Merge pull request RasaHQ#11809 from RasaHQ/merge-3.3.x-main-95d44dc

Merge 3.3.x into main

CHANGELOG.mdx

@@ -16,6 +16,14 @@ https://github.com/RasaHQ/rasa/tree/main/changelog/ . -->
 
 <!-- TOWNCRIER -->
 
+## [3.3.1] - 2022-11-09
+### Improved Documentation
+- [#11721](https://github.com/rasahq/rasa/issues/11721): Add docs on how to set up additional data lakes for Rasa Pro analytics pipeline.
+- [#11724](https://github.com/rasahq/rasa/issues/11724): Update migration guide and form docs with prescriptive recommendation on how to implement dynamic forms with custom slot mappings.
+
+### Improvements
+- [#11732](https://github.com/rasahq/rasa/issues/11732): Updated numpy and scikit learn version to fix vulnerabilities of those dependencies
+
 ## [3.3.0] - 2022-10-24
 ### Improvements
 - [#11561](https://github.com/rasahq/rasa/issues/11561): Added option `--offset-timestamps-by-seconds` to offset the timestamp of events when using `rasa export`
@@ -179,7 +187,7 @@ Yanked.
 ## [3.1.4] - 2022-06-21                          No significant changes.
 Upgrade dependent libraries with security vulnerabilities (Pillow, TensorFlow, ujson).
 
-## [3.1.3] - 2022-06-17                         
+## [3.1.3] - 2022-06-17
 ### Bugfixes
 - [#11129](https://github.com/rasahq/rasa/issues/11129): The azure botframework channel now validates the incoming JSON Web Tokens (including signature).
 

changelog/11768.doc.md

@@ -0,0 +1,2 @@
+The documentation was updated for Buttons using messages that start with '/'. 
+Previously, it wrongly stated that messages with '/' bypass NLU, which is not the case.

changelog/11799.doc.md

@@ -0,0 +1 @@
+Add documentation for Rasa Pro Services upgrades.

data/test_classes/custom_graph_components/nlu_meta_fallback.py

@@ -0,0 +1,42 @@
+from typing import Dict, Text, Any, List
+
+from rasa.engine.graph import GraphComponent, ExecutionContext
+from rasa.engine.recipes.default_recipe import DefaultV1Recipe
+from rasa.engine.storage.resource import Resource
+from rasa.engine.storage.storage import ModelStorage
+from rasa.shared.nlu.training_data.message import Message
+from rasa.shared.nlu.training_data.training_data import TrainingData
+from rasa.nlu.classifiers.fallback_classifier import FallbackClassifier
+
+
+@DefaultV1Recipe.register(
+    [DefaultV1Recipe.ComponentType.INTENT_CLASSIFIER], is_trainable=True
+)
+class MetaFallback(FallbackClassifier):
+
+    def __init__(
+        self,
+        config: Dict[Text, Any],
+        model_storage: ModelStorage,
+        resource: Resource,
+        execution_context: ExecutionContext,
+    ) -> None:
+        super().__init__(config)
+
+        self._model_storage = model_storage
+        self._resource = resource
+
+    @classmethod
+    def create(
+        cls,
+        config: Dict[Text, Any],
+        model_storage: ModelStorage,
+        resource: Resource,
+        execution_context: ExecutionContext,
+    ) -> FallbackClassifier:
+        """Creates a new untrained component (see parent class for full docstring)."""
+        return cls(config, model_storage, resource, execution_context)
+
+    def train(self, training_data: TrainingData) -> Resource:
+        # Do something here with the messages
+        return self._resource

data/test_classes/custom_graph_components/nlu_meta_intent_featurizer.py

@@ -0,0 +1,15 @@
+from rasa.engine.recipes.default_recipe import DefaultV1Recipe
+from rasa.shared.nlu.training_data.training_data import TrainingData
+from rasa.nlu.classifiers.diet_classifier import DIETClassifier
+
+
+@DefaultV1Recipe.register(
+    [DefaultV1Recipe.ComponentType.INTENT_CLASSIFIER,
+     DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR,
+     DefaultV1Recipe.ComponentType.MESSAGE_FEATURIZER], is_trainable=True
+)
+class DIETFeaturizer(DIETClassifier):
+
+    def process_training_data(self, training_data: TrainingData) -> TrainingData:
+        # classify and add the attributes to the messages on the training data
+        return training_data