def _rasa_service(
args: argparse.Namespace,
endpoints: AvailableEndpoints,
rasa_x_url: Optional[Text] = None,
credentials_path: Optional[Text] = None,
):
"""Starts the Rasa application."""
from rasa.core.run import serve_application
import rasa.utils.common
# needs separate logging configuration as it is started in its own process
rasa.utils.common.set_log_level(args.loglevel)
io_utils.configure_colored_logging(args.loglevel)
if not credentials_path:
credentials_path = _prepare_credentials_for_rasa_x(
args.credentials, rasa_x_url=rasa_x_url)
serve_application(
endpoints=endpoints,
port=args.port,
credentials=credentials_path,
cors=args.cors,
auth_token=args.auth_token,
enable_api=True,
jwt_secret=args.jwt_secret,
jwt_method=args.jwt_method,
ssl_certificate=args.ssl_certificate,
ssl_keyfile=args.ssl_keyfile,
ssl_ca_file=args.ssl_ca_file,
ssl_password=args.ssl_password,
)
def _rasa_service(
args: argparse.Namespace,
endpoints: "AvailableEndpoints",
rasa_x_url: Optional[Text] = None,
):
"""Starts the Rasa application."""
from rasa.core.run import serve_application
# needs separate logging configuration as it is started in its own process
logging.basicConfig(level=args.loglevel)
io_utils.configure_colored_logging(args.loglevel)
logging.getLogger("apscheduler").setLevel(logging.WARNING)
credentials_path = _prepare_credentials_for_rasa_x(args.credentials,
rasa_x_url=rasa_x_url)
serve_application(
endpoints=endpoints,
port=args.port,
credentials=credentials_path,
cors=args.cors,
auth_token=args.auth_token,
enable_api=True,
jwt_secret=args.jwt_secret,
jwt_method=args.jwt_method,
)
def main():
from rasa.utils.io import configure_colored_logging
import rasa.core.cli.train
from rasa.core.utils import set_default_subparser
# Running as standalone python application
arg_parser = create_argument_parser()
set_default_subparser(arg_parser, 'default')
cmdline_arguments = arg_parser.parse_args()
additional_args = _additional_arguments(cmdline_arguments)
configure_colored_logging(cmdline_arguments.loglevel)
loop = asyncio.get_event_loop()
training_stories = loop.run_until_complete(
rasa.core.cli.train.stories_from_cli_args(cmdline_arguments))
if cmdline_arguments.mode == 'default':
loop.run_until_complete(do_default_training(cmdline_arguments,
training_stories,
additional_args))
elif cmdline_arguments.mode == 'interactive':
do_interactive_learning(cmdline_arguments,
training_stories,
additional_args)
elif cmdline_arguments.mode == 'compare':
loop.run_until_complete(do_compare_training(cmdline_arguments,
training_stories,
additional_args))
def logging_setup():
logger = logging.getLogger(__name__)
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("absl").setLevel(logging.ERROR)
logging.getLogger("transformers").setLevel(logging.ERROR)
logging.getLogger("rasa").setLevel(logging.ERROR)
io_utils.configure_colored_logging(logging.INFO)
return logger
def _configure_logging(args):
from rasa.core.utils import configure_file_logging
from rasa.utils.common import set_log_level
log_level = args.loglevel or DEFAULT_LOG_LEVEL_RASA_X
if isinstance(log_level, str):
log_level = logging.getLevelName(log_level)
logging.basicConfig(level=log_level)
io_utils.configure_colored_logging(args.loglevel)
set_log_level(log_level)
configure_file_logging(log_level, args.log_file)
logging.getLogger("werkzeug").setLevel(logging.WARNING)
logging.getLogger("engineio").setLevel(logging.WARNING)
logging.getLogger("pika").setLevel(logging.WARNING)
logging.getLogger("socketio").setLevel(logging.ERROR)
if not log_level == logging.DEBUG:
logging.getLogger().setLevel(logging.WARNING)
logging.getLogger("py.warnings").setLevel(logging.ERROR)
argument.model = DEFAULT_MODELS_PATH
argument.connector = "cmdline"
argument.enable_api = False
argument.endpoints = None
argument.credentials = None
argument.remote_storage = None
run(argument)
def train_nlu_core_model() -> None:
train(
domain=DEFAULT_DOMAIN_PATH,
config=DEFAULT_CONFIG_PATH,
training_files=DEFAULT_DATA_PATH,
fixed_model_name="restaurant_rasa_model",
force_training=False,
)
if __name__ == "__main__":
configure_colored_logging(loglevel="ERROR")
parser = create_argument_parser()
cmdline_arguments = parser.parse_args()
train_nlu_core_model()
if cmdline_arguments.shell:
run_rasa_shell()