rasa 3.0.0 on Python PyPI

#6487: Remove backwards compatibility code with Rasa Open Source 1.x, Rasa Enterprise 0.35, and other outdated
backwards compatibility code in rasa.cli.x, rasa.core.utils, rasa.model_testing, rasa.model_training
and rasa.shared.core.events.
#8569: Removed Python 3.6 support as it reaches its end of life in December 2021.
#8862: Follow through on removing deprecation warnings for synchronous EventBroker methods.
#8864: Follow through on deprecation warnings for policies and policy ensembles.
#8867: Follow through on deprecation warnings for rasa.shared.data.
#8868: Follow through on deprecation warnings for the Domain. Most importantly this will
enforce the schema of the forms section in the domain file.
This further includes the removal of the UnfeaturizedSlot type.
#8869: Remove deprecated change_form_to and set_form_validation methods from DialogueStateTracker.
#8870: Remove the support of Markdown training data format. This includes:
- reading and writing of story files in Markdown format
- reading and writing of NLU data in Markdown format
- reading and writing of retrieval intent data in Markdown format
- all the Markdown examples and tests that use Markdown
#8871: Removed automatic renaming of deprecated action
action_deactivate_form to action_deactivate_loop.
action_deactivate_form will just be treated like other
non-existing actions from now on.
#8872: Remove deprecated sorted_intent_examples method from TrainingData.
#8873: Raising RasaException instead of deprecation warning when using
class_from_module_path for loading types other than classes.
#8874: Specifying the retrieve_events_from_previous_conversation_sessions kwarg for the any TrackerStore was deprecated and has now been removed.
Please use the retrieve_full_tracker() method instead.
Deserialization of pickled trackers was deprecated and has now been removed.
Rasa will perform any future save operations of trackers using json serialisation.
Removed catch for missing (deprecated) session_date when saving trackers in DynamoTrackerStore.
#8879: Removed the deprecated dialogue policy state featurizers: BinarySingleStateFeature and LabelTokenizerSingleStateFeaturizer.
Removed the deprecated method encode_all_actions of SingleStateFeaturizer. Use encode_all_labels instead.
#8880: Follow through with removing deprecated policies: FormPolicy, MappingPolicy, FallbackPolicy, TwoStageFallbackPolicy, and SklearnPolicy.
Remove warning about default value of max_history in MemoizationPolicy. The default value is now None.
#8881: Follow through on deprecation warnings and remove code, tests, and docs for ConveRTTokenizer, LanguageModelTokenizer and HFTransformersNLP.
#8883: rasa.shared.nlu.training_data.message.Message method get_combined_intent_response_key has been removed. get_full_intent should now be used in its place.
#8974: Intent IDs sent with events (to kafka and elsewhere) have been removed, intent
names can be used instead (or if numerical values are needed for backwards
compatibility, one can also hash the names to get previous ID values, ie.
hash(intent_name) is the old ID values). Intent IDs have been removed because
they were providing no extra value and integers that large were problematic for
some event broker implementations.
#9236: Remove loop argument from train method in rasa.
This argument became redundant when Python 3.6 support was dropped as asyncio.run became available in Python 3.7.
#9390: Remove template_variables and e2e arguments from get_stories method of TrainingDataImporter.
This argument was used in Markdown data format and became redundant once Markdown was removed.
#9399: weight_sparsity has been removed. Developers should replace it with connection_density in the following way: connection_density = 1-weight_sparsity.
softmax is not available as a loss_type anymore.
The linear_norm option has been removed as possible value for model_confidence. Please, use softmax instead.
minibatch has been removed as a value for tensorboard_log_level, use batch instead.
Removed deprecation warnings related to the removed component config values.
#9404: Follow through on removing deprecation warnings raised in these modules:
- rasa/server.py
- rasa/core/agent.py
- rasa/core/actions/action.py
- rasa/core/channels/mattermost.py
- rasa/core/nlg/generator.py
- rasa/nlu/registry.py
#9432: Remove deprecation warnings associated with the "number_additional_patterns" parameter of
rasa.nlu.featurizers.sparse_featurizer.regex_featurizer.RegexFeaturizer.
This parameter is no longer needed for incremental training.
Remove deprecation warnings associated with the "additional_vocabulary_size" parameter of
rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer.CountVectorsFeaturizer.
This parameter is no longer needed for incremental training.
Remove deprecated functions training_states_actions_and_entities and
training_states_and_actions from rasa.core.featurizers.tracker_featurizers.TrackerFeaturizer.
Use training_states_labels_and_entities and training_states_and_labels instead.
#9455: Follow through on deprecation warning for NGramFeaturizer
#9598: The CLI commands rasa data convert config and rasa data convert responses which
converted from the Rasa Open Source 1 to the Rasa Open Source 2 formats were removed.
Please use a Rasa Open Source 2 installation to convert your training data before
moving to Rasa Open Source 3.
#9766: rasa.core.agent.Agent.visualize was removed. Please use rasa visualize or
rasa.core.visualize.visualize instead.
#9972: Removed slot auto-fill functionality, making the key invalid to use in the domain file.
The auto_fill parameter was also removed from the constructor of the Slot class.
In order to continue filling slots with entities of the same name, you now have to define a from_entity mapping in the slots section of the domain.
To learn more about how to migrate your 2.0 assistant, please read the migration guide.

Features

#10150: Training data version upgraded from 2.0 to 3.0 due to breaking changes to format in Rasa Open Source 3.0
#10170: A new experimental feature called Markers has been added.
Markers allow you to define points of interest in conversations as a set of conditions that need to be met.
A new command rasa evaluate markers allows you to apply these conditions to your existing tracker stores
and outputs the points at which the conditions were satisfied.
#9803: Rasa Open Source now uses the model configuration to build a
directed acyclic graph.
This graph describes the dependencies between the items in your model configuration and
how data flows between them. This has two major benefits:
- Rasa Open Source can use the computational graph to optimize the execution of your
  model. Examples for this are efficient caching of training steps or executing
  independent steps in parallel.
- Rasa Open Source can represent different model architectures flexibly. As long as the
  graph remains acyclic Rasa Open Source can in theory pass any data to any graph
  component based on the model configuration without having to tie the underlying
  software architecture to the used model architecture.
This change required changes to custom policies and custom NLU components. See the
documentation for a detailed
migration guide.
#9972: Added explicit mechanism for slot filling that allows slots to be set and/or updated throughout the conversation.
This mechanism is enabled by defining global slot mappings in the slots section of the domain file.
In order to support this new functionality, implemented a new default action: action_extract_slots. This new action runs after each user turn and checks if any slots can be filled with information extracted from the last user message based on defined slot mappings.
Since slot mappings were moved away from the forms section of the domain file, converted the form's required_slots to a list of slot names.
In order to restrict certain mappings to a form, you can now use the conditions key in the mapping to define the applicable active_loop, like so:
```
slots:
  location:
    type: text
    influence_conversation: false
    mappings:
    - type: from_entity
      entity: city
      conditions:
      - active_loop: booking_form
```
To learn more about how to migrate your 2.0 assistant, please read the migration guide.

Improvements

#10189: Updated the /status endpoint response payload, and relevant documentation, to return/reflect the updated 3.0 keys/values.
#7619: Bump TensorFlow version to 2.6.
This update brings some security benefits (see TensorFlow
release notes
for details). However, internal experiments suggest that it is also associated with
increased train and inference time, as well as increased memory usage.
You can read more about why we decided to update TensorFlow, and what the expected
impact is here.
If you experience a significant increase in train time, inference time, and/or memory
usage, please let us know in the forum.
Users can no longer set TF_DETERMINISTIC_OPS=1 if they are using GPU(s) because a
tf.errors.UnimplementedError will be thrown by TensorFlow (read more
here).
:::caution
This breaks backward compatibility of previously trained models.
It is not possible to load models trained with previous versions of Rasa Open Source. Please re-train
your assistant before trying to use this version.
#8057: Added authentication support for connecting to external RabbitMQ servers.
Currently user has to hardcode a username and a password in a URL in order to connect to an external RabbitMQ server.

#8459: 1) Failed test stories will display full retrieval intents.

Retrieval intents will be extracted during action prediction in test stories so that we won't have unnecessary mismatches anymore.

Let's take this example story:

- story: test story
  steps:
  - user: |
      what is your name?
    intent: chitchat/ask_name
  - action: utter_chitchat/ask_name
  - intent: bye
  - action: utter_bye

Before:

  steps:
  - intent: chitchat   # 1) intent is not displayed in it's original form
  - action: utter_chitchat/ask_name  # predicted: utter_chitchat  
                  # 2) retrieval intent is not extracted during action prediction and we have a mismatch

  - intent: bye  # some other fail
  - action: utter_bye # some other fail

Both 1) and 2) problems are solved.

Now:

  steps:
  - intent: chitchat/ask_name
  - action: utter_chitchat/ask_name
  - intent: bye  # some other fail
  - action: utter_bye # some other fail

#8469: Added -i command line option to make RASA listen on a specific ip-address instead of any network interface
#8760: rasa data validate now checks that forms referenced in active_loop directives are defined in the domain
#8914: Every conversation event now includes in its metadata the ID of the model which loaded at the time it was created.
#8924: Send indices of user message tokens along with the UserUttered event through the event broker to Rasa X.
#8929: Added optional flag to convert intent ID hashes from integer to string in the KafkaEventBroker.
#8933: Make it possible to use or functionality for slot_was_set events.
#9068: Upgraded the spaCy dependency from version 3.0 to 3.1.
#9133: Implemented fingerprint methods in these classes:
- Event
- Slot
- DialogueStateTracker
#9193: Added debug message that logs when a response condition is used.
#9485: The naming scheme for trained models was changed. Unless you provide a
--fixed-model-name to rasa train, Rasa Open Source will now generate a new model
name using the schema <timestamp>-<random name>.tar.gz, e.g.
- 20211018-094821-composite-pita.tar.gz (for a model containing a trained NLU and dialogue model)
- nlu-20211018-094821-composite-pita.tar.gz (for a model containing only a trained NLU model but not a dialogue model)
- core-20211018-094821-composite-pita.tar.gz (for a model containing only a trained dialogue model but no NLU model)
#9604: Due to changes in the model architecture the behavior of rasa train --dry-run changed.
The exit codes now have the following meaning:
- 0 means that the model does not require an expensive retraining. However, the responses might still require updating by running rasa train`
- 1 means that one or multiple components require to be retrained.
- 8 means that the --force flag was used and hence any cached results are ignored
  and the entire model is retrained.
#9642: Machine learning components like DIETClassifier, ResponseSelector and TEDPolicy using a ranking_length parameter will no longer report renormalised
confidences for the top predictions by default.
A new parameter renormalize_confidences is added to these components which if set to True, renormalizes the confidences of top ranking_length number of predictions to sum up to 1. The default value is False, which means no renormalization will be applied by default. It is advised to leave it to False but if you are trying to reproduce the results from previous versions of Rasa Open Source, you can set it to True.
Renormalization will only be applied if model_confidence=softmax is used.

Bugfixes

#10079: Fixed validation behavior and logging output around unused intents and utterances.
#8614: rasa test nlu --cross-validation uses autoconfiguration when no pipeline is defined instead of failing
#9195: Update DynamoDb tracker store to correctly retrieve all sender_ids from a DynamoDb table.
#9629: Fix for failed_test_stories.yml not printing the correct message when the extracted entity
specified in a test story is incorrect.
#9852: Fix CVE-2021-41127

Improved Documentation

#10095: Added new docs for Markers.
#10230: Update pip in same command which installs rasa and clarify supported version in
docs.
#8568: Update pika consumer code in Event Brokers documentation.
#8930: Adds documentation on how to use CRFEntityExtractor with features from a dense featurizer (e.g. LanguageModelFeaturizer).
#9366: Updated docs (Domain, Forms, Default Actions, Migration Guide, CLI) to provide more detail over the new slot mappings changes.
#9711: Updated documentation publishing mechanisms to build one version of the documentation
for each major version of Rasa Open Source, starting from 2.x upwards. Previously, we were building one
version of the documentation for each minor version of Rasa Open Source, resulting in a poor user
experience and high maintenance costs.

Miscellaneous internal changes

#10065, #10084, #10086, #10131, #9078, #9131, #9135, #9557