add_docstring_to_source

docstring_gen.docstring_generator.add_docstring_to_source(path: Union[str, Path], include_auto_gen_txt: bool = True, recreate_auto_gen_docs: bool = False, model: str = 'gpt-3.5-turbo', temperature: float = 0.2, max_tokens: int = 250, top_p: float = 1.0, n: int = 3) -> None

Reads a Jupyter notebook or Python file, or a directory containing these files, and adds docstrings to classes and methods that do not have them.

Parameters:

Name	Type	Description	Default
path	Union[str, Path]	The path to the Jupyter notebook or Python file, or a directory containing these files.	required
include_auto_gen_txt	bool	If set to True, a note indicating that the docstring was autogenerated by docstring-gen library will be added to the end.	True
recreate_auto_gen_docs	bool	If set to True, the autogenerated docstrings from the previous runs will be replaced with the new one.	False
model	str	The name of the Codex model that will be used to generate docstrings.	'gpt-3.5-turbo'
temperature	float	Setting the temperature close to zero produces better results, whereas higher temperatures produce more complex, and sometimes irrelevant docstrings.	0.2
max_tokens	int	The maximum number of tokens to be used when generating a docstring for a function or class. Please note that a higher number will deplete your token quota faster.	250
top_p	float	You can also specify a top-P value from 0-1 to achieve similar results to changing the temperature. According to the Open AI documentation, it is generally recommended to change either this or the temperature but not both.	1.0
n	int	The number of docstrings to be generated for each function or class, with the best one being added to the source code. Please note that a higher number will deplete your token quota faster.	3

Returns:

Type	Description
None	None

Note

The above docstring is autogenerated by docstring-gen library (https://docstring-gen.airt.ai)

Source code in docstring_gen/docstring_generator.py

def add_docstring_to_source(
    path: Union[str, Path],
    include_auto_gen_txt: bool = True,
    recreate_auto_gen_docs: bool = False,
    model: str = "gpt-3.5-turbo",
    temperature: float = 0.2,
    max_tokens: int = 250,
    top_p: float = 1.0,
    n: int = 3,
) -> None:
    """Reads a Jupyter notebook or Python file, or a directory containing these files, and adds docstrings to classes and methods that do not have them.

    Args:
        path: The path to the Jupyter notebook or Python file, or a directory containing these files.
        include_auto_gen_txt: If set to True, a note indicating that the docstring was autogenerated by docstring-gen library will be added to the end.
        recreate_auto_gen_docs: If set to True, the autogenerated docstrings from the previous runs will be replaced with the new one.
        model: The name of the Codex model that will be used to generate docstrings.
        temperature: Setting the temperature close to zero produces better results, whereas higher temperatures produce more complex, and sometimes irrelevant docstrings.
        max_tokens: The maximum number of tokens to be used when generating a docstring for a function or class. Please note that a higher number will deplete your token quota faster.
        top_p: You can also specify a top-P value from 0-1 to achieve similar results to changing the temperature. According to the Open AI documentation, it is generally recommended to change either this or the temperature but not both.
        n: The number of docstrings to be generated for each function or class, with the best one being added to the source code. Please note that a higher number will deplete your token quota faster.

    Returns:
        None

    !!! note

        The above docstring is autogenerated by docstring-gen library (https://docstring-gen.airt.ai)
    """
    path = Path(path)
    files = _get_files(path) if path.is_dir() else [path]
    frequency_penalty = 0.0
    presence_penalty = 0.0
    stop = ['"""']

    for file in files:
        if file.suffix == ".ipynb":
            _add_docstring_to_nb(
                file=file,
                include_auto_gen_txt=include_auto_gen_txt,
                recreate_auto_gen_docs=recreate_auto_gen_docs,
                model=model,
                temperature=temperature,
                max_tokens=max_tokens,
                top_p=top_p,
                frequency_penalty=frequency_penalty,
                presence_penalty=presence_penalty,
                stop=stop,
                n=n,
            )
        else:
            _add_docstring_to_py(
                file=file,
                include_auto_gen_txt=include_auto_gen_txt,
                recreate_auto_gen_docs=recreate_auto_gen_docs,
                model=model,
                temperature=temperature,
                max_tokens=max_tokens,
                top_p=top_p,
                frequency_penalty=frequency_penalty,
                presence_penalty=presence_penalty,
                stop=stop,
                n=n,
            )