Model Config
Behaviour of pydantic can be controlled via the Config
class on a model.
Options:
title
- the title for the generated JSON Schema
anystr_strip_whitespace
- whether to strip leading and trailing whitespace for str & byte types (default:
False
) min_anystr_length
- the min length for str & byte types (default:
0
) max_anystr_length
- the max length for str & byte types (default:
2 ** 16
) validate_all
- whether to validate field defaults (default:
False
) extra
- whether to ignore, allow, or forbid extra attributes during model initialization. Accepts the string values of
'ignore'
,'allow'
, or'forbid'
, or values of theExtra
enum (default:Extra.ignore
).'forbid'
will cause validation to fail if extra attributes are included,'ignore'
will silently ignore any extra attributes, and'allow'
will assign the attributes to the model. allow_mutation
- whether or not models are faux-immutable, i.e. whether
__setattr__
is allowed (default:True
) use_enum_values
- whether to populate models with the
value
property of enums, rather than the raw enum. This may be useful if you want to serialisemodel.dict()
later (default:False
) fields
- a
dict
containing schema information for each field; this is equivalent to using the schema class (default:None
) validate_assignment
- whether to perform validation on assignment to attributes (default:
False
) allow_population_by_field_name
- whether an aliased field may be populated by its name as given by the model
attribute, as well as the alias (default:
False
)
Note
The name of this configuration setting was changed in v1.0 from
allow_population_by_alias
to allow_population_by_field_name
.
error_msg_templates
- a
dict
used to override the 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 an instance of the type). If
False
,RuntimeError
will be raised on model declaration (default:False
). See an example in Field Types. orm_mode
- whether to allow usage of ORM mode
getter_dict
- a custom class (which should inherit from
GetterDict
) to use when decomposing ORM classes for validation, for use withorm_mode
alias_generator
- a callable that takes a field name and returns an alias for it
keep_untouched
- a tuple of types (e.g. descriptors) for a model's default values that should not be changed during model creation and will not be included in the model schemas. Note: this means that attributes on the model with defaults of this type, not annotations of this type, will be left alone.
schema_extra
- a
dict
used to extend/update the generated JSON Schema, or a callable to post-process it; see Schema customization json_loads
- a custom function for decoding JSON; see custom JSON (de)serialisation
json_dumps
- a custom function for encoding JSON; see custom JSON (de)serialisation
json_encoders
- a
dict
used to customise the way types are encoded to JSON; see JSON Serialisation
from pydantic import BaseModel, ValidationError class Model(BaseModel): v: str class Config: max_anystr_length = 10 error_msg_templates = { 'value_error.any_str.max_length': 'max_length:{limit_value}', } try: Model(v='x' * 20) except ValidationError as e: print(e) """ 1 validation error for Model v max_length:10 (type=value_error.any_str.max_length; limit_value=10) """
(This script is complete, it should run "as is")
Similarly, if using the @dataclass
decorator:
from datetime import datetime from pydantic import ValidationError from pydantic.dataclasses import dataclass class MyConfig: max_anystr_length = 10 validate_assignment = True error_msg_templates = { 'value_error.any_str.max_length': 'max_length:{limit_value}', } @dataclass(config=MyConfig) class User: id: int name: str = 'John Doe' signup_ts: datetime = None user = User(id='42', signup_ts='2032-06-21T12:00') try: user.name = 'x' * 20 except ValidationError as e: print(e) """ 1 validation error for User name max_length:10 (type=value_error.any_str.max_length; limit_value=10) """
(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
:
from pydantic import BaseModel def to_camel(string: str) -> str: return ''.join(word.capitalize() for word in string.split('_')) class Voice(BaseModel): name: str language_code: str class Config: alias_generator = to_camel voice = Voice(Name='Filiz', LanguageCode='tr-TR') print(voice.language_code) #> tr-TR print(voice.dict(by_alias=True)) #> {'Name': 'Filiz', 'LanguageCode': 'tr-TR'}
(This script is complete, it should run "as is")
Here camel case refers to "upper camel case" aka pascal case
e.g. CamelCase
. If you'd like instead to use lower camel case e.g. camelCase
,
it should be trivial to modify the to_camel
function above.
Alias Precedence🔗
Warning
Alias priority logic changed in v1.4 to resolve buggy and unexpected behaviour in previous versions. In some circumstances this may represent a breaking change, see #1178 and the precedence order below for details.
In the case where a field's alias may be defined in multiple places, the selected value is determined as follows (in descending order of priority):
- Set via
Field(..., alias=<alias>)
, directly on the model - Defined in
Config.fields
, directly on the model - Set via
Field(..., alias=<alias>)
, on a parent model - Defined in
Config.fields
, on a parent model - Generated by
alias_generator
, regardless of whether it's on the model or a parent
Note
This means an alias_generator
defined on a child model does not take priority over an alias defined
on a field in a parent model.
For example:
from pydantic import BaseModel, Field class Voice(BaseModel): name: str = Field(None, alias='ActorName') language_code: str = None mood: str = None class Character(Voice): act: int = 1 class Config: fields = {'language_code': 'lang'} @classmethod def alias_generator(cls, string: str) -> str: # this is the same as `alias_generator = to_camel` above return ''.join(word.capitalize() for word in string.split('_')) print(Character.schema(by_alias=True)) """ { 'title': 'Character', 'type': 'object', 'properties': { 'ActorName': {'title': 'Actorname', 'type': 'string'}, 'lang': {'title': 'Lang', 'type': 'string'}, 'Mood': {'title': 'Mood', 'type': 'string'}, 'Act': {'title': 'Act', 'default': 1, 'type': 'integer'}, }, } """
(This script is complete, it should run "as is")