pydantic/docs/usage/model_config.md

Behaviour of pydantic can be controlled via the Config class on a model.

Options:

title

title for the generated JSON Schema

anystr_strip_whitespace

strip or not trailing and leading whitespace for str & byte types (default: False)

min_anystr_length

min length for str & byte types (default: 0)

max_anystr_length

max length for str & byte types (default: 2 ** 16)

validate_all

whether or not to validate field defaults (default: False)

extra

whether to ignore, allow or forbid extra attributes in model. Can use either string values of ignore, allow or forbid, or use Extra enum (default is Extra.ignore)

allow_mutation

whether or not models are faux-immutable, e.g. setattr fails (default: True)

use_enum_values

whether to populate models with the value property of enums, rather than the raw enum - useful if you want to serialise model.dict() later (default: False)

fields

schema information on each field, this is equivilant to using the schema class (default: None)

validate_assignment

whether to perform validation on assignment to attributes or not (default: False)

allow_population_by_alias

whether or not an aliased field may be populated by its name as given by the model attribute, rather than strictly the alias; please be sure to read the warning below before enabling this (default: False)

!!! warning Think twice before enabling allow_population_by_alias! Enabling it could cause previously correct code to become subtly incorrect. As an example, say you have a field named card_number with the alias cardNumber. With population by alias disabled (the default), trying to parse an object with only the key card_number will fail. However, if you enable population by alias, the card_number field can now be populated from cardNumber or card_number, and the previously-invalid example object would now be valid. This may be desired for some use cases, but in others (like the one given here, perhaps!), relaxing strictness with respect to aliases could introduce bugs.

error_msg_templates

let's you to override default error message templates. Pass in a dictionary with keys matching the error messages you want to override (default: {})

arbitrary_types_allowed

whether to allow arbitrary user types for fields (they are validated simply by checking if the value is instance of that type). If False - RuntimeError will be raised on model declaration (default: False)

orm_mode

allows usage of ORM mode

getter_dict

custom class (should inherit from GetterDict) to use when decomposing ORM classes for validation, use with orm_mode

alias_generator

callable that takes field name and returns alias for it

keep_untouched

tuple of types (e. g. descriptors) that won't change during model creation and won't be included in the model schemas

schema_extra

takes a dict to extend/update the generated JSON Schema

json_loads

custom function for decoding JSON, see custom JSON (de)serialisation

json_dumps

custom function for encoding JSON, see custom JSON (de)serialisation

json_encoders

customise the way types are encoded to JSON, see JSON Serialisation

{!./examples/config.py!}

(This script is complete, it should run "as is")

Version for models based on @dataclass decorator:

{!./examples/ex_dataclasses_config.py!}

(This script is complete, it should run "as is")

Alias Generator

If data source field names do not match your code style (e. g. CamelCase fields), you can automatically generate aliases using alias_generator:

{!./examples/alias_generator_config.py!}

(This script is complete, it should run "as is")