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"]]
)
def visualize(
domain_path: Text,
stories_path: Text,
nlu_data_path: Text,
output_path: Text,
max_history: int,
) -> None:
"""Visualizes stories as graph.
Args:
domain_path: Path to the domain file.
stories_path: Path to the stories files.
nlu_data_path: Path to the NLU training data which can be used to interpolate
intents with actual examples in the graph.
output_path: Path where the created graph should be persisted.
max_history: Max history to use for the story visualization.
"""
import rasa.shared.core.training_data.visualization
try:
domain = Domain.load(domain_path)
except InvalidDomain as e:
print_error(
f"Could not load domain due to: '{e}'. To specify a valid domain path use "
f"the '--domain' argument.")
return
# this is optional, only needed if the `/greet` type of
# messages in the stories should be replaced with actual
# messages (e.g. `hello`)
if nlu_data_path is not None:
import rasa.shared.nlu.training_data.loading
nlu_training_data = rasa.shared.nlu.training_data.loading.load_data(
nlu_data_path)
else:
nlu_training_data = None
logger.info("Starting to visualize stories...")
telemetry.track_visualization()
story_steps = loading.load_data_from_resource(stories_path, domain)
rasa.shared.core.training_data.visualization.visualize_stories(
story_steps,
domain,
output_path,
max_history,
nlu_training_data=nlu_training_data,
)
full_output_path = "file://{}".format(os.path.abspath(output_path))
logger.info(f"Finished graph creation. Saved into {full_output_path}")
import webbrowser
webbrowser.open(full_output_path)
async def visualize(
config_path: Text,
domain_path: Text,
stories_path: Text,
nlu_data_path: Text,
output_path: Text,
max_history: int,
):
from rasa.core.agent import Agent
from rasa.core import config
try:
policies = config.load(config_path)
except ValueError as e:
print_error(
"Could not load config due to: '{}'. To specify a valid config file use "
"the '--config' argument.".format(e))
return
try:
agent = Agent(domain=domain_path, policies=policies)
except InvalidDomain as e:
print_error(
"Could not load domain due to: '{}'. To specify a valid domain path use "
"the '--domain' argument.".format(e))
return
# this is optional, only needed if the `/greet` type of
# messages in the stories should be replaced with actual
# messages (e.g. `hello`)
if nlu_data_path is not None:
import rasa.shared.nlu.training_data.loading
nlu_training_data = rasa.shared.nlu.training_data.loading.load_data(
nlu_data_path)
else:
nlu_training_data = None
logger.info("Starting to visualize stories...")
telemetry.track_visualization()
await agent.visualize(stories_path,
output_path,
max_history,
nlu_training_data=nlu_training_data)
full_output_path = "file://{}".format(os.path.abspath(output_path))
logger.info(f"Finished graph creation. Saved into {full_output_path}")
import webbrowser
webbrowser.open(full_output_path)
