desc: | Rasa Changelog |
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All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning starting with version 1.0.
[Unreleased 1.0.8.aX] - master
- support for specifying full database urls in the
SQLTrackerStore
configuration - maximum number of predictions can be set via the environment variable
MAX_NUMBER_OF_PREDICTIONS
(default is 10)
- loading of additional training data with the
SkillSelector
- strip trailing slashes in endpoint URLs
- added argument
--rasa-x-port
to specify the port of Rasa X when running Rasa X locally viarasa x
- slack notifications from bots correctly render text
- fixed usage of
--log-file
argument forrasa run
andrasa shell
- check if correct tracker store is configured in local mode
- fixed backwards incompatible utils changes
- fixed spacy being a required dependency (regression)
- 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 usingCREATE INDEX event_idx_sender_id ON events (sender_id);
.
- NLU evaluation in cross-validation mode now also provides intent/entity reports, confusion matrix, etc.
- 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
- added
domain_warnings()
method toDomain
which returns a dict containing the diff between supplied {actions, intents, entities, slots} and what's contained in the domain
- 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
- updated installation command in docs for Rasa X
- 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 viautter_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 inconfig.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
- renamed all CLI parameters containing any
_
to use dashes-
instead (GNU standard) - renamed
rasa_core
package torasa.core
- for interactive learning only include manually annotated and ner_crf entities in nlu export
- made
message_id
an additional argument tointerpreter.parse
- changed removing punctuation logic in
WhitespaceTokenizer
training_processes
in the Rasa NLU data router have been renamed toworker_processes
- created a common utils package
rasa.utils
for nlu and core, common methods likeread_yaml
moved there - removed
--num_threads
from run command (server will be asynchronous but running in a single thread) - the
_check_token()
method inRasaChat
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 toutter_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
andrasa interactive
are actually used, removed arguments that were not used at all (e.g.--core
forrasa test
)
- removed possibility to execute
python -m rasa_core.train
etc. (e.g. scripts inrasa.core
andrasa.nlu
). Use the CLI for rasa instead, e.g.rasa train core
. - removed
_sklearn_numpy_warning_fix
from theSklearnIntentClassifier
- removed
Dispatcher
class from core - removed projects: the Rasa NLU server now has a maximum of one model at a time loaded.
- 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