Pydantic: Config: Control pydantic behaviour

You can control the Pydantic behavior by providing Config class to a model or pydantic data class.

 

Below table summarizes all the possible options.

 

Option	Description
title	the title for the generated JSON Schema
anystr_strip_whitespace	whether to strip leading and trailing whitespace for str & byte types (default: False)
anystr_lower	whether to make all characters lowercase 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 the Extra 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 serialise model.dict() later (default: False)
fields	a dict containing schema information for each field; this is equivalent to using the Field 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)
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 with orm_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

 

Example 1: Using json_encoders.

 

json_customize_serialization.py

from pydantic import BaseModel, ValidationError
from datetime import datetime

class Employee(BaseModel):
    id: int
    name: str
    age: int
    dateOfBirth: datetime

    class Config:
        json_encoders = {
            datetime: lambda v: v.timestamp()
        }

emp1 = Employee(id = 1, name = 'Ptr', age = 23, dateOfBirth = datetime(1988, 6, 6, 12, 13, 14))
emp1Json = emp1.json()

print('emp1Json -> ' + emp1Json)

 

Output

emp1Json -> {"id": 1, "name": "Ptr", "age": 23, "dateOfBirth": 581582594.0}

Example 2: Using orm_mode.

 

from_orm_1.py

from typing import List
from sqlalchemy import Column, Integer, String
from sqlalchemy.dialects.postgresql import ARRAY
from sqlalchemy.ext.declarative import declarative_base
from pydantic import BaseModel, constr

Base = declarative_base()

class EmployeeOrm(Base):
    __tablename__ = 'employees'
    id = Column(Integer, primary_key=True, nullable=False)
    name = Column(String(63), unique=True)
    age = Column(Integer, nullable=False)


class EmployeeModel(BaseModel):
    id: int
    name: constr(max_length=63)
    age: int

    class Config:
        orm_mode = True


empOrm = EmployeeOrm(
    id=123,
    name='Krishna',
    age=31
)
print('empOrm -> ' + str(empOrm))
empModel = EmployeeModel.from_orm(empOrm)
print('empModel -> ' + str(empModel))

Output

empOrm -> <__main__.EmployeeOrm object at 0x1083af670>
empModel -> id=123 name='Krishna' age=31

Example 3: Using error_msg_templates and max_anystr_length

 

error_message_template_1.py

from pydantic import BaseModel, ValidationError

class Model(BaseModel):
    name: str

    class Config:
        max_anystr_length = 9
        error_msg_templates = {
            'value_error.any_str.max_length': 'max_length:{limit_value}',
        }


try:
    Model(name='krishna12345678')
except ValidationError as e:
    print(e)

Output

1 validation error for Model
name
  max_length:9 (type=value_error.any_str.max_length; limit_value=9)

Example 4: Define immutable classes

By setting 'allow_mutation' to False, we can define immutable models.

 

immutable_models.py

from pydantic import BaseModel

class Employee(BaseModel):
    id: int
    name: str
    age: int

    class Config:
        allow_mutation = False

emp1 = Employee(id = 1, name = 'Krishna', age = 23)

print('updating employee name')
emp1.name = 'Ram'

Output

Traceback (most recent call last):
  File "/Users/krishna/pydantic/immutable_models.py", line 14, in <module>
    emp1.name = 'Ram'
  File "pydantic/main.py", line 424, in pydantic.main.BaseModel.__setattr__
TypeError: "Employee" is immutable and does not support item assignment

 

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