Just use pip install pydantic-partial to install the library.

Note: pydantic-partial is compatible with pydantic versions 1.9, 1.10 and even 2.x (🥳) on Python 3.9, 3.10, 3.11 and 3.12. This is also ensured running all tests on all those versions using tox.

Create partial models from your normal pydantic models. Partial models will allow some or all fields to be optional and thus not be required when creating the model instance.

Partial models can be used to support PATCH HTTP requests where the user only wants to update some fields of the model and normal validation for required fields is not required. It may also be used to have partial response DTOs where you want to skip certain fields, this can be useful in combination with exclude_none. It is - like shown in these examples - intended to be used with API use cases, so when using pydantic with for example FastAPI.

Disclaimer: This is still an early release of pydantic-partial. Things might change in the future. PR welcome. ;-)

pydantic-partial provides a mixin to generate partial model classes. The mixin can be used like this:

import pydantic
from pydantic_partial import PartialModelMixin

# Something model, then can be used as a partial, too:
class Something(PartialModelMixin, pydantic.BaseModel):
    name: str
    age: int


# Create a full partial model
FullSomethingPartial = Something.model_as_partial()
FullSomethingPartial()  # Same as FullSomethingPartial(name=None, age=None)

You also may create partial models without using the mixin:

import pydantic
from pydantic_partial import create_partial_model

# Something model, without the mixin:
class Something(pydantic.BaseModel):
    name: str
    age: int


# Create a full partial model
FullSomethingPartial = create_partial_model(Something)
FullSomethingPartial()  # Same as FullSomethingPartial(name=None, age=None)

pydantic-partial can be used to create partial models that only change some of the fields to being optional. Just pass the list of fields to be optional to the as_partial() or create_partial_model() function.

import pydantic
from pydantic_partial import create_partial_model

class Something(pydantic.BaseModel):
    name: str
    age: int

# Create a partial model only for the name attribute
FullSomethingPartial = create_partial_model(Something, 'name')
FullSomethingPartial(age=40)  # Same as FullSomethingPartial(name=None, age=40)
# This would still raise an error: FullSomethingPartial(age=None, ...)

Partial models can be created changing the field of all nested models to being optional, too.

from typing import List

import pydantic
from pydantic_partial import PartialModelMixin, create_partial_model

class InnerSomething(PartialModelMixin, pydantic.BaseModel):
    name: str

class OuterSomething(pydantic.BaseModel):
    name: str
    things: List[InnerSomething]

# Create a full partial model
RecursiveOuterSomethingPartial = create_partial_model(OuterSomething, recursive=True)
RecursiveOuterSomethingPartial(things=[
    {},
])

Note: The inner model MUST extend the PartialModelMixin mixin. Otherwise pydantic-partial will not be able to detect which fields may allow to being converted to partial models.

Also note: My recommendation would be to always create such recursive partials by creating partials for all the required models and then override the fields on you outer partial model class. This is way more explicit.

pydantic-partial cannot generate new class types that actually are supported by the Python typing system rules. This means that the partial models will only be recognized as the same as their original model classes - type checkers will not know about the partial model changes and thus will think all those partial fields are still required.

This is due to the fact that Python itself has no concept of partials. pydantic-partial could (in theory) provide plugins for mypy for example to "patch" this in, but this would be a massive amount of work while being kind of a bad hack. The real solution would be to have a partial type in Python itself, but this is not planned for the near future as far as I know.

My recommendation is to use pydantic-partial only for API use cases where you do not need to work with the partial aspects of the models - they are just the DTOs (data transfer objects) you are using. If you need to use partial models in other cases you might get errors by your type checker - if you use one. Please be aware of this.

Note: Not having a good solution in Python itself for this is the reason pydantic does not support partial models in the first place. pydantic-partial is just a really good workaround for this issue.
See issue 2 in this project and issue 1673 in the pydantic project for reference.

Having that all said: If anyone wants to get a working plugin for mypy or others ready, I'm going to very much support this.

If you want to contribute to this project, feel free to just fork the project, create a dev branch in your fork and then create a pull request (PR). If you are unsure about whether your changes really suit the project please create an issue first, to talk about this.