pydantic-django 0.0.3

Pydantic model support for Django ORM

Pydantic-Django

An attempt to implement a Pydantic model interface for Django ORM. The goal of this project is to support all of Pydantic's features to provide as many (useful) conveniences for Django models as possible.

Important: this project should be considered an experimental work-in-progress. The current API design and behaviour is not finalised, specific version support is not yet determined, and there is still a lot of things to test yet.

Also, I typically haven't used metaclasses/classes like this previously, so there may be some details in the implementation to refine.

Seems to work okay so far. :)

Installation

pip install pydantic-django

Usage

Requirements: Python 3.7+, Django 3

An example of basic schema usage:

from users.models import User
from pydantic_django import PydanticDjangoModel

class PydanticUser(PydanticDjangoModel):
    """An example user schema."""

    class Config:
        model = User

schema = PydanticUser.schema()

The schema call above would return something like this:

{
    'description': 'An example user schema.',
    'properties': {
        'created_at': {'format': 'date-time', 'title': 'Created At', 'type': 'string'},
        'email': {'maxLength': 254, 'title': 'Email', 'type': 'string'},
        'first_name': {'maxLength': 50, 'title': 'First Name', 'type': 'string'},
        'groups': {'items': {'type': 'integer'}, 'title': 'Id', 'type': 'array'},
        'id': {'title': 'Id', 'type': 'integer'},
        'last_name': {'maxLength': 50, 'title': 'Last Name', 'type': 'string'},
        'messages': {'items': {'type': 'integer'}, 'title': 'Id', 'type': 'array'},
        'profile': {'title': 'Id', 'type': 'integer'},
        'updated_at': {'format': 'date-time', 'title': 'Updated At', 'type': 'string'},
    },
    'required': ['first_name', 'email', 'created_at', 'updated_at', 'groups'],
    'title': 'PydanticUser',
    'type': 'object',
}

There are a few ways to populate the models with values, the first is using the from_django method:

user = User.objects.create(
    first_name="Jordan", 
    last_name="Eremieff", 
    email="[e-mail]"
)

pydantic_user = PydanticUser.from_django(user)

Alternatively, the Pydantic model can be used to create a new object:

pydantic_user = PydanticUser.create(
    first_name="Jordan", 
    last_name="Eremieff", 
    email="[e-mail]"
)

Or retrieve an existing one:

pydantic_user = PydanticUser.get(id=user.id)

The object in each case can be validated and export the values in the same way:

user_json = pydantic_user.json()

To produce a result such as:

{
    "profile": null, 
    "messages": [], 
    "id": 1, 
    "first_name": "Jordan", 
    "last_name": "Eremieff", 
    "email": "[e-mail]", 
    "created_at": "2020-08-09T13:45:04.395787+00:00",
    "updated_at": "2020-08-09T13:45:04.395828+00:00", 
    "groups": []
}

It can also use standard Python type annotations in conjunction with the fields retrieved automatically from the database, and the configuration class supports exclude and include options:

class PydanticUser(PydanticDjangoModel):
    first_name: Optional[str]
    last_name: str

    class Config:
        model = User
        include = ["first_name", "last_name"]

In this example, the first name and last name annotations override the fields that would normally be picked up from the Django model automatically, and the include list filters out the other fields from the schema definition.

The first_name field here is required in the database and the last_name field is optional, but using the type annotations this can be determined for the specific schema:

{
    'description': 'A user of the application.',
    'properties': {
            'first_name': {'title': 'First Name', 'type': 'string'},
            'last_name': {'title': 'Last Name', 'type': 'string'}
            },
    'required': ['last_name'],
    'title': 'PydanticUser',
    'type': 'object'
}

It can do a bit more than this, but you'll have to check out the testing application and test cases as a reference for now.

Roadmap

• Automatic schema generation from Django models
• Basic queryset interface for CRUD operations
• Include & exclude field filtering
• Default factory support
• Support basic field types
• Sub-model support for forward and reverse relations
• Postgres field types
• Look into custom validators and configurations
• Support for multi-object querysets
• More comprehensive support for Django features
• HTML schema generation
• Create a complete application example
• Look into performance & benchmarking
• More test coverage & type annotations