def shell(args: argparse.Namespace) -> None:
"""Talk with a bot though the command line."""
from rasa.cli.utils import get_validated_path
from rasa.shared.constants import DEFAULT_MODELS_PATH
args.connector = "cmdline"
model = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
try:
model = get_latest_model(model)
except ModelNotFound:
print_error("No model found. Train a model before running the "
"server using `rasa train`.")
return
metadata = LocalModelStorage.metadata_from_archive(model)
if metadata.training_type == TrainingType.NLU:
import rasa.nlu.run
telemetry.track_shell_started("nlu")
rasa.nlu.run.run_cmdline(model)
else:
import rasa.cli.run
telemetry.track_shell_started("rasa")
rasa.cli.run.run(args)
def shell(args: argparse.Namespace) -> None:
from rasa.cli.utils import get_validated_path
from rasa.shared.constants import DEFAULT_MODELS_PATH
from rasa.model import get_model, get_model_subdirectories
args.connector = "cmdline"
model = get_validated_path(args.model, "model", DEFAULT_MODELS_PATH)
try:
model_path = get_model(model)
except ModelNotFound:
print_error(
"No model found. Train a model before running the "
"server using `rasa train`."
)
return
core_model, nlu_model = get_model_subdirectories(model_path)
if not core_model:
import rasa.nlu.run
telemetry.track_shell_started("nlu")
rasa.nlu.run.run_cmdline(nlu_model)
else:
import rasa.cli.run
telemetry.track_shell_started("rasa")
rasa.cli.run.run(args)
async def test_events_schema(
monkeypatch: MonkeyPatch, default_agent: Agent, config_path: Text
):
# this allows us to patch the printing part used in debug mode to collect the
# reported events
monkeypatch.setenv("RASA_TELEMETRY_DEBUG", "true")
monkeypatch.setenv("RASA_TELEMETRY_ENABLED", "true")
mock = Mock()
monkeypatch.setattr(telemetry, "print_telemetry_event", mock)
with open(TELEMETRY_EVENTS_JSON) as f:
schemas = json.load(f)["events"]
initial = asyncio.all_tasks()
# Generate all known backend telemetry events, and then use events.json to
# validate their schema.
training_data = TrainingDataImporter.load_from_config(config_path)
with telemetry.track_model_training(training_data, "rasa"):
await asyncio.sleep(1)
telemetry.track_telemetry_disabled()
telemetry.track_data_split(0.5, "nlu")
telemetry.track_validate_files(True)
telemetry.track_data_convert("yaml", "nlu")
telemetry.track_tracker_export(5, TrackerStore(domain=None), EventBroker())
telemetry.track_interactive_learning_start(True, False)
telemetry.track_server_start([CmdlineInput()], None, None, 42, True)
telemetry.track_project_init("tests/")
telemetry.track_shell_started("nlu")
telemetry.track_rasa_x_local()
telemetry.track_visualization()
telemetry.track_core_model_test(5, True, default_agent)
telemetry.track_nlu_model_test(TrainingData())
pending = asyncio.all_tasks() - initial
await asyncio.gather(*pending)
assert mock.call_count == 15
for args, _ in mock.call_args_list:
event = args[0]
# `metrics_id` automatically gets added to all event but is
# not part of the schema so we need to remove it before validation
del event["properties"]["metrics_id"]
jsonschema.validate(
instance=event["properties"], schema=schemas[event["event"]]
)
