CHANGELOG.rst

Change Log

All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning starting with version 0.2.0.

[0.12.0] - 2018-11-11

Warning

This is major new version with a lot of changes under the hood as well as on the API level. Please take a careful look at the migration guide before updating. You need to retrain your models.

Added

• new connector for the Cisco Webex Teams chat
• openapi documentation of server API
• NLU data learned through interactive learning will now be stored in a separate markdown-format file (any previous NLU data is merged)
• Command line interface for interactive learning now displays policy confidence alongside the action name
• added action prediction confidence & policy to ActionExecuted event
• the Core policy configuration can now be set in a config.yaml file. This makes training custom policies possible.
• both the date and the time at which a model was trained are now included in the policy's metadata when it is persisted
• show visualization of conversation while doing interactive learning
• option for end-to-end evaluation of Rasa Core and NLU examples in evaluate.py script
• /conversations/{sender_id}/story endpoint for returning the end-to-end story describing a conversation
• docker-compose file to start a rasa core server together with nlu, an action server, and duckling
• http server (rasa_core.run --enable-api) evaluation endpoint
• ability to add tracker_store using endpoints.yml
• ability load custom tracker store modules using the endpoints.yml
• ability to add an event broker using an endpoint configuration file
• raise an exception when server.py is used instead of rasa_core.run --enable-api
• add documentation on how to configure endpoints within a configuration file
• auth_source parameter in MongoTrackerStore defining the database to authenticate against
• missing instructions on setting up the facebook connector
• environment variables specified with ${env_variable} in a yaml configuration file are now replaced with the value of the environment variable
• detailed documentation on how to deploy Rasa with Docker
• make wait_time_between_pulls configurable through endpoint configuration
• add FormPolicy to handle form action prediction
• add ActionExecutionRejection exception and ActionExecutionRejected event
• add default action ActionDeactivateForm()
• add formbot example
• add ability to turn off auto slot filling with entity for each slot in domain.yml
• add InvalidDomain exception
• add active_form_... to state dictionary
• add active_form and latest_action_name properties to DialogueStateTracker
• add Form and FormValidation events
• add REQUESTED_SLOT constant
• add ability to read action_listen from stories
• added train/eval scripts to compare policies

Changed

• improved response format for /predict endpoint
• all error messages from the server are now in json format
• agent.log_message now returns a tracker instead of the trackers state
• the core container does not load the nlu model by default anymore. Instead it can be connected to a nlu server.
• stories are now visualized as .html page instead of an image
• move and deduplicate restaurantbot nlu data from franken_data.json to nlu_data.md
• forms were completely reworked, see changelog in rasa_core_sdk
• state featurization if some form is active changed
• Domain raises InvalidDomain exception
• interactive learning is now started with rasa_core.train interactive
• passing a policy config file to train a model is now required
• flags for output of evaluate script have been merged to one flag --output where you provide a folder where any output from the script should be stored

Removed

• removed graphviz dependency
• policy config related flags in training script (see migration guide)

Fixed

• fixed an issue with boolean slots where False and None had the same value (breaking model compatibility with models that use a boolean slot)
• use utf8 everywhere when handling file IO
• argument --connector on run script accepts custom channel module names
• properly handle non ascii categorical slot values, e.g. 大于100亿元
• fixed HTTP server attempting to authenticate based on incorrect path to the correct JWT data field
• all sender ids from channels are now handled as str. Sender ids from old messages with an int id are converted to str.
• legacy pep8 errors

[0.11.12] - 2018-10-11

Changed

• Remove livechat widget from docs

[0.11.11] - 2018-10-05

Fixed

• Add missing name() to facebook Messenger class

[0.11.10] - 2018-10-05

Fixed

• backport fix to JWT schema

[0.11.9] - 2018-10-04

Changed

• pin tensorflow 1.10.0

[0.11.8] - 2018-09-28

Fixed

• cancel reminders if there has been a restarted event after the reminder

Changed

• JWT authentication now checks user roles. The admin role may access all endpoints. For endpoints which contain a sender_id parameter, users with the user role may only call endpoints where the sender_id matches the user's username.

[0.11.7] - 2018-09-26

Added

• custom message method in rocketchat channel

Fixed

• don't fail if rasa and rest input channels are used together
• wrong paramter name in rocketchat channel methods
• Software 2.0 link on interactive learning documentation page went to Tesla's homepage, now it links to Karpathy blogpost

[0.11.6] - 2018-09-20

Added

• UserMessage and UserUttered classes have a new attribute input_channel that stores the name of the InputChannel through which the message was received

[0.11.5] - 2018-09-20

Fixed

• numpy version incompatibility between rasa core and tensorflow

[0.11.4] - 2018-09-19

Added

• a flag --fail_on_prediction_errors to the evaluate.py script -if used when running the evaluation, the script will fail with a non 0 exit code if there is at least one prediction error. This can be used on CIs to validate models against test stories.
• JWT support: parameters to allow clients to authenticate requests to the rasa_core.server using JWT's in addition to normal token based auth
• added socket.io input / output channel
• UserMessage and UserUttered classes have a new attribute input_channel that stores the name of the InputChannel through which the message was received

Changed

• dump failed stories after evaluation in the normal story format instead of as a text file
• do not run actions during evaluation. instead, action are only predicted and validated against the gold story.
• improved the online learning experience on the CLI
• made finetuning during online learning optional (use --finetune if you want to enable it)

Removed

• package pytest-services since it wasn't necessary

Fixed

• fixed an issue with the followup (there was a name confusion, sometimes the followup action would be set to the non existent follow_up_action attribute instead of followup_action)

[0.11.3] - 2018-09-04

Added

• callback output channel, receives messages and uses a REST endpoint to respond with messages

Changed

• channel input creation moved to the channel, every channel can now customize how it gets created from the credentials file

[0.11.2] - 2018-09-04

Changed

• improved documentation for events (e.g. including json serialisation)

Removed

• outdated documentation for removed endpoints in the server (/parse & /continue)

Fixed

• read in fallback command line args

[0.11.1] - 2018-08-30

Fixed

• increased minimal compatible model version to 0.11.0

[0.11.0] - 2018-08-30

Warning

This is major new version with a lot of changes under the hood as well as on the API level. Please take a careful look at the migration guide before updating. You need to retrain your models.

Added

• added microsoft botframework input and output channels
• added rocket chat input and output channels
• script parameter --quiet to set the log level to WARNING
• information about the python version a model has been trained with to the model metadata
• more emoji support for PY2
• intent confidence support in RegexInterpreter
• added paramter to train script to pull training data from an url instead of a stories file
• added new policy: embedding_policy implemented in tensorflow

Changed

• default log level for all scripts has been changed from WARNING to INFO.
• format of the credentials file to allow specifying the credentials for multiple channels
• webhook URLs for the input channels have changed and need to be reset
• deprecated using rasa_core.server as a script - use rasa_core.run --enable_api instead
• collecting output channel will no properly collect events for images, buttons, and attachments

Removed

• removed the deprecated TopicSet event
• removed tracker.follow_up_action - use the FollowupAction event instead
• removed action_factory: remote from domain file - the domain is always run over http
• removed OnlineLearningPolicy - use the training.online script instead

Fixed

• lots of type annotations
• some invalid documentation references
• changed all logger.warn to logger.warning

[0.10.4] - 2018-08-08

Added

• more emoji support for PY2
• intent confidence support in RegexInterpreter

[0.10.3] - 2018-08-03

Changed

• updated to Rasa NLU 0.13
• improved documentation quickstart

Fixed

• server request argument handling on python 3
• creation of training data story graph - removes more nodes and speeds up the training

[0.10.2] - 2018-07-24

Added

• new RasaChatInput channel
• option to ignore entities for certain intents

Fixed

• loading of NLU model

[0.10.1] - 2018-07-18

Changed

• documentation changes

[0.10.0] - 2018-07-17

Warning

This is a major new release with backward incompatible changes. Old trained models can not be read with the new version - you need to retrain your model. View the migration for details.

Added

• allow bot responses to be managed externally (instead of putting them into the domain.yml)
• options to prevent slack from making re-deliver message upon meeting failure condition. the default is to ignore http_timeout.
• added ability to create domain from yaml string and export a domain to a yaml string
• added server endpoint to fetch domain as json or yaml
• new default action ActionDefaultFallback
• event streaming to a RabbitMQ message broker using Pika
• docs section on event brokers
• Agent() class supports a model_server EndpointConfig, which it regularly queries to fetch dialogue models
• this can be used with rasa_core.server with the --endpoint option (the key for this the model server config is model)
• docs on model fetching from a URL

Changed

• changed the logic inside AugmentedMemoizationPolicy to recall actions only if they are the same in training stories
• moved AugmentedMemoizationPolicy to memoization.py
• wrapped initialization of BackgroundScheduler in try/except to allow running on jupyterhub / binderhub/ colaboratory
• fixed order of events logged on a tracker: action executed is now always logged before bot utterances that action created

Removed

• removed support for topics

[0.9.6] - 2018-06-18

Fixed

• fixed fallback policy data generation

[0.9.5] - 2018-06-14

Fixed

• handling of max history configuration in policies
• fixed instantiation issues of fallback policy

[0.9.4] - 2018-06-07

Fixed

• fixed evaluation script
• fixed story file loading (previously some story files with checkpoints could create wrong training data)
• improved speed of data loading

[0.9.3] - 2018-05-30

Fixed

• added token auth to all endpoints of the core server

[0.9.2] - 2018-05-30

Fixed

• fix handling of max_history parameter in AugmentedMemoizationPolicy

[0.9.1] - 2018-05-29

Fixed

• persistence of training data collected during online learning if default file path is used
• the agent() method used in some rasa_core.server endpoints is re-run at every new call of the ensure_loaded_agent decorator
• fixed OR usage of intents

[0.9.0] - 2018-05-24

Warning

This is a major new release with backward incompatible changes. Old trained models can not be read with the new version - you need to retrain your model.

Added

• supported loading training data from a folder - loads all stories from all files in that directory
• parameter to specify NLU project when instantiating a RasaNLUInterpreter
• simple /respond endpoint to get bot response to a user message
• /conversations endpoint for listing sender ids of running conversations
• added a Mattermost channel that allows Rasa Core to communicate via a Mattermost app
• added a Twilio channel that allows Rasa Core to communicate via SMS
• FallbackPolicy for executing a default message if NLU or core model confidence is low.
• FormAction class to make it easier to collect multiple pieces of information with fewer stories.
• Dockerfile for rasa_core.server with a dialogue and Rasa NLU model

Changed

• moved server from klein to flask
• updated dependency fbmessenger from 4.3.1 to 5.0.0
• updated Rasa NLU to 0.12.x
• updated all the dependencies to the latest versions

Fixed

• List slot is now populated with a list
• Slack connector: slack_channel kwarg is used to send messages either back to the user or to a static channel
• properly log to a file when using the run script
• documentation fix on stories

[0.8.6] - 2018-04-18

Fixed

• pin rasa nlu version to 0.11.4 (0.12.x only works with master)

[0.8.5] - 2018-03-19

Fixed

• updated google analytics docs survey code

[0.8.4] - 2018-03-14

Fixed

• pin pykwalify<=1.6.0 as update to 1.6.1 breaks compatibility

[0.8.3] - 2018-02-28

Fixed

• pin fbmessenger version to avoid major update

[0.8.2] - 2018-02-13

Added

• script to reload a dumped trackers state and to continue the conversation at the end of the stored dialogue

Changed

• minor updates to dependencies

Fixed

• fixed datetime serialisation of reminder event

[0.8.1] - 2018-02-01

Fixed

• removed deque to support python 3.5
• Documentation improvements to tutorials
• serialisation of date time value for ReminderScheduled event

[0.8.0] - 2018-01-30

This is a major version change. Make sure to take a look at the migration in the documentation for advice on how to update existing projects.

Added

• --debug and --verbose flags to scripts (train.py, run.py, server.py) to set the log level
• support for story cycles when using checkpoints
• added a new machine learning policy SklearnPolicy that uses an sklearn classifier to predict actions (logistic regression by default)
• warn if action emits events when using a model that it did never emit in any of the stories the model was trained on
• support for event pushing and endpoints to retrieve the tracker state from the server
• Timestamp to every event
• added a Slack channel that allows Rasa Core to communicate via a Slack app
• added a Telegram channel that allows Rasa Core to communicate via a Telegram bot

Changed

• rewrite of the whole FB connector: replaced pymessenger library with fbmessenger
• story file utterance format changed from * _intent_greet[name=Rasa] to * intent_greet{"name": "Rasa"} (old format is still supported but deprecated)
• persist action names in domain during model persistence
• improved travis build speed by not using miniconda
• don't fail with an exception but with a helpful error message if an utterance template contains a variable that can not be filled
• domain doesn't fail on unknown actions but emits a warning instead. this is to support reading logs from older conversation if one recently removed an action from the domain

Fixed

• proper evaluation of stories with checkpoints
• proper visualisation of stories with checkpoints
• fixed float slot min max value handling
• fixed non integer feature decoding, e.g. used for memoization policy
• properly log to specified file when starting Rasa Core server
• properly calculate offset of last reset event after loading tracker from tracker store
• UserUtteranceReverted action incorrectly triggered actions to be replayed

[0.7.9] - 2017-11-29

Fixed

• visualisation using Networkx version 2.x
• add output about line of failing intent when parsing story files

[0.7.8] - 2017-11-27

Fixed

• Pypi readme rendering

[0.7.7] - 2017-11-24

Added

• log bot utterances to tracker

Fixed

• documentation improvements in README
• renamed interpreter argument to rasa core server

[0.7.6] - 2017-11-15

Fixed

• moodbot example train command in docs

[0.7.5] - 2017-11-14

Changed

• "sender_id" (and "DEFAULT_SENDER_ID") keyword consistency issue #56

Fixed

• improved moodbot example - more nlu examples as well as better fitting of dialogue model

[0.7.4] - 2017-11-09

Changed

• added method to tracker to retrieve the latest entities #68

[0.7.3] - 2017-10-31

Added

• parameter to specify font size when rendering story visualization

Fixed

• fixed documentation of story visualization

[0.7.2] - 2017-10-30

Added

• added facebook bot example
• added support for conditional checkpoints. a checkpoint can be restricted to only allow one to use it if certain slots are set. see docs for details
• utterance templates in domain yaml support buttons and images
• validate domain yaml and raise exception on invalid file
• run script to load models and handle messages from an input channel

Changed

• small dropout in standard keras model to decrease reliance on exact intents
• a LOT of documentation improvements

Fixed

• fixed http error if action listen is not confirmed. #42

[0.7.1] - 2017-10-06

Fixed

• issues with restart events. They created wrong a messed up history leading to wrong predictions

[0.7.0] - 2017-10-04

Added

• support for Rasa Core usage as a server with remote action execution

Changed

• switched to max code line length 80
• removed action id - use action.name() instead. if an action implementation overrides the name, it should include the action_ prefix (as it is not automatically added anymore)
• renamed rasa_dm.util to rasa_dm.utils
• renamed the whole package to rasa_core (so rasa_dm is gone!)
• renamed Reminder attribute id to name
• a lot of documentation improvements. docs are now at https://rasa.com/docs/core
• use hashing when writing memorized turns into persistence - requires retraining of all models that are trained with a version prior to this
• changed agent.handle_message(...) interface for easier usage

[0.6.0] - 2017-08-27

Added

• support for multiple policies (e.g. one memoization and a Keras policy at the same time)
• loading domains from yaml files instead of defining them with python code
• added an api layer (called Agent) for you to use for 95% of the things you want to do (training, persistence, loading models)
• support for reminders

Changed

• large refactoring of code base

[0.5.0] - 2017-06-18

Added

• ScoringPolicy added to policy implementations (less strict than standard default policy)
• RasaNLUInterpreter to run a nlu instance within dm (instead of using the http interface)
• more tests

Changed

• UserUtterance now holds the complete parse data from nlu (e.g. to access attributes other than entities or intent)
• Turn has a reference to a UserUtterance instead of directly storing intent & entities (allows access to other data)
• Simplified interface of output channels
• order of actions in the DefaultPolicy in possible_actions (ActionListen now always has index 0)

Fixed

• RedisTrackerStore checks if tracker is stored before accessing it (otherwise a None access exception is thrown)
• RegexInterpreter checks if the regex actually matches the message instead of assuming it always does
• str implementation for all events
• Controller can be started without an input channel (e.g. messages need to be fed into the queue manually)

[0.2.0] - 2017-05-18

First released version.