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Pydantic is a data validation and settings management library for Python, leveraging Python's type annotations. It is designed to provide robust data validation and parsing using Python's type hints. Pydantic is commonly used in applications where data integrity is crucial, such as API development, configuration management, and data processing pipelines.
For more information on Pydantic, visit the official documentation.
When using Pydantic, you might encounter the error code value_error.byte_size
. This error typically arises when a field expected to be a byte size receives an input that does not conform to the expected format.
During data validation, you may see an error message similar to the following:
{
"loc": ["field_name"],
"msg": "value is not a valid byte size",
"type": "value_error.byte_size"
}
The value_error.byte_size
error is triggered when Pydantic expects a field to represent a byte size, but the input does not match the expected format. Pydantic expects byte sizes to be specified in a format that it can interpret, such as integers representing bytes or strings with units like 'KB', 'MB', etc.
To resolve this issue, ensure that the data provided for byte size fields is in a valid format. Here are the steps you can follow:
Ensure that the input is either an integer representing bytes or a string with a valid unit. Valid examples include:
1024
(interpreted as 1024 bytes)'1KB'
(interpreted as 1024 bytes)'2MB'
(interpreted as 2097152 bytes)If the input data is incorrect, modify it to match the expected format. For instance, if you have a field that should be '1GB', ensure it is represented as '1GB'
or 1073741824
(in bytes).
After correcting the input, re-run the Pydantic validation to ensure the error is resolved. If the issue persists, double-check the input format and units.
For more detailed information on handling byte sizes in Pydantic, refer to the Pydantic Byte Size Documentation.
For general troubleshooting tips, visit the Pydantic Error Handling Guide.
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