New Response Selector training data format

examples/responseselectorbot/data/responses.yml

@@ -1,12 +1,12 @@
 version: "2.0"
 
 responses:
-  chitchat/ask_name:
+  utter_chitchat/ask_name:
   - image: "https://i.imgur.com/zTvA58i.jpeg"
     text: hello, my name is retrieval bot.
   - text: Oh yeah, I am called the retrieval bot.
 
-  chitchat/ask_weather:
+  utter_chitchat/ask_weather:
   - text: Oh, it does look sunny right now in Berlin.
     image: "https://i.imgur.com/vwv7aHN.png"
   - text: I am not sure of the whole week but I can see the sun is out today.

examples/responseselectorbot/data/rules.yml

@@ -15,4 +15,4 @@ rules:
 - rule: Response with a chitchat utterance whenever user indulges in some chitchat
   steps:
   - intent: chitchat
-  - action: respond_chitchat
+  - action: utter_chitchat

examples/responseselectorbot/domain.yml

@@ -10,9 +10,6 @@ intents:
   - bot_challenge
   - chitchat
 
-actions:
-  - respond_chitchat
-
 responses:
   utter_greet:
   - text: "Hey! How are you?"

rasa/core/actions/action.py

@@ -15,7 +15,6 @@
     REQUESTED_SLOT,
     USER_INTENT_OUT_OF_SCOPE,
     UTTER_PREFIX,
-    RESPOND_PREFIX,
 )
 from rasa.nlu.constants import (
     RESPONSE_SELECTOR_DEFAULT_INTENT,
@@ -92,6 +91,11 @@ def default_action_names() -> List[Text]:
     return [a.name() for a in default_actions()] + [RULE_SNIPPET_ACTION_NAME]
 
 
+def construct_retrieval_action_names(retrieval_intents: List[Text]) -> List[Text]:
+
+    return [f"{UTTER_PREFIX}{intent}" for intent in retrieval_intents]
+
+
 def combine_user_with_default_actions(user_actions: List[Text]) -> List[Text]:
     # remove all user actions that overwrite default actions
     # this logic is a bit reversed, you'd think that we should remove
@@ -115,11 +119,21 @@ def combine_with_templates(
     return actions + unique_template_names
 
 
+def is_retrieval_action(action_name: Text, retrieval_intents: List[Text]) -> bool:
+
+    return (
+        True
+        if retrieval_intents and action_name.split(UTTER_PREFIX)[1] in retrieval_intents
+        else False
+    )
+
+
 def action_from_name(
     name: Text,
     action_endpoint: Optional[EndpointConfig],
     user_actions: List[Text],
     should_use_form_action: bool = False,
+    retrieval_intents: List[Text] = [],
 ) -> "Action":
     """Return an action instance for the name."""
 
@@ -128,9 +142,11 @@ def action_from_name(
     if name in defaults and name not in user_actions:
         return defaults[name]
     elif name.startswith(UTTER_PREFIX):
-        return ActionUtterTemplate(name)
-    elif name.startswith(RESPOND_PREFIX):
-        return ActionRetrieveResponse(name)
+        return (
+            ActionRetrieveResponse(name)
+            if is_retrieval_action(name, retrieval_intents)
+            else ActionUtterTemplate(name)
+        )
     elif should_use_form_action:
         from rasa.core.actions.forms import FormAction
 
@@ -220,7 +236,7 @@ def __init__(self, name: Text, silent_fail: Optional[bool] = False):
         self.silent_fail = silent_fail
 
     def intent_name_from_action(self) -> Text:
-        return self.action_name.split(RESPOND_PREFIX)[1]
+        return self.action_name.split(UTTER_PREFIX)[1]
 
     async def run(
         self,

rasa/core/constants.py

@@ -66,7 +66,6 @@
 # it is the highest to prioritize form to the rest of the policies
 FORM_POLICY_PRIORITY = 5
 UTTER_PREFIX = "utter_"
-RESPOND_PREFIX = "respond_"
 
 DIALOGUE = "dialogue"
 

rasa/core/domain.py

@@ -30,11 +30,13 @@
     SLOT_LAST_OBJECT_TYPE,
     SLOT_LISTED_ITEMS,
     DEFAULT_INTENTS,
+    UTTER_PREFIX,
 )
 from rasa.core.events import SlotSet, UserUttered
 from rasa.shared.core.slots import Slot, UnfeaturizedSlot, CategoricalSlot
 from rasa.utils.endpoints import EndpointConfig
 from rasa.utils.validation import InvalidYamlFileError, validate_yaml_schema
+from rasa.nlu.training_data import TrainingData
 
 logger = logging.getLogger(__name__)
 
@@ -47,6 +49,7 @@
 USED_ENTITIES_KEY = "used_entities"
 USE_ENTITIES_KEY = "use_entities"
 IGNORE_ENTITIES_KEY = "ignore_entities"
+IS_RETRIEVAL_INTENT_KEY = "is_retrieval_intent"
 
 KEY_SLOTS = "slots"
 KEY_INTENTS = "intents"
@@ -311,6 +314,7 @@ def _transform_intent_properties_for_internal_use(
 
         properties.setdefault(USE_ENTITIES_KEY, True)
         properties.setdefault(IGNORE_ENTITIES_KEY, [])
+        properties.setdefault(IS_RETRIEVAL_INTENT_KEY, False)
         if not properties[USE_ENTITIES_KEY]:  # this covers False, None and []
             properties[USE_ENTITIES_KEY] = []
 
@@ -343,6 +347,18 @@ def _transform_intent_properties_for_internal_use(
 
         return intent
 
+    def _update_retrieval_intent_properties(self, retrieval_intents: List[Text]):
+        for retrieval_intent in retrieval_intents:
+            self.intent_properties[retrieval_intent][IS_RETRIEVAL_INTENT_KEY] = True
+
+    @lazy_property
+    def retrieval_intents(self) -> List[Text]:
+        return [
+            intent
+            for intent in self.intent_properties
+            if self.intent_properties[intent][IS_RETRIEVAL_INTENT_KEY]
+        ]
+
     @classmethod
     def collect_intent_properties(
         cls, intents: List[Union[Text, Dict[Text, Any]]], entities: List[Text]
@@ -385,7 +401,13 @@ def _intent_properties(
     ) -> Tuple[Text, Dict[Text, Any]]:
         if not isinstance(intent, dict):
             intent_name = intent
-            intent = {intent_name: {USE_ENTITIES_KEY: True, IGNORE_ENTITIES_KEY: []}}
+            intent = {
+                intent_name: {
+                    USE_ENTITIES_KEY: True,
+                    IGNORE_ENTITIES_KEY: [],
+                    IS_RETRIEVAL_INTENT_KEY: False,
+                }
+            }
         else:
             intent_name = list(intent.keys())[0]
 
@@ -537,6 +559,7 @@ def action_for_name(
             action_endpoint,
             self.user_actions_and_forms,
             should_use_form_action,
+            self.retrieval_intents,
         )
 
     def action_for_index(
@@ -635,7 +658,6 @@ def input_state_map(self) -> Dict[Text, int]:
     @lazy_property
     def input_states(self) -> List[Text]:
         """Returns all available states."""
-
         return (
             self.intent_states
             + self.entity_states

rasa/importers/importer.py

@@ -10,6 +10,7 @@
 from rasa.importers.autoconfig import TrainingType
 import rasa.utils.io as io_utils
 import rasa.utils.common as common_utils
+from rasa.core.actions import action
 
 logger = logging.getLogger(__name__)
 
@@ -251,10 +252,35 @@ async def get_domain(self) -> Domain:
         domains = [importer.get_domain() for importer in self._importers]
         domains = await asyncio.gather(*domains)
 
-        return reduce(
+        # Check if NLU data has any retrieval intents,
+        # if yes add corresponding retrieval actions with `utter_` prefix automatically to an empty domain.
+        nlu_data = await self.get_nlu_data()
+        if nlu_data.retrieval_intents:
+            domains.append(
+                self._get_domain_with_retrieval_actions(nlu_data.retrieval_intents)
+            )
+
+        combined_domain = reduce(
             lambda merged, other: merged.merge(other), domains, Domain.empty()
         )
 
+        # Make the domain known which intents are retrieval intents
+        combined_domain._update_retrieval_intent_properties(nlu_data.retrieval_intents)
+
+        return combined_domain
+
+    @staticmethod
+    def _get_domain_with_retrieval_actions(retrieval_intents):
+
+        return Domain(
+            [],
+            [],
+            [],
+            {},
+            action.construct_retrieval_action_names(retrieval_intents),
+            [],
+        )
+
     async def get_stories(
         self,
         template_variables: Optional[Dict] = None,

rasa/nlu/selectors/response_selector.py

@@ -87,6 +87,7 @@
     TEXT,
     INTENT_NAME_KEY,
 )
+from rasa.nlu.training_data import training_data
 
 from rasa.utils.tensorflow.model_data import RasaModelData
 from rasa.utils.tensorflow.models import RasaModel
@@ -350,13 +351,14 @@ def _resolve_intent_response_key(
         for key, responses in self.responses.items():
 
             # First check if the predicted label was the key itself
-            if hash(key) == label.get("id"):
-                return key
+            search_key = training_data.template_key_to_intent_response_key(key)
+            if hash(search_key) == label.get("id"):
+                return search_key
 
             # Otherwise loop over the responses to check if the text has a direct match
             for response in responses:
                 if hash(response.get(TEXT, "")) == label.get("id"):
-                    return key
+                    return search_key
         return None
 
     def process(self, message: Message, **kwargs: Any) -> None:
@@ -370,7 +372,9 @@ def process(self, message: Message, **kwargs: Any) -> None:
         label_intent_response_key = (
             self._resolve_intent_response_key(top_label) or top_label[INTENT_NAME_KEY]
         )
-        label_response_templates = self.responses.get(label_intent_response_key)
+        label_response_templates = self.responses.get(
+            training_data.intent_response_key_to_template_key(label_intent_response_key)
+        )
 
         if label_intent_response_key and not label_response_templates:
             # response templates seem to be unavailable,

rasa/nlu/training_data/training_data.py

@@ -22,6 +22,7 @@
     ENTITIES,
     TEXT,
 )
+from rasa.core.constants import UTTER_PREFIX
 from rasa.nlu.training_data.message import Message
 from rasa.nlu.training_data.util import check_duplicate_synonym
 from rasa.nlu.utils import list_to_str
@@ -31,6 +32,14 @@
 logger = logging.getLogger(__name__)
 
 
+def intent_response_key_to_template_key(intent_response_key: Text) -> Text:
+    return f"{UTTER_PREFIX}{intent_response_key}"
+
+
+def template_key_to_intent_response_key(template_key: Text) -> Text:
+    return template_key.split(UTTER_PREFIX)[1]
+
+
 class TrainingData:
     """Holds loaded intent and entity training data."""
 
@@ -240,8 +249,10 @@ def _fill_response_phrases(self) -> None:
                 continue
 
             # look for corresponding bot utterance
-            story_lookup_intent = example.get_full_intent()
-            assistant_utterances = self.responses.get(story_lookup_intent, [])
+            story_lookup_key = intent_response_key_to_template_key(
+                example.get_full_intent()
+            )
+            assistant_utterances = self.responses.get(story_lookup_key, [])
             if assistant_utterances:
 
                 # Use the first response text as training label if needed downstream
@@ -251,7 +262,7 @@ def _fill_response_phrases(self) -> None:
 
                 # If no text attribute was found use the key for training
                 if not example.get(RESPONSE):
-                    example.set(RESPONSE, story_lookup_intent)
+                    example.set(RESPONSE, story_lookup_key)
 
     def nlu_as_json(self, **kwargs: Any) -> Text:
         """Represent this set of training examples as json."""