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CHANGELOG.rst

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desc:Rasa Changelog

Rasa 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.

[Unreleased 1.0.0rc1] - `master`_

Added

  • added arguments to set the file paths for interactive training
  • added quick reply representation for command-line output
  • added option to specify custom button type for Facebook buttons
  • added tracker store persisting trackers into a SQL database (SQLTrackerStore)
  • added rasa command line interface and API
  • Rasa Stack HTTP training endpoint at POST /jobs. This endpoint will train a combined Rasa Core and NLU model
  • ReminderCancelled(action_name) event to cancel given action_name reminder for current user
  • Rasa Stack HTTP intent evaluation endpoint at POST /intentEvaluation. This endpoints performs an intent evaluation of a Rasa Stack model
  • option to create template for new utterance action in interactive learning
  • you can now choose actions previously created in the same session

in interactive learning - add formatter 'black' - channel-specific utterances via the - "channel": key in utterance templates - arbitrary json messages via the - "custom": key in utterance templates and

via utter_custom_json() method in custom actions
  • support to load sub skills (domain, stories, nlu data)
  • support to select which sub skills to load through import section in config.yml
  • add rasa interactive core to command line interface
  • support for spaCy 2.1
  • a model for an agent can now also be loaded from a remote storage
  • log level can be set via environment variable LOG_LEVEL

Changed

  • renamed all CLI parameters containing any _ to use dashes - instead (GNU standard)
  • renamed rasa_core package to rasa.core
  • for interactive learning only include manually annotated and ner_crf entities in nlu export
  • made message_id an additional argument to interpreter.parse
  • changed removing punctuation logic in WhitespaceTokenizer
  • training_processes in the Rasa NLU data router have been renamed to worker_processes
  • created a common utils package rasa.utils for nlu and core, common methods like read_yaml moved there
  • removed --num_threads from run command (server will be asynchronous but running in a single thread)
  • the _check_token() method in RasaChat now authenticates against /auth/verify instead of /user
  • removed --pre_load from run command (Rasa NLU server will just have a maximum of one model and that model will be loaded by default)
  • changed file format of a stored trained model from the Rasa NLU server to tar.gz
  • rasa train uses fallback config if an invalid config is given
  • rasa test core compares multiple models if a list of model files is provided for the argument --model
  • rasa train falls back to rasa train core/rasa train nlu if the corresponding training data are missing
  • Merged rasa.core and rasa.nlu server into a single server. See swagger file in docs/_static/spec/server.yaml for available endpoints.
  • utter_custom_message() method in rasa_core_sdk has been renamed to utter_elements()
  • updated dependencies. as part of this, models for spacy need to be reinstalled for 2.1 (from 2.0)

Removed

  • removed possibility to execute python -m rasa_core.train etc. (e.g. scripts in rasa.core and rasa.nlu). Use the CLI for rasa instead, e.g. rasa train core.
  • removed _sklearn_numpy_warning_fix from the SklearnIntentClassifier
  • removed Dispatcher class from core
  • removed projects: the Rasa NLU server now has a maximum of one model at a time loaded.

Fixed

  • evaluating core stories with two stage fallback gave an error, trying to handle None for a policy
  • the /evaluate route for the Rasa NLU server now runs evaluation in a parallel process, which prevents the currently loaded model unloading
  • added missing implementation of the keys() function for the Redis Tracker Store
  • in interactive learning: only updates entity values if user changes annotation
  • log options from the command line interface are applied (they overwrite the environment variable)
  • all message arguments (kwargs in dispatcher.utter methods, as well as template args) are now sent through to output channels