---

An extremely flexible and configurable data model conversion library.

Important

Adaptix is ready for production! The beta version only means there may be some backward incompatible changes, so you need to pin a specific version.

📚 Documentation

TL;DR

Install

pip install adaptix==3.0.0b9

Use for model loading and dumping.

from dataclasses import dataclass

from adaptix import Retort


@dataclass
class Book:
    title: str
    price: int


data = {
    "title": "Fahrenheit 451",
    "price": 100,
}

# Retort is meant to be global constant or just one-time created
retort = Retort()

book = retort.load(data, Book)
assert book == Book(title="Fahrenheit 451", price=100)
assert retort.dump(book) == data

Use for converting one model to another.

from dataclasses import dataclass

from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column

from adaptix.conversion import get_converter


class Base(DeclarativeBase):
    pass


class Book(Base):
    __tablename__ = 'books'

    id: Mapped[int] = mapped_column(primary_key=True)
    title: Mapped[str]
    price: Mapped[int]


@dataclass
class BookDTO:
    id: int
    title: str
    price: int


convert_book_to_dto = get_converter(Book, BookDTO)

assert (
    convert_book_to_dto(Book(id=183, title="Fahrenheit 451", price=100))
    ==
    BookDTO(id=183, title="Fahrenheit 451", price=100)
)

Use cases

• Validation and transformation of received data for your API.
• Conversion between data models and DTOs.
• Config loading/dumping via codec that produces/takes dict.
• Storing JSON in a database and representing it as a model inside the application code.
• Creating API clients that convert a model to JSON sending to the server.
• Persisting entities at cache storage.
• Implementing fast and primitive ORM.

Advantages

• Sane defaults for JSON processing, no configuration is needed for simple cases.
• Separated model definition and rules of conversion that allow preserving SRP and have different representations for one model.
• Speed. It is one of the fastest data parsing and serialization libraries.
• There is no forced model representation, adaptix can adjust to your needs.
• Support dozens of types, including different model kinds: @dataclass, TypedDict, NamedTuple, attrs, sqlalchemy and pydantic.
• Working with self-referenced data types (such as linked lists or trees).
• Saving path where an exception is raised (including unexpected errors).
• Machine-readable errors that could be dumped.
• Support for user-defined generic models.
• Automatic name style conversion (e.g. snake_case to camelCase).
• Predicate system that allows to concisely and precisely override some behavior.
• Disabling additional checks to speed up data loading from trusted sources.
• No auto casting by default. The loader does not try to guess value from plenty of input formats.