pydantic-marshals 0.3.17

Library for creating partial pydantic models (automatic converters) from different mappings

Pydantic Marshals

Library for creating partial pydantic models (automatic converters) from different mappings. Currently, it consists of basic boilerplate parts and functional implementation for sqlalchemy 2.0+ (included via extra)

Base Interface

TBA

Implementations

TBA

SQLAlchemy: Basic usage

# sqlalchemy 2.0+ is required
from sqlalchemy import ForeignKey, String, Text
from sqlalchemy.orm import Mapped, mapped_column, relationship

from pydantic_marshals.sqlalchemy import MappedModel

class Avatar(Base):
    __tablename__ = "avatars"
    id: Mapped[int] = mapped_column(primary_key=True)
    IdModel = MappedModel.create(columns=[id])

class User(Base):
    __tablename__ = "users"
    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(100))
    description: Mapped[str | None] = mapped_column(Text())
    admin: Mapped[bool] = mapped_column()  # empty `mapped_column()` is required for models

    avatar_id: Mapped[int] = mapped_column(ForeignKey("avatars.id"))
    avatar: Mapped[Avatar] = relationship()

    @property
    def representation(self) -> str:
        return f"User #{self.id}: {self.name}"

    BaseModel = MappedModel.create(columns=[id])
    CreateModel = MappedModel.create(columns=[name, description])
    PatchModel = CreateModel.as_patch()
    IndexModel = MappedModel.create(properties=[representation])
    FullModel = BaseModel.extend(
        columns=[admin],
        relationships=[(avatar, Avatar.IdModel)],
        includes=[CreateModel, IndexModel],
    )


with sessionmaker.begin() as session:
    user = User(name="alex", description="cool person", avatar=Avatar(), admin=False)
    session.add(user)
    session.flush()

    print(User.BaseModel.model_validate(user).model_dump())
    # {"id": 0}
    print(User.PatchModel.model_validate({}).model_dump(exclude_defaults=True))
    # {}
    print(User.PatchModel.model_validate({"description": None}).model_dump(exclude_defaults=True))
    # {"description": None}
    print(User.CreateModel.model_validate(user).model_dump())
    # {"name": "alex", "description": "cool person"}
    print(User.IndexModel.model_validate(user).model_dump())
    # {"representation": "User #0: alex"}
    print(User.FullModel.model_validate(user).model_dump())
    # {
    #   "id": 0,
    #   "name": "alex",
    #   "description": "cool person",
    #   "representation": "User #0: alex",
    #   "avatar": {"id": 0},
    #   "admin": False
    # }

Assert Contains

The "assert contains" is an interface for validating data, mainly used in testing. Use "assert-contains" extra to install this module:

pip install pydantic-marshals[assert-contains]

Documentation:

• Usage with Examples

Local development

1. Clone the repository
2. Setup python (the library is made with python 3.10+)
3. Install poetry (should work with v1.4.1)
4. Install dependencies
5. Install pre-commit hooks

Commands to use:

pip install poetry==1.4.1
poetry install
pre-commit install