validate filepath in pydantic model with other fields

Lussy04

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24-01-2025

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Pydantic is a fantastic Python library for data validation and parsing. It's particularly useful when you need to ensure data conforms to a specific structure, especially when dealing with complex data like filepaths alongside other fields. This article will guide you through validating filepaths within a Pydantic model, ensuring that the path exists and potentially incorporating other field-based validation logic.

Setting up the Pydantic Model

Let's start by creating a basic Pydantic model. We'll include a filepath field for validation, along with another field, filename, for demonstrating combined validation.

from pydantic import BaseModel, validator, Field, ValidationError
from pathlib import Path
import os

class FileModel(BaseModel):
    filepath: Path = Field(..., description="The path to the file")
    filename: str = Field(..., description="The name of the file")

    @validator('filepath')
    def validate_filepath(cls, filepath):
        if not filepath.exists():
            raise ValueError("Filepath does not exist")
        return filepath

    @validator('filename')
    def validate_filename(cls, filename, values):
        if not filename.endswith(".txt"):
            raise ValueError("Filename must end with '.txt'")
        # Access other fields in values dict.
        if filename != values['filepath'].name:
            raise ValueError("Filename must match the filename in the filepath")
        return filename

This model defines two fields: filepath (a Path object) and filename (a string). The @validator decorator adds custom validation logic. The validate_filepath function checks if the provided path exists. The validate_filename function adds a constraint: The filename must end in ".txt" and match the filename extracted from the filepath. Note the use of values to access other fields during validation.

Advanced Validation Techniques

The example above shows basic existence checks. Let's expand on this with more sophisticated validation:

1. Checking File Type:

    @validator('filepath')
    def validate_file_type(cls, filepath):
        if not filepath.is_file():
            raise ValueError("Path is not a file")
        return filepath

This ensures the path points to a regular file, not a directory.

2. Permissions Checks:

import os
    @validator('filepath')
    def validate_permissions(cls, filepath):
        if not os.access(filepath, os.R_OK):
            raise ValueError("Insufficient read permissions")
        return filepath

This verifies read access to the file. You could extend this to check write or execute permissions as needed.

3. Size Limits:

    @validator('filepath')
    def validate_file_size(cls, filepath):
        if filepath.stat().st_size > 1024 * 1024:  # 1 MB limit
            raise ValueError("File size exceeds the limit")
        return filepath

This adds a size constraint (1MB in this example). Adjust the limit as per your requirements.

4. Regular Expression Validation for Filenames:

import re
    @validator('filename')
    def validate_filename_regex(cls, filename):
        if not re.match(r"^[a-zA-Z0-9_-]+\.txt{{content}}quot;, filename):
            raise ValueError("Invalid filename format")
        return filename

This uses a regular expression to enforce a specific naming convention.

Handling Validation Errors

When validation fails, Pydantic raises a ValidationError. You should handle this exception gracefully in your application:

try:
    file_data = FileModel(filepath=Path("/path/to/my/file.txt"), filename="my_file.txt")
    print(file_data)
except ValidationError as e:
    print(e)

This try-except block catches the error and prints the details, helping debug validation issues.

Conclusion

Pydantic's powerful validation features, combined with custom validators and the ability to access other fields within validators, make it ideal for managing complex data structures, especially when dealing with filepaths. Remember to handle potential exceptions to ensure your application remains robust. By implementing these techniques, you can confidently validate your filepaths and related data, improving the integrity and reliability of your Python applications.