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Description:

hikaritanchi 1.3.6

tanchi
A signature parser for hikari's command handler tanjun.
Finally be able to define your commands without those bloody decorator chains!
Example
@component.with_slash_command
@tanchi.as_slash_command(default_to_ephemeral=True)
async def command(
ctx: tanjun.abc.SlashContext,
integer: int = 0,
flag: bool = False,
channel: typing.Optional[hikari.GuildTextChannel] = None,
):
"""Small tanchi command

Parameters
----------
integer : int
Any integer value.
flag : bool
Whether this flag should be enabled.
channel : hikari.GuildTextChannel
The channel to target.
"""

Documentation?
Ordinary Types
All builtin types supported in slash commands (str, int, float, bool) do not need any special care.
option: str

Choices
Choices can either be made with typing.Literal or enum.Enum
option: typing.Literal["Foo", "Bar", "Baz"]

class MyEnum(enum.IntEnum):
foo = 1
bar = 2
baz = 3

option: MyEnum

Ranges
Integer and float options support min and max boundaries. These can be set with tanchi.Range.
The type of the option is discerned from the boundaries.
int_option: tanchi.Range[1, 10]
float_option: tanchi.Range[0.0, 1.0]

Channels
Channels types may be enforced with the help of typing.Union. If you want all channel types use hikari.GuildChannel
option: typing.Union[hikari.GuildTextChannel, hikari.GuildNewsChannel]

Converters
Types are implicitly converted if a builtin tanjun converter is available.
option: hikari.Emoji

To provide your own converter you can use tanchi.Converted.
option: tanchi.Converted[int, round]

Autocomplete
Instead of using a decorator, autocompleters can be provided directly in the annotation with tanchi.Autocompleted.
option: tanchi.Autocompleted[autocomplete_callback]

Since converters and autocompletion are often used together you can provide a converter directly.
option: tanchi.Autocompleted[autocomplete_callback, converter_callback]

MyPy compatibility
Because mypy does not respect __class_getitem__ you'll most likely have to use typing.Annotated for some cases.
option: typing.Annotated[int, tanchi.Range(1, 10)]
option: typing.Annotated[int, tanchi.Converted(range)]
option: typing.Annotated[str, tanchi.Autocompleted(autocomplete_callback)]

Docstrings
Tanchi parses descriptions from docstrings.
Examples of all supported formats:
ReST
"""Command description on a single line

Parameters
----------
foo : OptionType
Description for the option named "foo"
bar:
Description for the option named "bar"
"""

Google
"""Command description on a single line

Args:
foo (OptionType): Description for the option named "foo"
bar: Description for the option named "bar"
"""

Autocompletion Examples
Instead of using context.set_choices you can choose to return options as either a sequence or a mapping.
@tanchi.as_slash_command()
async def command(context: tanjun.abc.SlashContext, option: str) -> None:
...

@tanchi.with_autocomplete(command, "option")
def autocomplete_names(context: tanjun.abc.AutocompleteContext, option: str):
return [word for word in WORDS if option.lower() in word.lower()]

Returning the options is also supported inside Autocompleted