Metadata-Version: 2.1
Name: djantic2
Version: 1.0.3
Summary: Pydantic model support for Django ORM
Home-page: https://github.com/jonathan-s/djantic2/
Author: Jonathan Sundqvist
Author-email: git@co.argpar.se
License: MIT
Description: <h1 style="text-align: center;">Djantic2</h1>
        <p style="text-align: center;">
            <em><a href="https://pydantic-docs.helpmanual.io/">Pydantic</a> model support for <a href="https://www.djangoproject.com/"> Django</a></em>
        </p>
        <p style="text-align: center;">
            <a href="https://github.com/jonathan-s/djantic2/actions/workflows/test.yml">
            <img src="https://img.shields.io/github/actions/workflow/status/jonathan-s/djantic2/test.yml?branch=main" alt="GitHub Workflow Status (Test)" >
            </a>
            <a href="https://pypi.org/project/djantic2" target="_blank">
                <img src="https://img.shields.io/pypi/v/djantic2" alt="PyPi package">
            </a>
            <a href="https://pypi.org/project/djantic2" target="_blank">
                <img src="https://img.shields.io/pypi/pyversions/djantic2" alt="Supported Python versions">
            </a>
            <a href="https://pypi.org/project/djantic2" target="_blank">
                <img src="https://img.shields.io/pypi/djversions/djantic2?label=django" alt="Supported Django versions">
            </a>
        </p>
        
        ---
        
        Djantic2 is a fork of djantic which works with pydantic >2, it is a library that provides a configurable utility class for automatically creating a Pydantic model instance for any Django model class. It is intended to support all of the underlying Pydantic model functionality such as JSON schema generation and introduces custom behaviour for exporting Django model instance data.
        
        ## Quickstart
        
        Install using pip:
        
        ```shell
        pip install djantic2
        ```
        
        Create a model schema:
        
        ```python
        from users.models import User
        from pydantic import ConfigDict
        
        from djantic import ModelSchema
        
        class UserSchema(ModelSchema):
            model_config = ConfigDict(model=User, include=["id", "first_name"])
        
        print(UserSchema.schema())
        ```
        
        **Output:**
        
        ```python
        {
            "description": "A user of the application.",
            "properties": {
                "id": {
                    "anyOf": [{"type": "integer"}, {"type": "null"}],
                    "default": None,
                    "description": "id",
                    "title": "Id",
                },
                "first_name": {
                    "description": "first_name",
                    "maxLength": 50,
                    "title": "First Name",
                    "type": "string",
                },
            },
            "required": ["first_name"],
            "title": "UserSchema",
            "type": "object",
        }
        ```
        
        See https://pydantic-docs.helpmanual.io/usage/models/ for more.
        
        ### Loading and exporting model instances
        
        Use the `from_orm` method on the model schema to load a Django model instance for <a href="https://pydantic-docs.helpmanual.io/usage/exporting_models/">export</a>:
        
        ```python
        user = User.objects.create(
            first_name="Jordan",
            last_name="Eremieff",
            email="jordan@eremieff.com"
        )
        
        user_schema = UserSchema.from_orm(user)
        print(user_schema.json(indent=2))
        ```
        
        **Output:**
        
        ```json
        {
            "profile": null,
            "id": 1,
            "first_name": "Jordan",
            "last_name": "Eremieff",
            "email": "jordan@eremieff.com",
            "created_at": "2020-08-15T16:50:30.606345+00:00",
            "updated_at": "2020-08-15T16:50:30.606452+00:00"
        }
        ```
        
        ### Using multiple level relations
        
        Djantic supports multiple level relations. This includes foreign keys, many-to-many, and one-to-one relationships.
        
        Consider the following example Django model and Djantic model schema definitions for a number of related database records:
        
        ```python
        # models.py
        from django.db import models
        
        class OrderUser(models.Model):
            email = models.EmailField(unique=True)
        
        
        class OrderUserProfile(models.Model):
            address = models.CharField(max_length=255)
            user = models.OneToOneField(OrderUser, on_delete=models.CASCADE, related_name='profile')
        
        
        class Order(models.Model):
            total_price = models.DecimalField(max_digits=8, decimal_places=5, default=0)
            user = models.ForeignKey(
                OrderUser, on_delete=models.CASCADE, related_name="orders"
            )
        
        
        class OrderItem(models.Model):
            price = models.DecimalField(max_digits=8, decimal_places=5, default=0)
            quantity = models.IntegerField(default=0)
            order = models.ForeignKey(
                Order, on_delete=models.CASCADE, related_name="items"
            )
        
        
        class OrderItemDetail(models.Model):
            name = models.CharField(max_length=30)
            order_item = models.ForeignKey(
                OrderItem, on_delete=models.CASCADE, related_name="details"
            )
        ```
        
        ```python
        # schemas.py
        from djantic import ModelSchema
        from pydantic import ConfigDict
        
        from orders.models import OrderItemDetail, OrderItem, Order, OrderUserProfile
        
        
        class OrderItemDetailSchema(ModelSchema):
            model_config = ConfigDict(model=OrderItemDetail)
        
        
        class OrderItemSchema(ModelSchema):
            details: List[OrderItemDetailSchema]
            model_config = ConfigDict(model=OrderItem)
        
        
        class OrderSchema(ModelSchema):
            items: List[OrderItemSchema]
            model_config = ConfigDict(model=Order)
        
        
        class OrderUserProfileSchema(ModelSchema):
            model_config = ConfigDict(model=OrderUserProfile)
        
        
        class OrderUserSchema(ModelSchema):
            orders: List[OrderSchema]
            profile: OrderUserProfileSchema
            model_config = ConfigDict(model=OrderUser)
        ```
        
        Now let's assume you're interested in exporting the order and profile information for a particular user into a JSON format that contains the details accross all of the related item objects:
        
        ```python
        user = OrderUser.objects.first()
        print(OrderUserSchema.from_orm(user).json(ident=4))
        ```
        
        **Output:**
        ```json
        {
            "profile": {
                "id": 1,
                "address": "",
                "user": 1
            },
            "orders": [
                {
                    "items": [
                        {
                            "details": [
                                {
                                    "id": 1,
                                    "name": "",
                                    "order_item": 1
                                }
                            ],
                            "id": 1,
                            "price": 0.0,
                            "quantity": 0,
                            "order": 1
                        }
                    ],
                    "id": 1,
                    "total_price": 0.0,
                    "user": 1
                }
            ],
            "id": 1,
            "email": ""
        }
        ```
        
        The model schema definitions are composable and support customization of the output according to the auto-generated fields and any additional annotations.
        
        ### Including and excluding fields
        
        The fields exposed in the model instance may be configured using two options: `include` and `exclude`. These represent iterables that should contain a list of field name strings. Only one of these options may be set at the same time, and if neither are set then the default behaviour is to include all of the fields from the Django model.
        
        For example, to include all of the fields from a user model <i>except</i> a field named `email_address`, you would use the `exclude` option:
        
        ```python
        from pydantic import ConfigDict
        
        class UserSchema(ModelSchema):
            model_config = ConfigDict(model=User, exclude=["email_address"])
        ```
        
        In addition to this, you may also limit the fields to <i>only</i> include annotations from the model schema class by setting the `include` option to a special string value: `"__annotations__"`.
        
        ```python
        from pydantic import ConfigDict
        
        class ProfileSchema(ModelSchema):
                website: str
                model_config = ConfigDict(model=Profile, include="__annotations__")
        
        
            assert ProfileSchema.schema() == {
                "title": "ProfileSchema",
                "description": "A user's profile.",
                "type": "object",
                "properties": {
                    "website": {
                        "title": "Website",
                        "type": "string"
                    }
                },
                "required": [
                    "website"
                ]
            }
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Framework :: Django
Classifier: Framework :: Django :: 3.2
Classifier: Framework :: Django :: 4.0
Classifier: Framework :: Django :: 4.2
Classifier: Framework :: Django :: 5.0
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Description-Content-Type: text/markdown
