def create_argument_parser() -> argparse.ArgumentParser:
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
prog="rasa",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Rasa command line interface. Rasa allows you to build "
"your own conversational assistants 🤖. The 'rasa' command "
"allows you to easily run most common commands like "
"creating a new bot, training or evaluating models.")
parser.add_argument("--version",
action='store_true',
default=argparse.SUPPRESS,
help="Print installed Rasa version")
parent_parser = argparse.ArgumentParser(add_help=False)
add_logging_option_arguments(parent_parser)
parent_parsers = [parent_parser]
subparsers = parser.add_subparsers(help='Rasa commands')
scaffold.add_subparser(subparsers, parents=parent_parsers)
run.add_subparser(subparsers, parents=parent_parsers)
shell.add_subparser(subparsers, parents=parent_parsers)
train.add_subparser(subparsers, parents=parent_parsers)
interactive.add_subparser(subparsers, parents=parent_parsers)
test.add_subparser(subparsers, parents=parent_parsers)
show.add_subparser(subparsers, parents=parent_parsers)
data.add_subparser(subparsers, parents=parent_parsers)
up.add_subparser(subparsers, parents=parent_parsers)
return parser
def test_train_data_in_project_dir(monkeypatch: MonkeyPatch, tmp_path: Path):
"""Test cache directory placement.
Tests cache directories for training data are in project root, not
where `rasa init` is run.
"""
# We would like to test CLI but can't run it with popen because we want
# to be able to monkeypatch it. Solution is to call functions inside CLI
# module. Initial project folder should have been created before
# `init_project`, that's what we do here.
monkeypatch.chdir(tmp_path)
new_project_folder_path = tmp_path / "new-project-folder"
new_project_folder_path.mkdir()
# Simulate CLI run arguments.
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
scaffold.add_subparser(subparsers, parents=[])
args = parser.parse_args([
"init",
"--no-prompt",
])
# Simple config which should train fast.
def mock_get_config(*args):
return {
"language": "en",
"pipeline": [{
"name": "KeywordIntentClassifier"
}],
"policies": [{
"name": "RulePolicy"
}],
"recipe": "default.v1",
}
monkeypatch.setattr(
"rasa.shared.importers.importer.CombinedDataImporter.get_config",
mock_get_config,
)
# Cache dir is auto patched to be a temp directory, this makes it
# go back to local project folder so we can test it is created correctly.
with enable_cache(Path(".rasa", "cache")):
mock_stdin([])
scaffold.init_project(args, str(new_project_folder_path))
assert os.getcwd() == str(new_project_folder_path)
assert os.path.exists(".rasa/cache")
def create_argument_parser() -> argparse.ArgumentParser:
"""Parse all the command line arguments for the training script."""
parser = argparse.ArgumentParser(
prog="rasa",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Rasa command line interface. Rasa allows you to build "
"your own conversational assistants 🤖. The 'rasa' command "
"allows you to easily run most common commands like "
"creating a new bot, training or evaluating models.",
)
parser.add_argument(
"--version",
action="store_true",
default=argparse.SUPPRESS,
help="Print installed Rasa version",
)
parent_parser = argparse.ArgumentParser(add_help=False) # 一级命令解析器
add_logging_options(parent_parser)
parent_parsers = [parent_parser]
subparsers = parser.add_subparsers(help="Rasa commands") # 二级命令解析器
scaffold.add_subparser(subparsers, parents=parent_parsers) # 解析rasa init
run.add_subparser(subparsers, parents=parent_parsers) # 解析rasa run
shell.add_subparser(subparsers, parents=parent_parsers) # 解析rasa shell
train.add_subparser(subparsers, parents=parent_parsers) # 解析 rasa train
interactive.add_subparser(subparsers, parents=parent_parsers) # 解析rasa interactive
test.add_subparser(subparsers, parents=parent_parsers) # 解析rasa test
visualize.add_subparser(subparsers, parents=parent_parsers) # 解析rasa visualize
data.add_subparser(subparsers, parents=parent_parsers) # 解析rasa data
x.add_subparser(subparsers, parents=parent_parsers) # 解析rasa x
return parser
