Warning: This document is for an old version of Rasa Core.

Source code for rasa_core.agent

import time
import logging
import os
import shutil
import tempfile
import typing
import uuid
from gevent.pywsgi import WSGIServer
from requests.exceptions import InvalidURL, RequestException
from threading import Thread
from typing import Text, List, Optional, Callable, Any, Dict, Union

from rasa_core import training, constants, utils
from rasa_core.channels import UserMessage, OutputChannel, InputChannel
from rasa_core.constants import DEFAULT_REQUEST_TIMEOUT
from rasa_core.dispatcher import Dispatcher
from rasa_core.domain import Domain, check_domain_sanity, InvalidDomain
from rasa_core.exceptions import AgentNotReady
from rasa_core.interpreter import NaturalLanguageInterpreter
from rasa_core.nlg import NaturalLanguageGenerator
from rasa_core.policies import Policy, FormPolicy
from rasa_core.policies.ensemble import SimplePolicyEnsemble, PolicyEnsemble
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.processor import MessageProcessor
from rasa_core.tracker_store import InMemoryTrackerStore
from rasa_core.trackers import DialogueStateTracker
from rasa_core.utils import EndpointConfig
from rasa_nlu.utils import is_url

logger = logging.getLogger(__name__)

if typing.TYPE_CHECKING:
    # noinspection PyPep8Naming
    from rasa_core.nlg import NaturalLanguageGenerator as NLG
    from rasa_core.tracker_store import TrackerStore

[docs]def load_from_server(interpreter: Optional[NaturalLanguageInterpreter] = None, generator: Optional[Union[EndpointConfig, 'NLG']] = None, tracker_store: Optional['TrackerStore'] = None, action_endpoint: Optional[EndpointConfig] = None, model_server: Optional[EndpointConfig] = None, ) -> 'Agent': """Load a persisted model from a server.""" agent = Agent(interpreter=interpreter, generator=generator, tracker_store=tracker_store, action_endpoint=action_endpoint) wait_time_between_pulls = model_server.kwargs.get( 'wait_time_between_pulls', 100 ) if wait_time_between_pulls is not None and ( isinstance(wait_time_between_pulls, int) or wait_time_between_pulls.isdigit()): # continuously pull the model every `wait_time_between_pulls` seconds start_model_pulling_in_worker(model_server, int(wait_time_between_pulls), agent) else: # just pull the model once _update_model_from_server(model_server, agent) return agent
def _init_model_from_server(model_server: EndpointConfig ) -> Optional[typing.Tuple[Text, Text]]: """Initialise a Rasa Core model from a URL.""" if not is_url(model_server.url): raise InvalidURL(model_server.url) model_directory = tempfile.mkdtemp() fingerprint = _pull_model_and_fingerprint(model_server, model_directory, fingerprint=None) return fingerprint, model_directory def _is_stack_model(model_directory: Text) -> bool: """Decide whether a persisted model is a stack or a core model.""" return os.path.exists(os.path.join(model_directory, "fingerprint.json")) def _load_and_set_updated_model(agent: 'Agent', model_directory: Text, fingerprint: Text): """Load the persisted model into memory and set the model on the agent.""" if _is_stack_model(model_directory): from rasa_core.interpreter import RasaNLUInterpreter nlu_model = os.path.join(model_directory, "nlu") core_model = os.path.join(model_directory, "core") interpreter = RasaNLUInterpreter(model_directory=nlu_model) else: interpreter = agent.interpreter core_model = model_directory domain_path = os.path.join(os.path.abspath(core_model), "domain.yml") domain = Domain.load(domain_path) # noinspection PyBroadException try: policy_ensemble = PolicyEnsemble.load(core_model) agent.update_model(domain, policy_ensemble, fingerprint, interpreter) except Exception: logger.exception("Failed to load policy and update agent. " "The previous model will stay loaded instead.") def _update_model_from_server(model_server: EndpointConfig, agent: 'Agent' ) -> None: """Load a zipped Rasa Core model from a URL and update the passed agent.""" if not is_url(model_server.url): raise InvalidURL(model_server.url) model_directory = tempfile.mkdtemp() new_model_fingerprint = _pull_model_and_fingerprint( model_server, model_directory, agent.fingerprint) if new_model_fingerprint: _load_and_set_updated_model(agent, model_directory, new_model_fingerprint) else: logger.debug("No new model found at " "URL {}".format(model_server.url)) def _pull_model_and_fingerprint(model_server: EndpointConfig, model_directory: Text, fingerprint: Optional[Text] ) -> Optional[Text]: """Queries the model server and returns the value of the response's <ETag> header which contains the model hash.""" header = {"If-None-Match": fingerprint} try: logger.debug("Requesting model from server {}..." "".format(model_server.url)) response = model_server.request(method="GET", headers=header, timeout=DEFAULT_REQUEST_TIMEOUT) except RequestException as e: logger.warning("Tried to fetch model from server, but couldn't reach " "server. We'll retry later... Error: {}." "".format(e)) return None if response.status_code in [204, 304]: logger.debug("Model server returned {} status code, indicating " "that no new model is available. " "Current fingerprint: {}" "".format(response.status_code, fingerprint)) return response.headers.get("ETag") elif response.status_code == 404: logger.debug("Model server didn't find a model for our request. " "Probably no one did train a model for the project " "and tag combination yet.") return None elif response.status_code != 200: logger.warning("Tried to fetch model from server, but server response " "status code is {}. We'll retry later..." "".format(response.status_code)) return None utils.unarchive(response.content, model_directory) logger.debug("Unzipped model to '{}'" "".format(os.path.abspath(model_directory))) # get the new fingerprint return response.headers.get("ETag") def _run_model_pulling_worker(model_server: EndpointConfig, wait_time_between_pulls: int, agent: 'Agent') -> None: while True: _update_model_from_server(model_server, agent) time.sleep(wait_time_between_pulls)
[docs]def start_model_pulling_in_worker(model_server: EndpointConfig, wait_time_between_pulls: int, agent: 'Agent') -> None: worker = Thread(target=_run_model_pulling_worker, args=(model_server, wait_time_between_pulls, agent)) worker.setDaemon(True) worker.start()
[docs]class Agent(object): """The Agent class provides a convenient interface for the most important Rasa Core functionality. This includes training, handling messages, loading a dialogue model, getting the next action, and handling a channel.""" def __init__( self, domain: Union[Text, Domain] = None, policies: Union[PolicyEnsemble, List[Policy], None] = None, interpreter: Optional[NaturalLanguageInterpreter] = None, generator: Union[EndpointConfig, 'NLG', None] = None, tracker_store: Optional['TrackerStore'] = None, action_endpoint: Optional[EndpointConfig] = None, fingerprint: Optional[Text] = None ): # Initializing variables with the passed parameters. self.domain = self._create_domain(domain) if self.domain: self.domain.add_requested_slot() self.policy_ensemble = self._create_ensemble(policies) if self._form_policy_not_present(): raise InvalidDomain( "You have defined a form action, but haven't added the " "FormPolicy to your policy ensemble." ) self.interpreter = NaturalLanguageInterpreter.create(interpreter) self.nlg = NaturalLanguageGenerator.create(generator, self.domain) self.tracker_store = self.create_tracker_store( tracker_store, self.domain) self.action_endpoint = action_endpoint self._set_fingerprint(fingerprint)
[docs] def update_model(self, domain: Union[Text, Domain], policy_ensemble: PolicyEnsemble, fingerprint: Optional[Text], interpreter: Optional[NaturalLanguageInterpreter] = None ) -> None: self.domain = domain self.policy_ensemble = policy_ensemble if interpreter: self.interpreter = NaturalLanguageInterpreter.create(interpreter) self._set_fingerprint(fingerprint) # update domain on all instances self.tracker_store.domain = domain if hasattr(self.nlg, "templates"): self.nlg.templates = domain.templates or []
[docs] @classmethod def load(cls, path: Text, interpreter: Optional[NaturalLanguageInterpreter] = None, generator: Union[EndpointConfig, 'NLG'] = None, tracker_store: Optional['TrackerStore'] = None, action_endpoint: Optional[EndpointConfig] = None, ) -> 'Agent': """Load a persisted model from the passed path.""" if not path: raise ValueError("You need to provide a valid directory where " "to load the agent from when calling " "`Agent.load`.") if os.path.isfile(path): raise ValueError("You are trying to load a MODEL from a file " "('{}'), which is not possible. \n" "The persisted path should be a directory " "containing the various model files. \n\n" "If you want to load training data instead of " "a model, use `agent.load_data(...)` " "instead.".format(path)) domain = Domain.load(os.path.join(path, "domain.yml")) ensemble = PolicyEnsemble.load(path) if path else None # ensures the domain hasn't changed between test and train domain.compare_with_specification(path) return cls(domain=domain, policies=ensemble, interpreter=interpreter, generator=generator, tracker_store=tracker_store, action_endpoint=action_endpoint)
[docs] def is_ready(self): """Check if all necessary components are instantiated to use agent.""" return (self.interpreter is not None and self.tracker_store is not None and self.policy_ensemble is not None)
[docs] def handle_message( self, message: UserMessage, message_preprocessor: Optional[Callable[[Text], Text]] = None, **kwargs ) -> Optional[List[Text]]: """Handle a single message.""" if not isinstance(message, UserMessage): logger.warning("Passing a text to `agent.handle_message(...)` is " "deprecated. Rather use `agent.handle_text(...)`.") return self.handle_text(message, message_preprocessor=message_preprocessor, **kwargs) def noop(_): logger.info("Ignoring message as there is no agent to handle it.") return None if not self.is_ready(): return noop(message) # processor = self.create_processor(message_preprocessor) return processor.handle_message(message)
# noinspection PyUnusedLocal
[docs] def predict_next( self, sender_id: Text, **kwargs: Any ) -> Dict[Text, Any]: """Handle a single message.""" processor = self.create_processor() return processor.predict_next(sender_id)
# noinspection PyUnusedLocal
[docs] def log_message( self, message: UserMessage, message_preprocessor: Optional[Callable[[Text], Text]] = None, **kwargs: Any ) -> DialogueStateTracker: """Append a message to a dialogue - does not predict actions.""" processor = self.create_processor(message_preprocessor) return processor.log_message(message)
[docs] def execute_action( self, sender_id: Text, action: Text, output_channel: OutputChannel, policy: Text, confidence: float ) -> DialogueStateTracker: """Handle a single message.""" processor = self.create_processor() dispatcher = Dispatcher(sender_id, output_channel, self.nlg) return processor.execute_action(sender_id, action, dispatcher, policy, confidence)
[docs] def handle_text( self, text_message: Union[Text, Dict[Text, Any]], message_preprocessor: Optional[Callable[[Text], Text]] = None, output_channel: Optional[OutputChannel] = None, sender_id: Optional[Text] = UserMessage.DEFAULT_SENDER_ID ) -> Optional[List[Dict[Text, Any]]]: """Handle a single message. If a message preprocessor is passed, the message will be passed to that function first and the return value is then used as the input for the dialogue engine. The return value of this function depends on the ``output_channel``. If the output channel is not set, set to ``None``, or set to ``CollectingOutputChannel`` this function will return the messages the bot wants to respond. :Example: >>> from rasa_core.agent import Agent >>> from rasa_core.interpreter import RasaNLUInterpreter >>> interpreter = RasaNLUInterpreter( ... "examples/restaurantbot/models/nlu/current") >>> agent = Agent.load("examples/restaurantbot/models/dialogue", ... interpreter=interpreter) >>> agent.handle_text("hello") [u'how can I help you?'] """ if isinstance(text_message, str): text_message = {"text": text_message} msg = UserMessage(text_message.get("text"), output_channel, sender_id) return self.handle_message(msg, message_preprocessor)
[docs] def toggle_memoization( self, activate: bool ) -> None: """Toggles the memoization on and off. If a memoization policy is present in the ensemble, this will toggle the prediction of that policy. When set to ``False`` the Memoization policies present in the policy ensemble will not make any predictions. Hence, the prediction result from the ensemble always needs to come from a different policy (e.g. ``KerasPolicy``). Useful to test prediction capabilities of an ensemble when ignoring memorized turns from the training data.""" if not self.policy_ensemble: return for p in self.policy_ensemble.policies: # explicitly ignore inheritance (e.g. augmented memoization policy) if type(p) == MemoizationPolicy: p.toggle(activate)
[docs] def continue_training(self, trackers: List[DialogueStateTracker], **kwargs: Any ) -> None: if not self.is_ready(): raise AgentNotReady("Can't continue training without a policy " "ensemble.") self.policy_ensemble.continue_training(trackers, self.domain, **kwargs) self._set_fingerprint()
def _max_history(self): """Find maximum max_history.""" max_histories = [policy.featurizer.max_history for policy in self.policy_ensemble.policies if hasattr(policy.featurizer, 'max_history')] return max(max_histories or [0]) def _are_all_featurizers_using_a_max_history(self): """Check if all featurizers are MaxHistoryTrackerFeaturizer.""" for policy in self.policy_ensemble.policies: if (policy.featurizer and not hasattr(policy.featurizer, 'max_history')): return False return True
[docs] def load_data(self, resource_name: Text, remove_duplicates: bool = True, unique_last_num_states: Optional[int] = None, augmentation_factor: int = 20, tracker_limit: Optional[int] = None, use_story_concatenation: bool = True, debug_plots: bool = False, exclusion_percentage: int = None ) -> List[DialogueStateTracker]: """Load training data from a resource.""" max_history = self._max_history() if unique_last_num_states is None: # for speed up of data generation # automatically detect unique_last_num_states # if it was not set and # if all featurizers are MaxHistoryTrackerFeaturizer if self._are_all_featurizers_using_a_max_history(): unique_last_num_states = max_history elif unique_last_num_states < max_history: # possibility of data loss logger.warning("unique_last_num_states={} but " "maximum max_history={}." "Possibility of data loss. " "It is recommended to set " "unique_last_num_states to " "at least maximum max_history." "".format(unique_last_num_states, max_history)) return training.load_data(resource_name, self.domain, remove_duplicates, unique_last_num_states, augmentation_factor, tracker_limit, use_story_concatenation, debug_plots, exclusion_percentage=exclusion_percentage)
[docs] def train(self, training_trackers: List[DialogueStateTracker], **kwargs: Any ) -> None: """Train the policies / policy ensemble using dialogue data from file. Args: training_trackers: trackers to train on **kwargs: additional arguments passed to the underlying ML trainer (e.g. keras parameters) """ if not self.is_ready(): raise AgentNotReady("Can't train without a policy ensemble.") # deprecation tests if kwargs.get('featurizer'): raise Exception("Passing `featurizer` " "to `agent.train(...)` is not supported anymore. " "Pass appropriate featurizer directly " "to the policy configuration instead. More info " "https://legacy-docs.rasa.com/docs/core " " /migrations.html") if kwargs.get('epochs') or kwargs.get('max_history') or kwargs.get( 'batch_size'): raise Exception("Passing policy configuration parameters " "to `agent.train(...)` is not supported " "anymore. Specify parameters directly in the " "policy configuration instead. More info " "https://legacy-docs.rasa.com/docs/core " " /migrations.html") if isinstance(training_trackers, str): # the user most likely passed in a file name to load training # data from raise Exception("Passing a file name to `agent.train(...)` is " "not supported anymore. Rather load the data with " "`data = agent.load_data(file_name)` and pass it " "to `agent.train(data)`.") logger.debug("Agent trainer got kwargs: {}".format(kwargs)) check_domain_sanity(self.domain) self.policy_ensemble.train(training_trackers, self.domain, **kwargs) self._set_fingerprint()
[docs] def handle_channels(self, channels: List[InputChannel], http_port: int = constants.DEFAULT_SERVER_PORT, serve_forever: bool = True, route: Text = "/webhooks/") -> WSGIServer: """Start a webserver attaching the input channels and handling msgs. If ``serve_forever`` is set to ``True``, this call will be blocking. Otherwise the webserver will be started, and the method will return afterwards.""" from flask import Flask import rasa_core app = Flask(__name__) rasa_core.channels.channel.register(channels, app, self.handle_message, route=route) http_server = WSGIServer(('', http_port), app) http_server.start() if serve_forever: http_server.serve_forever() return http_server
def _set_fingerprint(self, fingerprint: Optional[Text] = None) -> None: if fingerprint: self.fingerprint = fingerprint else: self.fingerprint = uuid.uuid4().hex @staticmethod def _clear_model_directory(model_path: Text) -> None: """Remove existing files from model directory. Only removes files if the directory seems to contain a previously persisted model. Otherwise does nothing to avoid deleting `/` by accident.""" if not os.path.exists(model_path): return domain_spec_path = os.path.join(model_path, 'metadata.json') # check if there were a model before if os.path.exists(domain_spec_path): logger.info("Model directory {} exists and contains old " "model files. All files will be overwritten." "".format(model_path)) shutil.rmtree(model_path) else: logger.debug("Model directory {} exists, but does not contain " "all old model files. Some files might be " "overwritten.".format(model_path))
[docs] def persist(self, model_path: Text, dump_flattened_stories: bool = False) -> None: """Persists this agent into a directory for later loading and usage.""" if not self.is_ready(): raise AgentNotReady("Can't persist without a policy ensemble.") self._clear_model_directory(model_path) self.policy_ensemble.persist(model_path, dump_flattened_stories) self.domain.persist(os.path.join(model_path, "domain.yml")) self.domain.persist_specification(model_path) logger.info("Persisted model to '{}'" "".format(os.path.abspath(model_path)))
[docs] def visualize(self, resource_name: Text, output_file: Text, max_history: Optional[int] = None, nlu_training_data: Optional[Text] = None, should_merge_nodes: bool = True, fontsize: int = 12 ) -> None: from rasa_core.training.visualization import visualize_stories from rasa_core.training.dsl import StoryFileReader """Visualize the loaded training data from the resource.""" # if the user doesn't provide a max history, we will use the # largest value from any policy max_history = max_history or self._max_history() story_steps = StoryFileReader.read_from_folder(resource_name, self.domain) visualize_stories(story_steps, self.domain, output_file, max_history, self.interpreter, nlu_training_data, should_merge_nodes, fontsize)
def _ensure_agent_is_ready(self) -> None: """Checks that an interpreter and a tracker store are set. Necessary before a processor can be instantiated from this agent. Raises an exception if any argument is missing.""" if not self.is_ready(): raise AgentNotReady("Agent needs to be prepared before usage. " "You need to set an interpreter, a policy " "ensemble as well as a tracker store.")
[docs] def create_processor(self, preprocessor: Optional[Callable[[Text], Text]] = None ) -> MessageProcessor: """Instantiates a processor based on the set state of the agent.""" # Checks that the interpreter and tracker store are set and # creates a processor self._ensure_agent_is_ready() return MessageProcessor( self.interpreter, self.policy_ensemble, self.domain, self.tracker_store, self.nlg, action_endpoint=self.action_endpoint, message_preprocessor=preprocessor)
@staticmethod def _create_domain(domain: Union[None, Domain, Text]) -> Domain: if isinstance(domain, str): return Domain.load(domain) elif isinstance(domain, Domain): return domain elif domain is not None: raise ValueError( "Invalid param `domain`. Expected a path to a domain " "specification or a domain instance. But got " "type '{}' with value '{}'".format(type(domain), domain))
[docs] @staticmethod def create_tracker_store(store: Optional['TrackerStore'], domain: Domain) -> 'TrackerStore': if store is not None: store.domain = domain return store else: return InMemoryTrackerStore(domain)
@staticmethod def _create_ensemble( policies: Union[List[Policy], PolicyEnsemble, None] ) -> Optional[PolicyEnsemble]: if policies is None: return None if isinstance(policies, list): return SimplePolicyEnsemble(policies) elif isinstance(policies, PolicyEnsemble): return policies else: passed_type = type(policies).__name__ raise ValueError( "Invalid param `policies`. Passed object is " "of type '{}', but should be policy, an array of " "policies, or a policy ensemble".format(passed_type)) def _form_policy_not_present(self) -> bool: """Check whether form policy is not present if there is a form action in the domain """ return (self.domain and self.domain.form_names and not any(isinstance(p, FormPolicy) for p in self.policy_ensemble.policies))