Merge branch '2.0.x' into update_poetry_gha

changelog/7200.bugfix.md

@@ -0,0 +1 @@
+Fix a bug because of which only one retrieval intent was present in `all_retrieval_intent` key of the output of `ResponseSelector` even if there were multiple retrieval intents present in the training data.

rasa/nlu/selectors/response_selector.py

@@ -300,7 +300,12 @@ def preprocess_train_data(self, training_data: TrainingData) -> RasaModelData:
         """Prepares data for training.
 
         Performs sanity checks on training data, extracts encodings for labels.
+
+        Args:
+            training_data: training data to preprocessed.
         """
+        # Collect all retrieval intents present in the data before filtering
+        self.all_retrieval_intents = list(training_data.retrieval_intents)
 
         if self.retrieval_intent:
             training_data = training_data.filter_training_examples(
@@ -321,7 +326,6 @@ def preprocess_train_data(self, training_data: TrainingData) -> RasaModelData:
         )
 
         self.responses = training_data.responses
-        self.all_retrieval_intents = list(training_data.retrieval_intents)
 
         if not label_id_index_mapping:
             # no labels are present to train

tests/nlu/selectors/test_selectors.py

@@ -18,6 +18,8 @@
     CHECKPOINT_MODEL,
 )
 from rasa.nlu.selectors.response_selector import ResponseSelector
+from rasa.shared.nlu.training_data.message import Message
+from rasa.shared.nlu.training_data.training_data import TrainingData
 
 
 @pytest.mark.parametrize(
@@ -95,6 +97,34 @@ def test_train_selector(pipeline, component_builder, tmpdir):
         assert rank.get("intent_response_key") is not None
 
 
+def test_preprocess_selector_multiple_retrieval_intents():
+
+    # use some available data
+    training_data = rasa.shared.nlu.training_data.loading.load_data(
+        "data/examples/rasa/demo-rasa.md"
+    )
+    training_data_responses = rasa.shared.nlu.training_data.loading.load_data(
+        "data/examples/rasa/demo-rasa-responses.md"
+    )
+    training_data_extra_intent = TrainingData(
+        [
+            Message.build(
+                text="Is it possible to detect the version?", intent="faq/q1"
+            ),
+            Message.build(text="How can I get a new virtual env", intent="faq/q2"),
+        ]
+    )
+    training_data = training_data.merge(training_data_responses).merge(
+        training_data_extra_intent
+    )
+
+    response_selector = ResponseSelector()
+
+    response_selector.preprocess_train_data(training_data)
+
+    assert sorted(response_selector.all_retrieval_intents) == ["chitchat", "faq"]
+
+
 @pytest.mark.parametrize(
     "use_text_as_label, label_values",
     [