dynapydantic is an extension to the pydantic Python
package that allow for dynamic tracking of pydantic.BaseModel subclasses.
This project can be installed via PyPI:
pip install dynapydantic
or with conda via the conda-forge channel:
conda install dynapydantic
Consider the following simple class setup:
import pydantic
class Base(pydantic.BaseModel):
pass
class A(Base):
field: int
class B(Base):
field: str
class Model(pydantic.BaseModel):
val: BaseAs expected, we can use A's and B's for Model.val:
>>> m = Model(val=A(field=1))
>>> m
Model(val=A(field=1))However, we quickly run into trouble when serializing and validating:
>>> m.model_dump()
{'base': {}}
>>> m.model_dump(serialize_as_any=True)
{'val': {'field': 1}}
>>> Model.model_validate(m.model_dump(serialize_as_any=True))
Model(val=Base())Pydantic provides a solution for serialization via serialize_as_any (and
its corresponding field annotation SerializeAsAny), but offers no native
solution for the validation half. Currently, the canonical way of doing this
is to annotate the field as a discriminated union of all subclasses. Often, a
single field in the model is chosen as the "discriminator". This library,
dynapydantic, automates this process.
Let's reframe the above problem with dynapydantic:
import dynapydantic
import pydantic
class Base(
dynapydantic.SubclassTrackingModel,
discriminator_field="name",
discriminator_value_generator=lambda t: t.__name__,
):
pass
class A(Base):
field: int
class B(Base):
field: str
class Model(pydantic.BaseModel):
val: dynapydantic.Polymorphic[Base]Now, the same set of operations works as intended:
>>> m = Model(val=A(field=1))
>>> m
Model(val=A(field=1, name='A'))
>>> m.model_dump()
{'val': {'field': 1, 'name': 'A'}}
>>> Model.model_validate(m.model_dump())
Model(val=A(field=1, name='A')The core entity in this library is the dynapydantic.TrackingGroup:
import typing as ty
import dynapydantic
import pydantic
mygroup = dynapydantic.TrackingGroup(
name="mygroup",
discriminator_field="name"
)
@mygroup.register("A")
class A(pydantic.BaseModel):
"""A class to be tracked, will be tracked as "A"."""
a: int
@mygroup.register()
class B(pydantic.BaseModel):
"""Another class, will be tracked as "B"."""
name: ty.Literal["B"] = "B"
a: int
class Model(pydantic.BaseModel):
"""A model that can have A or B"""
field: mygroup.union() # call after all subclasses have been registered
print(Model(field={"name": "A", "a": 4})) # field=A(a=4, name='A')
print(Model(field={"name": "B", "a": 5})) # field=B(name='B', a=5)The union() method produces a discriminated union
of all registered pydantic.BaseModel subclasses. It also accepts an
annotated=False keyword argument to produce a plain typing.Union for use
in type annotations, but since this is a runtime-computed union, this will not
work with static type checkers. This union is based on a discriminator field,
which was configured by the discriminator_field argument to TrackingGroup.
The field can be created by hand, as was shown with B, or dynapydantic
will inject it for you, as was shown with A.
TrackingGroup has a few opt-in features to make it more powerful and easier to use:
discriminator_value_generator: This parameter is a optional callback function that is called with each class that gets registered and produces a default value for the discriminator field. This allows the user to callregister()without a value for the discriminator. For example, passing:lambda cls: cls.__name__would use the name of the class as the discriminator value.plugin_entry_point: This parameter indicates todynapydanticthat there might be models to be discovered in other packages. Packages are discovered by the Python entrypoint mechanism. See thetests/exampledirectory for an example of how this works.
The most common use case of this pattern is to automatically register subclasses
of a given pydantic.BaseModel. This is supported via the use of
dynapydantic.SubclassTrackingModel. For example:
import typing as ty
import dynapydantic
import pydantic
class Base(
dynapydantic.SubclassTrackingModel,
discriminator_field="name",
discriminator_value_generator=lambda cls: cls.__name__,
):
"""Base model, will track its subclasses"""
# The TrackingGroup can be specified here like model_config, or passed in
# kwargs of the class declaration, just like how model_config works with
# pydantic.BaseModel. If you do it like this, you have to give the tracking
# group a name, whereas using kwargs will generate the name for you.
# tracking_config: ty.ClassVar[dynapydantic.TrackingGroup] = dynapydantic.TrackingGroup(
# name="BaseSubclasses",
# discriminator_field="name",
# discriminator_value_generator=lambda cls: cls.__name__,
# )
class Intermediate(Base, exclude_from_union=True):
"""Subclasses can opt out of being tracked"""
class Derived1(Intermediate):
"""Non-direct descendants are registered"""
a: int
class Derived2(Intermediate):
"""You can override the value generator if desired"""
name: ty.Literal["Custom"] = "Custom"
a: int
print(Base.registered_subclasses())
# {'Derived1': <class '__main__.Derived1'>, 'Custom': <class '__main__.Derived2'>}
# if plugin_entry_point was specificed, load plugin packages
# Base.load_plugins()
class Model(pydantic.BaseModel):
"""A model that can have any registered Base subclass"""
field: dynapydantic.Polymorphic[Base]
print(Model(field={"name": "Derived1", "a": 4}))
# field=Derived1(a=4, name='Derived1')
print(Model(field={"name": "Custom", "a": 5}))
# field=Derived2(name='Custom', a=5)It is important to note that the subclasses that are supported are those that
were defined prior to defining the model that uses dynapydantic.Polymorphic
(Model in the above example). If you declare additional subclasses afterwards,
you must call .model_rebuild(force=True) on the model that uses the subclass
union.