def test_broker_from_config():
cfg = utils.read_endpoint_config(EVENT_BROKER_ENDPOINT_FILE,
"event_broker")
actual = PikaProducer.from_endpoint_config(cfg)
expected = PikaProducer("localhost", "username", "password", "queue")
assert actual.host == expected.host
assert actual.credentials.username == expected.credentials.username
assert actual.queue == expected.queue
def run(core_dir, nlu_dir):
pika_broker = None
if ENABLE_ANALYTICS:
pika_broker = PikaProducer(url, username, password, queue=queue)
configs = {
'user': os.getenv('ROCKETCHAT_BOT_USERNAME'),
'password': os.getenv('ROCKETCHAT_BOT_PASSWORD'),
'server_url': os.getenv('ROCKETCHAT_URL'),
}
input_channel = RocketChatInput(user=configs['user'],
password=configs['password'],
server_url=configs['server_url'])
_tracker_store = InMemoryTrackerStore(domain=None,
event_broker=pika_broker)
_endpoints = AvailableEndpoints.read_endpoints(None)
_interpreter = NaturalLanguageInterpreter.create(nlu_dir)
_agent = load_agent(core_dir,
interpreter=_interpreter,
tracker_store=_tracker_store,
endpoints=_endpoints)
http_server = start_server([input_channel], "", "", 5005, _agent)
try:
http_server.serve_forever()
except Exception as exc:
logger.exception(exc)
def create_agent(model: Text,
endpoints: Text = None) -> 'Agent':
from rasa_core.broker import PikaProducer
from rasa_core.interpreter import RasaNLUInterpreter
import rasa_core.run
from rasa_core.tracker_store import TrackerStore
from rasa_core.utils import AvailableEndpoints
core_path, nlu_path = get_model_subdirectories(model)
_endpoints = AvailableEndpoints.read_endpoints(endpoints)
_interpreter = None
if os.path.exists(nlu_path):
_interpreter = RasaNLUInterpreter(model_directory=nlu_path)
else:
_interpreter = None
logging.info("No NLU model found. Running without NLU.")
_broker = PikaProducer.from_endpoint_config(_endpoints.event_broker)
_tracker_store = TrackerStore.find_tracker_store(None,
_endpoints.tracker_store,
_broker)
return rasa_core.run.load_agent(core_path, interpreter=_interpreter,
tracker_store=_tracker_store,
endpoints=_endpoints)
def do_interactive_learning(cmdline_args, stories, additional_arguments):
_endpoints = AvailableEndpoints.read_endpoints(cmdline_args.endpoints)
_interpreter = NaturalLanguageInterpreter.create(cmdline_args.nlu,
_endpoints.nlu)
if cmdline_args.core:
if cmdline_args.finetune:
raise ValueError("--core can only be used without "
"--finetune flag.")
logger.info("Loading a pre-trained model. This means that "
"all training-related parameters will be ignored.")
_broker = PikaProducer.from_endpoint_config(_endpoints.event_broker)
_tracker_store = TrackerStore.find_tracker_store(
None, _endpoints.tracker_store, _broker)
_agent = Agent.load(cmdline_args.core,
interpreter=_interpreter,
generator=_endpoints.nlg,
tracker_store=_tracker_store,
action_endpoint=_endpoints.action)
else:
if cmdline_args.out:
model_directory = cmdline_args.out
else:
model_directory = tempfile.mkdtemp(suffix="_core_model")
_agent = train_dialogue_model(cmdline_args.domain, stories,
model_directory, _interpreter,
_endpoints, cmdline_args.dump_stories,
cmdline_args.config[0], None,
additional_arguments)
interactive.run_interactive_learning(
_agent,
stories,
finetune=cmdline_args.finetune,
skip_visualization=cmdline_args.skip_visualization)
def do_interactive_learning(cmdline_args, stories, additional_arguments):
_endpoints = AvailableEndpoints.read_endpoints(cmdline_args.endpoints)
_interpreter = NaturalLanguageInterpreter.create(cmdline_args.nlu,
_endpoints.nlu)
if (isinstance(cmdline_args.config, list)
and len(cmdline_args.config) > 1):
raise ValueError("You can only pass one config file at a time")
if cmdline_args.core and cmdline_args.finetune:
raise ValueError("--core can only be used without --finetune flag.")
elif cmdline_args.core:
logger.info("loading a pre-trained model. "
"all training-related parameters will be ignored")
_broker = PikaProducer.from_endpoint_config(_endpoints.event_broker)
_tracker_store = TrackerStore.find_tracker_store(
None, _endpoints.tracker_store, _broker)
_agent = Agent.load(cmdline_args.core,
interpreter=_interpreter,
generator=_endpoints.nlg,
tracker_store=_tracker_store,
action_endpoint=_endpoints.action)
else:
if not cmdline_args.out:
raise ValueError("you must provide a path where the model "
"will be saved using -o / --out")
_agent = train_dialogue_model(cmdline_args.domain, stories,
cmdline_args.out, _interpreter,
_endpoints, cmdline_args.dump_stories,
cmdline_args.config[0], None,
additional_arguments)
interactive.run_interactive_learning(
_agent,
stories,
finetune=cmdline_args.finetune,
skip_visualization=cmdline_args.skip_visualization)
arg_parser = create_argument_parser()
cmdline_args = arg_parser.parse_args()
logging.getLogger('werkzeug').setLevel(logging.WARN)
logging.getLogger('matplotlib').setLevel(logging.WARN)
utils.configure_colored_logging(cmdline_args.loglevel)
utils.configure_file_logging(cmdline_args.loglevel,
cmdline_args.log_file)
logger.info("Rasa process starting")
_endpoints = AvailableEndpoints.read_endpoints(cmdline_args.endpoints)
_interpreter = NaturalLanguageInterpreter.create(cmdline_args.nlu,
_endpoints.nlu)
_broker = PikaProducer.from_endpoint_config(_endpoints.event_broker)
_tracker_store = TrackerStore.find_tracker_store(
None, _endpoints.tracker_store, _broker)
_agent = load_agent(cmdline_args.core,
interpreter=_interpreter,
tracker_store=_tracker_store,
endpoints=_endpoints)
serve_application(_agent,
cmdline_args.connector,
cmdline_args.port,
cmdline_args.credentials,
cmdline_args.cors,
cmdline_args.auth_token,
cmdline_args.enable_api,
cmdline_args.jwt_secret,
def test_broker_not_in_config():
cfg = utils.read_endpoint_config(DEFAULT_ENDPOINTS_FILE, "event_broker")
actual = PikaProducer.from_endpoint_config(cfg)
assert actual is None