All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning starting with version 1.0.

• #8001: Fixed bug where the conversation does not lock before handling a reminder event.

• #7972: Fix a bug where, if a user injects an intent using the HTTP API, slot auto-filling is not performed on the entities provided.

• #7970: Fixed a YAML validation error which happened when executing multiple validations concurrently. This could e.g. happen when sending concurrent requests to server endpoints which process YAML training data.

• #5673: Expose diagnostic data for action and NLU predictions.

  Add diagnostic_data field to the Message and Prediction objects, which contain information about attention weights and other intermediate results of the inference computation. This information can be used for debugging and fine-tuning, e.g. with RasaLit.

  For examples of how to access the diagnostic data, see here.

• #5986: Using the TrainingDataImporter interface to load the data in rasa test core.

  Failed test stories are now referenced by their absolute path instead of the relative path.

• #7292: Improve error handling and Sentry tracking:

  • Raise MarkdownException when training data in Markdown format cannot be read.
  • Raise InvalidEntityFormatException error instead of json.JSONDecodeError when entity format is in valid in training data.
  • Gracefully handle empty sections in endpoint config files.
  • Introduce ConnectionException error and raise it when TrackerStore and EventBroker cannot connect to 3rd party services, instead of raising exceptions from 3rd party libraries.
  • Improve rasa.shared.utils.common.class_from_module_path function by making sure it always returns a class. The function currently raises a deprecation warning if it detects an anomaly.
  • Ignore MemoryError and asyncio.CancelledError in Sentry.
  • rasa.shared.utils.validation.validate_training_data now raises a SchemaValidationError when validation fails (this error inherits jsonschema.ValidationError, ensuring backwards compatibility).

• #7303: Allow PolicyEnsemble in cases where calling individual policy's load method returns None.

• #7420: User message metadata can now be accessed via the default slot session_started_metadata during the execution of a custom action_session_start.

  from typing import Any, Text, Dict, List
  from rasa_sdk import Action, Tracker
  from rasa_sdk.events import SlotSet, SessionStarted, ActionExecuted, EventType
  
  
  class ActionSessionStart(Action):
      def name(self) -> Text:
          return "action_session_start"
  
      async def run(
        self, dispatcher, tracker: Tracker, domain: Dict[Text, Any]
      ) -> List[Dict[Text, Any]]:
          metadata = tracker.get_slot("session_started_metadata")
  
          # Do something with the metadata
          print(metadata)
  
          # the session should begin with a `session_started` event and an `action_listen`
          # as a user message follows
          return [SessionStarted(), ActionExecuted("action_listen")]

• #7579: Add BILOU tagging schema for entity extraction in end-to-end TEDPolicy.

• #7616: Added two new parameters constrain_similarities and model_confidence to machine learning (ML) components - DIETClassifier, ResponseSelector and TEDPolicy.

  Setting constrain_similarities=True adds a sigmoid cross-entropy loss on all similarity values to restrict them to an approximate range in DotProductLoss. This should help the models to perform better on real world test sets. By default, the parameter is set to False to preserve the old behaviour, but users are encouraged to set it to True and re-train their assistants as it will be set to True by default from Rasa Open Source 3.0.0 onwards.

  Parameter model_confidence affects how model's confidence for each label is computed during inference. It can take three values:

  1. softmax - Similarities between input and label embeddings are post-processed with a softmax function, as a result of which confidence for all labels sum up to 1.
  2. cosine - Cosine similarity between input label embeddings. Confidence for each label will be in the range [-1,1].
  3. inner - Dot product similarity between input and label embeddings. Confidence for each label will be in an unbounded range.

  Setting model_confidence=cosine should help users tune the fallback thresholds of their assistant better. The default value is softmax to preserve the old behaviour, but we recommend using cosine as that will be the new default value from Rasa Open Source 3.0.0 onwards. The value of this option does not affect how confidences are computed for entity predictions in DIETClassifier and TEDPolicy.

  With both the above recommendations, users should configure their ML component, e.g. DIETClassifier, as

  - name: DIETClassifier
    model_confidence: cosine
    constrain_similarities: True
    ...

  Once the assistant is re-trained with the above configuration, users should also tune fallback confidence thresholds.

  Configuration option loss_type=softmax is now deprecated and will be removed in Rasa Open Source 3.0.0 . Use loss_type=cross_entropy instead.

  The default auto-configuration is changed to use constrain_similarities=True and model_confidence=cosine in ML components so that new users start with the recommended configuration.

• #7817: Use simple random uniform distribution of integers in negative sampling, because negative sampling with tf.while_loop and random shuffle inside creates a memory leak.

• #7848: Added support to configure exchange_name for pika event broker.

• #7867: If MaxHistoryTrackerFeaturizer is used, invert the dialogue sequence before passing it to the transformer so that the last dialogue input becomes the first one and therefore always have the same positional encoding.

• #7420: Fixed an error when using the endpoint GET /conversations/<conversation_id:path>/story with a tracker which contained slots.

• #7707: Add the option to configure whether extracted entities should be split by comma (",") or not to TEDPolicy. Fixes crash when this parameter is accessed during extraction.

• #7710: When switching forms, the next form will always correctly ask for the first required slot.

  Before, the next form did not ask for the slot if it was the same slot as the requested slot of the previous form.

• #7749: Fix the bug when RulePolicy handling loop predictions are overwritten by e2e TEDPolicy.

• #7751: When switching forms, the next form is cleanly activated.

  Before, the next form was correctly activated, but the previous form had wrongly uttered the response that asked for the requested slot when slot validation for that slot had failed.

• #7829: Fix a bug in incremental training when passing a specific model path with the --finetune argument.

• #7867: Fix the role of unidirectional_encoder in TED. This parameter is only applied to transformers for text, action_text and label_action_text.

• #7420, #7515, #7574, #7601

• #7069: Updated error message when using incompatible model versions.

• #7885: Limit numpy version to < 1.2 as tensorflow is not compatible with numpy versions >= 1.2. pip versions <= 20.2 don't resolve dependencies conflicts correctly which could result in an incompatible numpy version and the following error:

  NotImplementedError: Cannot convert a symbolic Tensor (strided_slice_6:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

• #7861: Correctly include the confused_with field in the test report for the POST /model/test/intents endpoint.

• #7764: Fixes a bug in forms where the next slot asked was not consistent after returning to a form from an unhappy path.

• #7731: Add support for in RasaYAMLWriter for writing intent and example metadata back into NLU YAML files.

• #4311: Fixed a bug with Domain.is_domain_file() that could raise an Exception in case the potential domain file is not a valid YAML.

• #7717: Fix wrong warning The method 'EventBroker.close' was changed to be asynchronous when the EventBroker.close was actually asynchronous.

• #7720: Fix incremental training for cases when training data does not contain entities but DIETClassifier is configured to perform entity recognition also.

  Now, the instance of RasaModelData inside DIETClassifier does not contain entities as a feature for training if there is no training data present for entity recognition.

• #7603: Fixed key-error bug on rasa data validate stories.

• #7711

• #7520: Improve the warning in case the RulePolicy or the deprecated MappingPolicy are missing from the model's policies configuration. Changed the info log to a warning as one of this policies should be added to the model configuration.

• #7692: Explicitly specify the crypto extra dependency of pyjwt to ensure that the cryptography dependency is installed. cryptography is strictly required to be able to be able to verify JWT tokens.

• #7622: Correctly retrieve intent ranking from UserUttered even during default affirmation action implementation.

• #7684: Fixed a problem when using the POST /model/test/intents endpoint together with a model server. The error looked as follows:

  ERROR    rasa.core.agent:agent.py:327 Could not load model due to Detected inconsistent loop usage. Trying to schedule a task on a new event loop, but scheduler was created with a different event loop. Make sure there is only one event loop in use and that the scheduler is running on that one.
  

  This also fixes a problem where testing a model from a model server would change the production model.

• #7592: Fixed incompatibility between Rasa Open Source 2.2.x and Rasa X < 0.35.

• #7557: Fixed a problem where a form wouldn't reject when the FormValidationAction re-implemented required_slots.

• #7585: Fixed an error when using the SQLTrackerStore with a Postgres database and the parameter login_db specified.

  The error was:

  psycopg2.errors.SyntaxError: syntax error at end of input
  rasa-production_1  | LINE 1: SELECT 1 FROM pg_catalog.pg_database WHERE datname = ?

• #6410: Domain.random_template_for is deprecated and will be removed in Rasa Open Source 3.0.0. You can alternatively use the TemplatedNaturalLanguageGenerator.

  Domain.action_names is deprecated and will be removed in Rasa Open Source 3.0.0. Please use Domain.action_names_or_texts instead.

• #7458: Interfaces for Policy.__init__ and Policy.load have changed. See migration guide for details.

• #7495: Deprecate training and test data in Markdown 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

  Support for Markdown data will be removed entirely in Rasa Open Source 3.0.0.

  Please convert your existing Markdown data by using the commands from the migration guide:

  rasa data convert nlu -f yaml --data={SOURCE_DIR} --out={TARGET_DIR}
  rasa data convert nlg -f yaml --data={SOURCE_DIR} --out={TARGET_DIR}
  rasa data convert core -f yaml --data={SOURCE_DIR} --out={TARGET_DIR}

• #7529: Domain.add_categorical_slot_default_value, Domain.add_requested_slot and Domain.add_knowledge_base_slots are deprecated and will be removed in Rasa Open Source 3.0.0. Their internal versions are now called during the Domain creation. Calling them manually is no longer required.

• #6971: Incremental training of models in a pipeline is now supported.

  If you have added new NLU training examples or new stories/rules for dialogue manager, you don't need to train the pipeline from scratch. Instead, you can initialize the pipeline with a previously trained model and continue finetuning the model on the complete dataset consisting of new training examples. To do so, use rasa train --finetune. For more detailed explanation of the command, check out the docs on incremental training.

  Added a configuration parameter additional_vocabulary_size to CountVectorsFeaturizer and number_additional_patterns to RegexFeaturizer. These parameters are useful to configure when using incremental training for your pipelines.

• #7408: Add the option to use cross-validation to the POST /model/test/intents endpoint. To use cross-validation specify the query parameter cross_validation_folds in addition to the training data in YAML format.

  Add option to run NLU evaluation (POST /model/test/intents) and model training (POST /model/train) asynchronously. To trigger asynchronous processing specify a callback URL in the query parameter callback_url which Rasa Open Source should send the results to. This URL will also be called in case of errors.

• #7496: Make TED Policy an end-to-end policy. Namely, make it possible to train TED on stories that contain intent and entities or user text and bot actions or bot text. If you don't have text in your stories, TED will behave the same way as before. Add possibility to predict entities using TED.

  Here's an example of a dialogue in the Rasa story format:

  stories:
  - story: collect restaurant booking info  # name of the story - just for debugging
    steps:
    - intent: greet                          # user message with no entities
    - action: utter_ask_howcanhelp           # action that the bot should execute
    - intent: inform                         # user message with entities
      entities:
      - location: "rome"
      - price: "cheap"
    - bot: On it                             # actual text that bot can output
    - action: utter_ask_cuisine
    - user: I would like [spanish](cuisine). # actual text that user input
    - action: utter_ask_num_people
  

  Some model options for TEDPolicy got renamed. Please update your configuration files using the following mapping:

  Old model option	New model option
  transformer_size	dictionary “transformer_size” with keys
  	“text”, “action_text”, “label_action_text”, “dialogue”
  number_of_transformer_layers	dictionary “number_of_transformer_layers” with keys
  	“text”, “action_text”, “label_action_text”, “dialogue”
  dense_dimension	dictionary “dense_dimension” with keys
  	“text”, “action_text”, “label_action_text”, “intent”,
  	“action_name”, “label_action_name”, “entities”, “slots”,
  	“active_loop”

• #3998: Added a message showing the location where the failed stories file was saved.

• #7232: Add support for the top-level response keys quick_replies, attachment and elements refered to in rasa.core.channels.OutputChannel.send_reponse, as well as metadata.

• #7257: Changed the format of the histogram of confidence values for both correct and incorrect predictions produced by running rasa test.

• #7284: Run bandit checks on pull requests. Introduce make static-checks command to run all static checks locally.

• #7397: Add rasa train --dry-run command that allows to check if training needs to be performed and what exactly needs to be retrained.

• #7408: POST /model/test/intents now returns the report field for intent_evaluation, entity_evaluation and response_selection_evaluation as machine-readable JSON payload instead of string.

• #7436: Make rasa data validate stories work for end-to-end.

  The rasa data validate stories function now considers the tokenized user text instead of the plain text that is part of a state. This is closer to what Rasa Core actually uses to distinguish states and thus captures more story structure problems.

• #6804: Rename language_list to supported_language_list for JiebaTokenizer.
• #7244: A float slot returns unambiguous values - [1.0, <value>] if successfully converted, [0.0, 0.0] if not. This makes it possible to distinguish an empty float slot from a slot set to 0.0. :::caution This change is model-breaking. Please retrain your models. :::
• #7306: Fix an erroneous attribute for Redis key prefix in rasa.core.tracker_store.RedisTrackerStore: 'RedisTrackerStore' object has no attribute 'prefix'.
• #7407: Remove token when its text (for example, whitespace) can't be tokenized by LM tokenizer (from LanguageModelFeaturizer).
• #7408: Temporary directories which were created during requests to the HTTP API are now cleaned up correctly once the request was processed.
• #7422: Add option use_word_boundaries for RegexFeaturizer and RegexEntityExtractor. To correctly process languages such as Chinese that don't use whitespace for word separation, the user needs to add the use_word_boundaries: False option to those two components.
• #7529: Correctly fingerprint the default domain slots. Previously this led to the issue that rasa train core would always retrain the model even if the training data hasn't changed.

• #7313: Return the "Migrate from" entry to the docs sidebar.

• #7167

• #7426: Removed multidict from the project dependencies. multidict continues to be a second order dependency of Rasa Open Source but will be determined by the dependencies which use it instead of by Rasa Open Source directly.

  This resolves issues like the following:

  sanic 20.9.1 has requirement multidict==5.0.0, but you'll have multidict 4.6.0 which is incompatible.

• #7316: SingleStateFeaturizer checks whether it was trained with RegexInterpreter as nlu interpreter. If that is the case, RegexInterpreter is used during prediction.

• #7390: Make sure the responses are synced between NLU training data and the Domain even if there're no retrieval intents in the NLU training data.

• #7417: Categorical slots will have a default value set when just updating nlg data in the domain.

  Previously this resulted in InvalidDomain being thrown.

• #7418: - Preserve domain slot ordering while dumping it back to the file.

  • Preserve multiline text examples of responses defined in domain and NLU training data.

• #7235: Slots that use initial_value won't cause rule contradiction errors when conversation_start: true is used. Previously, two rules that differed only in their use of conversation_start would be flagged as contradicting when a slot used initial_value.

  In checking for incomplete rules, an action will be required to have set only those slots that the same action has set in another rule. Previously, an action was expected to have set also slots which, despite being present after this action in another rule, were not actually set by this action.

• #7345: Fixed Rasa Open Source not being able to fetch models from certain URLs.

• #7338: Sender ID is correctly set when copying the tracker and sending it to the action server (instead of sending the default value). This fixes a problem where the action server would only retrieve trackers with a sender_id default.

• #7136: The Policy interface was changed to return a PolicyPrediction object when predict_action_probabilities is called. Returning a list of probabilities directly is deprecated and support for this will be removed in Rasa Open Source 3.0.

  You can adapt your custom policy by wrapping your probabilities in a PolicyPrediction object:

  from rasa.core.policies.policy import Policy, PolicyPrediction
  # ... other imports
  
  def predict_action_probabilities(
          self,
          tracker: DialogueStateTracker,
          domain: Domain,
          interpreter: NaturalLanguageInterpreter,
          **kwargs: Any,
      ) -> PolicyPrediction:
      probabilities = ... # an action prediction of your policy
      return PolicyPrediction(probabilities, "policy_name", policy_priority=self.priority)

  The same change was applied to the PolicyEnsemble interface. Instead of returning a tuple of action probabilities and policy name, it is now returning a PolicyPrediction object. Support for the old PolicyEnsemble interface will be removed in Rasa Open Source 3.0.

  :::caution This change is model-breaking. Please retrain your models.

  :::

• #7263: The Pika Event Broker no longer supports the environment variables RABBITMQ_SSL_CA_FILE and RABBITMQ_SSL_KEY_PASSWORD. You can alternatively specify RABBITMQ_SSL_CA_FILE in the RabbitMQ connection URL as described in the RabbitMQ documentation.

  event_broker:
   type: pika
   url: "amqps://user:password@host?cacertfile=path_to_ca_cert&password=private_key_password"
   queues:
   - my_queue
  
  

  Support for RABBITMQ_SSL_KEY_PASSWORD was removed entirely.

  The method Event Broker.close was changed to be asynchronous. Support for synchronous implementations will be removed in Rasa Open Source 3.0.0. To adapt your implementation add the async keyword:

  from rasa.core.brokers.broker import EventBroker
  
  class MyEventBroker(EventBroker):
  
      async def close(self) -> None:
          # clean up event broker resources

• #7136: Policies can now return obligatory and optional events as part of their prediction. Obligatory events are always applied to the current conversation tracker. Optional events are only applied to the conversation tracker in case the policy wins.

• #4341: Changed Agent.load method to support pathlib paths.

• #5715: If you are using the feature Entity Roles and Groups, you should now also list the roles and groups in your domain file if you want roles and groups to influence your conversations. For example:

  entities:
    - city:
        roles:
          - from
          - to
    - name
    - topping:
        groups:
          - 1
          - 2
    - size:
        groups:
          - 1
          - 2
  

  Entity roles and groups can now influence dialogue predictions. For more information see the section Entity Roles and Groups influencing dialogue predictions.

• #6285: Predictions of the FallbackClassifier are ignored when evaluating the NLU model Note that the FallbackClassifier predictions still apply to test stories.

• #6474: Adapt the training data reader and emulator for wit.ai to their latest format. Update the instructions in the migrate from wit.ai documentation to run Rasa Open Source in wit.ai emulation mode.

• #6498: Adding configurable prefixes to Redis Tracker and Lock Stores so that a single Redis instance (and logical DB) can support multiple conversation trackers and locks. By default, conversations will be prefixed with tracker:... and all locks prefixed with lock:.... Additionally, you can add an alphanumeric-only prefix: value in endpoints.yml such that keys in redis will take the form value:tracker:... and value:lock:... respectively.

• #6571: Log the model's relative path when using CLI commands.

• #6852: Adds the option to configure whether extracted entities should be split by comma (",") or not. The default behaviour is True - i.e. split any list of extracted entities by comma. This makes sense for a list of ingredients in a recipie, for example "avocado, tofu, cauliflower", however doesn't make sense for an address such as "Schönhauser Allee 175, 10119 Berlin, Germany".

  In the latter case, add a new option to your config, e.g. if you are using the DIETClassifier this becomes:

  ...
  - name: DIETClassifier
    split_entities_by_comma: False
  ...

  in which case, none of the extracted entities will be split by comma. To switch it on/off for specific entity types you can use:

  ...
  - name: DIETClassifier
    split_entities_by_comma:
      address: True
      ingredient: False
  ...

  where both address and ingredient are two entity types.

  This feature is also available for CRFEntityExtractor.

• #6860: Fetching test stories from the HTTP API endpoint GET /conversations/<conversation_id>/story no longer triggers an update of the conversation session.

  Added a new boolean query parameter all_sessions (default: false) to the HTTP API endpoint for fetching test stories (GET /conversations/<conversation_id>/story).

  When setting ?all_sessions=true, the endpoint returns test stories for all conversation sessions for conversation_id. When setting ?all_sessions=all_sessions, or when omitting the all_sessions parameter, a single test story is returned for conversation_id. In cases where multiple conversation sessions exist, only the last story is returned.

  Specifying the retrieve_events_from_previous_conversation_sessions kwarg for the Tracker Store class is deprecated and will be removed in Rasa Open Source 3.0. Please use the retrieve_full_tracker() method instead.

• #6865: Improve the rasa data convert nlg command and introduce the rasa data convert responses command to simplify the migration from pre-2.0 response selector format to the new format.

• #6966: Added warning for when an option is provided for a component that is not listed as a key in the defaults for that component.

• #6977: Forms no longer reject their execution before a potential custom action for validating / extracting slots was executed. Forms continue to reject in two cases automatically:

  • A slot was requested to be filled, but no slot mapping applied to the latest user message and there was no custom action for potentially extracting other slots.
  • A slot was requested to be filled, but the custom action for validating / extracting slots didn't return any slot event.

  Additionally you can also reject the form execution manually by returning a ActionExecutionRejected event within your custom action for validating / extracting slots.

• #7027: Remove dependency between ConveRTTokenizer and ConveRTFeaturizer. The ConveRTTokenizer is now deprecated, and the ConveRTFeaturizer can be used with any other Tokenizer.

  Remove dependency between HFTransformersNLP, LanguageModelTokenizer, and LanguageModelFeaturizer. Both HFTransformersNLP and LanguageModelTokenizer are now deprecated. LanguageModelFeaturizer implements the behavior of the stack and can be used with any other Tokenizer.

• #7061: Gray out "Download" button in Rasa Playground when the project is not yet ready to be downloaded.

• #7068: Slot mappings for Forms in the domain are now optional. If you do not provide any slot mappings as part of the domain, you need to provide custom slot mappings through a custom action. A form without slot mappings is specified as follows:

  forms:
    my_form:
      # no mappings
  

  The action for forms can now be overridden by defining a custom action with the same name as the form. This can be used to keep using the deprecated Rasa Open Source FormAction which is implemented within the Rasa SDK. Note that it is not recommended to override the form action for anything else than using the deprecated Rasa SDK FormAction.

• #7102: Changed the default model weights loaded for HFTransformersNLP component.

  Use a language agnostic sentence embedding model as the default model. These model weights should help improve performance on intent classification and response selection.

• #7122: Add validations for slot mappings. If a slot mapping is not valid, an InvalidDomain error is raised.

• #7132: Adapt the training data reader and emulator for LUIS to their latest format and add support for roles. Update the instructions in the "Migrate from LUIS" documentation page to reflect the recent changes made to the UI of LUIS.

• #7160: Adapt the training data reader and emulator for DialogFlow to their latest format and add support for regex entities.

• #7263: The Pika Event Broker was reimplemented with the [aio-pika library[(https://aio-pika.readthedocs.io/en/latest/). Messages will now be published to RabbitMQ asynchronously which improves the prediction performance.

• #7278: The confidence of the FallbackClassifier predictions is set to 1 - top intent confidence.

• #5974: ActionRestart will now trigger ActionSessionStart as a followup action.

• #6582: Fixed a bug with rasa data split nlu which caused the resulting train / test ratio to sometimes differ from the ratio specified by the user or by default.

  The splitting algorithm ensures that every intent and response class appears in both the training and the test set. This means that each split must contain at least as many examples as there are classes, which for small datasets can contradict the requested training fraction. When this happens, the command issues a warning to the user that the requested training fraction can't be satisfied.

• #6721: Fixed bug where slots with influence_conversation=false affected the action prediction if they were set manually using the POST /conversations/<conversation_id/tracker/events endpoint in the HTTP API.

• #6760: Update Pika event broker to be a separate process and make it use a multiprocessing.Queue to send and process messages. This change should help avoid situations when events stop being sent after a while.

• #6973: Ignore rules when validating stories

• #6986: - Updated Slack Connector for new Slack Events API

• #7001: Update Rasa Playground "Download" button to work correctly depending on the current chat state.

• #7002: Test stories can now contain both: normal intents and retrieval intents. The failed_test_stories.yml, generated by rasa test, also specifies the full retrieval intent now. Previously rasa test would fail on test stories that specified retrieval intents.

• #7031: The converter tool is now able to convert test stories that contain a number as entity type.

• #7034: The converter tool now converts test stories and stories that contain full retrieval intents correctly. Previously the response keys were deleted during conversion to YAML.

• #7204: The slack connector requires a configuration for slack_signing_secret to make the connector more secure. The configuration value needs to be added to your credentials.yml if you are using the slack connector.

• #7246: Fixed model fingerprinting - it should avoid some more unecessary retrainings now.

• #7253: Fixed a problem when slots of type text or list were referenced by name only in the training data and this was treated as an empty value. This means that the two following stories are equivalent in case the slot type is text:

  stories:
  - story: Story referencing slot by name
    steps:
    - intent: greet
    - slot_was_set:
      - name
  
  - story: Story referencing slot with name and value
    steps:
    - intent: greet
    - slot_was_set:
      - name: "some name"
  

  Note that you still need to specify values for all other slot types as only text and list slots are featurized in a binary fashion.

• #6973: Correct data validation docs

• #6470, #7015, #7090

• #7235: Slots that use initial_value won't cause rule contradiction errors when conversation_start: true is used. Previously, two rules that differed only in their use of conversation_start would be flagged as contradicting when a slot used initial_value.

  In checking for incomplete rules, an action will be required to have set only those slots that the same action has set in another rule. Previously, an action was expected to have set also slots which, despite being present after this action in another rule, were not actually set by this action.

• #5974: ActionRestart will now trigger ActionSessionStart as a followup action.

• #7317: Fixed Rasa Open Source not being able to fetch models from certain URLs.

  This addresses an issue introduced in 2.0.3 where rasa-production could not use the models from rasa-x in Rasa X server mode.

• #7316: SingleStateFeaturizer checks whether it was trained with RegexInterpreter as NLU interpreter. If that is the case, RegexInterpreter is used during prediction.

• #6629: Fixed a bug that occurred when setting multiple Sanic workers in combination with a custom Lock Store. Previously, if the number was set higher than 1 and you were using a custom lock store, it would reject because of a strict check to use a Redis Lock Store.
• #7176: Fixed a bug in the TwoStageFallback action which reverted too many events after the user successfully rephrased.

• #7200: 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.

• #7140: Fixed error when starting Rasa X locally without a proper git setup.
• #7186: Properly validate incoming webhook requests for the Slack connector to be authentic.

• #7089: Fix ConveRTTokenizer failing because of wrong model URL by making the model_url parameter of ConveRTTokenizer mandatory.

  Since the ConveRT model was taken offline, we can no longer use the earlier public URL of the model. Additionally, since the licence for the model is unknown, we cannot host it ourselves. Users can still use the component by setting model_url to a community/self-hosted model URL or path to a local directory containing model files. For example:

  pipeline:
      - name: ConveRTTokenizer
        model_url: <remote/local path to model>

• #7108: Update example formbot to use FormValidationAction for slot validation

• #6691: Fix description of previous event in output of rasa data validate stories
• #7053: Fixed command line coloring for windows command lines running an encoding other than utf-8.

• #7057

• #7018: Create correct KafkaProducer for PLAINTEXT and SASL_SSL security protocols.
• #7033: - Fix YAMLStoryReader not being able to represent OR statements in conversion mode.
  • Fix MarkdownStoryWriter not being able to write stories with OR statements (when loaded in conversion mode).

• #5757: Removed previously deprecated packages rasa_nlu and rasa_core.

  Use imports from rasa.core and rasa.nlu instead.

• #5758: Removed previously deprecated classes:

  • event brokers (EventChannel and FileProducer, KafkaProducer, PikaProducer, SQLProducer)
  • intent classifier EmbeddingIntentClassifier
  • policy KerasPolicy

  Removed previously deprecated methods:

  • Agent.handle_channels
  • TrackerStore.create_tracker_store

  Removed support for pipeline templates in config.yml

  Removed deprecated training data keys entity_examples and intent_examples from json training data format.

• #5834: Removed restaurantbot example as it was confusing and not a great way to build a bot.

• #6296: LabelTokenizerSingleStateFeaturizer is deprecated. To replicate LabelTokenizerSingleStateFeaturizer functionality, add a Tokenizer with intent_tokenization_flag: True and CountVectorsFeaturizer to the NLU pipeline. An example of elements to be added to the pipeline is shown in the improvement changelog 6296`.

  BinarySingleStateFeaturizer is deprecated and will be removed in the future. We recommend to switch to SingleStateFeaturizer.

• #6354: Specifying the parameters force and save_to_default_model_directory as part of the JSON payload when training a model using POST /model/train is now deprecated. Please use the query parameters force_training and save_to_default_model_directory instead. See the API documentation for more information.

• #6409: The conversation event form was renamed to active_loop. Rasa Open Source will continue to be able to read and process old form events. Note that serialized trackers will no longer have the active_form field. Instead the active_loop field will contain the same information. Story representations in Markdown and YAML will use active_loop instead of form to represent the event.

• #6453: Removed support for queue argument in PikaEventBroker (use queues instead).

  Domain file:

  • Removed support for templates key (use responses instead).
  • Removed support for string responses (use dictionaries instead).

  NLU Component:

  • Removed support for provides attribute, it's not needed anymore.
  • Removed support for requires attribute (use required_components() instead).

  Removed _guess_format() utils method from rasa.nlu.training_data.loading (use guess_format instead).

  Removed several config options for TED Policy, DIETClassifier and ResponseSelector:

  • hidden_layers_sizes_pre_dial
  • hidden_layers_sizes_bot
  • droprate
  • droprate_a
  • droprate_b
  • hidden_layers_sizes_a
  • hidden_layers_sizes_b
  • num_transformer_layers
  • num_heads
  • dense_dim
  • embed_dim
  • num_neg
  • mu_pos
  • mu_neg
  • use_max_sim_neg
  • C2
  • C_emb
  • evaluate_every_num_epochs
  • evaluate_on_num_examples

  Please check the documentation for more information.

• #6463: The conversation event form_validation was renamed to loop_interrupted. Rasa Open Source will continue to be able to read and process old form_validation events.

• #6658: SklearnPolicy was deprecated. TEDPolicy is the preferred machine-learning policy for dialogue models.

• #6809: Slots of type unfeaturized are now deprecated and will be removed in Rasa Open Source 3.0. Instead you should use the property influence_conversation: false for every slot type as described in the migration guide.

• #6934: Conversation sessions are now enabled by default if your Domain does not contain a session configuration. Previously a missing session configuration was treated as if conversation sessions were disabled. You can explicitly disable conversation sessions using the following snippet:

  session_config:
    # A session expiration time of `0`
    # disables conversation sessions
    session_expiration_time: 0
  

• #6952: Using the default action action_deactivate_form to deactivate the currently active loop / Form is deprecated. Please use action_deactivate_loop instead.

• #4745: Added template name to the metadata of bot utterance events.

  BotUttered event contains a template_name property in its metadata for any new bot message.

• #5086: Added a --num-threads CLI argument that can be passed to rasa train and will be used to train NLU components.

• #5510: You can now define what kind of features should be used by what component (see Choosing a Pipeline).

  You can set an alias via the option alias for every featurizer in your pipeline. The alias can be anything, by default it is set to the full featurizer class name. You can then specify, for example, on the DIETClassifier what features from which featurizers should be used. If you don't set the option featurizers all available features will be used. This is also the default behavior. Check components to see what components have the option featurizers available.

  Here is an example pipeline that shows the new option. We define an alias for all featurizers in the pipeline. All features will be used in the DIETClassifier. However, the ResponseSelector only takes the features from the ConveRTFeaturizer and the CountVectorsFeaturizer (word level).

  pipeline:
  - name: ConveRTTokenizer
  - name: ConveRTFeaturizer
    alias: "convert"
  - name: CountVectorsFeaturizer
    alias: "cvf_word"
  - name: CountVectorsFeaturizer
    alias: "cvf_char"
    analyzer: char_wb
    min_ngram: 1
    max_ngram: 4
  - name: RegexFeaturizer
    alias: "regex"
  - name: LexicalSyntacticFeaturizer
    alias: "lsf"
  - name: DIETClassifier:
  - name: ResponseSelector
    epochs: 50
    featurizers: ["convert", "cvf_word"]
  - name: EntitySynonymMapper
  

  :::caution This change is model-breaking. Please retrain your models.

  :::

• #5837: Added --port commandline argument to the interactive learning mode to allow changing the port for the Rasa server running in the background.

• #5957: Add new entity extractor RegexEntityExtractor. The entity extractor extracts entities using the lookup tables and regexes defined in the training data. For more information see RegexEntityExtractor.

• #5996: Introduced a new YAML format for Core training data and implemented a parser for it. Rasa Open Source can now read stories in both Markdown and YAML format.

• #6020: You can now enable threaded message responses from Rasa through the Slack connector. This option is enabled using an optional configuration in the credentials.yml file

      slack:
        slack_token:
        slack_channel:
        use_threads: True

  Button support has also been added in the Slack connector.

• #6065: Add support for rules data and forms in YAML format.

• #6066: The NLU interpreter is now passed to the Policies during training and inference time. Note that this requires an additional parameter interpreter in the method predict_action_probabilities of the Policy interface. In case a custom Policy implementation doesn't provide this parameter Rasa Open Source will print a warning and omit passing the interpreter.

• #6088: Added the new dialogue policy RulePolicy which will replace the old “rule-like” policies Mapping Policy, Fallback Policy, Two-Stage Fallback Policy, and Form Policy. These policies are now deprecated and will be removed in the future. Please see the rules documentation for more information.

  Added new NLU component FallbackClassifier which predicts an intent nlu_fallback in case the confidence was below a given threshold. The intent nlu_fallback may then be used to write stories / rules to handle the fallback in case of low NLU confidence.

  pipeline:
  - # Other NLU components ...
  - name: FallbackClassifier
    # If the highest ranked intent has a confidence lower than the threshold then
    # the NLU pipeline predicts an intent `nlu_fallback` which you can then be used in
    # stories / rules to implement an appropriate fallback.
    threshold: 0.5
  

• #6132: Added possibility to split the domain into separate files. All YAML files under the path specified with --domain will be scanned for domain information (e.g. intents, actions, etc) and then combined into a single domain.

  The default value for --domain is still domain.yml.

• #6275: Add optional metadata argument to NaturalLanguageInterpreter's parse method.

• #6354: The Rasa Open Source API endpoint POST /model/train now supports training data in YAML format. Please specify the header Content-Type: application/yaml when training a model using YAML training data. See the API documentation for more information.

• #6374: Added a YAML schema and a writer for 2.0 Training Core data.

• #6404: Users can now use the rasa data convert {nlu|core} -f yaml command to convert training data from Markdown format to YAML format.

• #6536: Add option use_lemma to CountVectorsFeaturizer. By default it is set to True.

  use_lemma indicates whether the featurizer should use the lemma of a word for counting (if available) or not. If this option is set to False it will use the word as it is.

• #4536: Add support for Python 3.8.

• #5368: Changed the project structure for Rasa projects initialized with the CLI (using the rasa init command): actions.py -> actions/actions.py. actions is now a Python package (it contains a file actions/__init__.py). In addition, the __init__.py at the root of the project has been removed.

• #5481: DIETClassifier now also assigns a confidence value to entity predictions.

• #5637: Added behavior to the rasa --version command. It will now also list information about the operating system, python version and rasa-sdk. This will make it easier for users to file bug reports.

• #5743: Support for additional training metadata.

  Training data messages now to support kwargs and the Rasa JSON data reader includes all fields when instantiating a training data instance.

• #5748: Standardize testing output. The following test output can be produced for intents, responses, entities and stories:

  • report: a detailed report with testing metrics per label (e.g. precision, recall, accuracy, etc.)
  • errors: a file that contains incorrect predictions
  • successes: a file that contains correct predictions
  • confusion matrix: plot of confusion matrix
  • histogram: plot of confidence distribution (not available for stories)

• #5756: To avoid the problem of our entity extractors predicting entity labels for just a part of the words, we introduced a cleaning method after the prediction was done. We should avoid the incorrect prediction in the first place. To achieve this we will not tokenize words into sub-words anymore. We take the mean feature vectors of the sub-words as the feature vector of the word.

  :::caution This change is model breaking. Please, retrain your models.

  :::

• #5759: Move option case_sensitive from the tokenizers to the featurizers.

  • Remove the option from the WhitespaceTokenizer and ConveRTTokenizer.
  • Add option case_sensitive to the RegexFeaturizer.

• #5766: If a user sends a voice message to the bot using Facebook, users messages was set to the attachments URL. The same is now also done for the rest of attachment types (image, video, and file).

• #5794: Creating a Domain using Domain.fromDict can no longer alter the input dictionary. Previously, there could be problems when the input dictionary was re-used for other things after creating the Domain from it.

• #5805: The debug-level logs when instantiating an SQLTrackerStore no longer show the password in plain text. Now, the URL is displayed with the password hidden, e.g. postgresql://username:***@localhost:5432.

• #5855: Shorten the information in tqdm during training ML algorithms based on the log level. If you train your model in debug mode, all available metrics will be shown during training, otherwise, the information is shorten.

• #5913: Ignore conversation test directory tests/ when importing a project using MultiProjectImporter and use_e2e is False. Previously, any story data found in a project subdirectory would be imported as training data.

• #5985: Implemented model checkpointing for DIET (including the response selector) and TED. The best model during training will be stored instead of just the last model. The model is evaluated on the basis of evaluate_every_number_of_epochs and evaluate_on_number_of_examples.

  Checkpointing is enabled iff the following is set for the models in the config.yml file:

  • checkpoint_model: True
  • evaluate_on_number_of_examples > 0

  The model is stored to whatever location has been specified with the --out parameter when calling rasa train nlu/core ....

• #6024: rasa data split nlu now makes sure that there is at least one example per intent and response in the test data.

• #6039: The method ensure_consistent_bilou_tagging now also considers the confidence values of the predicted tags when updating the BILOU tags.

• #6045: We updated the way how we save and use features in our NLU pipeline.

  The message object now has a dedicated field, called features, to store the features that are generated in the NLU pipeline. We adapted all our featurizers in a way that sequence and sentence features are stored independently. This allows us to keep different kind of features for the sequence and the sentence. For example, the LexicalSyntacticFeaturizer does not produce any sentence features anymore as our experiments showed that those did not bring any performance gain just quite a lot of additional values to store.

  We also modified the DIET architecture to process the sequence and sentence features independently at first. The features are concatenated just before the transformer.

  We also removed the __CLS__ token again. Our Tokenizers will not add this token anymore.

  :::caution This change is model-breaking. Please retrain your models.

  :::

• #6052: Add endpoint kwarg to rasa.jupyter.chat to enable using a custom action server while chatting with a model in a jupyter notebook.

• #6055: Support for rasa conversation id with special characters on the server side - necessary for some channels (e.g. Viber)

• #6123: Add support for proxy use in slack input channel.

• #6134: Log the number of examples per intent during training. Logging can be enabled using rasa train --debug.

• #6237: Support for other remote storages can be achieved by using an external library.

• #6273: Add output_channel query param to /conversations/<conversation_id>/tracker/events route, along with boolean execute_side_effects to optionally schedule/cancel reminders, and forward bot messages to output channel.

• #6276: Allow Rasa to boot when model loading exception occurs. Forward HTTP Error responses to standard log output.

• #6294: Rename DucklingHTTPExtractor to DucklingEntityExtractor.

• #6296: * Modified functionality of SingleStateFeaturizer.

  SingleStateFeaturizer uses trained NLU Interpreter to featurize intents and action names. This modified SingleStateFeaturizer can replicate LabelTokenizerSingleStateFeaturizer functionality. This component is deprecated from now on. To replicate LabelTokenizerSingleStateFeaturizer functionality, add a Tokenizer with intent_tokenization_flag: True and CountVectorsFeaturizer to the NLU pipeline. Please update your configuration file.

  For example: yaml language: en pipeline: - name: WhitespaceTokenizer intent_tokenization_flag: True - name: CountVectorsFeaturizer

  Please train both NLU and Core (using rasa train) to use a trained tokenizer and featurizer for core featurization.

  The new SingleStateFeaturizer stores slots, entities and forms in sparse features for more lightweight storage.

  BinarySingleStateFeaturizer is deprecated and will be removed in the future. We recommend to switch to SingleStateFeaturizer.

  • Modified TEDPolicy to handle sparse features. As a result, TEDPolicy may require more epochs than before to converge.

  • Default TEDPolicy featurizer changed to MaxHistoryTrackerFeaturizer with infinite max history (takes all dialogue turns into account).

  • Default batch size for TED increased from [8,32] to [64, 256]

• #6323: Response selector templates now support all features that domain utterances do. They use the yaml format instead of markdown now. This means you can now use buttons, images, ... in your FAQ or chitchat responses (assuming they are using the response selector).

  As a consequence, training data form in markdown has to have the file suffix .md from now on to allow proper file type detection-

• #6457: Support for test stories written in yaml format.

• #6466: Response Selectors are now trained on retrieval intent labels by default instead of the actual response text. For most models, this should improve training time and accuracy of the ResponseSelector.

  If you want to revert to the pre-2.0 default behavior, add the use_text_as_label=true parameter to your ResponseSelector component.

  You can now also have multiple response templates for a single sub-intent of a retrieval intent. The first response template containing the text attribute is picked for training(if use_text_as_label=True) and a random template is picked for bot's utterance just as how other utter_ templates are picked.

  All response selector related evaluation artifacts - report.json, successes.json, errors.json, confusion_matrix.png now use the sub-intent of the retrieval intent as the target and predicted labels instead of the actual response text.

  The output schema of ResponseSelector has changed - full_retrieval_intent and name have been deprecated in favour of intent_response_key and response_templates respectively. Additionally a key all_retrieval_intents is added to the response selector output which will hold a list of all retrieval intents(faq,chitchat, etc.) that are present in the training data.An example output looks like this -

  "response_selector": {
      "all_retrieval_intents": ["faq"],
      "default": {
        "response": {
          "id": 1388783286124361986, "confidence": 1.0, "intent_response_key": "faq/is_legit",
          "response_templates": [
            {
              "text": "absolutely",
              "image": "https://i.imgur.com/nGF1K8f.jpg"
            },
            {
              "text": "I think so."
            }
          ],
        },
        "ranking": [
          {
            "id": 1388783286124361986,
            "confidence": 1.0,
            "intent_response_key": "faq/is_legit"
          },
        ]
  

  An example bot demonstrating how to use the ResponseSelector is added to the examples folder.

• #6472: Do not modify conversation tracker's latest_input_channel property when using POST /trigger_intent or ReminderScheduled.

• #6555: Do not set the output dimension of the sparse-to-dense layers to the same dimension as the dense features.

  Update default value of dense_dimension and concat_dimension for text in DIETClassifier to 128.

• #6591: Retrieval actions with respond_ prefix are now replaced with usual utterance actions with utter_ prefix.

  If you were using retrieval actions before, rename all of them to start with utter_ prefix. For example, respond_chitchat becomes utter_chitchat. Also, in order to keep the response templates more consistent, you should now add the utter_ prefix to all response templates defined for retrieval intents. For example, a response template chitchat/ask_name becomes utter_chitchat/ask_name. Note that the NLU examples for this will still be under chitchat/ask_name intent. The example responseselectorbot should help clarify these changes further.

• #6613: Added telemetry reporting. Rasa uses telemetry to report anonymous usage information. This information is essential to help improve Rasa Open Source for all users. Reporting will be opt-out. More information can be found in our telemetry documentation.

• #6757: Update extract_other_slots method inside FormAction to fill a slot from an entity with a different name if corresponding slot mapping of from_entity type is unique.

• #6809: Slots of any type can now be ignored during a conversation. To do so, specify the property influence_conversation: false for the slot.

  slot:
    a_slot:
      type: text
      influence_conversation: false

  The property influence_conversation is set to true by default. See the documentation for slots for more information.

  A new slot type any was added. Slots of this type can store any value. Slots of type any are always ignored during conversations.

• #6856: Improved exception handling within Rasa Open Source.

  All exceptions that are somewhat expected (e.g. errors in file formats like configurations or training data) will share a common base class RasaException.

  ::warning Backwards Incompatibility Base class for the exception raised when an action can not be found has been changed from a NameError to a ValueError. ::

  Some other exceptions have also slightly changed:

  • raise YamlSyntaxException instead of YAMLError (from ruamel) when failing to load a yaml file with information about the line where loading failed
  • introduced MissingDependencyException as an exception raised if packages need to be installed

• #6900: Debug logs from matplotlib libraries are now hidden by default and are configurable with the LOG_LEVEL_LIBRARIES environment variable.

• #6943: Update KafkaEventBroker to support SASL_SSL and PLAINTEXT protocols.

• #3597: Fixed issue where temporary model directories were not removed after pulling from a model server.

  If the model pulled from the server was invalid, this could lead to large amounts of local storage usage.

• #5038: Fixed a bug in the CountVectorsFeaturizer which resulted in the very first message after loading a model to be processed incorrectly due to the vocabulary not being loaded yet.

• #5135: Fixed Rasa shell skipping button messages if buttons are attached to a message previous to the latest.

• #5385: Stack level for FutureWarning updated to level 2.

• #5453: If custom utter message contains no value or integer value, then it fails returning custom utter message. Fixed by converting the template to type string.

• #5617: Don't create TensorBoard log files during prediction.

• #5638: Fixed DIET breaking with empty spaCy model.

• #5737: Pinned the library version for the Azure Cloud Storage to 2.1.0 since the persistor is currently not compatible with later versions of the azure-storage-blob library.

• #5755: Remove clean_up_entities from extractors that extract pre-defined entities. Just keep the clean up method for entity extractors that extract custom entities.

• #5792: Fixed issue where the DucklingHTTPExtractor component would not work if its url contained a trailing slash.

• #5808: Changed to variable CERT_URI in hangouts.py to a string type

• #5850: Slots will be correctly interpolated for button responses.

  Previously this resulted in no interpolation due to a bug.

• #5905: Remove option token_pattern from CountVectorsFeaturizer. Instead all tokenizers now have the option token_pattern. If a regular expression is set, the tokenizer will apply the token pattern.

• #5921: Allow user to retry failed file exports in interactive training.

• #5964: Fixed a bug when custom metadata passed with the utterance always restarted the session.

• #5998: WhitespaceTokenizer does not remove vowel signs in Hindi anymore.

• #6042: Convert entity values coming from DucklingHTTPExtractor to string during evaluation to avoid mismatches due to different types.

• #6053: Update FeatureSignature to store just the feature dimension instead of the complete shape. This change fixes the usage of the option share_hidden_layers in the DIETClassifier.

• #6087: Unescape the \n, \t, \r, \f, \b tokens on reading nlu data from markdown files.

  On converting json files into markdown, the tokens mentioned above are espaced. These tokens need to be unescaped on loading the data from markdown to ensure that the data is treated in the same way.

• #6120: Fix the way training data is generated in rasa test nlu when using the -P flag. Each percentage of the training dataset used to be formed as a part of the last sampled training dataset and not as a sample from the original training dataset.

• #6143: Prevent WhitespaceTokenizer from outputting empty list of tokens.

• #6198: Add EntityExtractor as a required component for EntitySynonymMapper in a pipeline.

• #6222: Better handling of input sequences longer than the maximum sequence length that the HFTransformersNLP models can handle.

  During training, messages with longer sequence length should result in an error, whereas during inference they are gracefully handled but a debug message is logged. Ideally, passing messages longer than the acceptable maximum sequence lengths of each model should be avoided.

• #6231: When using the DynamoTrackerStore, if there are more than 100 DynamoDB tables, the tracker could attempt to re-create an existing table if that table was not among the first 100 listed by the dynamo API.

• #6282: Fixed a deprication warning that pops up due to changes in numpy

• #6291: Update rasabaster to fix an issue with syntax highlighting on "Prototype an Assistant" page.

  Update default stories and rules on "Prototype an Assistant" page.

• #6419: Fixed a bug in the serialise method of the EvaluationStore class which resulted in a wrong end-to-end evaluation of the predicted entities.

• #6535: Forms with slot mappings defined in domain.yml must now be a dictionary (with form names as keys). The previous syntax where forms was simply a list of form names is still supported.

• #6577: Remove BILOU tag prefix from role and group labels when creating entities.

• #6601: Fixed a bug in the featurization of the boolean slot type. Previously, to set a slot value to "true", you had to set it to "1", which is in conflict with the documentation. In older versions true (without quotes) was also possible, but now raised an error during yaml validation.

• #6603: Fixed a bug in rasa interactive. Now it exports the stories and nlu training data as yml file.

• #6711: Fixed slots not being featurized before first user utterance.

  Fixed AugmentedMemoizationPolicy to forget the first action on the first going back

• #6741: Fixed the remote URL of ConveRT model as it was recently updated by its authors.

• #6755: Treat the length of OOV token as 1 to fix token align issue when OOV occurred.

• #6757: Fixed the bug when entity was extracted even if it had a role or group but roles or groups were not expected.

• #6803: Fixed the bug that caused supported_language_list of Component to not work correctly.

  To avoid confusion, only one of supported_language_list and not_supported_language_list can be set to not None now

• #6897: Fixed issue where responses including text: "" and no custom key would incorrectly fail domain validation.

• #6898: Fixed issue where extra keys other than title and payload inside of buttons made a response fail domain validation.

• #6919: Do not filter training data in model.py but on component side.

• #6929: Check if a model was provided when executing rasa test core. If not, print a useful error message and stop.

• #6805: Transfer only response templates for retrieval intents from domain to NLU Training Data.

  This avoids retraining the NLU model if one of the non retrieval intent response templates are edited.

• #4441: Added documentation on ambiguity_threshold parameter in Fallback Actions page.
• #4605: Remove outdated whitespace tokenizer warning in Testing Your Assistant documentation.
• #5640: Updated Facebook Messenger channel docs with supported attachment information
• #5675: Update rasa shell documentation to explain how to recreate external channel session behavior.
• #5811: Event brokers documentation should say url instead of host.
• #5952: Update rasa init documentation to include tests/conversation_tests.md in the resulting directory tree.
• #6819: Update "Validating Form Input" section to include details about how FormValidationAction class makes it easier to validate form slots in custom actions and how to use it.
• #6823: Update the examples in the API docs to use YAML instead of Markdown

• #5784, #5788, #6199, #6403, #6735

• #7772: Backported the Rasa Open Source 2 PikaEventBroker implementation to address problems when using it with multiple Sanic workers.

• #7439: The url option now supports a list of servers url: ['10.0.0.158:32803','10.0.0.158:32804']. Removed group_id because it is not a valid Kafka producer parameter.

• #7638: Fixed a bug that occurred when setting multiple Sanic workers in combination with a custom Lock Store. Previously, if the number was set higher than 1 and you were using a custom lock store, it would reject because of a strict check to use a Redis Lock Store.
• #7722: Fix a bug where, if a user injects an intent using the HTTP API, slot auto-filling is not performed on the entities provided.

• #7575: Fix scikit-learn crashing during evaluation of ResponseSelector predictions.

• #6251: Kafka Producer connection now remains active across sends. Added support for group and client id. The Kafka producer also adds support for the PLAINTEXT and SASL_SSL protocols.

  DynamoDB table exists check fixed bug when more than 100 tables exist.

• #6814: Replace use of python-telegram-bot package with pyTelegramBotAPI

• #7423: Use response selector keys (sub-intents) as labels for plotting the confusion matrix during NLU evaluation to improve readability.

• #7340: Fixed an issues with the DynamoDB TrackerStore creating a new table entry/object for each TrackerStore update. The column session_date has been deprecated and should be removed manually in existing DynamoDB tables.

• #7219: Prevent the message handling process in PikaEventBroker from being terminated.

• #6703: Update Pika event broker to be a separate process and make it use a multiprocessing.Queue to send and process messages. This change should help avoid situations when events stop being sent after a while.

• #3597: Fixed issue where temporary model directories were not removed after pulling from a model server. If the model pulled from the server was invalid, this could lead to large amounts of local storage usage.
• #6755: Treat the length of OOV token as 1 to fix token align issue when OOV occurred.
• #6899: Fixed MappingPolicy not predicting action_listen after the mapped action while running rasa test.

• #6900: Debug logs from matplotlib libraries are now hidden by default and are configurable with the LOG_LEVEL_LIBRARIES environment variable.

• #6741: Fixed the remote URL of ConveRT model as it was recently updated by its authors. Also made the remote URL configurable at runtime in the corresponding tokenizer's and featurizer's configuration.

• #6577: Remove BILOU tag prefix from role and group labels when creating entities.

• #6549: Fix slow training of CRFEntityExtractor when using Entity Roles and Groups.

• #6044: Do not deepcopy slots when instantiating trackers. This leads to a significant speedup when training on domains with a large number of slots.

• #6226: Added more debugging logs to the Lock Stores to simplify debugging in case of

  connection problems.

  Added a new parameter socket_timeout to the RedisLockStore. If Redis doesn't answer within socket_timeout seconds to requests from Rasa Open Source, an error is raised. This avoids seemingly infinitely blocking connections and exposes connection problems early.

• #5182: Fixed a bug where domain fields such as store_entities_as_slots were overridden with defaults and therefore ignored.
• #6191: If two entities are separated by a comma (or any other symbol), extract them as two separate entities.
• #6340: If two entities are separated by a single space and uses BILOU tagging, extract them as two separate entities based on their BILOU tags.

• #6280: Fixed TypeError: expected string or bytes-like object issue caused by integer, boolean, and null values in templates.

• #6255: Rasa Open Source will no longer add responses to the actions section of the domain when persisting the domain as a file. This addresses related problems in Rasa X when Integrated Version Control introduced big diffs due to the added utterances in the actions section.

• #6160: Consider entity roles/groups during interactive learning.

• #6075: Add 'Access-Control-Expose-Headers' for 'filename' header
• #6137: Fixed a bug where an invalid language variable prevents rasa from finding training examples when importing Dialogflow data.

• #6150: Add not_supported_language_list to component to be able to define languages that a component can NOT handle.

  WhitespaceTokenizer is not able to process languages which are not separated by whitespace. WhitespaceTokenizer will throw an error if it is used with Chinese, Japanese, and Thai.

• #6150: WhitespaceTokenizer only removes emoji if complete token matches emoji regex.

• #6143: Prevent WhitespaceTokenizer from outputting empty list of tokens.

• #6119: Explicitly remove all emojis which appear as unicode characters from the output of regex.sub inside WhitespaceTokenizer.

• #5998: WhitespaceTokenizer does not remove vowel signs in Hindi anymore.

• #6031: Previously, specifying a lock store in the endpoint configuration with a type other than redis or in_memory would lead to an AttributeError: 'str' object has no attribute 'type'. This bug is fixed now.

• #6032: Fix Interpreter parsed an intent ... warning when using the /model/parse endpoint with an NLU-only model.

• #6042: Convert entity values coming from any entity extractor to string during evaluation to avoid mismatches due to different types.

• #6078: The assistant will respond through the webex channel to any user (room) communicating to it. Before the bot responded only to a fixed roomId set in the credentials.yml config file.

• #3900: Reduced duplicate logs and warnings when running rasa train.

• #5972: Remove the clean_up_entities method from the DIETClassifier and CRFEntityExtractor as it let to incorrect entity predictions.

• #5976: Fix server crashes that occurred when Rasa Open Source pulls a model from a model server and an exception was thrown during model loading (such as a domain with invalid YAML).

• #5521: Responses used in ResponseSelector now support new lines with explicitly adding \\n between them.

• #5758: Fixed a bug in rasa export) which caused Rasa Open Source to only migrate conversation events from the last Session configuration.

• #5794: Creating a Domain using Domain.fromDict can no longer alter the input dictionary. Previously, there could be problems when the input dictionary was re-used for other things after creating the Domain from it.

• #5617: Don't create TensorBoard log files during prediction.

• #5638: Fix: DIET breaks with empty spaCy model

• #5755: Remove clean_up_entities from extractors that extract pre-defined entities. Just keep the clean up method for entity extractors that extract custom entities.

• #5792: Fixed issue where the DucklingHTTPExtractor component would not work if its url contained a trailing slash.

• #5825: Fix list index out of range error in ensure_consistent_bilou_tagging.

• #5788

• #3765: Add support for entities with roles and grouping of entities in Rasa NLU.

  You can now define a role and/or group label in addition to the entity type for entities. Use the role label if an entity can play different roles in your assistant. For example, a city can be a destination or a departure city. The group label can be used to group multiple entities together. For example, you could group different pizza orders, so that you know what toppings goes with which pizza and what size which pizza has. For more details see Entities Roles and Groups.

  To fill slots from entities with a specific role/group, you need to either use forms or use a custom action. We updated the tracker method get_latest_entity_values to take an optional role/group label. If you want to use a form, you can add the specific role/group label of interest to the slot mapping function from_entity (see Forms).

  :::note Composite entities are currently just supported by the DIETClassifier and CRFEntityExtractor.

  :::

• #5465: Update training data format for NLU to support entities with a role or group label.

  You can now specify synonyms, roles, and groups of entities using the following data format: Markdown:

  [LA]{"entity": "location", "role": "city", "group": "CA", "value": "Los Angeles"}
  

  JSON:

  "entities": [
      {
          "start": 10,
          "end": 12,
          "value": "Los Angeles",
          "entity": "location",
          "role": "city",
          "group": "CA",
      }
  ]
  

  The markdown format [LA](location:Los Angeles) is deprecated. To update your training data file just execute the following command on the terminal of your choice: sed -i -E 's/\\[([^)]+)\\]\\(([^)]+):([^)]+)\\)/[\\1]{"entity": "\\2", "value": "\\3"}/g' nlu.md

  For more information about the new data format see Training Data Format.

• #2224: Suppressed pika logs when establishing the connection. These log messages mostly happened when Rasa X and RabbitMQ were started at the same time. Since RabbitMQ can take a few seconds to initialize, Rasa X has to re-try until the connection is established. In case you suspect a different problem (such as failing authentication) you can re-enable the pika logs by setting the log level to DEBUG. To run Rasa Open Source in debug mode, use the --debug flag. To run Rasa X in debug mode, set the environment variable DEBUG_MODE to true.

• #3419: Include the source filename of a story in the failed stories

  Include the source filename of a story in the failed stories to make it easier to identify the file which contains the failed story.

• #5544: Add confusion matrix and “confused_with” to response selection evaluation

  If you are using ResponseSelectors, they now produce similiar outputs during NLU evaluation. Misclassfied responses are listed in a “confused_with” attribute in the evaluation report. Similiarily, a confusion matrix of all responses is plotted.

• #5578: Added socketio to the compatible channels for Reminders and External Events.

• #5595: Update POST /model/train endpoint to accept retrieval action responses at the responses key of the JSON payload.

• #5627: All Rasa Open Source images are now using Python 3.7 instead of Python 3.6.

• #5635: Update dependencies based on the dependabot check.

• #5636: Add dropout between FFNN and DenseForSparse layers in DIETClassifier, ResponseSelector and EmbeddingIntentClassifier controlled by use_dense_input_dropout config parameter.

• #5646: DIETClassifier only counts as extractor in rasa test if it was actually trained for entity recognition.

• #5669: Remove regularization gradient for variables that don't have prediction gradient.

• #5672: Raise a warning in CRFEntityExtractor and DIETClassifier if entities are not correctly annotated in the training data, e.g. their start and end values do not match any start and end values of tokens.

• #5690: Add full_retrieval_intent property to ResponseSelector rankings

• #5717: Change default values for hyper-parameters in EmbeddingIntentClassifier and DIETClassifier

  Use scale_loss=False in DIETClassifier. Reduce the number of dense dimensions for sparse features of text from 512 to 256 in EmbeddingIntentClassifier.

• #5230: Fixed issue where posting to certain callback channel URLs would return a 500 error on successful posts due to invalid response format.

• #5475: One word can just have one entity label.

  If you are using, for example, ConveRTTokenizer words can be split into multiple tokens. Our entity extractors assign entity labels per token. So, it might happen, that a word, that was split into two tokens, got assigned two different entity labels. This is now fixed. One word can just have one entity label at a time.

• #5509: An entity label should always cover a complete word.

  If you are using, for example, ConveRTTokenizer words can be split into multiple tokens. Our entity extractors assign entity labels per token. So, it might happen, that just a part of a word has an entity label. This is now fixed. An entity label always covers a complete word.

• #5574: Fixed an issue that happened when metadata is passed in a new session.

  Now the metadata is correctly passed to the ActionSessionStart.

• #5672: Updated Python dependency ruamel.yaml to >=0.16. We recommend to use at least 0.16.10 due to the security issue CVE-2019-20478 which is present in in prior versions.

• #5556, #5587, #5614, #5631, #5633

• #4606: The stream reading timeout for rasa shell\ is now configurable by using the environment variable ``RASA_SHELL_STREAM_READING_TIMEOUT_IN_SECONDS. This can help to fix problems when using rasa shell` with custom actions which run 10 seconds or longer.

• #5709: Reverted changes in 1.9.6 that led to model incompatibility. Upgrade to 1.9.7 to fix self.sequence_lengths_for(tf_batch_data[TEXT_SEQ_LENGTH][0]) IndexError: list index out of range error without needing to retrain earlier 1.9 models.

  Therefore, all 1.9 models except for 1.9.6 will be compatible; a model trained on 1.9.6 will need to be retrained on 1.9.7.

• #5426: Fix rasa test nlu plotting when using multiple runs.

• #5489: Fixed issue where max_number_of_predictions was not considered when running end-to-end testing.

• #5626

• #5533: Support for PostgreSQL schemas in SQLTrackerStore. The SQLTrackerStore accesses schemas defined by the POSTGRESQL_SCHEMA environment variable if connected to a PostgreSQL database.

  The schema is added to the connection string option's -csearch_path key, e.g. -options=-csearch_path=<SCHEMA_NAME> (see the PostgreSQL docs for more details). As before, if no POSTGRESQL_SCHEMA is defined, Rasa uses the database's default schema (public).

  The schema has to exist in the database before connecting, i.e. it needs to have been created with

  CREATE SCHEMA schema_name;
  

• #5547: Fixed ambiguous logging in DIETClassifier by adding the name of the calling class to the log message.

• #5529: Fix memory leak problem on increasing number of calls to /model/parse endpoint.

• #5505: Set default value for weight_sparsity in ResponseSelector to 0. This fixes a bug in the default behavior of ResponseSelector which was accidentally introduced in rasa==1.8.0. Users should update to this version and re-train their models if ResponseSelector was used in their pipeline.

• #5497: Fix documentation to bring back Sara.

• #5492: Fix an issue where the deprecated queue parameter for the Pika Event Broker was ignored and Rasa Open Source published the events to the rasa_core_events queue instead. Note that this does not change the fact that the queue argument is deprecated in favor of the queues argument.

• #5006: Channel hangouts for Rasa integration with Google Hangouts Chat is now supported out-of-the-box.

• #5389: Add an optional path to a specific directory to download and cache the pre-trained model weights for HFTransformersNLP.

• #5422: Add options tensorboard_log_directory and tensorboard_log_level to EmbeddingIntentClassifier, DIETClasifier, ResponseSelector, EmbeddingPolicy and TEDPolicy.

  By default tensorboard_log_directory is None. If a valid directory is provided, metrics are written during training. After the model is trained you can take a look at the training metrics in tensorboard. Execute tensorboard --logdir <path-to-given-directory>.

  Metrics can either be written after every epoch (default) or for every training step. You can specify when to write metrics using the variable tensorboard_log_level. Valid values are 'epoch' and 'minibatch'.

  We also write down a model summary, i.e. layers with inputs and types, to the given directory.

• #4756: Make response timeout configurable. rasa run, rasa shell and rasa x can now be started with --response-timeout <int> to configure a response timeout of <int> seconds.

• #4826: Add full retrieval intent name to message data ResponseSelector will now add the full retrieval intent name e.g. faq/which_version to the prediction, making it accessible from the tracker.

• #5258: Added PikaEventBroker (Pika Event Broker) support for publishing to multiple queues. Messages are now published to a fanout exchange with name rasa-exchange (see exchange-fanout for more information on fanout exchanges).

  The former queue key is deprecated. Queues should now be specified as a list in the endpoints.yml event broker config under a new key queues. Example config:

  event_broker:
    type: pika
    url: localhost
    username: username
    password: password
    queues:
      - queue-1
      - queue-2
      - queue-3
  

• #5416: Change rasa init to include tests/conversation_tests.md file by default.

• #5446: The endpoint PUT /conversations/<conversation_id>/tracker/events no longer adds session start events (to learn more about conversation sessions, please see Session configuration) in addition to the events which were sent in the request payload. To achieve the old behavior send a GET /conversations/<conversation_id>/tracker request before appending events.

• #5482: Make scale_loss for intents behave the same way as in versions below 1.8, but only scale if some of the examples in a batch has probability of the golden label more than 0.5. Introduce scale_loss for entities in DIETClassifier.

• #5205: Fixed the bug when FormPolicy was overwriting MappingPolicy prediction (e.g. /restart). Priorities for Mapping Policy and Form Policy are no longer linear: FormPolicy priority is 5, but its prediction is ignored if MappingPolicy is used for prediction.

• #5215: Fixed issue related to storing Python float values as decimal.Decimal objects in DynamoDB tracker stores. All decimal.Decimal objects are now converted to float on tracker retrieval.

  Added a new docs section on DynamoTrackerStore.

• #5356: Fixed bug where FallbackPolicy would always fall back if the fallback action is action_listen.

• #5361: Fixed bug where starting or ending a response with \\n\\n led to one of the responses returned being empty.

• #5405: Fixes issue where model always gets retrained if multiple NLU/story files are in a directory, by sorting the list of files.

• #5444: Fixed ambiguous logging in DIETClassifier by adding the name of the calling class to the log message.

• #2237: Restructure the “Evaluating models” documentation page and rename this page to Testing Your Assistant.

• #5302: Improved documentation on how to build and deploy an action server image for use on other servers such as Rasa X deployments.

• #5340

• #5405: Fixes issue where model always gets retrained if multiple NLU/story files are in a directory, by sorting the list of files.

• #5444: Fixed ambiguous logging in DIETClassifier by adding the name of the calling class to the log message.

• #5506: Set default value for weight_sparsity in ResponseSelector to 0. This fixes a bug in the default behavior of ResponseSelector which was accidentally introduced in rasa==1.8.0. Users should update to this version or rasa>=1.9.3 and re-train their models if ResponseSelector was used in their pipeline.

• #5302: Improved documentation on how to build and deploy an action server image for use on other servers such as Rasa X deployments.

• #5438: Fixed bug when installing rasa with poetry.

• #5413: Fixed bug with EmbeddingIntentClassifier, where results weren't the same as in 1.7.x. Fixed by setting weight sparsity to 0.

• #5404: Explain how to run commands as root user in Rasa SDK Docker images since version 1.8.0. Since version 1.8.0 the Rasa SDK Docker images does not longer run as root user by default. For commands which require root user usage, you have to switch back to the root user in your Docker image as described in Building an Action Server Image.

• #5402: Made improvements to Building Assistants tutorial

• #5354: Fixed issue with using language models like xlnet along with entity_recognition set to True inside DIETClassifier.

• #5330, #5348

• #4991: Removed Agent.continue_training and the dump_flattened_stories parameter from Agent.persist.

• #5266: Properties Component.provides and Component.requires are deprecated. Use Component.required_components() instead.

• #2674: Add default value __other__ to values of a CategoricalSlot.

  All values not mentioned in the list of values of a CategoricalSlot will be mapped to __other__ for featurization.

• #4088: Add story structure validation functionality (e.g. rasa data validate stories –max-history 5).

• #5065: Add LexicalSyntacticFeaturizer to sparse featurizers.

  LexicalSyntacticFeaturizer does the same featurization as the CRFEntityExtractor. We extracted the featurization into a separate component so that the features can be reused and featurization is independent from the entity extraction.

• #5187: Integrate language models from HuggingFace's Transformers Library.

  Add a new NLP component HFTransformersNLP which tokenizes and featurizes incoming messages using a specified pre-trained model with the Transformers library as the backend. Add LanguageModelTokenizer and LanguageModelFeaturizer which use the information from HFTransformersNLP and sets them correctly for message object. Language models currently supported: BERT, OpenAIGPT, GPT-2, XLNet, DistilBert, RoBERTa.

• #5225: Added a new CLI command rasa export to publish tracker events from a persistent tracker store using an event broker. See Export Conversations to an Event Broker, Tracker Stores and Event Brokers for more details.

• #5230: Refactor how GPU and CPU environments are configured for TensorFlow 2.0.

  Please refer to the documentation on Configuring TensorFlow to understand which environment variables to set in what scenarios. A couple of examples are shown below as well:

  # This specifies to use 1024 MB of memory from GPU with logical ID 0 and 2048 MB of memory from GPU with logical ID 1
  TF_GPU_MEMORY_ALLOC="0:1024, 1:2048"
  
  # Specifies that at most 3 CPU threads can be used to parallelize multiple non-blocking operations
  TF_INTER_OP_PARALLELISM_THREADS="3"
  
  # Specifies that at most 2 CPU threads can be used to parallelize a particular operation.
  TF_INTRA_OP_PARALLELISM_THREADS="2"

• #5266: Added a new NLU component DIETClassifier and a new policy TEDPolicy.

  DIET (Dual Intent and Entity Transformer) is a multi-task architecture for intent classification and entity recognition. You can read more about this component in the DIETClassifier documentation. The new component will replace the EmbeddingIntentClassifier and the CRFEntityExtractor in the future. Those two components are deprecated from now on. See migration guide for details on how to switch to the new component.

  TEDPolicy is the new name for EmbeddingPolicy. EmbeddingPolicy is deprecated from now on. The functionality of TEDPolicy and EmbeddingPolicy is the same. Please update your configuration file to use the new name for the policy.

• #663: The sentence vector of the SpacyFeaturizer and MitieFeaturizer can be calculated using max or mean pooling.

  To specify the pooling operation, set the option pooling for the SpacyFeaturizer or the MitieFeaturizer in your configuration file. The default pooling operation is mean. The mean pooling operation also does not take into account words, that do not have a word vector.

• #3975: Added command line argument --conversation-id to rasa interactive. If the argument is not given, conversation_id defaults to a random uuid.

• #4653: Added a new command-line argument --init-dir to command rasa init to specify the directory in which the project is initialised.

• #4682: Added support to send images with the twilio output channel.

• #4817: Part of Slack sanitization: Multiple garbled URL's in a string coming from slack will be converted into actual strings. Example: health check of <http://eemdb.net|eemdb.net> and <http://eemdb1.net|eemdb1.net> to health check of eemdb.net and eemdb1.net

• #5117: New command-line argument –conversation-id will be added and wiil give the ability to set specific conversation ID for each shell session, if not passed will be random.

• #5211: Messages sent to the Pika Event Broker are now persisted. This guarantees the RabbitMQ will re-send previously received messages after a crash. Note that this does not help for the case where messages are sent to an unavailable RabbitMQ instance.

• #5250: Added support for mattermost connector to use bot accounts.

• #5266: We updated our code to TensorFlow 2.

• #5317: Events exported using rasa export receive a message header if published through a PikaEventBroker. The header is added to the message's BasicProperties.headers under the rasa-export-process-id key (rasa.core.constants.RASA_EXPORT_PROCESS_ID_HEADER_NAME). The value is a UUID4 generated at each call of rasa export. The resulting header is a key-value pair that looks as follows:

  'rasa-export-process-id': 'd3b3d3ffe2bd4f379ccf21214ccfb261'
  

• #5292: Added followlinks=True to os.walk calls, to allow the use of symlinks in training, NLU and domain data.

• #4811: Support invoking a SlackBot by direct messaging or @<app name> mentions.

• #4006: Fixed timestamp parsing warning when using DucklingHTTPExtractor

• #4601: Fixed issue with action_restart getting overridden by action_listen when the MappingPolicy and the TwoStageFallbackPolicy are used together.

• #5201: Fixed incorrectly raised Error encountered in pipelines with a ResponseSelector and NLG.

  When NLU training data is split before NLU pipeline comparison, NLG responses were not also persisted and therefore training for a pipeline including the ResponseSelector would fail.

  NLG responses are now persisted along with NLU data to a /train directory in the run_x/xx%_exclusion folder.

• #5277: Fixed sending custom json with Twilio channel

• #5174: Updated the documentation to properly suggest not to explicitly add utterance actions to the domain.

• #5189: Added user guide for reminders and external events, including reminderbot demo.

• #3923, #4597, #4903, #5180, #5189, #5266, #699

• #5068: Tracker stores supporting conversation sessions (SQLTrackerStore and MongoTrackerStore) do not save the tracker state to database immediately after starting a new conversation session. This leads to the number of events being saved in addition to the already-existing ones to be calculated correctly.

  This fixes action_listen events being saved twice at the beginning of conversation sessions.

• #5231: Fix segmentation fault when running rasa train or rasa shell.

• #5286: Fix doc links on “Deploying your Assistant” page

• #5197: Fixed incompatibility of Oracle with the SQLTrackerStore, by using a Sequence for the primary key columns. This does not change anything for SQL databases other than Oracle. If you are using Oracle, please create a sequence with the instructions in the SQLTrackerStore docs.

• #5197: Added section on setting up the SQLTrackerStore with Oracle

• #5210: Renamed “Running the Server” page to “Configuring the HTTP API”

• #5106: Fixed file loading of non proper UTF-8 story files, failing properly when checking for story files.

• #5162: Fix problem with multi-intents. Training with multi-intents using the CountVectorsFeaturizer together with EmbeddingIntentClassifier is working again.

• #5171: Fix bug ValueError: Cannot concatenate sparse features as sequence dimension does not match.

  When training a Rasa model that contains responses for just some of the intents, training was failing. Fixed the featurizers to return a consistent feature vector in case no response was given for a specific message.

• #5199: If no text features are present in EmbeddingIntentClassifier return the intent None.

• #5216: Resolve version conflicts: Pin version of cloudpickle to ~=1.2.0.

• #4964: The endpoint /conversations/<conversation_id>/execute is now deprecated. Instead, users should use the /conversations/<conversation_id>/trigger_intent endpoint and thus trigger intents instead of actions.

• #4978: Remove option use_cls_token from tokenizers and option return_sequence from featurizers.

  By default all tokenizer add a special token (__CLS__) to the end of the list of tokens. This token will be used to capture the features of the whole utterance.

  The featurizers will return a matrix of size (number-of-tokens x feature-dimension) by default. This allows to train sequence models. However, the feature vector of the __CLS__ token can be used to train non-sequence models. The corresponding classifier can decide what kind of features to use.

• #400: Rename templates key in domain to responses.

  templates key will still work for backwards compatibility but will raise a future warning.

• #4902: Added a new configuration parameter, ranking_length to the EmbeddingPolicy, EmbeddingIntentClassifier, and ResponseSelector classes.

• #4964: External events and reminders now trigger intents (and entities) instead of actions.

  Add new endpoint /conversations/<conversation_id>/trigger_intent, which lets the user specify an intent and a list of entities that is injected into the conversation in place of a user message. The bot then predicts and executes a response action.

• #4978: Add ConveRTTokenizer.

  The tokenizer should be used whenever the ConveRTFeaturizer is used.

  Every tokenizer now supports the following configuration options: intent_tokenization_flag: Flag to check whether to split intents (default False). intent_split_symbol: Symbol on which intent should be split (default _)

• #1988: Remove the need of specifying utter actions in the actions section explicitly if these actions are already listed in the templates section.

• #4877: Entity examples that have been extracted using an external extractor are excluded from Markdown dumping in MarkdownWriter.dumps(). The excluded external extractors are DucklingHTTPExtractor and SpacyEntityExtractor.

• #4902: The EmbeddingPolicy, EmbeddingIntentClassifier, and ResponseSelector now by default normalize confidence levels over the top 10 results. See Rasa 1.6 to Rasa 1.7 for more details.

• #4964: ReminderCancelled can now cancel multiple reminders if no name is given. It still cancels a single reminder if the reminder's name is specified.

• #4774: Requests to /model/train do not longer block other requests to the Rasa server.

• #4896: Fixed default behavior of rasa test core --evaluate-model-directory when called without --model. Previously, the latest model file was used as --model. Now the default model directory is used instead.

  New behavior of rasa test core --evaluate-model-directory when given an existing file as argument for --model: Previously, this led to an error. Now a warning is displayed and the directory containing the given file is used as --model.

• #5040: Updated the dependency networkx from 2.3.0 to 2.4.0. The old version created incompatibilities when using pip.

  There is an imcompatibility between Rasa dependecy requests 2.22.0 and the own depedency from Rasa for networkx raising errors upon pip install. There is also a bug corrected in requirements.txt which used ~= instead of ==. All of these are fixed using networkx 2.4.0.

• #5057: Fixed compatibility issue with Microsoft Bot Framework Emulator if service_url lacked a trailing /.

• #5092: DynamoDB tracker store decimal values will now be rounded on save. Previously values exceeding 38 digits caused an unhandled error.

• #4458, #4664, #4780, #5029

• #4994: Switching back to a TensorFlow release which only includes CPU support to reduce the size of the dependencies. If you want to use the TensorFlow package with GPU support, please run pip install tensorflow-gpu==1.15.0.

• #5111: Fixes Exception 'Loop' object has no attribute '_ready' error when running rasa init.

• #5126: Updated the end-to-end ValueError you recieve when you have a invalid story format to point to the updated doc link.

• #4989: Use an empty domain in case a model is loaded which has no domain (avoids errors when accessing agent.doman.<some attribute>).

• #4995: Replace error message with warning in tokenizers and featurizers if default parameter not set.

• #5019: Pin sanic patch version instead of minor version. Fixes sanic _run_request_middleware() error.

• #5032: Fix wrong calculation of additional conversation events when saving the conversation. This led to conversation events not being saved.

• #5032: Fix wrong order of conversation events when pushing events to conversations via POST /conversations/<conversation_id>/tracker/events.

• #4935: Removed ner_features as a feature name from CRFEntityExtractor, use text_dense_features instead.

  The following settings match the previous NGramFeaturizer:

  pipeline:
  - name: 'CountVectorsFeaturizer'
    analyzer: 'char_wb'
    min_ngram: 3
    max_ngram: 17
    max_features: 10
    min_df: 5
  

• #4957: To use custom features in the CRFEntityExtractor use text_dense_features instead of ner_features. If text_dense_features are present in the feature set, the CRFEntityExtractor will automatically make use of them. Just make sure to add a dense featurizer in front of the CRFEntityExtractor in your pipeline and set the flag return_sequence to True for that featurizer.

• #4990: Deprecated Agent.continue_training. Instead, a model should be retrained.

• #684: Specifying lookup tables directly in the NLU file is now deprecated. Please specify them in an external file.

• #4795: Replaced the warnings about missing templates, intents etc. in validator.py by debug messages.

• #4830: Added conversation sessions to trackers.

  A conversation session represents the dialog between the assistant and a user. Conversation sessions can begin in three ways: 1. the user begins the conversation with the assistant, 2. the user sends their first message after a configurable period of inactivity, or 3. a manual session start is triggered with the /session_start intent message. The period of inactivity after which a new conversation session is triggered is defined in the domain using the session_expiration_time key in the session_config section. The introduction of conversation sessions comprises the following changes:

  • Added a new event SessionStarted that marks the beginning of a new conversation session.

  • Added a new default action ActionSessionStart. This action takes all SlotSet events from the previous session and applies it to the next session.

  • Added a new default intent session_start which triggers the start of a new conversation session.

  • SQLTrackerStore and MongoTrackerStore only retrieve events from the last session from the database.

  :::note The session behavior is disabled for existing projects, i.e. existing domains without session config section.

  :::

• #4935: Preparation for an upcoming change in the EmbeddingIntentClassifier:

  Add option use_cls_token to all tokenizers. If it is set to True, the token __CLS__ will be added to the end of the list of tokens. Default is set to False. No need to change the default value for now.

  Add option return_sequence to all featurizers. By default all featurizers return a matrix of size (1 x feature-dimension). If the option return_sequence is set to True, the corresponding featurizer will return a matrix of size (token-length x feature-dimension). See Text Featurizers. Default value is set to False. However, you might want to set it to True if you want to use custom features in the CRFEntityExtractor. See passing custom features to the CRFEntityExtractor

  Changed some featurizers to use sparse features, which should reduce memory usage with large amounts of training data significantly. Read more: Text Featurizers .

  :::caution These changes break model compatibility. You will need to retrain your old models!

  :::

• #3549: Added --no-plot option for rasa test command, which disables rendering of confusion matrix and histogram. By default plots will be rendered.

• #4086: If matplotlib couldn't set up a default backend, it will be set automatically to TkAgg/Agg one

• #4647: Add the option \random_seed`to the`rasa data split nlu`` command to generate reproducible train/test splits.

• #4734: Changed url __init__() arguments for custom tracker stores to host to reflect the __init__ arguments of currently supported tracker stores. Note that in endpoints.yml, these are still declared as url.

• #4751: The kafka-python dependency has become as an “extra” dependency. To use the KafkaEventConsumer, rasa has to be installed with the [kafka] option, i.e.

  $ pip install rasa[kafka]

• #4801: Allow creation of natural language interpreter and generator by classname reference in endpoints.yml.

• #4834: Made it explicit that interactive learning does not work with NLU-only models.

  Interactive learning no longer trains NLU-only models if no model is provided and no core data is provided.

• #4899: The intent_report.json created by rasa test now creates an extra field confused_with for each intent. This is a dictionary containing the names of the most common false positives when this intent should be predicted, and the number of such false positives.

• #4976: rasa test nlu --cross-validation now also includes an evaluation of the response selector. As a result, the train and test F1-score, accuracy and precision is logged for the response selector. A report is also generated in the results folder by the name response_selection_report.json

• #4635: If a wait_time_between_pulls is configured for the model server in endpoints.yml, this will be used instead of the default one when running Rasa X.

• #4759: Training Luis data with luis_schema_version higher than 4.x.x will show a warning instead of throwing an exception.

• #4799: Running rasa interactive with no NLU data now works, with the functionality of rasa interactive core.

• #4917: When loading models from S3, namespaces (folders within a bucket) are now respected. Previously, this would result in an error upon loading the model.

• #4925: “rasa init” will ask if user wants to train a model

• #4942: Pin multidict dependency to 4.6.1 to prevent sanic from breaking, see the Sanic GitHub issue for more info.

• #4985: Fix errors during training and testing of ResponseSelector.

• #4933: Improved error message that appears when an incorrect parameter is passed to a policy.

• #4914: Added rasa/nlu/schemas/config.yml to wheel package

• #4942: Pin multidict dependency to 4.6.1 to prevent sanic from breaking, see the Sanic GitHub issue

• #3684: rasa interactive will skip the story visualization of training stories in case there are more than 200 stories. Stories created during interactive learning will be visualized as before.

• #4792: The log level for SocketIO loggers, including websockets.protocol, engineio.server, and socketio.server, is now handled by the LOG_LEVEL_LIBRARIES environment variable, where the default log level is ERROR.

• #4873: Updated all example bots and documentation to use the updated dispatcher.utter_message() method from rasa-sdk==1.5.0.

• #3684: rasa interactive will not load training stories in case the visualization is skipped.

• #4789: Fixed error where spacy models where not found in the docker images.

• #4802: Fixed unnecessary kwargs unpacking in rasa.test.test_core call in rasa.test.test function.

• #4898: Training data files now get loaded in the same order (especially relevant to subdirectories) each time to ensure training consistency when using a random seed.

• #4918: Locks for tickets in LockStore are immediately issued without a redundant check for their availability.

• #4844: Added towncrier to automatically collect changelog entries.

• #4869: Document the pipeline for pretrained_embeddings_convert in the pre-configured pipelines section.

• #4894: Proactively Reaching Out to the User Using Actions now correctly links to the endpoint specification.

• When NLU training data is dumped as Markdown file the intents are not longer ordered alphabetically, but in the original order of given training data

• End to end stories now support literal payloads which specify entities, e.g. greet: /greet{"name": "John"}

• Slots will be correctly interpolated if there are lists in custom response templates.

• Fixed compatibility issues with rasa-sdk 1.5

• Updated /status endpoint to show correct path to model archive

• Added data validator that checks if domain object returned is empty. If so, exit early from the command rasa data validate.

• Added the KeywordIntentClassifier.

• Added documentation for AugmentedMemoizationPolicy.

• Fall back to InMemoryTrackerStore in case there is any problem with the current tracker store.

• Arbitrary metadata can now be attached to any Event subclass. The data must be stored under the metadata key when reading the event from a JSON object or dictionary.

• Add command line argument rasa x --config CONFIG, to specify path to the policy and NLU pipeline configuration of your bot (default: config.yml).

• Added a new NLU featurizer - ConveRTFeaturizer based on ConveRT model released by PolyAI.

• Added a new preconfigured pipeline - pretrained_embeddings_convert.

• Do not retrain the entire Core model if only the templates section of the domain is changed.

• Upgraded jsonschema version.

• Remove duplicate messages when creating training data (issues/1446).

• MultiProjectImporter now imports files in the order of the import statements

• Fixed server hanging forever on leaving rasa shell before first message

• Fixed rasa init showing traceback error when user does Keyboard Interrupt before choosing a project path

• CountVectorsFeaturizer featurizes intents only if its analyzer is set to word

• Fixed bug where facebooks generic template was not rendered when buttons were None

• Fixed default intents unnecessarily raising undefined parsing error

• Fixed Rasa X not working when any tracker store was configured for Rasa.

• Use the matplotlib backend agg in case the tkinter package is not installed.

• NLU-only models no longer throw warnings about parsing features not defined in the domain

• Fixed bug that stopped Dockerfiles from building version 1.4.4.

• Fixed format guessing for e2e stories with intent restated as /intent

• PikaEventProducer adds the RabbitMQ App ID message property to published messages with the value of the RASA_ENVIRONMENT environment variable. The message property will not be assigned if this environment variable isn't set.

• Updated Mattermost connector documentation to be more clear.

• Updated format strings to f-strings where appropriate.

• Updated tensorflow requirement to 1.15.0

• Dump domain using UTF-8 (to avoid \\UXXXX sequences in the dumped files)

• Fixed exporting NLU training data in json format from rasa interactive

• Fixed numpy deprecation warnings

• Fixed Connection reset by peer errors and bot response delays when using the RabbitMQ event broker.

• TensorFlow deprecation warnings are no longer shown when running rasa x

• Fixed 'Namespace' object has no attribute 'persist_nlu_data' error during interactive learning

• Pinned networkx~=2.3.0 to fix visualization in rasa interactive and Rasa X

• Fixed No model found error when using rasa run actions with “actions” as a directory.

Regression: changes from 1.2.12 were missing from 1.4.0, readded them

• add flag to CLI to persist NLU training data if needed

• log a warning if the Interpreter picks up an intent or an entity that does not exist in the domain file.

• added DynamoTrackerStore to support persistence of agents running on AWS

• added docstrings for TrackerStore classes

• added buttons and images to mattermost.

• CRFEntityExtractor updated to accept arbitrary token-level features like word vectors (issues/4214)

• SpacyFeaturizer updated to add ner_features for CRFEntityExtractor

• Sanitizing incoming messages from slack to remove slack formatting like <mailto:[email protected]|[email protected]> or <http://url.com|url.com> and substitute it with original content

• Added the ability to configure the number of Sanic worker processes in the HTTP server (rasa.server) and input channel server (rasa.core.agent.handle_channels()). The number of workers can be set using the environment variable SANIC_WORKERS (default: 1). A value of >1 is allowed only in combination with RedisLockStore as the lock store.

• Botframework channel can handle uploaded files in UserMessage metadata.

• Added data validator that checks there is no duplicated example data across multiples intents

• Unknown sections in markdown format (NLU data) are not ignored anymore, but instead an error is raised.

• It is now easier to add metadata to a UserMessage in existing channels. You can do so by overwriting the method get_metadata. The return value of this method will be passed to the UserMessage object.

• Tests can now be run in parallel

• Serialise DialogueStateTracker as json instead of pickle. DEPRECATION warning: Deserialisation of pickled trackers will be deprecated in version 2.0. For now, trackers are still loaded from pickle but will be dumped as json in any subsequent save operations.

• Event brokers are now also passed to custom tracker stores (using the event_broker parameter)

• Don't run the Rasa Docker image as root.

• Use multi-stage builds to reduce the size of the Rasa Docker image.

• Updated the /status api route to use the actual model file location instead of the tmp location.

• Removed Python 3.5 support

• fixed missing tkinter dependency for running tests on Ubuntu

• fixed issue with conversation JSON serialization

• fixed the hanging HTTP call with ner_duckling_http pipeline

• fixed Interactive Learning intent payload messages saving in nlu files

• fixed DucklingHTTPExtractor dimensions by actually applying to the request

• Can now pass a package as an argument to the --actions parameter of the rasa run actions command.

• Fixed visualization of stories with entities which led to a failing visualization in Rasa X

• Port of 1.2.10 (support for RabbitMQ TLS authentication and port key in event broker endpoint config).

• Port of 1.2.11 (support for passing a CA file for SSL certificate verification via the –ssl-ca-file flag).

• Fixed the hanging HTTP call with ner_duckling_http pipeline.

• Fixed text processing of intent attribute inside CountVectorFeaturizer.

• Fixed argument of type 'NoneType' is not iterable when using rasa shell, rasa interactive / rasa run

• Policies now only get imported if they are actually used. This removes TensorFlow warnings when starting Rasa X

• Fixed error Object of type 'MaxHistoryTrackerFeaturizer' is not JSON serializable when running rasa train core

• Default channel send_ methods no longer support kwargs as they caused issues in incompatible channels

• re-added TLS, SRV dependencies for PyMongo

• socketio can now be run without turning on the --enable-api flag

• MappingPolicy no longer fails when the latest action doesn't have a policy

• Added the ability for users to specify a conversation id to send a message to when using the RasaChat input channel.

• Fixed issue where rasa init would fail without spaCy being installed

• Added the ability to set the backlog parameter in Sanics run() method using the SANIC_BACKLOG environment variable. This parameter sets the number of unaccepted connections the server allows before refusing new connections. A default value of 100 is used if the variable is not set.

• Status endpoint (/status) now also returns the number of training processes currently running

• Added the ability to properly deal with spaCy Doc-objects created on empty strings as discussed in issue #4445. Only training samples that actually bear content are sent to self.nlp.pipe for every given attribute. Non-content-bearing samples are converted to empty Doc-objects. The resulting lists are merged with their preserved order and properly returned.

• asyncio warnings are now only printed if the callback takes more than 100ms (up from 1ms).

• agent.load_model_from_server no longer affects logging.

• The endpoint POST /model/train no longer supports specifying an output directory for the trained model using the field out. Instead you can choose whether you want to save the trained model in the default model directory (models) (default behavior) or in a temporary directory by specifying the save_to_default_model_directory field in the training request.

• Added a check to avoid training CountVectorizer for a particular attribute of a message if no text is provided for that attribute across the training data.

• Default one-hot representation for label featurization inside EmbeddingIntentClassifier if label features don't exist.

• Policy ensemble no longer incorrectly wrings “missing mapping policy” when mapping policy is present.

• “text” from utter_custom_json now correctly saved to tracker when using telegram channel

• Removed computation of intent_spacy_doc. As a result, none of the spacy components process intents now.

• SQL tracker events are retrieved ordered by timestamps. This fixes interactive learning events being shown in the wrong order.

• Pin gast to == 0.2.2

• Added option to persist nlu training data (default: False)

• option to save stories in e2e format for interactive learning

• bot messages contain the timestamp of the BotUttered event, which can be used in channels

• FallbackPolicy can now be configured to trigger when the difference between confidences of two predicted intents is too narrow

• experimental training data importer which supports training with data of multiple sub bots. Please see the docs for more information.

• throw error during training when triggers are defined in the domain without MappingPolicy being present in the policy ensemble

• The tracker is now available within the interpreter's parse method, giving the ability to create interpreter classes that use the tracker state (eg. slot values) during the parsing of the message. More details on motivation of this change see issues/3015.

• add example bot knowledgebasebot to showcase the usage of ActionQueryKnowledgeBase

• softmax starspace loss for both EmbeddingPolicy and EmbeddingIntentClassifier

• balanced batching strategy for both EmbeddingPolicy and EmbeddingIntentClassifier

• max_history parameter for EmbeddingPolicy

• Successful predictions of the NER are written to a file if --successes is set when running rasa test nlu

• Incorrect predictions of the NER are written to a file by default. You can disable it via --no-errors.

• New NLU component ResponseSelector added for the task of response selection

• Message data attribute can contain two more keys - response_key, response depending on the training data

• New action type implemented by ActionRetrieveResponse class and identified with response_ prefix

• Vocabulary sharing inside CountVectorsFeaturizer with use_shared_vocab flag. If set to True, vocabulary of corpus is shared between text, intent and response attributes of message

• Added an option to share the hidden layer weights of text input and label input inside EmbeddingIntentClassifier using the flag share_hidden_layers

• New type of training data file in NLU which stores response phrases for response selection task.

• Add flag intent_split_symbol and intent_tokenization_flag to all WhitespaceTokenizer, JiebaTokenizer and SpacyTokenizer

• Added evaluation for response selector. Creates a report response_selection_report.json inside --out directory.

• argument --config-endpoint to specify the URL from which rasa x pulls the runtime configuration (endpoints and credentials)

• LockStore class storing instances of TicketLock for every conversation_id

• environment variables SQL_POOL_SIZE (default: 50) and SQL_MAX_OVERFLOW (default: 100) can be set to control the pool size and maximum pool overflow for SQLTrackerStore when used with the postgresql dialect

• Add a bot_challenge intent and a utter_iamabot action to all example projects and the rasa init bot.

• Allow sending attachments when using the socketio channel

• rasa data validate will fail with a non-zero exit code if validation fails

• added character-level CountVectorsFeaturizer with empirically found parameters into the supervised_embeddings NLU pipeline template

• NLU evaluations now also stores its output in the output directory like the core evaluation

• show warning in case a default path is used instead of a provided, invalid path

• compare mode of rasa train core allows the whole core config comparison, naming style of models trained for comparison is changed (this is a breaking change)

• pika keeps a single connection open, instead of open and closing on each incoming event

• RasaChatInput fetches the public key from the Rasa X API. The key is used to decode the bearer token containing the conversation ID. This requires rasa-x>=0.20.2.

• more specific exception message when loading custom components depending on whether component's path or class name is invalid or can't be found in the global namespace

• change priorities so that the MemoizationPolicy has higher priority than the MappingPolicy

• substitute LSTM with Transformer in EmbeddingPolicy

• EmbeddingPolicy can now use MaxHistoryTrackerFeaturizer

• non zero evaluate_on_num_examples in EmbeddingPolicy and EmbeddingIntentClassifier is the size of hold out validation set that is excluded from training data

• defaults parameters and architectures for both EmbeddingPolicy and EmbeddingIntentClassifier are changed (this is a breaking change)

• evaluation of NER does not include 'no-entity' anymore

• --successes for rasa test nlu is now boolean values. If set incorrect/successful predictions are saved in a file.

• --errors is renamed to --no-errors and is now a boolean value. By default incorrect predictions are saved in a file. If --no-errors is set predictions are not written to a file.

• Remove label_tokenization_flag and label_split_symbol from EmbeddingIntentClassifier. Instead move these parameters to Tokenizers.

• Process features of all attributes of a message, i.e. - text, intent and response inside the respective component itself. For e.g. - intent of a message is now tokenized inside the tokenizer itself.

• Deprecate as_markdown and as_json in favour of nlu_as_markdown and nlu_as_json respectively.

• pin python-engineio >= 3.9.3

• update python-socketio req to >= 4.3.1

• rasa test nlu with a folder of configuration files

• MappingPolicy standard featurizer is set to None

• Removed text parameter from send_attachment function in slack.py to avoid duplication of text output to slackbot

• server /status endpoint reports status when an NLU-only model is loaded

• Removed --report argument from rasa test nlu. All output files are stored in the --out directory.

• Support for transit encryption with Redis via use_ssl: True in the tracker store config in endpoints.yml

• Support for passing a CA file for SSL certificate verification via the –ssl-ca-file flag

• Added support for RabbitMQ TLS authentication. The following environment variables need to be set: RABBITMQ_SSL_CLIENT_CERTIFICATE - path to the SSL client certificate (required) RABBITMQ_SSL_CLIENT_KEY - path to the SSL client key (required) RABBITMQ_SSL_CA_FILE - path to the SSL CA file (optional, for certificate verification) RABBITMQ_SSL_KEY_PASSWORD - SSL private key password (optional)

• Added ability to define the RabbitMQ port using the port key in the event_broker endpoint config.

• Correctly pass SSL flag values to x CLI command (backport of

• SQL tracker events are retrieved ordered by timestamps. This fixes interactive learning events being shown in the wrong order. Backport of 1.3.2 patch (PR #4427).

• Added query dictionary argument to SQLTrackerStore which will be appended to the SQL connection URL as query parameters.

• fixed bug that occurred when sending template elements through a channel that doesn't support them

• SSL support for rasa run command. Certificate can be specified using --ssl-certificate and --ssl-keyfile.

• made default augmentation value consistent across repo

• '/restart' will now also restart the bot if the tracker is paused

• the SocketIO input channel now allows accesses from other origins (fixes SocketIO channel on Rasa X)

• messages with multiple entities are now handled properly with e2e evaluation

• data/test_evaluations/end_to_end_story.md was re-written in the restaurantbot domain

• messages with multiple entities are now handled properly with e2e evaluation

• data/test_evaluations/end_to_end_story.md was re-written in the restaurantbot domain

• Free text input was not allowed in the Rasa shell when the response template contained buttons, which has now been fixed.

• UserUttered events always got the same timestamp

• Docs now have an EDIT THIS PAGE button

• Flood control exceeded error in Telegram connector which happened because the webhook was set twice

• add root route to server started without --enable-api parameter

• add --evaluate-model-directory to rasa test core to evaluate models from rasa train core -c <config-1> <config-2>

• option to send messages to the user by calling POST /conversations/{conversation_id}/execute

• Agent.update_model() and Agent.handle_message() now work without needing to set a domain or a policy ensemble

• Update pytype to 2019.7.11

• new event broker class: SQLProducer. This event broker is now used when running locally with Rasa X

• API requests are not longer logged to rasa_core.log by default in order to avoid problems when running on OpenShift (use --log-file rasa_core.log to retain the old behavior)

• metadata attribute added to UserMessage

• rasa test core can handle compressed model files

• rasa can handle story files containing multi line comments

• template will retain { if escaped with {. e.g. {{“foo”: {bar}}} will result in {“foo”: “replaced value”}

• TrainingFileImporter interface to support customizing the process of loading training data

• fill slots for custom templates

• Agent.update_model() and Agent.handle_message() now work without needing to set a domain or a policy ensemble

• update pytype to 2019.7.11

• interactive learning bug where reverted user utterances were dumped to training data

• added timeout to terminal input channel to avoid freezing input in case of server errors

• fill slots for image, buttons, quick_replies and attachments in templates

• rasa train core in comparison mode stores the model files compressed (tar.gz files)

• slot setting in interactive learning with the TwoStageFallbackPolicy

• added optional pymongo dependencies [tls, srv] to requirements.txt for better mongodb support

• case_sensitive option added to WhiteSpaceTokenizer with true as default.

• validation no longer throws an error during interactive learning

• fixed wrong cleaning of use_entities in case it was a list and not True

• updated the server endpoint /model/parse to handle also messages with the intent prefix

• fixed bug where “No model found” message appeared after successfully running the bot

• debug logs now print to rasa_core.log when running rasa x -vv or rasa run -vv

• rest channel supports setting a message's input_channel through a field input_channel in the request body

• recommended syntax for empty use_entities and ignore_entities in the domain file has been updated from False or None to an empty list ([])

• rasa run without --enable-api does not require a local model anymore

• using rasa run with --enable-api to run a server now prints “running Rasa server” instead of “running Rasa Core server”

• actions, intents, and utterances created in rasa interactive can no longer be empty

• debug logging now tells you which tracker store is connected

• the response of /model/train now includes a response header for the trained model filename

• Validator class to help developing by checking if the files have any errors

• project's code is now linted using flake8

• info log when credentials were provided for multiple channels and channel in --connector argument was specified at the same time

• validate export paths in interactive learning

• deprecate rasa.core.agent.handle_channels(...)\. Please use ``rasa.run(...)orrasa.core.run.configure_app` instead.

• Agent.load() also accepts tar.gz model file

• revert the stripping of trailing slashes in endpoint URLs since this can lead to problems in case the trailing slash is actually wanted

• starter packs were removed from Github and are therefore no longer tested by Travis script

• all temporal model files are now deleted after stopping the Rasa server

• rasa shell nlu now outputs unicode characters instead of \\uxxxx codes

• fixed PUT /model with model_server by deserializing the model_server to EndpointConfig.

• x in AnySlotDict is now True for any x, which fixes empty slot warnings in interactive learning

• rasa train now also includes NLU files in other formats than the Rasa format

• rasa train core no longer crashes without a --domain arg

• rasa interactive now looks for endpoints in endpoints.yml if no --endpoints arg is passed

• custom files, e.g. custom components and channels, load correctly when using the command line interface

• MappingPolicy now works correctly when used as part of a PolicyEnsemble

• unfeaturize single entities

• added agent readiness check to the /status resource

• removed leading underscore from name of '_create_initial_project' function.

• fixed bug where facebook quick replies were not rendering

• take FB quick reply payload rather than text as input

• fixed bug where training_data path in metadata.json was an absolute path

• fixed any inconsistent type annotations in code and some bugs revealed by type checker

• fixed duplicate events appearing in tracker when using a PostgreSQL tracker store

• fixed compatibility with Rasa SDK

• bot responses can contain custom messages besides other message types

• nlu configs can now be directly compared for performance on a dataset in rasa test nlu

• update the tracker in interactive learning through reverting and appending events instead of replacing the tracker

• POST /conversations/{conversation_id}/tracker/events supports a list of events

• fixed creation of RasaNLUHttpInterpreter

• form actions are included in domain warnings

• default actions, which are overriden by custom actions and are listed in the domain are excluded from domain warnings

• SQL data column type to Text for compatibility with MySQL

• non-featurizer training parameters don't break SklearnPolicy anymore

• revert PR #3739 (as this is a breaking change): set PikaProducer and KafkaProducer default queues back to rasa_core_events

• support for specifying full database urls in the SQLTrackerStore configuration

• maximum number of predictions can be set via the environment variable MAX_NUMBER_OF_PREDICTIONS (default is 10)

• default PikaProducer and KafkaProducer queues to rasa_production_events

• exclude unfeaturized slots from domain warnings

• loading of additional training data with the SkillSelector

• strip trailing slashes in endpoint URLs

• added argument --rasa-x-port to specify the port of Rasa X when running Rasa X locally via rasa x

• slack notifications from bots correctly render text

• fixed usage of --log-file argument for rasa run and rasa shell

• check if correct tracker store is configured in local mode

• fixed backwards incompatible utils changes

• fixed spacy being a required dependency (regression)

• automatic creation of index on the sender_id column when using an SQL tracker store. If you have an existing data and you are running into performance issues, please make sure to add an index manually using CREATE INDEX event_idx_sender_id ON events (sender_id);.

• NLU evaluation in cross-validation mode now also provides intent/entity reports, confusion matrix, etc.

• non-ascii characters render correctly in stories generated from interactive learning

• validate domain file before usage, e.g. print proper error messages if domain file is invalid instead of raising errors

• added domain_warnings() method to Domain which returns a dict containing the diff between supplied {actions, intents, entities, slots} and what's contained in the domain

• fix lookup table files failed to load issues/3622

• buttons can now be properly selected during cmdline chat or when in interactive learning

• set slots correctly when events are added through the API

• mapping policy no longer ignores NLU threshold

• mapping policy priority is correctly persisted

• updated installation command in docs for Rasa X

• added arguments to set the file paths for interactive training

• added quick reply representation for command-line output

• added option to specify custom button type for Facebook buttons

• added tracker store persisting trackers into a SQL database (SQLTrackerStore)

• added rasa command line interface and API

• Rasa HTTP training endpoint at POST /jobs. This endpoint will train a combined Rasa Core and NLU model

• ReminderCancelled(action_name) event to cancel given action_name reminder for current user

• Rasa HTTP intent evaluation endpoint at POST /intentEvaluation. This endpoints performs an intent evaluation of a Rasa model

• option to create template for new utterance action in interactive learning

• you can now choose actions previously created in the same session in interactive learning

• add formatter 'black'

• channel-specific utterances via the - "channel": key in utterance templates

• arbitrary json messages via the - "custom": key in utterance templates and via utter_custom_json() method in custom actions

• support to load sub skills (domain, stories, nlu data)

• support to select which sub skills to load through import section in config.yml

• support for spaCy 2.1

• a model for an agent can now also be loaded from a remote storage

• log level can be set via environment variable LOG_LEVEL

• add --store-uncompressed to train command to not compress Rasa model

• log level of libraries, such as tensorflow, can be set via environment variable LOG_LEVEL_LIBRARIES

• if no spaCy model is linked upon building a spaCy pipeline, an appropriate error message is now raised with instructions for linking one

• renamed all CLI parameters containing any _ to use dashes - instead (GNU standard)

• renamed rasa_core package to rasa.core

• for interactive learning only include manually annotated and ner_crf entities in nlu export

• made message_id an additional argument to interpreter.parse

• changed removing punctuation logic in WhitespaceTokenizer

• training_processes in the Rasa NLU data router have been renamed to worker_processes

• created a common utils package rasa.utils for nlu and core, common methods like read_yaml moved there

• removed --num_threads from run command (server will be asynchronous but running in a single thread)

• the _check_token() method in RasaChat now authenticates against /auth/verify instead of /user

• removed --pre_load from run command (Rasa NLU server will just have a maximum of one model and that model will be loaded by default)

• changed file format of a stored trained model from the Rasa NLU server to tar.gz

• train command uses fallback config if an invalid config is given

• test command now compares multiple models if a list of model files is provided for the argument --model

• Merged rasa.core and rasa.nlu server into a single server. See swagger file in docs/_static/spec/server.yaml for available endpoints.

• utter_custom_message() method in rasa_core_sdk has been renamed to utter_elements()

• updated dependencies. as part of this, models for spacy need to be reinstalled for 2.1 (from 2.0)

• make sure all command line arguments for rasa test and rasa interactive are actually used, removed arguments that were not used at all (e.g. --core for rasa test)

• removed possibility to execute python -m rasa_core.train etc. (e.g. scripts in rasa.core and rasa.nlu). Use the CLI for rasa instead, e.g. rasa train core.

• removed _sklearn_numpy_warning_fix from the SklearnIntentClassifier

• removed Dispatcher class from core

• removed projects: the Rasa NLU server now has a maximum of one model at a time loaded.

• evaluating core stories with two stage fallback gave an error, trying to handle None for a policy

• the /evaluate route for the Rasa NLU server now runs evaluation in a parallel process, which prevents the currently loaded model unloading

• added missing implementation of the keys() function for the Redis Tracker Store

• in interactive learning: only updates entity values if user changes annotation

• log options from the command line interface are applied (they overwrite the environment variable)

• all message arguments (kwargs in dispatcher.utter methods, as well as template args) are now sent through to output channels

• utterance templates defined in actions are checked for existence upon training a new agent, and a warning is thrown before training if one is missing