Build Methods
The ModelFactory class exposes two build methods:
.build(**kwargs)- builds a single instance of the factory's model.batch(size: int, **kwargs)- build a list of size n instances
from pydantic import BaseModel
from pydantic_factories import ModelFactory
class Person(BaseModel):
...
class PersonFactory(ModelFactory):
__model__ = Person
single_result = PersonFactory.build() # a single Person instance
batch_result = PersonFactory.batch(
size=5
) # list[Person, Person, Person, Person, Person]
Any kwargs you pass to .build, .batch or any of the persistence methods, will take precedence over
whatever defaults are defined on the factory class itself.
By default, when building a pydantic class, kwargs are validated, to avoid input validation you can use the factory_use_construct param.
from pydantic import BaseModel
from pydantic_factories import ModelFactory
class Person(BaseModel):
...
class PersonFactory(ModelFactory):
__model__ = Person
PersonFactory.build(id=5) # Raises a validation error
result = PersonFactory.build(
factory_use_construct=True, id=5
) # Build a Person with invalid id
Partial Parameters
Factories can randomly generate missing parameters for child factories. For example:
from pydantic_factories import ModelFactory
from pydantic import BaseModel
class Pet(BaseModel):
name: str
age: int
class Person(BaseModel):
name: str
pets: list[Pet]
age: int
class PersonFactory(ModelFactory[Person]):
__model__ = Person
When building a person without specifying the Person and pets ages, all these fields will be randomly generated:
from pydantic_factories import ModelFactory
from pydantic import BaseModel
class Pet(BaseModel):
name: str
age: int
class Person(BaseModel):
name: str
pets: list[Pet]
age: int
class PersonFactory(ModelFactory[Person]):
__model__ = Person
data = {
"name": "John",
"pets": [
{"name": "dog"},
{"name": "cat"},
],
}
person = PersonFactory.build(**data)
print(person.json(indent=2))