Question 1

How to handle an array of objects in the request body with Flask-Pydantic?

Accepted Answer

Set the `request_body_many` parameter to `True` in the `validate` decorator. This allows the request body to contain an array of objects validated against the Pydantic model, and if not an array, a 400 response is returned, as mentioned in the README.

Question 2

Flask-Pydantic vs Flask-Marshmallow: which is better for validation?

Accepted Answer

Flask-Pydantic excels if you're already using Pydantic and type hints, offering seamless integration and reduced boilerplate. Flask-Marshmallow has a longer history and more ecosystem tools, but requires manual schema definitions. Choose based on your team's familiarity and project needs.

Question 3

Can Flask-Pydantic be used in asynchronous Flask apps?

Accepted Answer

Flask-Pydantic is designed for synchronous Flask applications and might not fully leverage async features. While Pydantic supports async, Flask's default sync nature means you may need to handle async validation manually if using async extensions.

Question 4

How to customize validation error responses in Flask-Pydantic?

Accepted Answer

Set `FLASK_PYDANTIC_VALIDATION_ERROR_RAISE` to `True` in Flask config to raise a `ValidationError` exception, then use Flask's error handler to catch it and format custom responses, as described in the configuration section.

Question 5

Does Flask-Pydantic support Pydantic v2 features?

Accepted Answer

Check the project's PyPI or GitHub for compatibility updates. The README doesn't specify, but as a Pallets-Eco project, it likely supports recent versions, though always verify for specific Pydantic v2 validators or config changes.

Question 6

Setting up Flask-Pydantic with Flask blueprints?

Accepted Answer

Apply the `validate` decorator within blueprint route functions just like in main app routes. Ensure the decorator order is correct, with `@blueprint.route` preceding `@validate`, similar to the basic examples.

Flask-Pydantic

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