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 _convert_nlu_data(args: argparse.Namespace) -> None:
import rasa.nlu.convert
if args.format in ["json", "yaml"]:
rasa.nlu.convert.convert_training_data(args.data, args.out,
args.format, args.language)
telemetry.track_data_convert(args.format, "nlu")
else:
rasa.shared.utils.cli.print_error_and_exit(
"Could not recognize output format. Supported output formats: 'json' "
"and 'yaml'. Specify the desired output format with '--format'.")
def _convert_nlg_data(args: argparse.Namespace) -> None:
from rasa.nlu.training_data.converters.nlg_markdown_to_yaml_converter import (
NLGMarkdownToYamlConverter, )
if args.format == "yaml":
rasa.utils.common.run_in_loop(
_convert_to_yaml(args, NLGMarkdownToYamlConverter()))
telemetry.track_data_convert(args.format, "nlg")
else:
rasa.shared.utils.cli.print_error_and_exit(
"Could not recognize output format. Supported output formats: "
"'yaml'. Specify the desired output format with '--format'.")
def _migrate_responses(args: argparse.Namespace) -> None:
"""Migrate retrieval intent responses to the new 2.0 format.
It does so modifying the stories and domain files.
"""
if args.format == "yaml":
rasa.utils.common.run_in_loop(
_convert_to_yaml(args.out, args.domain,
DomainResponsePrefixConverter()))
rasa.utils.common.run_in_loop(
_convert_to_yaml(args.out, args.data,
StoryResponsePrefixConverter()))
telemetry.track_data_convert(args.format, "responses")
else:
rasa.shared.utils.cli.print_error_and_exit(
"Could not recognize output format. Supported output formats: "
"'yaml'. Specify the desired output format with '--format'.")
def _convert_nlu_data(args: argparse.Namespace) -> None:
from rasa.nlu.training_data.converters.nlu_markdown_to_yaml_converter import (
NLUMarkdownToYamlConverter, )
if args.format in ["json", "md"]:
rasa.nlu.convert.convert_training_data(args.data, args.out,
args.format, args.language)
telemetry.track_data_convert(args.format, "nlu")
elif args.format == "yaml":
rasa.utils.common.run_in_loop(
_convert_to_yaml(args.out, args.data,
NLUMarkdownToYamlConverter()))
telemetry.track_data_convert(args.format, "nlu")
else:
rasa.shared.utils.cli.print_error_and_exit(
"Could not recognize output format. Supported output formats: 'json', "
"'md', 'yaml'. Specify the desired output format with '--format'.")
def _migrate_model_config(args: argparse.Namespace) -> None:
"""Migrates old "rule-like" policies to the new `RulePolicy`.
Updates the config, domain, and generates the required rules.
Args:
args: The commandline args with the required paths.
"""
import rasa.core.config
configuration_file = Path(args.config)
model_configuration = _get_configuration(configuration_file)
domain_file = Path(args.domain)
domain = _get_domain(domain_file)
rule_output_file = _get_rules_path(args.out)
(
model_configuration,
domain,
new_rules,
) = rasa.core.config.migrate_mapping_policy_to_rules(
model_configuration, domain)
model_configuration, fallback_rule = rasa.core.config.migrate_fallback_policies(
model_configuration)
if new_rules:
_backup(domain_file)
domain.persist_clean(domain_file)
if fallback_rule:
new_rules.append(fallback_rule)
if new_rules or model_configuration["policies"]:
_backup(configuration_file)
rasa.shared.utils.io.write_yaml(model_configuration,
configuration_file)
_dump_rules(rule_output_file, new_rules)
telemetry.track_data_convert("yaml", "config")
_print_success_message(new_rules, rule_output_file)
