Pydantic supports the generation of JSON Schema. This can be used to automatically support the validation of collections. The implementation should support the following:
Enabling/disabling validation via a validate flag in the model’s Mongo class.
Customization of the generated schema (this may be done directly via Pydantic)
Converting the schema to a MongoDB compatible format (stripping unsupported Keys, embedding referenced models etc.)
Using a custom validator instead of the automatically generated schema.
The implementation should be compatible with polymorphic and abstract documents:
Abstract documents in their function as blueprints can not be validated themselves. They can however enable validation options for any subclass by default.
Polymorphic models can be validated. Validation has to be enabled or disabled at the root level and is implemented as an $or combination of all sub-schemas. Submodels can use custom validation rules that will be used instead of the automatically generated schemas.
Using a strict validation mode may be interesting. Such a mode may enforce validation not via the $or operator but by using a more complex boolean predicate based on the model’s _type (Something like _type in … and _type=abc => $schema).
Pydantic supports the generation of JSON Schema. This can be used to automatically support the validation of collections. The implementation should support the following:
validateflag in the model’sMongoclass.$orcombination of all sub-schemas. Submodels can use custom validation rules that will be used instead of the automatically generated schemas.$oroperator but by using a more complex boolean predicate based on the model’s_type(Something like_type in …and_type=abc => $schema).