2.2.3

Bugfixes

• #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.

2.2.2

Bugfixes

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

1.10.20

Bugfixes

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

2.2.1

Bugfixes

• #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 = ?

2.2.0

Deprecations and Removals

• #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.

Features

• #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”

Improvements

• #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.

Bugfixes

• #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.

Improved Documentation

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

Miscellaneous internal changes

• #7167

1.10.19

Improvements

• #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.

2.2.0a1

Pre-release version

2.1.3

Improvements

• #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.

Bugfixes

• #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.

2.1.2

Bugfixes

• #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.

2.0.8

Bugfixes

• #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.