Rasa Open Source Change Log

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

[3.6.13] - 2023-10-23#

Rasa 3.6.13 (2023-10-23)

Bugfixes#

• #12927: Fix wrong conflicts that occur when rasa validate stories is run with slots that have active_loop set to null in mapping conditions.

[3.6.12] - 2023-10-10#

Rasa 3.6.12 (2023-10-10)

Bugfixes#

• #12904: Refresh headers used in requests (e.g. action server requests) made by EndpointConfig using its headers attribute.
• #12906: Upgrade pillow to 10.0.1 to address security vulnerability CVE-2023-4863 found in 10.0.0 version.

[3.6.11] - 2023-10-05#

Rasa 3.6.11 (2023-10-05)

Bugfixes#

• #12722: Intent names will not be falsely abbreviated in interactive training (fixes OSS-413).

  This will also fix a bug where forced user utterances (using the regex matcher) will be reverted even though they are present in the domain.

• #12886: Cache EndpointConfig session object using cached_property decorator instead of recreating this object on every request. Initialize these connection pools for action server and model server endpoints as part of the Sanic after_server_start listener. Also close connection pools during Sanic after_server_stop listener.

[3.6.10] - 2023-09-26#

Rasa 3.6.10 (2023-09-26)

Improvements#

• #12827: Improved handling of last batch during DIET and TED training. The last batch is discarded if it contains less than half a batch size of data.
• #12852: Added username to the connection parameters for RedisLockStore and RedisTrackerStore
• #1493: Telemetry data is only send for licensed users.

Improved Documentation#

• #12868: Remove the Playground from docs.

[3.6.9] - 2023-09-15#

Rasa 3.6.9 (2023-09-15)

Improvements#

• #12778: Added additional method fingerprint_addon to the GraphComponent interface to allow inclusion of external data into the fingerprint calculation of a component

Bugfixes#

• #12790: Fixed KeyError which resulted when domain_responses doesn't exist as a keyword argument while using a custom action dispatcher with nlg server.

[3.6.8] - 2023-08-30#

Rasa 3.6.8 (2023-08-30)

No significant changes.

[3.6.7] - 2023-08-29#

Rasa 3.6.7 (2023-08-29)

Bugfixes#

• #12768: Updated certifi, cryptography, and scipy packages to address security vulnerabilities.

[3.6.6] - 2023-08-23#

Rasa 3.6.6 (2023-08-23)

Bugfixes#

• #12755: Updated setuptools and wheel to address security vulnerabilities.

[3.6.5] - 2023-08-17#

Rasa 3.6.5 (2023-08-17)

Improvements#

• #12696: Use the same session across requests in RasaNLUHttpInterpreter

Bugfixes#

• #12737: Resolve dependency incompatibility: Pin version of dnspython to ==2.3.0.

Improved Documentation#

• #12712: Updated PII docs with new section on how to use Rasa X/Enterprise with PII management solution, and a new note on debug logs being displayed after the bot message with rasa shell.

[3.6.4] - 2023-07-21#

Rasa 3.6.4 (2023-07-21)

Bugfixes#

• #12575: Extract conditional response variation and channel variation filtering logic into a separate component. Enable usage of this component in the NaturalLanguageGenerator subclasses (e.g. CallbackNaturalLanguageGenerator, TemplatedNaturalLanguageGenerator). Amend nlg_request_format to include a single response ID string field, instead of a list of IDs.

Improved Documentation#

• #12663: Updated commands with square brackets e.g (pip install rasa[spacy]) to use quotes (pip install 'rasa[spacy]') for compatibility with zsh in docs.

[3.6.3] - 2023-07-20#

Rasa 3.6.3 (2023-07-20)

Improvements#

• #12637: Added a human readable component to structlog using the event_info key and made it the default rendered key if present.

Bugfixes#

• #12638: Fix the issue with the most recent model not being selected if the owner or permissions where modified on the model file.
• #12661: Fixed BlockingIOError which occured as a result of too large data passed to strulogs.

Improved Documentation#

• #12659: Update action server documentation with new capability to extend Sanic features by using plugins. Update rasa-sdk dependency to version 3.6.1.
• #12663: Updated commands with square brackets e.g (pip install rasa[spacy]) to use quotes (pip install 'rasa[spacy]') for compatibility with zsh in docs.

[3.6.2] - 2023-07-06#

Rasa 3.6.2 (2023-07-06)

Bugfixes#

• #12602: Resolves the issue of importing TensorFlow on Docker for ARM64 architecture.

[3.6.1] - 2023-07-03#

Rasa 3.6.1 (2023-07-03)

Improvements#

• #12533: Add building multi-platform Docker image (amd64/arm64)
• #12543: Switch struct log to FilteringBoundLogger in order to retain log level set in the config.
• #12560: Add new anonymizable structlog keys.

Bugfixes#

• #12516: Add rasa_events to the list of anonymizable structlog keys and rename structlog keys.
• #12521: Introduce a validation step in rasa data validate and rasa train commands to identify non-existent paths and empty domains.
• #12556: Rich responses containing buttons with parentheses characters are now correctly parsed. Previously any characters found between the first identified pair of () in response button took precedence.

Improved Documentation#

• #12371: Update wording in Rasa Pro installation page.
• #12505: Document new PII Management section.
• #12527: Added Documentation for Realtime Markers Section.
• #12579: Add "Rasa Pro Change Log" to documentation.
• #12581: Document new Load Testing Guidelines section.
• #12597: Changes the formatting of realtime markers documentation page

[3.6.0] - 2023-06-14#

Rasa 3.6.0 (2023-06-14)

Deprecations and Removals#

• #12355: Removed Python 3.7 support as it reaches its end of life in June 2023

Improvements#

• #11222: Add optional property ids to the nlg server request body. IDs will be transmitted to the NLG server and can be used to identify the response variation that should be used.

• #11775: Add building Docker container for arm64 (e.g. to allow running Rasa inside docker on M1/M2).

• #12162: Add support for Location data from Whatsapp on Twilio Channel

• #12324: Add validation to rasa train to align validation expectations with rasa data validate. Add --skip-validation flag to disable validation and --fail-on-validation-warnings, --validation-max-history to rasa train to have the same options as rasa data validate.

• #12453: Updated tensorflow to version 2.11.1 for all platforms except Apple Silicon which stays on 2.11.0 as 2.11.1 is not available yet

• #12454: Slot mapping conditions accept active_loop specified as null in those cases when slots with this mapping condition should be filled only outside form contexts.

• #12487: Add an optional description key to the Markers Configuration format. This can be used to add documentation and context about marker's usage. For example, a markers.yml can look like

  Copy

  marker_name_provided:

  description: “Name slot has been set”

  slot_was_set: name

  marker_mood_expressed:

  description: “Unhappy or Great Mood was expressed”

  or:

  - intent: mood_unhappy

  - intent: mood_great

Bugfixes#

• #12467: Fix running custom form validation to update required slots at form activation when prefilled slots consist only of slots that are not requested by the form.

Improved Documentation#

• #12145: Explicitly set Node.js version to 12.x in order to run Docusaurus.
• #12160: Update obselete commands in docs README.
• #12168: Correct docker image name for deploy-rasa-pro-services in docs.
• #12169: Update Compatibility Matrix.
• #12266: Implement rasa data split stories to split stories data into train/test parts.
• #12362: Updated knowledge base action docs to reflect the improvements made in knowledge base actions in Rasa 3.6 version. This enhancement now allows users to query for the object attribute without the need for users to request a list of objects of a particular object type beforehand. The docs update mentions this under :::info New in 3.6 section.
• #12504: Fix dead link in Analytics documentation.

Miscellaneous internal changes#

• #12291, #12329, #12332, #12365, #12372, #12386, #12492

[3.5.12] - 2023-06-23#

Rasa 3.5.12 (2023-06-23)

Bugfixes#

• #12534: Rich responses containing buttons with parentheses characters are now correctly parsed. Previously any characters found between the first identified pair of () in response button took precedence.

Miscellaneous internal changes#

• #12512

[3.5.11] - 2023-06-08#

Rasa 3.5.11 (2023-06-08)

Bugfixes#

• #12467: Fix running custom form validation to update required slots at form activation when prefilled slots consist only of slots that are not requested by the form.

[3.5.10] - 2023-05-23#

Rasa 3.5.10 (2023-05-23)

Improved Documentation#

• #12437: Added documentation for spaces alpha

[3.5.9] - 2023-05-19#

Rasa 3.5.9 (2023-05-19)

No significant changes.

[3.5.8] - 2023-05-12#

Rasa 3.5.8 (2023-05-12)

Bugfixes#

• #12361: Explicitly handled BufferError exception - Local: Queue full in Kafka producer.

[3.5.7] - 2023-05-09#

Rasa 3.5.7 (2023-05-09)

Bugfixes#

• #12314: SlotSet events will be emitted when the value set by the custom action is the same as the existing value of the slot. This was fixed for AugmentedMemoizationPolicy to work properly with truncated trackers.

  To restore the previous behaviour, the custom action can return a SlotSet only if the slot value has changed. For example,

  Copy

  class CustomAction(Action):

  def name(self) -> Text:

  return "custom_action"

  def run(self, dispatcher: CollectingDispatcher,

  tracker: Tracker,

  domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:

  # current value of the slot

  slot_value = tracker.get_slot('my_slot')

  # value of the entity

  # this is parsed from the user utterance

  entity_value = next(tracker.get_latest_entity_values("entity_name"), None)

  if slot_value != entity_value:

  return[SlotSet("my_slot", entity_value)]

[3.5.6] - 2023-04-28#

Rasa 3.5.6 (2023-04-28)

Bugfixes#

• #12280: Addresses Regular Expression Denial of Service vulnerability in slack connector (https://owasp.org/www-community/attacks/Regular_expression_Denial_of_Service_-_ReDoS)
• #12325: Fix parsing of RabbitMQ URL provided in endpoints.yml file to include vhost path and query parameters. Re-allows inclusion of credentials in the URL as a regression fix (this was supported in 2.x).

[3.5.5] - 2023-04-20#

Rasa 3.5.5 (2023-04-20)

Bugfixes#

• #12268: Allow slot mapping parameter intent to accept a list of intent names (as strings), in addition to accepting an intent name as a single string.
• #12271: Fix BlockingIOError when running rasa shell on utterances with more than 5KB of text.
• #12286: Use ruamel.yaml round-trip loader in order to preserve all comments after appending assistant_id to config.yml.
• #12295: Fix AttributeError: 'NoneType' object has no attribute 'send_response' caused by retrieving tracker via GET /conversations/{conversation_id}/tracker endpoint when action_session_start is customized in a custom action. This was addressed by passing an instance of CollectingOutputChannel to the method retrieving the tracker from the MessageProcessor.

Improved Documentation#

• #12272: Updated AWS model loading documentation to indicate what should AWS_ENDPOINT_URL environment variable be set to. Added integration test for AWS model loading.
• #12279: Updated Rasa Pro Services documentation to add KAFKA_SSL_CA_LOCATION environment variable. Allows connections over SSL to Kafka
• #12290: Added note to CLI documentation to address encoding and color issues on certain Windows terminals

Miscellaneous internal changes#

• #12267

[3.5.4] - 2023-04-05#

Rasa 3.5.4 (2023-04-05)

Bugfixes#

• #12226: Fix issue with failures while publishing events to RabbitMQ after a RabbitMQ restart. The fix consists of pinning aio-pika dependency to 8.2.3, since this issue was introduced in aio-pika v8.2.4.
• #12232: Patch redis Race Conditiion vulnerability.

Miscellaneous internal changes#

• #12230, #12238

[3.5.3] - 2023-03-30#

Rasa 3.5.3 (2023-03-30)

Improved Documentation#

• #12209: Add new Rasa Pro page in docs, together with minimal content changes.

[3.5.2] - 2023-03-30#

Rasa 3.5.2 (2023-03-30)

Improvements#

• #12144: Add a self-reference of the synonym in the EntitySynonymMapper to handle entities extracted in a casing different to synonym case. (For example if a synonym austria is added, entities extracted with any alternate casing of the synonym will also be mapped to austria). It addresses ATO-616

Bugfixes#

• #12189: Make custom actions inheriting from rasa-sdk FormValidationAction parent class an exception of the selective_domain rule and always send them domain.
• #12193: Fix 2 issues detected with the HTTP API:
  • The GET /conversations/{conversation_id}/tracker endpoint was not returning the tracker with all sessions when include_events query parameter was set to ALL. The fix constituted in using TrackerStore.retrieve_full_tracker method instead of TrackerStore.retrieve method in the function handling the GET /conversations/{conversation_id}/tracker endpoint. Implemented or updated this method across all tracker store subclasses.
  • The GET /conversations/{conversation_id}/story endpoint was not returning all the stories for all sessions when all_sessions query parameter was set to true. The fix constituted in using all events of the tracker to be converted in stories instead of only the applied_events.

Improved Documentation#

• #12110: Add documentation for secrets managers.

[3.5.1] - 2023-03-24#

Rasa 3.5.1 (2023-03-24)

Bugfixes#

• #12174: Fixes training DIETCLassifier on the GPU.

  A deterministic GPU implementation of SparseTensorDenseMatmulOp is not currently available

Improved Documentation#

• #12127: Updated Test your assistant section to describe the new end-to-end testing feature. Also updated CLI and telemetry reference docs.
• #12169: Update Compatibility Matrix.

[3.5.0] - 2023-03-21#

Rasa 3.5.0 (2023-03-21)

Features#

• #12053: Add a new required key (assistant_id) to config.yml to uniquely identify assistants in deployment. The assistant identifier is extracted from the model metadata and added to the metadata of all dialogue events. Re-training will be required to include the assistant id in the event metadata.

  If the assistant identifier is missing from the config.yml or the default identifier value is not replaced, a random identifier is generated during each training.

  An assistant running without an identifier will issue a warning that dialogue events without identifier metadata will be streamed to the event broker.

Improvements#

• #11998: Add capability to send compressed body in HTTP request to action server. Use COMPRESS_ACTION_SERVER_REQUEST=True to turn the feature on.

Bugfixes#

• #12136: Address potentially missing events with Pika consumer due to weak references on asynchronous tasks, as specifcied in Python official documentation.
• #12155: Sets a global seed for numpy, TensorFlow, keras, Python and CuDNN, to ensure consistent random number generation.

Improved Documentation#

• #11893: Clarify in the docs, how rules are designed and how to use this behaviour to abort a rule

Miscellaneous internal changes#

• #11924, #11926, #12006, #12022, #12092, #12135, #12137, #12140, #12154

[3.4.14] - 2023-06-08#

Rasa 3.4.14 (2023-06-08)

Bugfixes#

• #12467: Fix running custom form validation to update required slots at form activation when prefilled slots consist only of slots that are not requested by the form.

[3.4.13] - 2023-05-19#

Rasa 3.4.13 (2023-05-19)

No significant changes.

[3.4.12] - 2023-05-12#

Rasa 3.4.12 (2023-05-12)

Bugfixes#

• #12361: Explicitly handled BufferError exception - Local: Queue full in Kafka producer.

[3.4.11] - 2023-05-09#

Rasa 3.4.11 (2023-05-09)

Bugfixes#

• #12325: Fix parsing of RabbitMQ URL provided in endpoints.yml file to include vhost path and query parameters. Re-allows inclusion of credentials in the URL as a regression fix (this was supported in 2.x).

• #12364: SlotSet events will be emitted when the value set by the custom action is the same as the existing value of the slot. This was fixed for AugmentedMemoizationPolicy to work properly with truncated trackers.

  To restore the previous behaviour, the custom action can return a SlotSet only if the slot value has changed. For example,

  Copy

  class CustomAction(Action):

  def name(self) -> Text:

  return "custom_action"

  def run(self, dispatcher: CollectingDispatcher,

  tracker: Tracker,

  domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:

  # current value of the slot

  slot_value = tracker.get_slot('my_slot')

  # value of the entity

  # this is parsed from the user utterance

  entity_value = next(tracker.get_latest_entity_values("entity_name"), None)

  if slot_value != entity_value:

  return[SlotSet("my_slot", entity_value)]

Miscellaneous internal changes#

• #12267

[3.4.10] - 2023-04-17#

Rasa 3.4.10 (2023-04-17)

Miscellaneous internal changes#

• #12255

[3.4.9] - 2023-04-05#

Miscellaneous internal changes#

• #12234

[3.4.8] - 2023-04-03#

Rasa 3.4.8 (2023-04-03)

Bugfixes#

• #12186: Fix issue with failures while publishing events to RabbitMQ after a RabbitMQ restart. The fix consists of pinning aio-pika dependency to 8.2.3, since this issue was introduced in aio-pika v8.2.4.

[3.4.7] - 2023-03-30#

Rasa 3.4.7 (2023-03-30)

Improvements#

• #12144: Add a self-reference of the synonym in the EntitySynonymMapper to handle entities extracted in a casing different to synonym case. (For example if a synonym austria is added, entities extracted with any alternate casing of the synonym will also be mapped to austria). It addresses ATO-616

Bugfixes#

• #12139: Fix 2 issues detected with the HTTP API:
  • The GET /conversations/{conversation_id}/tracker endpoint was not returning the tracker with all sessions when include_events query parameter was set to ALL. The fix constituted in using TrackerStore.retrieve_full_tracker method instead of TrackerStore.retrieve method in the function handling the GET /conversations/{conversation_id}/tracker endpoint. Implemented or updated this method across all tracker store subclasses.
  • The GET /conversations/{conversation_id}/story endpoint was not returning all the stories for all sessions when all_sessions query parameter was set to true. The fix constituted in using all events of the tracker to be converted in stories instead of only the applied_events.
• #12189: Make custom actions inheriting from rasa-sdk FormValidationAction parent class an exception of the selective_domain rule and always send them domain.

[3.4.6] - 2023-03-16#

Rasa 3.4.6 (2023-03-16)

Bugfixes#

• #12098: Fixes CountVectorFeaturizer to train when min_df != 1.

[3.4.5] - 2023-03-09#

Rasa 3.4.5 (2023-03-09)

Bugfixes#

• #12059: Check unresolved slots before initiating model training.
• #12096: Fixes the bug when a slot (with from_intent mapping which contains no input for intent parameter) will no longer fill for any intent that is not under the not_intent parameter.
• #12099: Fix validation metrics calculation when batch_size is dynamic.

Improved Documentation#

• #12062: Add a link to an existing docs section on how to test the audio channel on localhost.

[3.4.4] - 2023-02-17#

Rasa 3.4.4 (2023-02-17)

Improvements#

• #11997: Add capability to send compressed body in HTTP request to action server. Use COMPRESS_ACTION_SERVER_REQUEST=True to turn the feature on.

Bugfixes#

• #12052: Fix the error which resulted during merging multiple domain files where at least one of them contains custom actions that explicitly need send_domain set as True in the domain.

[3.4.3] - 2023-02-14#

Rasa 3.4.3 (2023-02-14)

Improvements#

• #12001: Add support for custom RulePolicy.
• #12013: Add capability to select which custom actions should receive domain when they are invoked.

Bugfixes#

• #11942: Fix calling the form validation action twice for the same user message triggering a form.
• #12033: Fix conditional response does not check other conditions if first condition matches.

Improved Documentation#

• #11976: Add section in tracker store docs to document the fallback tracker store mechanism.

[3.4.2] - 2023-01-27#

Rasa 3.4.2 (2023-01-27)

Bugfixes#

• #11926: Decision to publish docs should not consider next major and minor alpha release versions.

• #11968: Exit training/running Rasa model when SpaCy runtime version is not compatible with the specified SpaCy model version.

• #11971: The new custom logging feature was not working due to small syntax issue in the argparse level.

  Previously, the file name passed using the argument --logging-config-file was never retrieved so it never creates the new custom config file with the desired formatting.

Miscellaneous internal changes#

• #11924

[3.4.1] - 2023-01-19#

Rasa 3.4.1 (2023-01-19)

Bugfixes#

• #11923: Changed categorical slot comparison to be case insensitive.
• #11929: Exit training when transformer_size is not divisible by the number_of_attention_heads parameter and update the transformer documentations.

Improved Documentation#

• #11899: Update compatibility matrix between Rasa-plus and Rasa Pro services.

[3.4.0] - 2022-12-14#

Rasa 3.4.0 (2022-12-14)

Features#

• #11087: Add metadata to Websocket channel. Messages can now include a metadata object which will be included as metadata to Rasa. The metadata can be supplied on a user configurable key with the metadata_key setting in the socketio section of the credentials.yml.

Improvements#

• #11517: Added ./docker/Dockerfile_pretrained_embeddings_spacy_it to include Spacy's Italian pre-trained model it_core_news_md.
• #11765: Replace kafka-python dependency with confluent-kafka async Producer API.
• #11773: Add support for Python 3.10 version.
• #2546: Added CLI option --logging-config-file to enable configuration of custom logs formatting.

Bugfixes#

• #11869: Implements a new CLI option --jwt-private-key required to have complete support for asymmetric algorithms as specified originally in the docs.

Improved Documentation#

• #11766: Clarify in the documentation how to write testing stories if a user presses a button with payload.
• #11801: Clarify prioritisation of used slot asking option in forms in documentation.

Miscellaneous internal changes#

• #11742, #11800, #11817, #11877

[3.3.8] - 2023-04-06#

Miscellaneous internal changes#

• #12235

[3.3.7] - 2023-03-31#

Improvements#

• #12144: Add a self-reference of the synonym in the EntitySynonymMapper to handle entities extracted in a casing different to synonym case. (For example if a synonym austria is added, entities extracted with any alternate casing of the synonym will also be mapped to austria). It addresses ATO-616

Bugfixes#

• #12187: Fix issue with failures while publishing events to RabbitMQ after a RabbitMQ restart. The fix consists of pinning aio-pika dependency to 8.2.3, since this issue was introduced in aio-pika v8.2.4.
• #12192: Fix 2 issues detected with the HTTP API:
  • The GET /conversations/{conversation_id}/tracker endpoint was not returning the tracker with all sessions when include_events query parameter was set to ALL. The fix constituted in using TrackerStore.retrieve_full_tracker method instead of TrackerStore.retrieve method in the function handling the GET /conversations/{conversation_id}/tracker endpoint. Implemented or updated this method across all tracker store subclasses.
  • The GET /conversations/{conversation_id}/story endpoint was not returning all the stories for all sessions when all_sessions query parameter was set to true. The fix constituted in using all events of the tracker to be converted in stories instead of only the applied_events.

[3.3.6] - 2023-03-09#

Rasa 3.3.6 (2023-03-09)

Bugfixes#

• #12103: Fixes the bug when a slot (with from_intent mapping which contains no input for intent parameter) will no longer fill for any intent that is not under the not_intent parameter.
• #12117: Fix validation metrics calculation when batch_size is dynamic.

[3.3.5] - 2023-02-21#

No significant changes.

[3.3.4] - 2023-02-14#

Rasa 3.3.4 (2023-02-14)

Improvements#

• #11996: Add capability to send compressed body in HTTP request to action server. Use COMPRESS_ACTION_SERVER_REQUEST=True to turn the feature on.
• #12001: Add support for custom RulePolicy.

[3.3.3] - 2022-12-01#

Bugfixes#

• #11792: Bypass Windows path length restrictions upon saving and reading a model archive in rasa.engine.storage.LocalModelStorage.

Improvements#

• #11819: Updated tensorflow to 2.8.4.

[3.3.2] - 2022-11-30#

Improvements#

• #11762: Added support for camembert french bert model

Bugfixes#

• #11818: Fixes RuntimeWarning: coroutine 'Bot.set_webhook' was never awaited issue encountered when starting the rasa server, which caused the Telegram bot to be unresponsive.

Improved Documentation#

• #11768: The documentation was updated for Buttons using messages that start with '/'. Previously, it wrongly stated that messages with '/' bypass NLU, which is not the case.
• #11799: Add documentation for Rasa Pro Services upgrades.

[3.3.1] - 2022-11-09#

Improved Documentation#

• #11721: Add docs on how to set up additional data lakes for Rasa Pro analytics pipeline.
• #11724: Update migration guide and form docs with prescriptive recommendation on how to implement dynamic forms with custom slot mappings.

Improvements#

• #11732: Updated numpy and scikit learn version to fix vulnerabilities of those dependencies

[3.3.0] - 2022-10-24#

Improvements#

• #11561: Added option --offset-timestamps-by-seconds to offset the timestamp of events when using rasa export
• #11578: Rasa supports native installations on Apple Silicon (M1 / M2). Please follow the installation instructions and take a look at the limitations.
• #11596: Caching Message and Features fingerprints unless they are altered, saving up to 2/3 of fingerprinting time in our tests.
• #11598: Added package versions of component dependencies as an additional part of fingerprinting calculation. Upgrading an dependency will thus lead to a retraining of the component in the future. Also, by changing fingerprint calculation, the next training after this change will be a complete retraining.
• #11623: Export events continuously rather than loading all events in memory first when using rasa export. Events will be streamed right from the start rather than loading all events first and pushing them to the broker afterwards.
• #11568: Adds new dependency pluggy, with which it was possible to implement new plugin functionality. This plugin manager enables the extension and/or enhancement of the Rasa command line interface with functionality made available in the rasa-plus package.

Bugfixes#

• #11575: Fix BlockingIOError when running rasa interactive, after the upgrade of prompt-toolkit dependency.
• #11395: Fixes a bug that lead to initial slot values being incorporated into all rules by default, thus breaking most rules when the slot value changed from its initial value
• #11429: Made logistic regression classifier output a proper intent ranking and made ranking length configurable

Deprecations and Removals#

• #11190: Remove code related to Rasa X local mode as it is deprecated and scheduled for removal.

Miscellaneous internal changes#

• #11548

[3.2.13] - 2023-03-09#

Rasa 3.2.13 (2023-03-09)

Bugfixes#

• #12099: Fix validation metrics calculation when batch_size is dynamic.
• #12102: Fixes the bug when a slot (with from_intent mapping which contains no input for intent parameter) will no longer fill for any intent that is not under the not_intent parameter.

[3.2.12] - 2023-02-21#

Features#

• #11087: Add metadata to Websocket channel. Messages can now include a metadata object which will be included as metadata to Rasa. The metadata can be supplied on a user configurable key with the metadata_key setting in the socketio section of the credentials.yml.

Improvements#

• #11995: Add capability to send compressed body in HTTP request to action server. Use COMPRESS_ACTION_SERVER_REQUEST=True to turn the feature on.

Bugfixes#

• #11926: Decision to publish docs should not consider next major and minor alpha release versions.

[3.2.11] - 2022-12-05#

Improvements#

• #11596: Caching Message and Features fingerprints unless they are altered, saving up to 2/3 of fingerprinting time in our tests.

Bugfixes#

• #11829: Implements a new CLI option --jwt-private-key required to have complete support for asymmetric algorithms as specified originally in the docs.

[3.2.10] - 2022-09-29#

Bugfixes#

• #11588: Fixes scenarios in which a slot with from_trigger_intent mapping that specifies an active_loop condition was being filled despite that active_loop not being activated. In addition, fixes scenario in which a slot with from_trigger_intent mapping without a specified active_loop mapping condition is only filled if the form gets activated. Removes unnecessary validation warning that a slot with from_trigger_intent and a mapping condition should be included in the form's required_slots.
• #ato-161: Fixed a bug where DIETClassier crashed during training when both masked language modelling and evaluation during training were used.

Improved Documentation#

• #11571: Rasa SDK documentation lives now in Rasa Open Source documentation under the Rasa SDK category.

Miscellaneous internal changes#

• #11552

[3.2.9] - 2022-09-09#

Yanked.

[3.2.8] - 2022-09-08#

Bugfixes#

• #11540: Fix bug where KeywordIntentClassifier overrides preceding intent classifiers' predictions although the KeyWordIntentClassifier was not matching any keywords.

[3.2.7] - 2022-08-31#

Improvements#

• #11448: Improve rasa data validate command so that it uses custom importers when they are defined in config file.

Bugfixes#

• #11311: Re-instates the REST channel metadata feature. Metadata can be provided on the metadata key.

[3.2.6] - 2022-08-12#

Bugfixes#

• #11433: This fix makes sure that when a Domain object is loaded from multiple files where one file specifies a custom session config and the rest do not, the default session configuration does not override the custom session config.

Miscellaneous internal changes#

• #11426, #11434

[3.2.5] - 2022-08-05#

Bugfixes#

• #11294: Fix KeyError which resulted when action_two_stage_fallback got executed in a project whose domain contained slot mappings.
• #11390: Fixes regression in which slot mappings were prioritized according to reverse order as they were listed in the domain, instead of in order from first to last, as was implicitly expected in 2.x. Clarifies this implicit priority order in the docs.
• #11394: Enables the dispatching of bot messages returned as events by slot validation actions.

[3.2.4] - 2022-07-21#

Bugfixes#

• #11061: Added session_config key as valid domain key during domain loading from directory containing a separate domain file with session configuration.
• #11362: Run default action action_extract_slots after a custom action returns a UserUttered event to fill any applicable slots.
• #11368: Handle the case when an EndpointConfig object is given as parameter to the AwaitableTrackerStore.create() method.

[3.2.3] - 2022-07-18#

Bugfixes#

• #11305: - Fixed error in creating response when slack sends retry messages. Assigning None to response.text caused TypeError: Bad body type. Expected str, got NoneType.

  • Fixed Slack triggering timeout after 3 seconds if the action execution is too slow. Running on_new_message as an asyncio background task instead of a blocking await fixes this by immediately returning a response with code 200.

• #11326: Revert change in #10295 that removed running the form validation action on activation of the form before the loop is active.

• #11333: SlotSet events will be emitted when the value set by the current user turn is the same as the existing value.

  Previously, ActionExtractSlots would not emit any SlotSet events if the new value was the same as the existing one. This caused the augmented memoization policy to lose these slot values when truncating the tracker.

[3.2.2] - 2022-07-05#

Improved Documentation#

• #11207: Update documentation for customizable classes such as tracker stores, event brokers and lock stores.

Miscellaneous internal changes#

• #11296

[3.2.1] - 2022-06-17#

Bugfixes#

• #10430: Fix failed check in rasa data validate that verifies forms in rules or stories are consistent with the domain when the rule or story contains a default action as active_loop step.

[3.2.0] - 2022-06-14#

Deprecations and Removals#

• #10989: NLU training data in JSON format is deprecated and will be removed in Rasa Open Source 4.0. Please use rasa data convert nlu -f yaml --data <path to NLU data> to convert your NLU JSON data to YAML format before support for NLU JSON data is removed.

Improvements#

• #10696: Make TrackerStore interface methods asynchronous and supply an AwaitableTrackerstore wrapper for custom tracker stores which do not implement the methods as asynchronous.
• #10897: Added flag use_gpu to TEDPolicy and UnexpecTEDIntentPolicy that can be used to enable training on CPU even when a GPU is available.
• #11102: Add --endpoints command line parameter to rasa train parser.

Bugfixes#

• #11129: The azure botframework channel now validates the incoming JSON Web Tokens (including signature).

  Previously, JWTs were not validated at all.

• #9386: rasa shell now outputs custom json unicode characters instead of \uxxxx codes

Improved Documentation#

• #11130: Clarify aspects of the API spec GET /status endpoint: Correct response schema for model_id - a string, not an object.

  GET /conversations/{conversation_id}/tracker: Describe each of the enum options for include_events query parameter

  POST & PUT /conversations/{conversation_id}/tracker/eventss: Events schema added for each event type

  GET /conversations/{conversation_id}/story: Clarified the all_sessions query parameter and default behaviour.

  POST /model/test/intents : Remove JSON payload option since it is not supported

  POST /model/parse: Explain what emulation_mode is and how it affects response results

Miscellaneous internal changes#

• #11088, #11123, #9093, #9099

[3.1.7] - 2022-08-30#

Miscellaneous internal changes#

• #11522

[3.1.6] - 2022-07-20#

Bugfixes#

• #11362: Run default action action_extract_slots after a custom action returns a UserUttered event to fill any applicable slots.

[3.1.5] - 2022-07-15#

Bugfixes#

• #11333: SlotSet events will be emitted when the value set by the current user turn is the same as the existing value.

  Previously, ActionExtractSlots would not emit any SlotSet events if the new value was the same as the existing one. This caused the augmented memoization policy to lose these slot values when truncating the tracker.

[3.1.4] - 2022-06-21 No significant changes.#

Upgrade dependent libraries with security vulnerabilities (Pillow, TensorFlow, ujson).

[3.1.3] - 2022-06-17#

Bugfixes#

• #11129: The azure botframework channel now validates the incoming JSON Web Tokens (including signature).

  Previously, JWTs were not validated at all.

• #11197: Backports fix for failed check in rasa data validate that verifies forms in rules or stories are consistent with the domain when the rule or story contains a default action as active_loop step.

[3.1.2] - 2022-06-08#

Miscellaneous internal changes#

• #11156, #11173

[3.1.1] - 2022-06-03#

Bugfixes#

• #10480: Remove warning for Rasa X localmode not being supported when the --production flag is present.
• #10908: Pin requirement for scipy<1.8.0 since scipy>=1.8.0 is not backward compatible with scipy<1.8.0 and additionally requires Python>=3.8, while Rasa supports Python 3.7 as well.
• #11149: Fix the extraction of values for slots with mapping conditions from trigger intents that activate a form, which was possible in 2.x.

[3.1.0] - 2022-03-25#

Features#

• #10203: Add configuration options (via env variables) for library logging.

• #10473: Support other recipe types.

  This pull request also adds support for graph recipes, see details at https://rasa.com/docs/rasa/model-configuration and check Graph Recipe page.

  Graph recipe is a raw format for specifying executed graph directly. This is useful if you need a more powerful way to specify your model creation.

• #10545: Added optional ssl_keyfile, ssl_certfile, and ssl_ca_certs parameters to the Redis tracker store.

• #10650: Added LogisticRegressionClassifier to the NLU classifiers.

  This model is lightweight and might help in early prototyping. The training times typically decrease substantially, but the accuracy might be a bit lower too.

• #8762: Added support for Python 3.9.

Improvements#

• #10378: Bump TensorFlow version to 2.7.

  caution

  We can't guarantee the exact same output and hence model performance if your configuration uses LanguageModelFeaturizer. This applies to the case where the model is re-trained with the new rasa open source version without changing the configuration, random seeds, and data as well as to the case where a model trained with a previous version of rasa open source is loaded with this new version for inference.

  We suggest training a new model if you are upgrading to this version of Rasa Open Source.

• #10444: Make rasa data validate check for duplicated intents, forms, responses and slots when using domains split between multiple files.

• #10899: Add an influence_conversation flag to entites to provide a shorthand for ignoring an entity for all intents.

• #9789: Add --request-timeout command line argument to rasa shell, allowing users to configure the time a request can take before it's terminated.

Bugfixes#

• #10376: Validate regular expressions in nlu training data configuration.

• #10409: Unset the default values for num_threads and finetuning_epoch_fraction to None in order to fix cases when CLI defaults override the data from config.

• #10447: Update rasa data validate to not fail when active_loop is null

• #10570: Fixes Domain loading when domain config uses multiple yml files.

  Previously not all configures attributes were necessarily known when merging Domains, and in the case of entities were not being properly assigned to intents.

• #10606: Fix max_history truncation in AugmentedMemoizationPolicy to preserve the most recent UserUttered event. Previously, AugmentedMemoizationPolicy failed to predict next action after long sequences of actions (longer than max_history) because the policy did not have access to the most recent user message.

• #10634: Add RASA_ENVIRONMENT header in Kafka only if the environmental variable is set.

• #10767: Merge domain entities as lists of dicts, not lists of lists to support entity roles and groups across multiple domains.

• #9351: Add an option to specify --domain for rasa test nlu CLI command.

Improved Documentation#

• #10553: Fixed an over-indent in the Tokenizers section of the Components page of the docs.

Miscellaneous internal changes#

• #10143, #10507, #10568, #10601, #10658, #10746, #10807, #9094, #9096, #9097, #9098

[3.0.10] - 2022-03-15## [3.0.10] - 2022-03-15#

Bugfixes#

• #10675: Fix broken conversion from Rasa JSON NLU data to Rasa YAML NLU data.

[3.0.9] - 2022-03-11#

Bugfixes#

• #10412: Fix Socket IO connection issues by upgrading sanic to v21.12.

  The bug is caused by an invalid function signature and is fixed in v21.12.

  This update brings some deprecations in sanic:

  • Sanic and Blueprint may no longer have arbitrary properties attached to them
    • Fixed this by moving user defined properties to the instance.ctx object
  • Sanic and Blueprint forced to have compliant names
    • Fixed this by using string literal names instead of the module's name via __name__
  • sanic.exceptions.abort is Deprecated
    • Fixed by replacing it with sanic.exceptions.SanicException
  • sanic.response.StreamingHTTPResponse is deprecated
    • Fixed by replacing it with sanic.response.ResponseStream

• #10447: Update rasa data validate to not fail when active_loop is null

Improved Documentation#

• #10798: Updated the model_confidence parameter in TEDPolicy and DIETClassifier. The linear_norm is removed as it is no longer supported.
• #10940: Added an additional step to Receiving Messages section in slack.mdx documentation. After a slack update this additional step is needed to allow direct messages to the bot.
• #10957: Backport the updated deployment docs to 3.0.x.

[3.0.8] - 2022-02-11#

Improvements#

• #10394: Allow single tokens in rasa end-to-end test files to be annotated with multiple entities.

  Some entity extractors (s.a. RegexEntityExtractor) can generate multiple entities from a single expression. Before this change, rasa test would fail in this case, because test stories could not be annotated correctly. New annotation option is

  Copy

  stories:

  - story: Some story

  steps:

  - user: |

  cancel my [iphone][{"entity":"iphone", "value":"iphone"},{"entity":"smartphone", "value":"true"}{"entity":"mobile_service", "value":"true"}]

  intent: cancel_contract

Bugfixes#

• #10865: Fixed a bug where the POST /conversations/<conversation_id>/tracker/events endpoint repeated session start events when appending events to a new tracker.

[3.0.7] - 2022-02-09#

Bugfixes#

• #10516: Checkpoint weights were never loaded before. Implements overwriting checkpoint weights to the final model weights after training of DIETClassifier, ResponseSelector and TEDPolicy.
• #10782: Allow arbitrary keys under each slot in the domain to allow for custom slot types.
• #10840: Fix issue with missing running event loop in MainThread when starting Rasa Open Source for Rasa X with JWT secrets.

[3.0.6] - 2022-01-28#

Deprecations and Removals#

• #10590: Removed CompositionView.

Bugfixes#

• #10504: Fixes a bug which was caused by DIETClassifier (ResponseSelector, SklearnIntentClassifier and CRFEntityExtractor have the same issue) trying to process message which didn't have required features. Implements removing unfeaturized messages for the above-mentioned components before training and prediction.
• #10540: Enable slots with from_entity mapping that are not part of a form's required slots to be set during active loop.
• #10673: Catch ValueError for any port values that cannot be cast to integer and re-raise as RasaException during the initialisation of SQLTrackerStore.
• #10728: Use tf.function for model prediction to improve inference speed.
• #10761: Tie prompt-toolkit to ^2.0 to fix rasa-shell.

Improved Documentation#

• #10536: Update dynamic form behaviour docs section with an example on how to override required_slots in case of removal of a form required slot.

[3.0.5] - 2022-01-19#

Bugfixes#

• #10519: Corrects transformer_size parameter value (None by default) with a default size during loading in case ResponseSelector contains transformer layers.

Miscellaneous internal changes#

• #10385, #592

[3.0.4] - 2021-12-22#

Miscellaneous internal changes#

• #10572

[3.0.3] - 2021-12-16#

Bugfixes#

• #10448: Copy lookup tables to train and test folds in cross validation. Before, the generated folds did not have a copy of the lookup tables from the original NLU data, so that RegexEntityExtractor could not recognize any entities during the evaluation.
• #7645: Do not print warning when subintent actions have response.

Miscellaneous internal changes#

• #9945

[3.0.2] - 2021-12-09#

Bugfixes#

• #10374: Update SQLAlchemy version to a compatible one in case other dependencies force a lower version.
• #10391: Fix overriding of default config with custom config containing nested dictionaries. Before, the keys of a nested dictionary in the default config that were not specified in the custom config got lost.
• #10401: Add UserWarning to alert users trying to run rasa x CLI command with rasa version 3.0 or higher that rasa-x currently doesn't support rasa 3.x.

Improved Documentation#

• #10291: Added note to the slot mappings section of the migration guide to recommend checking dynamic form behavior on migrated assistants.

[3.0.1] - 2021-12-02#

Bugfixes#

• #10235: Fix previous slots getting filled after a restart. Previously events were searched from oldest to newest which meant we would find first occurrence of a message and use slots from thereafter. Now we use the last utterance or the restart event.

Miscellaneous internal changes#

• #10405

[3.0.0] - 2021-11-23#

Deprecations and Removals#

• #6487: Remove backwards compatibility code with Rasa Open Source 1.x, Rasa Enterprise 0.35, and other outdated backwards compatibility code in rasa.cli.x, rasa.core.utils, rasa.model_testing, rasa.model_training and rasa.shared.core.events.

• #8569: Removed Python 3.6 support as it reaches its end of life in December 2021.

• #8862: Follow through on removing deprecation warnings for synchronous EventBroker methods.

• #8864: Follow through on deprecation warnings for policies and policy ensembles.

• #8867: Follow through on deprecation warnings for rasa.shared.data.

• #8868: Follow through on deprecation warnings for the Domain. Most importantly this will enforce the schema of the forms section in the domain file. This further includes the removal of the UnfeaturizedSlot type.

• #8869: Remove deprecated change_form_to and set_form_validation methods from DialogueStateTracker.

• #8870: Remove the support of Markdown training data format. This includes:

  • reading and writing of story files in Markdown format
  • reading and writing of NLU data in Markdown format
  • reading and writing of retrieval intent data in Markdown format
  • all the Markdown examples and tests that use Markdown

• #8871: Removed automatic renaming of deprecated action action_deactivate_form to action_deactivate_loop. action_deactivate_form will just be treated like other non-existing actions from now on.

• #8872: Remove deprecated sorted_intent_examples method from TrainingData.

• #8873: Raising RasaException instead of deprecation warning when using class_from_module_path for loading types other than classes.

• #8874: Specifying the retrieve_events_from_previous_conversation_sessions kwarg for the any TrackerStore was deprecated and has now been removed. Please use the retrieve_full_tracker() method instead.

  Deserialization of pickled trackers was deprecated and has now been removed. Rasa will perform any future save operations of trackers using json serialisation.

  Removed catch for missing (deprecated) session_date when saving trackers in DynamoTrackerStore.

• #8879: Removed the deprecated dialogue policy state featurizers: BinarySingleStateFeature and LabelTokenizerSingleStateFeaturizer.

  Removed the deprecated method encode_all_actions of SingleStateFeaturizer. Use encode_all_labels instead.

• #8880: Follow through with removing deprecated policies: FormPolicy, MappingPolicy, FallbackPolicy, TwoStageFallbackPolicy, and SklearnPolicy.

  Remove warning about default value of max_history in MemoizationPolicy. The default value is now None.

• #8881: Follow through on deprecation warnings and remove code, tests, and docs for ConveRTTokenizer, LanguageModelTokenizer and HFTransformersNLP.

• #8883: rasa.shared.nlu.training_data.message.Message method get_combined_intent_response_key has been removed. get_full_intent should now be used in its place.

• #8974: Intent IDs sent with events (to kafka and elsewhere) have been removed, intent names can be used instead (or if numerical values are needed for backwards compatibility, one can also hash the names to get previous ID values, ie. hash(intent_name) is the old ID values). Intent IDs have been removed because they were providing no extra value and integers that large were problematic for some event broker implementations.

• #9236: Remove loop argument from train method in rasa. This argument became redundant when Python 3.6 support was dropped as asyncio.run became available in Python 3.7.

• #9390: Remove template_variables and e2e arguments from get_stories method of TrainingDataImporter. This argument was used in Markdown data format and became redundant once Markdown was removed.

• #9399: weight_sparsity has been removed. Developers should replace it with connection_density in the following way: connection_density = 1-weight_sparsity.

  softmax is not available as a loss_type anymore.

  The linear_norm option has been removed as possible value for model_confidence. Please, use softmax instead.

  minibatch has been removed as a value for tensorboard_log_level, use batch instead.

  Removed deprecation warnings related to the removed component config values.

• #9404: Follow through on removing deprecation warnings raised in these modules:

  • rasa/server.py

  • rasa/core/agent.py

  • rasa/core/actions/action.py

  • rasa/core/channels/mattermost.py

  • rasa/core/nlg/generator.py

  • rasa/nlu/registry.py

• #9432: Remove deprecation warnings associated with the "number_additional_patterns" parameter of rasa.nlu.featurizers.sparse_featurizer.regex_featurizer.RegexFeaturizer. This parameter is no longer needed for incremental training.

  Remove deprecation warnings associated with the "additional_vocabulary_size" parameter of rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer.CountVectorsFeaturizer. This parameter is no longer needed for incremental training.

  Remove deprecated functions training_states_actions_and_entities and training_states_and_actions from rasa.core.featurizers.tracker_featurizers.TrackerFeaturizer. Use training_states_labels_and_entities and training_states_and_labels instead.

• #9455: Follow through on deprecation warning for NGramFeaturizer

• #9598: The CLI commands rasa data convert config and rasa data convert responses which converted from the Rasa Open Source 1 to the Rasa Open Source 2 formats were removed. Please use a Rasa Open Source 2 installation to convert your training data before moving to Rasa Open Source 3.

• #9766: rasa.core.agent.Agent.visualize was removed. Please use rasa visualize or rasa.core.visualize.visualize instead.

• #9972: Removed slot auto-fill functionality, making the key invalid to use in the domain file. The auto_fill parameter was also removed from the constructor of the Slot class. In order to continue filling slots with entities of the same name, you now have to define a from_entity mapping in the slots section of the domain. To learn more about how to migrate your 2.0 assistant, please read the migration guide.

Features#

• #10150: Training data version upgraded from 2.0 to 3.0 due to breaking changes to format in Rasa Open Source 3.0

• #10170: A new experimental feature called Markers has been added. Markers allow you to define points of interest in conversations as a set of conditions that need to be met. A new command rasa evaluate markers allows you to apply these conditions to your existing tracker stores and outputs the points at which the conditions were satisfied.

• #9803: Rasa Open Source now uses the model configuration to build a

  directed acyclic graph. This graph describes the dependencies between the items in your model configuration and how data flows between them. This has two major benefits:

  • Rasa Open Source can use the computational graph to optimize the execution of your model. Examples for this are efficient caching of training steps or executing independent steps in parallel.
  • Rasa Open Source can represent different model architectures flexibly. As long as the graph remains acyclic Rasa Open Source can in theory pass any data to any graph component based on the model configuration without having to tie the underlying software architecture to the used model architecture.

  This change required changes to custom policies and custom NLU components. See the documentation for a detailed migration guide.

• #9972: Added explicit mechanism for slot filling that allows slots to be set and/or updated throughout the conversation. This mechanism is enabled by defining global slot mappings in the slots section of the domain file.

  In order to support this new functionality, implemented a new default action: action_extract_slots. This new action runs after each user turn and checks if any slots can be filled with information extracted from the last user message based on defined slot mappings.

  Since slot mappings were moved away from the forms section of the domain file, converted the form's required_slots to a list of slot names. In order to restrict certain mappings to a form, you can now use the conditions key in the mapping to define the applicable active_loop, like so:

  Copy

  slots:

  location:

  type: text

  influence_conversation: false

  mappings:

  - type: from_entity

  entity: city

  conditions:

  - active_loop: booking_form

  To learn more about how to migrate your 2.0 assistant, please read the migration guide.

Improvements#

• #10189: Updated the /status endpoint response payload, and relevant documentation, to return/reflect the updated 3.0 keys/values.

• #7619: Bump TensorFlow version to 2.6.

  This update brings some security benefits (see TensorFlow release notes for details). However, internal experiments suggest that it is also associated with increased train and inference time, as well as increased memory usage.

  You can read more about why we decided to update TensorFlow, and what the expected impact is here.

  If you experience a significant increase in train time, inference time, and/or memory usage, please let us know in the forum.

  Users can no longer set TF_DETERMINISTIC_OPS=1 if they are using GPU(s) because a tf.errors.UnimplementedError will be thrown by TensorFlow (read more here).

  :::caution This breaks backward compatibility of previously trained models. It is not possible to load models trained with previous versions of Rasa Open Source. Please re-train your assistant before trying to use this version.

• #8057: Added authentication support for connecting to external RabbitMQ servers. Currently user has to hardcode a username and a password in a URL in order to connect to an external RabbitMQ server.

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

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

  Let's take this example story:

  Copy

  - story: test story

  steps:

  - user: |

  what is your name?

  intent: chitchat/ask_name

  - action: utter_chitchat/ask_name

  - intent: bye

  - action: utter_bye

  Before:

  Copy

  steps:

  - intent: chitchat # 1) intent is not displayed in it's original form

  - action: utter_chitchat/ask_name # predicted: utter_chitchat

  # 2) retrieval intent is not extracted during action prediction and we have a mismatch

  - intent: bye # some other fail

  - action: utter_bye # some other fail

  Both 1) and 2) problems are solved.

  Now:

  Copy

  steps:

  - intent: chitchat/ask_name

  - action: utter_chitchat/ask_name

  - intent: bye # some other fail

  - action: utter_bye # some other fail

• #8469: Added -i command line option to make RASA listen on a specific ip-address instead of any network interface

• #8760: rasa data validate now checks that forms referenced in active_loop directives are defined in the domain

• #8914: Every conversation event now includes in its metadata the ID of the model which loaded at the time it was created.

• #8924: Send indices of user message tokens along with the UserUttered event through the event broker to Rasa X.

• #8929: Added optional flag to convert intent ID hashes from integer to string in the KafkaEventBroker.

• #8933: Make it possible to use or functionality for slot_was_set events.

• #9068: Upgraded the spaCy dependency from version 3.0 to 3.1.

• #9133: Implemented fingerprint methods in these classes:

  • Event
  • Slot
  • DialogueStateTracker

• #9193: Added debug message that logs when a response condition is used.

• #9485: The naming scheme for trained models was changed. Unless you provide a --fixed-model-name to rasa train, Rasa Open Source will now generate a new model name using the schema <timestamp>-<random name>.tar.gz, e.g.

  • 20211018-094821-composite-pita.tar.gz (for a model containing a trained NLU and dialogue model)
  • nlu-20211018-094821-composite-pita.tar.gz (for a model containing only a trained NLU model but not a dialogue model)
  • core-20211018-094821-composite-pita.tar.gz (for a model containing only a trained dialogue model but no NLU model)

• #9604: Due to changes in the model architecture the behavior of rasa train --dry-run changed. The exit codes now have the following meaning:

  • 0 means that the model does not require an expensive retraining. However, the responses might still require updating by running rasa train
  • 1 means that one or multiple components require to be retrained.
  • 8 means that the --force flag was used and hence any cached results are ignored and the entire model is retrained.

• #9642: Machine learning components like DIETClassifier, ResponseSelector and TEDPolicy using a ranking_length parameter will no longer report renormalised confidences for the top predictions by default.

  A new parameter renormalize_confidences is added to these components which if set to True, renormalizes the confidences of top ranking_length number of predictions to sum up to 1. The default value is False, which means no renormalization will be applied by default. It is advised to leave it to False but if you are trying to reproduce the results from previous versions of Rasa Open Source, you can set it to True.

  Renormalization will only be applied if model_confidence=softmax is used.

Bugfixes#

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

Improved Documentation#

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

Miscellaneous internal changes#

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

[2.8.16] - 2021-12-09#

Improvements#

• #10413: The value of the RASA_ENVIRONMENT environmental variable is sent as a header in messages logged by KafkaEventBroker. This value was previously only made available by PikaEventConsumer.

Bugfixes#

• #10458: Make action_metadata json serializable and make it available on the tracker. This is a backport of a fix in 3.0.0.

[2.8.15] - 2021-11-25#

Bugfixes#

• #10381: Validate regular expressions in nlu training data configuration.

[2.8.14] - 2021-11-18#

Bugfixes#

• #10241: Bump TensorFlow version to 2.6.2. We have plans to port this change to 3.x (see this issue).
• #10257: Downgrade google-auth to <2.

[2.8.13] - 2021-11-11#

Bugfixes#

• #9949: Fixed new intent creation in rasa interactive command. Previously, this failed with 500 from the server due to UnexpecTEDIntentPolicy trying to predict with the new intent not in domain.
• #9982: Install mitie library when preparing test runs. This step was missing before and tests were thus failing as we have many tests which rely on mitie library. Previously, make install-full was required.

Miscellaneous internal changes#

• #10146, #9989

[2.8.12] - 2021-10-21#

Bugfixes#

• #9771: Fixed a bug where rasa test --fail-on-prediction-errors would raise a WrongPredictionException for entities which were actually predicted correctly.

  This happened in two ways:

  1. if for a user message some entities were extracted multiple times (by multiple entity extractors) but listed only once in the test story,
  2. if the order in which entities from a message were extracted didn't match the order in which they were listed in the test story.

Improved Documentation#

• #9691: Improve the documentation for training TEDPolicy with data augmentation.

[2.8.11] - 2021-10-20#

Bugfixes#

• #9858: Updates dependency on sanic-jwt (1.5.0 -> ">=1.6.0, <1.7.0")

  This removes the need to pin the version of pyjwt as the newer version of sanic-jwt manages this properly.

[2.8.10] - 2021-10-14#

Bugfixes#

• #5657: Add List handling in the send_custom_json method on channels/facebook.py. Bellow are some examples that could cause en error before.

  Example 1: when the whole json is a List

  Copy

  [

  {

  "blocks": {

  "type": "progression_bar",

  "text": {"text": "progression 1", "level": "1"},

  }

  },

  {"sender": {"id": "example_id"}},

  ]

  Example 2: instead of being a Dict, blocks is a List when there are 2 type keys under it

  Copy

  {

  "blocks": [

  {"type": "title", "text": {"text": "Conversation progress"}},

  {

  "type": "progression_bar",

  "text": {"text": "Look how far we are...", "level": "1"},

  },

  ]

  }

• #7676: Fixed bug when using wit.ai training data to train. Training failed with an error similarly to this:

  Copy

  File "./venv/lib/python3.8/site-packages/rasa/nlu/classifiers/diet_classifier.py", line 803, in train

  self.check_correct_entity_annotations(training_data)

  File "./venv/lib/python3.8/site-packages/rasa/nlu/extractors/extractor.py", line 418, in check_correct_entity_annotations

  entities_repr = [

  File "./venv/lib/python3.8/site-packages/rasa/nlu/extractors/extractor.py", line 422, in <listcomp>

  entity[ENTITY_ATTRIBUTE_VALUE],

  KeyError: 'value'

• #9851: Fix CVE-2021-41127

[2.8.9] - 2021-10-08#

Improvements#

• #7619: Bump TensorFlow version to 2.6.

  This update brings some security benefits (see TensorFlow release notes for details). However, internal experiments suggest that it is also associated with increased train and inference time, as well as increased memory usage.

  You can read more about why we decided to update TensorFlow, and what the expected impact is here.

  If you experience a significant increase in train time, inference time, and/or memory usage, please let us know in the forum.

  Users can no longer set TF_DETERMINISTIC_OPS=1 if they are using GPU(s) because a tf.errors.UnimplementedError will be thrown by TensorFlow (read more here).

  caution

  This breaks backward compatibility of previously trained models. It is not possible to load models trained with previous versions of Rasa Open Source. Please re-train your assistant before trying to use this version.

[2.8.8] - 2021-10-06#

Improvements#

• #7250: Added a function to display the actual text of a Token when inspecting a Message in a pipeline, making it easier to debug.

Improved Documentation#

• #9780: Removing the experimental feature warning for conditional response variations from the Rasa docs. The behaviour of the feature remains unchanged.
• #9782: Updates quick install documentation with optional venv step, better pip install instructions, & M1 warning

[2.8.7] - 2021-09-20#

Bugfixes#

• #9678: Explicitly set the upper limit for currently compatible TensorFlow versions.

[2.8.6] - 2021-09-09#

Bugfixes#

• #9302: Fix rules not being applied when a featurised categorical slot has as one of its allowed values none, NoNe, None or a similar value.

[2.8.5] - 2021-09-06#

Bugfixes#

• #9476: AugmentedMemoizationPolicy is accelerated for large trackers
• #9542: Bump tensorflow to 2.3.4 to address security vulnerabilities

[2.8.4] - 2021-09-02#

Improvements#

• #5546: Increase speed of augmented lookup for AugmentedMemoizationPolicy

Bugfixes#

• #7362: Fix --data being treated as if non-optional on sub-commands of rasa data convert
• #9490: Fixes bug where hide_rule_turn was defaulting to None when ActionExecuted was deserialised.

Miscellaneous internal changes#

• #8682

[2.8.3] - 2021-08-19#

Bugfixes#

• #7695: Ignore checking that intent is in domain for E2E story utterances when running rasa data validate. Previously data validation would fail on E2E stories.

[2.8.2] - 2021-08-04#

Bugfixes#

• #9203: Fixes a bug which caused training of UnexpecTEDIntentPolicy to crash when end-to-end training stories were included in the training data.

  Stories with end-to-end training data will now be skipped for the training of UnexpecTEDIntentPolicy.

Improved Documentation#

• #8024: Removing the experimental feature warning for the story validation tool from the rasa docs. The behaviour of the feature remains unchanged.
• #8791: Removing the experimental feature warning for entity roles and groups from the rasa docs, and from the code where it previously appeared as a print statement. The behaviour of the feature remains otherwise unchanged.

[2.8.1] - 2021-07-22#

Improvements#

• #9085: Add support for cafile parameter in endpoints.yaml. This will load a custom local certificate file and use it when making requests to that endpoint.

  For example:

  Copy

  action_endpoint:

  url: https://localhost:5055/webhook

  cafile: ./cert.pem

  This means that requests to the action server localhost:5055 will use the certificate cert.pem located in the current working directory.

Bugfixes#

• #9182: Fixes wrong overriding of epochs parameter when TEDPolicy or UnexpecTEDIntentPolicy is not loaded in finetune mode.

[2.8.0] - 2021-07-12#

Deprecations and Removals#

• #9045: The option model_confidence=linear_norm is deprecated and will be removed in Rasa Open Source 3.0.0.

  Rasa Open Source 2.3.0 introduced linear_norm as a possible value for model_confidence parameter in machine learning components such as DIETClassifier, ResponseSelector and TEDPolicy. Based on user feedback, we have identified multiple problems with this option. Therefore, model_confidence=linear_norm is now deprecated and will be removed in Rasa Open Source 3.0.0. If you were using model_confidence=linear_norm for any of the mentioned components, we recommend to revert it back to model_confidence=softmax and re-train the assistant. After re-training, we also recommend to re-tune the thresholds for fallback components.

• #9091: The fallback mechanism for spaCy models has now been removed in Rasa 3.0.0.

  Rasa Open Source 2.5.0 introduced support for spaCy 3.0. This introduced a breaking feature because models would no longer be manually linked. To make the transition smooth Rasa would rely on the language parameter in the config.yml to fallback to a medium spaCy model if no model was configured for the SpacyNLP component. In Rasa Open Source 3.0.0 and onwards the SpacyNLP component will require the model name (like "en_core_web_md") to be passed explicitly.

Features#

• #8724: Added sasl_mechanism as an optional configurable parameters for the Kafka Producer.

• #8913: Introduces a new policy called UnexpecTEDIntentPolicy.

  UnexpecTEDIntentPolicy helps you review conversations and also allows your bot to react to unexpected user turns in conversations. It is an auxiliary policy that should only be used in conjunction with at least one other policy, as the only action that it can trigger is the special and newly introduced action_unlikely_intent action.

  The auto-configuration will include UnexpecTEDIntentPolicy in your configuration automatically, but you can also include it yourself in the policies section of the configuration:

  Copy

  policies:

  - name: UnexpecTEDIntentPolicy

  epochs: 200

  max_history: 5

  As part of the feature, it also introduces:

  • IntentMaxHistoryTrackerFeaturizer to featurize the trackers for UnexpecTEDIntentPolicy.
  • MultiLabelDotProductLoss to support UnexpecTEDIntentPolicy's multi-label training objective.
  • A new default action called action_unlikely_intent.

rasa test command has also been adapted to UnexpecTEDIntentPolicy:

• If a test story contains action_unlikely_intent and the policy ensemble does not trigger it, this leads to a test error (wrongly predicted action) and the corresponding story will be logged in failed_test_stories.yml.
• If the story does not contain action_unlikely_intent and Rasa Open Source does predict it then the prediction of action_unlikely_intent will be ignored for the evaluation (and hence not lead to a prediction error) but the story will be logged in a file called stories_with_warnings.yml.

The rasa data validate command will warn if action_unlikely_intent is included in the training stories. Accordingly, YAMLStoryWriter and MarkdownStoryWriter have been updated to not dump action_unlikely_intent when writing stories to a file.

:::caution The introduction of a new default action breaks backward compatibility of previously trained models. It is not possible to load models trained with previous versions of Rasa Open Source. Please re-train your assistant before trying to use this version.

:::

Improvements#

• #8127: Added detailed json schema validation for UserUttered, SlotSet, ActionExecuted and EntitiesAdded events both sent and received from the action server, as well as covered at high-level the validation of the rest of the 20 events. In case the events are invalid, a ValidationError will be raised.

• #8232: Users don't need to specify an additional buffer size for sparse featurizers anymore during incremental training.

  Space for new sparse features are created dynamically inside the downstream machine learning models - DIETClassifier, ResponseSelector. In other words, no extra buffer is created in advance for additional vocabulary items and space will be dynamically allocated for them inside the model.

  This means there's no need to specify additional_vocabulary_size for CountVectorsFeaturizer or number_additional_patterns for RegexFeaturizer. These parameters are now deprecated.

  Before

  Copy

  pipeline:

  - name: "WhitespaceTokenizer"

  - name: "RegexFeaturizer"

  number_additional_patterns: 100

  - name: "CountVectorsFeaturizer"

  additional_vocabulary_size: {text: 100, response: 20}

  Now

  Copy

  pipeline:

  - name: "WhitespaceTokenizer"

  - name: "RegexFeaturizer"

  - name: "CountVectorsFeaturizer"

  Also, all custom layers specifically built for machine learning models - RasaSequenceLayer, RasaFeatureCombiningLayer and ConcatenateSparseDenseFeatures now inherit from RasaCustomLayer so that they support flexible incremental training out of the box.

• #8295: Speed up the contradiction check of the RulePolicy by a factor of 3.

• #8801: Change the confidence score assigned by FallbackClassifier to fallback intent to be the same as the fallback threshold.

• #8926: Issue a UserWarning if a specified domain folder contains files that look like YML files but cannot be parsed successfully. Only invoked if user specifies a folder path in --domain paramater. Previously those invalid files in the specified folder were silently ignored. Does not apply to individually specified domain YAML files, e.g. --domain /some/path/domain.yml, those being invalid will still raise an exception.

Bugfixes#

• #8711: Fix for unnecessary retrain and duplication of folders in the model

Miscellaneous internal changes#

• #8241, #8525, #8694, #8704

[2.7.2] - 2021-08-09#

Bugfixes#

• #7695: Ignore checking that intent is in domain for E2E story utterances when running rasa data validate. Previously data validation would fail on E2E stories.
• #8711: Fix for unnecessary retrain and duplication of folders in the model

[2.7.1] - 2021-06-16#

Bugfixes#

• #7286: Best model checkpoint allows for metrics to be equal to previous best if at least one metric improves, rather than strict improvement for each metric.

• #8200: Fixes a bug where multiple plots overlap each other and are rendered incorrectly when comparing performance across multiple NLU pipelines.

• #8812: Don't evaluate entities if no entities present in test data.

  Also, catch exception in plot_paired_histogram when data is empty.

[2.7.0] - 2021-06-03#

Improvements#

• #7691: Changed the default config to train the RulePolicy before the TEDPolicy. This means that conflicting rule/stories will be identified before a potentially slow training of the TEDPolicy.
• #7799: Updated validator used by rasa data validate to verify that actions used in stories and rules are present in the domain and that form slots match domain slots.
• #7912: Rename plot_histogram to plot_paired_histogram and fix missing bars in the plot.
• #8225: Changed --data option type in the ``rasa data validate``` command to allow more than one path to be passed.

Bugfixes#

• #8152: The file failed_test_stories.yml (generated by rasa test) now also includes the wrongly predicted entity as a comment next to the entity of a user utterance. Additionally, the comment printed next to the intent of a user utterance is printed only if the intent was wrongly predicted (irrelevantly if there was a wrongly predicted entity or not in the specific user utterance).
• #8309: Added check in PikaEventBroker constructor: if port cannot be cast to integer, raise RasaException
• #8388: Fixed bug where missing intent warnings appear when running rasa test
• #8611: Update should_retrain function to return the correct fingerprint comparison result even when there is a problem with model unpacking.
• #8719: Handle correctly Telegram edited message.

Miscellaneous internal changes#

• #8591, #8641, #8654, #8658, #8802

[2.6.3] - 2021-05-28#

Bugfixes#

• #8046: ResponseSelector can now be trained with the transformer enabled (i.e. when a positive number_of_transformer_layers is provided) even if one doesn't specify the transformer's size. Previously, not specifying transformer_size led to an error.
• #8386: Return EntityEvaluationResult during evaluation of test stories only if parsed_message is not None.
• #8546: Ignore OSError in Sentry reporting.
• #8547: Replaced ValueError with RasaException in TED model _check_data method.
• #8639: Changed import to fix agent creation in Jupyter.

Miscellaneous internal changes#

• #7906, #8544, #8725, #8726, #8727, #8728

[2.6.2] - 2021-05-18#

Bugfixes#

• #8364: Fixed a bug where ListSlots were filled with single items in case only one matching entity was extracted for this slot.

  Values applied to ListSlots will be converted to a List in case they aren't one.

• #8581: Fix bug with false rule conflicts

  This essentially reverts PR 8446, except for the tests. The PR is redundant due to PR 8646.

• #8590: Handle AttributeError thrown by empty slot mappings in domain form through refactoring.

• #8631: Fixed incorrect The action 'utter_<response selector intent>' is used in the stories, but is not a valid utterance action error when running rasa data validate with response selector responses in the domain file.

Improved Documentation#

• #8079: Added a note to clarify best practice for resetting all slots after form deactivation.

Miscellaneous internal changes#

• #8587

[2.6.1] - 2021-05-11#

Bugfixes#

• #7908: Made SchemaError message available to validator so that the reason why reason schema validation fails during rasa data validate is displayed when response text value is null. Added warning message when deprecated MappingPolicy format is used in the domain.

• #8623: When there are multiple entities in a user message, they will get sorted when creating a representation of the current dialogue state.

  Previously, the ordering was random, leading to inconsistent state representations. This would sometimes lead to memoization policies failing to recall a memorised action.

[2.6.0] - 2021-05-06#

Deprecations and Removals#

• #261: In forms, the keyword required_slots should always precede the definition of slot mappings and the lack of it is deprecated. Please see the migration guide for more information.
• #8428: rasa.data.get_test_directory, rasa.data.get_core_nlu_directories, and rasa.shared.nlu.training_data.training_data.TrainingData::get_core_nlu_directories are deprecated and will be removed in Rasa Open Source 3.0.0.
• #8498: Update the minimum compatible model version to "2.6.0". This means all models trained with an earlier version will have to be retrained.

Features#

• #8103: Feature enhancement enabling JWT authentication for the Socket.IO channel. Users can define jwt_key and jwt_method as parameters in their credentials file for authentication.

• #8180: Allows a Rasa bot to be connected to a Twilio Voice channel. More details in the Twilio Voice docs

• #8532: Conditional response variations are supported in the domain.yml without requiring users to write custom actions code.

  A condition can be a list of slot-value mapping constraints.

Improvements#

• #261: Added an optional ignored_intents parameter in forms.

  • To use it, add the ignored_intents parameter in your domain.yml file after the forms name and provide a list of intents to ignore. Please see Forms for more information.
  • This can be used in case the user never wants to fill any slots of a form with the specified intent, e.g. chitchat.

• #5786: Add function to carry max_history to featurizer

• #7589: Improved the machine learning models' codebase by factoring out shared feature-processing logic into three custom layer classes:

  • ConcatenateSparseDenseFeatures combines multiple sparse and dense feature tensors into one.
  • RasaFeatureCombiningLayer additionally combines sequence-level and sentence-level features.
  • RasaSequenceLayer is used for attributes with sequence-level features; it additionally embeds the combined features with a transformer and facilitates masked language modeling.

• #7685: Added the following usability improvements with respect to entities getting extracted multiple times:

  • Added warnings for competing entity extractors at training time and for overlapping entities at inference time
  • Improved docs to help users handle overlapping entity problems.

• #7999: Replace weight_sparsity with connection_density in all transformer-based models and add guarantees about internal layers.

  We rename DenseWithSparseWeights into RandomlyConnectedDense, and guarantee that even at density zero the output is dense and every input is connected to at least one output. The former weight_sparsity parameter of DIET, TED, and the ResponseSelector, is now roughly equivalent to 1 - connection_density, except at very low densities (high sparsities).

  All layers and components that used to have a sparsity argument (Ffnn, TransformerRasaModel, MultiHeadAttention, TransformerEncoderLayer, TransformerEncoder) now have a density argument instead.

• #8074: Rasa test now prints a warning if the test stories contain bot utterances that are not part of the domain.

• #8263: Updated asyncio.Task.all_tasks to asyncio.all_tasks, with a fallback for python 3.6, which raises an AttributeError for asyncio.all_tasks. This removes the deprecation warning for the Task.all_tasks usage.

• #8461: Change variable name from i to array_2D

• #8560: Implement a new interface run_inference inside RasaModel which performs batch inferencing through tensorflow models.

  rasa_predict inside RasaModel has been made a private method now by changing it to _rasa_predict.

Bugfixes#

• #7005: Fixed a bug for plotting trackers with non-ascii texts during interactive training by enforcing utf-8 encoding
• #7589: Fix masked language modeling in DIET to only apply masking to token-level (sequence-level) features. Previously, masking was applied to both token-level and sentence-level features.
• #8300: Make it possible to use null entities in stories.
• #8333: Introduce a skip_validation flag in order to speed up reading YAML files that were already validated.
• #8341: Fixed a bug in interactive training that lead to crashes for long Chinese, Japanese, or Korean user or bot utterances.

[2.5.2] - 2021-06-16#

Features#

• #8892: Added sasl_mechanism as an optional configurable parameters for the Kafka Producer.

[2.5.1] - 2021-04-28#

Bugfixes#

• #8446: Fixed prediction for rules with multiple entities.
• #8545: Mitigated Matplotlib backend issue using lazy configuration and added a more explicit error message to guide users.

[2.5.0] - 2021-04-12#

Deprecations and Removals#

• #8141: The following import abbreviations were removed:
  • rasa.core.train: Please use rasa.core.train.train instead.
  • rasa.core.visualize: Please use rasa.core.visualize.visualize instead.
  • rasa.nlu.train: Please use rasa.nlu.train.train instead.
  • rasa.nlu.test: Please use rasa.nlu.test.run_evaluation instead.
  • rasa.nlu.cross_validate: Please use rasa.nlu.test.cross_validate instead.

Features#

• #7869: Upgraded Rasa to be compatible with spaCy 3.0.

  This means that we can support more features for more languages but there are also a few changes.

  SpaCy 3.0 deprecated the spacy link <language model> command so that means that from now on the full model name needs to be used in the config.yml file.

  Before

  Before you could run spacy link en en_core_web_md and then we would be able to pick up the correct model from the language parameter.

  Copy

  language: en

  pipeline:

  - name: SpacyNLP

  Now

  This behavior will be deprecated and instead you'll want to be explicit in config.yml.

  Copy

  language: en

  pipeline:

  - name: SpacyNLP

  model: en_core_web_md

  Fallback

  To make the transition easier, Rasa will try to fall back to a medium spaCy model when-ever a compatible language is configured for the entire pipeline in config.yml even if you don't specify a model. This fallback behavior is temporary and will be deprecated in Rasa 3.0.0.

  We've updated our docs to reflect these changes. All examples now show a direct link to the correct spaCy model. We've also added a warning to the SpaCyNLP docs that explains the fallback behavior.

Improvements#

• #4280: Improved CLI startup time.

• #4596: Add augmentation and num_threads arguments to API POST /model/train

  Fix boolean casting issue for force_training and save_to_default_model_directory arguments

• #7477: Add minimum compatible version to --version command

• #7660: Updated warning for unexpected slot events during prediction time to Rasa Open Source 2.0 YAML training data format.

• #7701: Hide dialogue turns predicted by RulePolicy in the tracker states for ML-only policies like TEDPolicy if those dialogue turns only appear as rules in the training data and do not appear in stories.

  Add set_shared_policy_states(...) method to all policies. This method sets _rule_only_data dict with keys:

  • rule_only_slots: Slot names, which only occur in rules but not in stories.
  • rule_only_loops: Loop names, which only occur in rules but not in stories.

  This information is needed for correct featurization to hide dialogue turns that appear only in rules.

• #8208: Faster reading of YAML NLU training data files.

• #8335: Added partition_by_sender flag to Kafka Producer to optionally associate events with Kafka partition based on sender_id.

Bugfixes#

• #7260: Fixed the 'loading model' message which was logged twice when using rasa run.

• #7379: Change training data validation to only count nlu training examples.

• #7450: Rule tracker states no longer include the initial value of slots. Rules now only require slot values when explicitly stated in the rule.

• #7640: rasa test, rasa test core and rasa test nlu no longer show temporary paths in case there are issues in the test files.

• #7690: Resolved memory problems with dense features and CRFEntityExtractor

• #7916: Handle empty intent and entity mapping in the domain.

  There is now an InvalidDomain exception raised if in the domain.yml file there are empty intent or entity mappings. An example of empty intent and entity mappings is the following :

  Copy

  intents:

  - greet:

  - goodbye:

  entities:

  - cuisine:

  - number:

• #8102: Fixed a bug in a form where slot mapping doesn't work if the predicted intent name is substring for another intent name.

• #8114: Fixes bug where stories could not be retrieved if entities had no start or end.

• #8178: Catch ChannelNotFoundEntity exception coming from the pika broker and raise as ConnectionException.

• #8337: Fix bug with NoReturn throwing an exception in Python 3.7.0 when running rasa train

• #8382: Throw RasaException instead of ValueError in situations when environment variables specified in YAML cannot be expanded.

• #8343: Updated python-engineio version for compatibility with python-socketio

Miscellaneous internal changes#

• #6511, #7640, #7827, #8056, #8117, #8141, #8240

[2.4.3] - 2021-03-26#

Bugfixes#

• #8114: Fixes bug where stories could not be retrieved if entities had no start or end.

[2.4.2] - 2021-03-25#

Bugfixes#

• #7835: Fix UnicodeException in is_key_in_yaml.
• #8258: Fixed the bug that events from previous conversation sessions would be re-saved in the SQLTrackerStore or MongoTrackerStore when retrieve_events_from_previous_conversation_sessions was true.

[2.4.1] - 2021-03-23#

Bugfixes#

• #8194: Fix TEDPolicy training e2e entities when no entities are present in the stories but there are entities in the domain.
• #8198: Fixed missing model configuration file validation.
• #8223: In Rasa 2.4.0, support for using template in utter_message when handling a custom action was wrongly deprecated. Both template and response are now supported, though note that template will be deprecated at Rasa 3.0.0.

[2.4.0] - 2021-03-11#

Deprecations and Removals#

• #6484: NLG Server

  • Changed request format to send response as well as template as a field. The template field will be removed in Rasa Open Source 3.0.0.

  rasa.core.agent

  • The terminology template is deprecated and replaced by response. Support for template from the NLG response will be removed in Rasa Open Source 3.0.0. Please see here for more details.

  rasa.core.nlg.generator

  • generate() now takes in utter_action as a parameter.
  • The terminology template is deprecated and replaced by response. Support for template in the NaturalLanguageGenerator will be removed in Rasa Open Source 3.0.0.

  rasa.shared.core.domain

  • The property templates is deprecated. Use responses instead. It will be removed in Rasa Open Source 3.0.0.
  • retrieval_intent_templates will be removed in Rasa Open Source 3.0.0. Please use retrieval_intent_responses instead.
  • is_retrieval_intent_template will be removed in Rasa Open Source 3.0.0. Please use is_retrieval_intent_response instead.
  • check_missing_templates will be removed in Rasa Open Source 3.0.0. Please use check_missing_responses instead.

  Response Selector

  • The field template_name will be deprecated in Rasa Open Source 3.0.0. Please use utter_action instead. Please see here for more details.
  • The field response_templates will be deprecated in Rasa Open Source 3.0.0. Please use responses instead. Please see here for more details.

Improvements#

• #7022: The following endpoints now require the existence of the conversation for the specified conversation ID, raising an exception and returning a 404 status code.

  • GET /conversations/<conversation_id:path>/story

  • POST /conversations/<conversation_id:path>/execute

  • POST /conversations/<conversation_id:path>/predict

• #7438: Simplify our training by overwriting train_step instead of fit for our custom models.

  This allows us to use the build-in callbacks from Keras, such as the Tensorboard Callback, which offers more functionality compared to what we had before.

  :::warning If you want to use Tensorboard for DIETClassifier, ResponseSelector, or TEDPolicy and log metrics after every (mini)batch, please use 'batch' instead of 'minibatch' as 'tensorboard_log_level'.

• #7578: When TED is configured to extract entities rasa test now evaluates them against the labels in the test stories. Results are saved in /results along with the results for the NLU components that extract entities.

• #7680: We're now running integration tests for Rasa Open Source, with initial coverage for SQLTrackerStore (with PostgreSQL), RedisLockStore (with Redis) and PikaEventBroker (with RabbitMQ). The integration tests are now part of our CI, and can also be ran locally using make test-integration (see Rasa Open Source README for more information).

• #7763: Allow tests to be located anywhere, not just in tests directory.

• #7893: Model configuration files are now validated whether they match the expected schema.

• #7952: Speed up YAMLStoryReader.is_key_in_yaml function by making it to check if key is in YAML without actually parsing the text file.

• #7953: Speed up YAML parsing by reusing parsers, making the process of environment variable interpolation optional, and by not adding duplicating implicit resolvers and YAML constructors to ruamel.yaml

• #7955: Drastically improved finger printing time for large story graphs

• #8000: Remove console logging of conversation level F1-score and precision since these calculations were not meaningful.

  Add conversation level accuracy to core policy results logged to file in story_report.json after running rasa test core or rasa test.

• #8100: Improved the lock store debug log message when the process has to queue because other messages have to be processed before this item.

Bugfixes#

• #4612: Fixed the bug that OR statements in stories would break the check whether a model needs to be retrained

• #7063: Update the spec of POST /model/test/intents and add tests for cases when JSON is provided.

  Fix the incorrect temporary file extension for the data that gets extracted from the payload provided in the body of POST /model/test/intents request.

• #7113: Fix for the cli command rasa data convert config when migrating Mapping Policy and no rules.

  Making rasa data convert config migrate correctly the Mapping Policy when no rules are available. It updates the config.yml file by removing the MappingPolicy and adding the RulePolicy instead. Also, it creates the data/rules.yml file even if empty in the case of no available rules.

• #7470: Allow to have slots with values that result to a dictionary under the key slot_was_set (in stories.yml file).

  An example would be to have the following story step in stories.yml:

  Copy

  - slot_was_set:

  - some_slot:

  some_key: 'some_value'

  other_key: 'other_value'

  This would be allowed if the some_slot is also set accordingly in the domain.yml with type any.

• #7662: Update the fingerprinting function to recognize changes in lookup files.

• #7932: Fixed a bug when interpolating environment variables in YAML files which included $ in their value. This led to the following stack trace:

  Copy

  ValueError: Error when trying to expand the environment variables in '${PASSWORD}'. Please make sure to also set these environment variables: '['$qwerty']'.

  (13 additional frame(s) were not displayed)

  ...

  File "rasa/utils/endpoints.py", line 26, in read_endpoint_config

  content = rasa.shared.utils.io.read_config_file(filename)

  File "rasa/shared/utils/io.py", line 527, in read_config_file

  content = read_yaml_file(filename)

  File "rasa/shared/utils/io.py", line 368, in read_yaml_file

  return read_yaml(read_file(filename, DEFAULT_ENCODING))

  File "rasa/shared/utils/io.py", line 349, in read_yaml

  return yaml_parser.load(content) or {}

  File "rasa/shared/utils/io.py", line 314, in env_var_constructor

  " variables: '{}'.".format(value, not_expanded)

• #7949: The REQUESTED_SLOT always belongs to the currently active form.

  Previously it was possible that after form switching, the REQUESTED_SLOT was for the previous form.

• #96: Update the LanguageModelFeaturizer tests to reflect new default model weights for bert, and skip all bert tests with default model weights on CI, run bert tests with bert-base-uncased on CI instead.

Improved Documentation#

• #8080: Update links to Sanic docs in the documentation.
• #8109: Update Rasa Playground to correctly use tracking_id when calling API methods.

Miscellaneous internal changes#

• #6484, #7737, #7879, #8016

[2.3.5] - 2021-06-16#

Features#

• #8860: Added sasl_mechanism as an optional configurable parameters for the Kafka Producer.

Improvements#

• #7955: Drastically improved finger printing time for large story graphs
• #8100: Improved the lock store debug log message when the process has to queue because other messages have to be processed before this item.

Bugfixes#

• #4612: Fixed the bug that OR statements in stories would break the check whether a model needs to be retrained
• #8649: Updated python-engineio dependency version for compatibility with python-socketio.

Improved Documentation#

• #8080: Update links to Sanic docs in the documentation.

[2.3.4] - 2021-02-26#

Bugfixes#

• #8014: Setting model_confidence=cosine in DIETClassifier, ResponseSelector and TEDPolicy is deprecated and will no longer be available. This was introduced in Rasa Open Source version 2.3.0 but post-release experiments suggest that using cosine similarity as model's confidences can change the ranking of predicted labels which is wrong.

  model_confidence=inner is deprecated and is replaced by model_confidence=linear_norm as the former produced an unbounded range of confidences which broke the logic of assistants in various other places.

  We encourage you to try model_confidence=linear_norm which will produce a linearly normalized version of dot product similarities with each value in the range [0,1]. This can be done with the following config:

  Copy

  - name: DIETClassifier

  model_confidence: linear_norm

  constrain_similarities: True

  This should ease up tuning fallback thresholds as confidences for wrong predictions are better distributed across the range [0, 1].

  If you trained a model with model_confidence=cosine or model_confidence=inner setting using previous versions of Rasa Open Source, please re-train by either removing the model_confidence option from the configuration or setting it to linear_norm.

  model_confidence=cosine is removed from the configuration generated by auto-configuration.

[2.3.3] - 2021-02-25#

Bugfixes#

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

[2.3.2] - 2021-02-22#

Bugfixes#

• #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.

[2.3.1] - 2021-02-17#

Bugfixes#

• #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.

[2.3.0] - 2021-02-11#

Improvements#

• #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.

  Copy

  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

  Copy

  - 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.

  EDIT: Some post-release experiments revealed that using model_confidence=cosine is wrong as it can change the order of predicted labels. That's why this option was removed in Rasa Open Source version 2.3.3. model_confidence=inner is deprecated as it produces an unbounded range of confidences which can break the logic of assistants in various other places. Please use model_confidence=linear_norm which will produce a linearly normalized version of dot product similarities with each value in the range [0,1]. Please read more about this change under the notes for release 2.3.4.

• #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.

Bugfixes#

• #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.

Miscellaneous internal changes#

• #7420, #7515, #7574, #7601

[2.2.10] - 2021-02-08#

Improvements#

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

Bugfixes#

• #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:

  Copy

  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

[2.2.9] - 2021-02-02#

Bugfixes#

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

[2.2.8] - 2021-01-28#

Bugfixes#

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

[2.2.7] - 2021-01-25#

Improvements#

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

Bugfixes#

• #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.

[2.2.6] - 2021-01-21#

Bugfixes#

• #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.

[2.2.5] - 2021-01-12#

Bugfixes#

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

Miscellaneous internal changes#

• #7711

[2.2.4] - 2021-01-08#

Improvements#

• #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.

Bugfixes#

• #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.

[2.2.3] - 2021-01-06#

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:

  Copy

  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] - 2020-12-21#

Bugfixes#

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

[2.2.1] - 2020-12-17#

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:

  Copy

  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] - 2020-12-16#

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:

  Copy

  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:

  Copy

  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

[2.1.3] - 2020-12-04#

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:

  Copy

  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] - 2020-11-27#

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.

[2.1.1] - 2020-11-23#

Bugfixes#

• #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.

[2.1.0] - 2020-11-17#

Deprecations and Removals#

• #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:

  Copy

  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.

  Copy

  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:

  Copy

  from rasa.core.brokers.broker import EventBroker

  class MyEventBroker(EventBroker):

  async def close(self) -> None:

  # clean up event broker resources

Features#

• #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.

Improvements#

• #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:

  Copy

  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:

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  ...

  - 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:

  Copy

  ...

  - 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:

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  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.

Bugfixes#

• #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:

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  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.

Improved Documentation#

• #6973: Correct data validation docs

Miscellaneous internal changes#

• #6470, #7015, #7090

[2.0.8] - 2020-11-26#

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.

[2.0.7] - 2020-11-24#

Bugfixes#

• #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.

[2.0.6] - 2020-11-10#

Bugfixes#

• #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.

[2.0.5] - 2020-11-10#

Bugfixes#

• #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.

[2.0.4] - 2020-11-08#

Bugfixes#

• #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.

[2.0.3] - 2020-10-29#

Bugfixes#

• #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:

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  pipeline:

  - name: ConveRTTokenizer

  model_url: <remote/local path to model>

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

[2.0.2] - 2020-10-22#

Bugfixes#

• #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.

Miscellaneous internal changes#

• #7057

[2.0.1] - 2020-10-20#

Bugfixes#

• #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).

[2.0.0] - 2020-10-07#

Deprecations and Removals#

• #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:

  domain.yml

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  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.

Features#

• #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).

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  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

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  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.

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  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.

Improvements#

• #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:

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  ```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 -

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  "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.

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  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.

Bugfixes#

• #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.

Improved Documentation#

• #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

Miscellaneous internal changes#

• #5784, #5788, #6199, #6403, #6735

[1.10.26] - 2021-06-17#

Features#

• #8876: Added sasl_mechanism as an optional configurable parameter for the Kafka Producer.

[1.10.25] - 2021-04-14#

Features#

• #8429: Added partition_by_sender flag to Kafka Producer to optionally associate events with Kafka partition based on sender_id.

Improvements#

• #8345: Improved the lock store debug log message when the process has to queue because other messages have to be processed before this item.

[1.10.24] - 2021-03-29#

Bugfixes#

• #8019: Added group_id parameter back to KafkaEventBroker to fix error when instantiating event broker with a config containing the group_id parameter which is only relevant to the event consumer

[1.10.23] - 2021-02-22#

Bugfixes#

• #7895: Fixed bug where the conversation does not lock before handling a reminder event.

[1.10.22] - 2021-02-05#

Bugfixes#

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

[1.10.21] - 2021-02-01#

Improvements#

• #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.

Bugfixes#

• #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.

[1.10.20] - 2020-12-18#

Bugfixes#

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

[1.10.19] - 2020-12-17#

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.

[1.10.18] - 2020-11-26#

Bugfixes#

• #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.

[1.10.17] - 2020-11-12#

Bugfixes#

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

[1.10.16] - 2020-10-15#

Bugfixes#

• #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.

[1.10.15] - 2020-10-09#

Bugfixes#

• #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.

Improvements#

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

[1.10.14] - 2020-09-23#

Bugfixes#

• #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.

[1.10.13] - 2020-09-22#

Bugfixes#

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

[1.10.12] - 2020-09-03#

Bugfixes#

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

[1.10.11] - 2020-08-21#

Improvements#

• #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.

Bugfixes#

• #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.

[1.10.10] - 2020-08-04#

Bugfixes#

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

[1.10.9] - 2020-07-29#

Improvements#

• #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.

Bugfixes#

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

[1.10.8] - 2020-07-15#

Bugfixes#

• #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.

[1.10.7] - 2020-07-07#

Features#

• #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.

Bugfixes#

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

[1.10.6] - 2020-07-06#

Bugfixes#

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

[1.10.5] - 2020-07-02#

Bugfixes#

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

[1.10.4] - 2020-07-01#

Bugfixes#

• #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.

[1.10.3] - 2020-06-12#

Improvements#

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

Bugfixes#

• #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).

[1.10.2] - 2020-06-03#

Bugfixes#

• #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.

[1.10.1] - 2020-05-15#

Improvements#

• #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.

Bugfixes#

• #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.

Miscellaneous internal changes#

• #5788

[1.10.0] - 2020-04-28#

Features#

• #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:

  Copy

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

  JSON:

  Copy

  "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.

Improvements#

• #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.

Bugfixes#

• #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.

Miscellaneous internal changes#

• #5556, #5587, #5614, #5631, #5633

[1.9.7] - 2020-04-23#

Improvements#

• #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.

Bugfixes#

• #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.

[1.9.6] - 2020-04-15#

Bugfixes#

• #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.

Miscellaneous internal changes#

• #5626

[1.9.5] - 2020-04-01#

Improvements#

• #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

  Copy

  CREATE SCHEMA schema_name;

Bugfixes#

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

[1.9.4] - 2020-03-30#

Bugfixes#

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

[1.9.3] - 2020-03-27#

Bugfixes#

• #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.

[1.9.2] - 2020-03-26#

Improved Documentation#

• #5497: Fix documentation to bring back Sara.

[1.9.1] - 2020-03-25#

Bugfixes#

• #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.

[1.9.0] - 2020-03-24#

Features#

• #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.

Improvements#

• #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:

  Copy

  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.

Bugfixes#

• #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.

Improved Documentation#

• #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.

Miscellaneous internal changes#

• #5340

[1.8.3] - 2020-03-27#

Bugfixes#

• #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.

Improved Documentation#

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

[1.8.2] - 2020-03-19#

Bugfixes#

• #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.

Improved Documentation#

• #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

[1.8.1] - 2020-03-06#

Bugfixes#

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

Miscellaneous internal changes#

• #5330, #5348

[1.8.0] - 2020-02-26#

Deprecations and Removals#

• #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.

Features#

• #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:

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  # 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.