors are raised.
Additional Resources
For more information on Pydantic and its validation capabilities, consider visiting the following resources:
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Pydantic A number is greater than the specified maximum value.
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