Pydantic A number field is not a multiple of the specified value.
Understanding Pydantic
Pydantic is a data validation and settings management library for Python, leveraging Python type annotations. It is designed to provide data validation and parsing using Python type hints, ensuring that the data conforms to the specified types and constraints. Pydantic is widely used for defining data models and ensuring data integrity in Python applications.
Identifying the Symptom
When using Pydantic, you may encounter the error code value_error.multiple_of. This error typically arises when a number field in your data model does not meet the 'multiple_of' constraint defined in your Pydantic model. The error message might look something like this:
{
"loc": ["field_name"],
"msg": "value is not a multiple of the specified value",
"type": "value_error.multiple_of"
}
Explaining the Issue
The value_error.multiple_of error occurs when a numeric field in your Pydantic model is expected to be a multiple of a certain value, but the provided input does not satisfy this condition. This constraint is useful for ensuring that numbers adhere to specific multiples, which can be critical in applications requiring precise calculations or measurements.
Example Scenario
Consider a scenario where you have a Pydantic model that requires a field to be a multiple of 5. If the input value is 7, Pydantic will raise a value_error.multiple_of because 7 is not a multiple of 5.
Steps to Fix the Issue
To resolve the value_error.multiple_of error, follow these steps:
Step 1: Review the Pydantic Model
Check the Pydantic model definition to identify the field with the 'multiple_of' constraint. Ensure that the constraint is correctly defined. Here is an example:
from pydantic import BaseModel, conint
class MyModel(BaseModel):
number: conint(multiple_of=5)
Step 2: Validate the Input Data
Ensure that the input data provided to the Pydantic model satisfies the 'multiple_of' constraint. For example, if the constraint is multiple_of=5, the input should be a number like 10, 15, 20, etc.
Step 3: Adjust the Input Data
If the input data does not meet the constraint, adjust it accordingly. For instance, if the input is 7 and the constraint is multiple_of=5, change the input to 10 or another valid multiple of 5.
Step 4: Test the Solution
After making the necessary adjustments, test the solution by running your application or script again to ensure that the error is resolved.
Additional Resources
For more information on Pydantic and its constraints, you can refer to the official Pydantic documentation. Additionally, you can explore this guide on field types to understand how to define various constraints in Pydantic models.
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