All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning starting with version 0.2.0.
[Unreleased 0.12.0.aX] - master
Note
This version is not yet released and is under active development.
- 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 - 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
- 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
- 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.
- Remove livechat widget from docs
- Add missing name() to facebook Messenger class
- backport fix to JWT schema
- pin tensorflow 1.10.0
- cancel reminders if there has been a restarted event after the reminder
- JWT authentication now checks user roles. The
admin
role may access all endpoints. For endpoints which contain asender_id
parameter, users with theuser
role may only call endpoints where thesender_id
matches the user'susername
.
- custom message method in rocketchat channel
- 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
UserMessage
andUserUttered
classes have a new attributeinput_channel
that stores the name of theInputChannel
through which the message was received
- numpy version incompatibility between rasa core and tensorflow
- a flag
--fail_on_prediction_errors
to theevaluate.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
andUserUttered
classes have a new attributeinput_channel
that stores the name of theInputChannel
through which the message was received
- 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)
- package pytest-services since it wasn't necessary
- 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 offollowup_action
)
- callback output channel, receives messages and uses a REST endpoint to respond with messages
- channel input creation moved to the channel, every channel can now customize how it gets created from the credentials file
- improved documentation for events (e.g. including json serialisation)
- outdated documentation for removed endpoints in the server
(
/parse
&/continue
)
- read in fallback command line args
- increased minimal compatible model version to 0.11.0
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 :ref:`migration` guide before updating. You need to retrain your models.
- added microsoft botframework input and output channels
- added rocket chat input and output channels
- script parameter
--quiet
to set the log level toWARNING
- 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: :ref:`embedding_policy` implemented in tensorflow
- default log level for all scripts has been changed from
WARNING
toINFO
. - 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 - userasa_core.run --enable_api
instead - collecting output channel will no properly collect events for images, buttons, and attachments
- removed the deprecated
TopicSet
event - removed
tracker.follow_up_action
- use theFollowupAction
event instead - removed
action_factory: remote
from domain file - the domain is always run over http - removed
OnlineLearningPolicy
- use thetraining.online
script instead
- lots of type annotations
- some invalid documentation references
- changed all
logger.warn
tologger.warning
- more emoji support for PY2
- intent confidence support in RegexInterpreter
- updated to Rasa NLU 0.13
- improved documentation quickstart
- server request argument handling on python 3
- creation of training data story graph - removes more nodes and speeds up the training
- new
RasaChatInput
channel - option to ignore entities for certain intents
- loading of NLU model
- documentation changes
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 :ref:`migration` for details.
- 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 usingPika
- docs section on event brokers
Agent()
class supports amodel_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 ismodel
) - docs on model fetching from a URL
- 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 support for topics
- fixed fallback policy data generation
- handling of max history configuration in policies
- fixed instantiation issues of fallback policy
- fixed evaluation script
- fixed story file loading (previously some story files with checkpoints could create wrong training data)
- improved speed of data loading
- added token auth to all endpoints of the core server
- fix handling of max_history parameter in AugmentedMemoizationPolicy
- persistence of training data collected during online learning if default file path is used
- the
agent()
method used in somerasa_core.server
endpoints is re-run at every new call of theensure_loaded_agent
decorator - fixed OR usage of intents
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.
- 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
- 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
- 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
- pin rasa nlu version to 0.11.4 (0.12.x only works with master)
- updated google analytics docs survey code
- pin
pykwalify<=1.6.0
as update to1.6.1
breaks compatibility
- pin
fbmessenger
version to avoid major update
- script to reload a dumped trackers state and to continue the conversation at the end of the stored dialogue
- minor updates to dependencies
- fixed datetime serialisation of reminder event
- removed deque to support python 3.5
- Documentation improvements to tutorials
- serialisation of date time value for
ReminderScheduled
event
This is a major version change. Make sure to take a look at the :ref:`migration` in the documentation for advice on how to update existing projects.
--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
- 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
- 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
- visualisation using Networkx version 2.x
- add output about line of failing intent when parsing story files
- Pypi readme rendering
- log bot utterances to tracker
- documentation improvements in README
- renamed interpreter argument to rasa core server
- moodbot example train command in docs
- "sender_id" (and "DEFAULT_SENDER_ID") keyword consistency issue #56
- improved moodbot example - more nlu examples as well as better fitting of dialogue model
- added method to tracker to retrieve the latest entities #68
- parameter to specify font size when rendering story visualization
- fixed documentation of story visualization
- 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
- small dropout in standard keras model to decrease reliance on exact intents
- a LOT of documentation improvements
- fixed http error if action listen is not confirmed. #42
- issues with restart events. They created wrong a messed up history leading to wrong predictions
- support for Rasa Core usage as a server with remote action execution
- switched to max code line length 80
- removed action id - use
action.name()
instead. if an action implementation overrides the name, it should include theaction_
prefix (as it is not automatically added anymore) - renamed
rasa_dm.util
torasa_dm.utils
- renamed the whole package to
rasa_core
(sorasa_dm
is gone!) - renamed
Reminder
attributeid
toname
- 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
- 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
- large refactoring of code base
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
UserUtterance
now holds the complete parse data from nlu (e.g. to access attributes other than entities or intent)Turn
has a reference to aUserUtterance
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)
RedisTrackerStore
checks if tracker is stored before accessing it (otherwise aNone
access exception is thrown)RegexInterpreter
checks if the regex actually matches the message instead of assuming it always doesstr
implementation for all eventsController
can be started without an input channel (e.g. messages need to be fed into the queue manually)
First released version.