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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 1.0.6.aX] - master

Added

Changed

Removed

Fixed

  • slack notifications from bots correctly render text
  • fixed usage of --log-file argument for rasa run and rasa shell

[1.0.6] - 2019-06-03

Fixed

  • fixed backwards incompatible utils changes

[1.0.5] - 2019-06-03

Fixed

  • fixed spacy being a required dependency (regression)

[1.0.4] - 2019-06-03

Added

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

Changed

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

[1.0.3] - 2019-05-30

Fixed

  • non-ascii characters render correctly in stories generated from interactive learning
  • validate domain file before usage, e.g. print proper error messages if domain file is invalid instead of raising errors

[1.0.2] - 2019-05-29

Added

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

Fixed

  • fix lookup table files failed to load issues/3622
  • buttons can now be properly selected during cmdline chat or when in interactive learning
  • set slots correctly when events are added through the API
  • mapping policy no longer ignores NLU threshold
  • mapping policy priority is correctly persisted

[1.0.1] - 2019-05-21

Fixed

  • updated installation command in docs for Rasa X

[1.0.0] - 2019-05-21

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
  • support for spaCy 2.1
  • a model for an agent can now also be loaded from a remote storage
  • log level can be set via environment variable LOG_LEVEL
  • add --store-uncompressed to train command to not compress Rasa model
  • log level of libraries, such as tensorflow, can be set via environment variable LOG_LEVEL_LIBRARIES
  • if no spaCy model is linked upon building a spaCy pipeline, an appropriate error message is now raised with instructions for linking one

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
  • train command uses fallback config if an invalid config is given
  • test command now compares multiple models if a list of model files is provided for the argument --model
  • Merged rasa.core and rasa.nlu server into a single server. See swagger file in docs/_static/spec/server.yaml for available endpoints.
  • utter_custom_message() method in rasa_core_sdk has been renamed to utter_elements()
  • updated dependencies. as part of this, models for spacy need to be reinstalled for 2.1 (from 2.0)
  • make sure all command line arguments for rasa test and rasa interactive are actually used, removed arguments that were not used at all (e.g. --core for rasa test)

Removed

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