clipstick 0.3.7

A pydantic cli creation tool based on Pydantic models.

Clipstick

A cli-tool based on Pydantic models.

There are many other tools out there that do kind of the same, but they all don't do quite exactly what I want.

The goal of clipstip is to use pydantic to model your cli by leveraging:

• The automatic casting of input variables.
• The powerful validation capabilities.
• Docstrings as cli documentation.
• No other mental model required than Typing and Pydantic.

Clipstick is inspired by tyro, which is excellent and more versatile than this tool. But in my opionion its primary focus is not building a cli tool along the lines of Argparse or Click but more on composing complex objects from the command line. Making tyro behave like a "traditional" cli requires additional Annotation flags, which I don't want.

Some other similar tools don't support pydantic v2, so I decided to create my own. Next to that I wanted to try and build my own parser instead of using Argparse because... why not.

Installation

pip install clipstick

Example

# examples/simple.py

from pydantic import BaseModel
from clipstick import parse


class SimpleModel(BaseModel):
    """A simple model demonstrating clipstick.

    This is used in help as describing the main command.
    """

    name: str
    """Your name. This is used in help describing name."""

    repeat_count: int = 10
    """How many times to repeat your name. Used in help describing repeat_count."""

    def main(self):
        for _ in range(self.repeat_count):
            print(f"hello: {self.name}")


if __name__ == "__main__":
    model = parse(SimpleModel)
    model.main()

python examples/simple.py -h gives you:

python examples/simple.py alex --repeat-count 3 gives you:

hello: alex
hello: alex
hello: alex

[!NOTE] The inclusion of the def main(self) method is not a requirement. clipstick generates a pydantic model based on provided cli arguments and gives it back to you for your further usage. Using def main() is one of the options to further process it.

Positional arguments

All properties in your pydantic model without a default value are converted to cli positional arguments.

# docs/source/positional_arg.py

from pydantic import BaseModel
from clipstick import parse


class MyModel(BaseModel):
    """My model with a required value."""

    my_value: int
    """My required value."""


if __name__ == "__main__":
    """your cli entrypoint"""
    model = parse(MyModel)

# >>> python docs/source/positional_arg.py 10

MyModel(my_value=10)

Keyword arguments

All fields with a default value are converted to cli optional arguments.

# docs/source/keyword_arg.py

from typing import Annotated
from pydantic import BaseModel
from clipstick import parse, short


class MyModel(BaseModel):
    """A model with a keyworded optional value"""

    my_value: int = 22
    """My value with a default."""
    another_value: Annotated[str, short("a")] = "value"
    """Value with a shorthand"""


if __name__ == "__main__":
    model = parse(MyModel)

# >>> python docs/source/keyword_arg.py --my-value 10

MyModel(my_value=10, another_value='value')

You can add a shorthand to a field by annotating it:

# docs/source/keyword_arg_with_short.py

from typing import Annotated
from pydantic import BaseModel
from clipstick import short


class MyModel(BaseModel):
    """A model with a keyworded optional value"""

    my_value: Annotated[int, short("m")] = 22  # <-- this adds a shorthand of `-m`.
    """My value with a default."""

Choices

Choices are supported by using the Literal type annotation.

# docs/source/choice_arg.py

from typing import Literal
from pydantic import BaseModel
from clipstick import parse


class MyModel(BaseModel):
    """My model with choice values."""

    my_value: Literal["option1", "option2"] = "option1"
    """A value with restricted values."""


if __name__ == "__main__":
    model = parse(MyModel)

# >>> python docs/source/choice_arg.py --my-value option2

MyModel(my_value='option2')

Booleans/Flags

# docs/source/boolean_required_arg.py

from pydantic import BaseModel
from clipstick import parse


class MyModel(BaseModel):
    """A model with a required boolean value."""

    verbose: bool
    """Some verbose thingy."""


if __name__ == "__main__":
    model = parse(MyModel)

# >>> python docs/source/boolean_required_arg.py --verbose

MyModel(verbose=True)

# >>> python docs/source/boolean_required_arg.py --no-verbose

MyModel(verbose=False)

Short annotations Annotated[int, short('m')] are also allowed.

Subcommands

Subcommands are possible by adding a property with a union of BaseModel, each defined as new path in the sub-command tree.

# docs/source/subcommand_arg.py

from pydantic import BaseModel
from clipstick import parse


class Routes(BaseModel):
    """Some climbing routes."""

    route_name: str
    """Name of a route."""


class Climbers(BaseModel):
    """Climbers model."""

    climber_name: str
    """Name of a climber."""


class MyModel(BaseModel):
    """The base model with a subcommand."""

    sub_command: Routes | Climbers


if __name__ == "__main__":
    model = parse(MyModel)

# >>> python docs/source/subcommand_arg.py climbers Ondra

MyModel(sub_command=Climbers(climber_name='Ondra'))

• Only one subcommand per model is allowed. (If you need more (and want to follow the more object-composition path), have a look at tyro)
• sub_command as a name is not required. Any name will do.
• Nesting of subcommands is possible.

Validators

Using pydantic as a model definition gives you many useful types (including checks) out of the box. For a list of pydantic types look here: https://docs.pydantic.dev/latest/api/types/

Examples

Below code shows a pydantic type of FilePath indicating the provided argument should point to an existing file.

# docs/source/types_file_exists.py

from pydantic import BaseModel, FilePath
from clipstick import parse


class MyModel(BaseModel):
    my_path: FilePath
    """provide an existing file location."""


if __name__ == "__main__":
    model = parse(MyModel)

Below code shows a pydantic type of PositiveInt indicating the provided argument should be a positive integer.

# docs/source/types_non_negative_int.py

from typing import Annotated
from pydantic import BaseModel, PositiveInt
from clipstick import parse, short


class MyModel(BaseModel):
    my_value: Annotated[PositiveInt, short("m")] = 10
    """Value must be positive"""


if __name__ == "__main__":
    model = parse(MyModel)