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.

[1.3.10] - 2019-10-18

Added

• Can now pass a package as an argument to the --actions parameter of the rasa run actions command.

Fixed

• Fixed visualization of stories with entities which led to a failing visualization in Rasa X

[1.3.9] - 2019-10-10

Added

• Port of 1.2.10 (support for RabbitMQ TLS authentication and port key in event broker endpoint config).

• Port of 1.2.11 (support for passing a CA file for SSL certificate verification via the –ssl-ca-file flag).

Fixed

• Fixed the hanging HTTP call with ner_duckling_http pipeline.

• Fixed text processing of intent attribute inside CountVectorFeaturizer.

• Fixed argument of type 'NoneType' is not iterable when using rasa shell, rasa interactive / rasa run

[1.3.8] - 2019-10-08

Changed

• Policies now only get imported if they are actually used. This removes TensorFlow warnings when starting Rasa X

Fixed

• Fixed error Object of type 'MaxHistoryTrackerFeaturizer' is not JSON serializable when running rasa train core

• Default channel send_ methods no longer support kwargs as they caused issues in incompatible channels

[1.3.7] - 2019-09-27

Fixed

• re-added TLS, SRV dependencies for PyMongo

• socketio can now be run without turning on the --enable-api flag

• MappingPolicy no longer fails when the latest action doesn’t have a policy

[1.3.6] - 2019-09-21

Added

• Added the ability for users to specify a conversation id to send a message to when using the RasaChat input channel.

[1.3.5] - 2019-09-20

Fixed

• Fixed issue where rasa init would fail without spaCy being installed

[1.3.4] - 2019-09-20

Added

• Added the ability to set the backlog parameter in Sanics run() method using the SANIC_BACKLOG environment variable. This parameter sets the number of unaccepted connections the server allows before refusing new connections. A default value of 100 is used if the variable is not set.

• Status endpoint (/status) now also returns the number of training processes currently running

Fixed

• Added the ability to properly deal with spaCy Doc-objects created on empty strings as discussed here. Only training samples that actually bear content are sent to self.nlp.pipe for every given attribute. Non-content-bearing samples are converted to empty Doc-objects. The resulting lists are merged with their preserved order and properly returned.

• asyncio warnings are now only printed if the callback takes more than 100ms (up from 1ms).

• agent.load_model_from_server no longer affects logging.

Changed

• The endpoint POST /model/train no longer supports specifying an output directory for the trained model using the field out. Instead you can choose whether you want to save the trained model in the default model directory (models) (default behavior) or in a temporary directory by specifying the save_to_default_model_directory field in the training request.

[1.3.3] - 2019-09-13

Fixed

• Added a check to avoid training CountVectorizer for a particular attribute of a message if no text is provided for that attribute across the training data.

• Default one-hot representation for label featurization inside EmbeddingIntentClassifier if label features don’t exist.

• Policy ensemble no longer incorrectly wrings “missing mapping policy” when mapping policy is present.

• “test” from utter_custom_json now correctly saved to tracker when using telegram channel

Removed

• Removed computation of intent_spacy_doc. As a result, none of the spacy components process intents now.

[1.3.2] - 2019-09-10

Fixed

• SQL tracker events are retrieved ordered by timestamps. This fixes interactive learning events being shown in the wrong order.

[1.3.1] - 2019-09-09

Changed

• Pin gast to == 0.2.2

[1.3.0] - 2019-09-05

Added

• Added option to persist nlu training data (default: False)

• option to save stories in e2e format for interactive learning

• bot messages contain the timestamp of the BotUttered event, which can be used in channels

• FallbackPolicy can now be configured to trigger when the difference between confidences of two predicted intents is too narrow

• experimental training data importer which supports training with data of multiple sub bots. Please see the docs for more information.

• throw error during training when triggers are defined in the domain without MappingPolicy being present in the policy ensemble

• The tracker is now available within the interpreter’s parse method, giving the ability to create interpreter classes that use the tracker state (e.g. slot values) during the parsing of the message. More details on motivation of this change see issues/3015.

• add example bot knowledgebasebot to showcase the usage of ActionQueryKnowledgeBase

• softmax starspace loss for both EmbeddingPolicy and EmbeddingIntentClassifier

• balanced batching strategy for both EmbeddingPolicy and EmbeddingIntentClassifier

• max_history parameter for EmbeddingPolicy

• Successful predictions of the NER are written to a file if --successes is set when running rasa test nlu

• Incorrect predictions of the NER are written to a file by default. You can disable it via --no-errors.

• New NLU component ResponseSelector added for the task of response selection

• Message data attribute can contain two more keys - response_key, response depending on the training data

• New action type implemented by ActionRetrieveResponse class and identified with response_ prefix

• Vocabulary sharing inside CountVectorsFeaturizer with use_shared_vocab flag. If set to True, vocabulary of corpus is shared between text, intent and response attributes of the message

• Added an option to share the hidden layer weights of text input and label input inside EmbeddingIntentClassifier using the flag share_hidden_layers

• New type of training data file in NLU which stores response phrases for response selection task.

• Add flags intent_split_symbol and intent_tokenization_flag to all WhitespaceTokenizer, JiebaTokenizer and SpacyTokenizer

• Added evaluation for response selector. Creates a report response_selection_report.json inside --out directory.

• argument --config-endpoint to specify the URL from which rasa x pulls the runtime configuration (endpoints and credentials)

• LockStore class storing instances of TicketLock for every conversation_id

• environment variables SQL_POOL_SIZE (default: 50) and SQL_MAX_OVERFLOW (default: 100) can be set to control the pool size and maximum pool overflow for SQLTrackerStore when used with the postgresql dialect

• Add a bot_challenge intent and a utter_iamabot action to all example projects and the rasa init bot.

• Allow sending attachments when using the socketio channel

• rasa data validate will fail with a non-zero exit code if validation fails

Changed

• added character-level CountVectorsFeaturizer with empirically found parameters into the supervised_embeddings NLU pipeline template

• NLU evaluations now also stores its output in the output directory like the core evaluation

• show warning in case a default path is used instead of a provided, invalid path

• compare mode of rasa train core allows the whole core config comparison, naming style of models trained for comparison is changed (this is a breaking change)

• pika keeps a single connection open, instead of open and closing on each incoming event

• RasaChatInput fetches the public key from the Rasa X API. The key is used to decode the bearer token containing the conversation ID. This requires rasa-x>=0.20.2.

• more specific exception message when loading custom components depending on whether component’s path or class name is invalid or can’t be found in the global namespace

• change priorities so that the MemoizationPolicy has higher priority than the MappingPolicy

• substitute LSTM with Transformer in EmbeddingPolicy

• EmbeddingPolicy can now use MaxHistoryTrackerFeaturizer

• non-zero evaluate_on_num_examples in EmbeddingPolicy and EmbeddingIntentClassifier is the size of holdout validation set that is excluded from training data

• defaults parameters and architectures for both EmbeddingPolicy and EmbeddingIntentClassifier are changed (this is a breaking change)

• evaluation of NER does not include ‘no-entity’ anymore

• --successes for rasa test nlu is now boolean values. If set incorrect/successful predictions are saved in a file.

• --errors is renamed to --no-errors and is now a boolean value. By default incorrect predictions are saved in a file. If --no-errors is set predictions are not written to a file.

• Remove label_tokenization_flag and label_split_symbol from EmbeddingIntentClassifier. Instead move these parameters to Tokenizers.

• Process features of all attributes of a message, i.e. - text, intent and response inside the respective component itself. For e.g. - intent of a message is now tokenized inside the tokenizer itself.

• Deprecate as_markdown and as_json in favor of nlu_as_markdown and nlu_as_json respectively.

• pin python-engineio >= 3.9.3

• update python-socketio req to >= 4.3.1

Fixed

• rasa test nlu with a folder of configuration files

• MappingPolicy standard featurizer is set to None

• Removed text parameter from send_attachment function in slack.py to avoid duplication of text output to slackbot

• server /status endpoint reports status when an NLU-only model is loaded

Removed

• Removed --report argument from rasa test nlu. All output files are stored in the --out directory.

[1.2.11] - 2019-10-09

Added

• Support for passing a CA file for SSL certificate verification via the –ssl-ca-file flag

[1.2.10] - 2019-10-08

Added

• Added support for RabbitMQ TLS authentication. The following environment variables need to be set: RABBITMQ_SSL_CLIENT_CERTIFICATE - path to the SSL client certificate (required) RABBITMQ_SSL_CLIENT_KEY - path to the SSL client key (required) RABBITMQ_SSL_CA_FILE - path to the SSL CA file (optional, for certificate verification) RABBITMQ_SSL_KEY_PASSWORD - SSL private key password (optional)

• Added ability to define the RabbitMQ port using the port key in the event_broker endpoint config.

[1.2.9] - 2019-09-17

Fixed

• Correctly pass SSL flag values to x CLI command

[1.2.8] - 2019-09-10

Fixed

• SQL tracker events are retrieved ordered by timestamps. This fixes interactive learning events being shown in the wrong order. Backport of 1.3.2 patch (PR #4427).

[1.2.7] - 2019-09-02

Fixed

• Added query dictionary argument to SQLTrackerStore which will be appended to the SQL connection URL as query parameters.

[1.2.6] - 2019-09-02

Fixed

• fixed bug that occurred when sending template elements through a channel that doesn’t support them

[1.2.5] - 2019-08-26

Added

• SSL support for rasa run command. Certificate can be specified using --ssl-certificate and --ssl-keyfile.

Fixed

• made default augmentation value consistent across repo

• '/restart' will now also restart the bot if the tracker is paused

[1.2.4] - 2019-08-23

Fixed

• the SocketIO input channel now allows access from other origins (fixes SocketIO channel on Rasa X)

[1.2.3] - 2019-08-15

Changed

• messages with multiple entities are now handled properly with e2e evaluation

• data/test_evaluations/end_to_end_story.md was rewritten in the restaurantbot domain

[1.2.2] - 2019-08-07

Fixed

• UserUttered events always got the same timestamp

[1.2.1] - 2019-08-06

Added

• Docs now have an EDIT THIS PAGE button

Fixed

• Flood control exceeded error in Telegram connector which happened because the webhook was set twice

[1.2.0] - 2019-08-01

Added

• add root route to server started without --enable-api parameter

• add --evaluate-model-directory to rasa test core to evaluate models from rasa train core -c <config-1> <config-2>

• option to send messages to the user by calling POST /conversations/{conversation_id}/execute

Changed

• Agent.update_model() and Agent.handle_message() now work without needing to set a domain or a policy ensemble

• Update pytype to 2019.7.11

• new event broker class: SQLProducer. This event broker is now used when running locally with Rasa X

• API requests are no longer logged to rasa_core.log by default in order to avoid problems when running on OpenShift (use --log-file rasa_core.log to retain the old behavior)

• metadata attribute added to UserMessage

Fixed

• rasa test core can handle compressed model files

• rasa can handle story files containing multi-line comments

• template will retain { if escaped with {. e.g. {{“foo”: {bar}}} will result in {“foo”: “replaced value”}

[1.1.8] - 2019-07-25

Added

• TrainingFileImporter interface to support customizing the process of loading training data

• fill slots for custom templates

Changed

• Agent.update_model() and Agent.handle_message() now work without needing to set a domain or a policy ensemble

• update pytype to 2019.7.11

Fixed

• interactive learning bug where reverted user utterances were dumped to training data

• added timeout to terminal input channel to avoid freezing input in case of server errors

• fill slots for image, buttons, quick_replies and attachments in templates

[1.1.7] - 2019-07-18

Added

• added optional pymongo dependencies [tls, srv] to requirements.txt for better mongodb support

• case_sensitive option added to WhiteSpaceTokenizer with true as default.

Fixed

• validation no longer throws an error during interactive learning

• fixed wrong cleaning of use_entities in case it was a list and not True

• updated the server endpoint /model/parse to handle also messages with the intent prefix

• fixed bug where “No model found” message appeared after successfully running the bot

• debug logs now print to rasa_core.log when running rasa x -vv or rasa run -vv

[1.1.6] - 2019-07-12

Added

• rest channel supports setting a message’s input_channel through a field input_channel in the request body

Changed

• recommended syntax for empty use_entities and ignore_entities in the domain file has been updated from False or None to an empty list ([])

Fixed

• rasa run without --enable-api does not require a local model anymore

• using rasa run with --enable-api to run a server now prints “running Rasa server” instead of “running Rasa Core server”

• actions, intents, and utterances created in rasa interactive can no longer be empty

[1.1.5] - 2019-07-10

Added

• debug logging now tells you which tracker store is connected

• the response of /model/train now includes a response header for the trained model filename

• Validator class to help develop by checking if the files have any errors

• project’s code is now linted using flake8

• info log when credentials were provided for multiple channels and channel in --connector argument was specified at the same time

• validate export paths in interactive learning

Changed

• deprecate rasa.core.agent.handle_channels(...). Please use rasa.run(...) or rasa.core.run.configure_app instead.

• Agent.load() also accepts tar.gz model file

Removed

• revert the stripping of trailing slashes in endpoint URLs since this can lead to problems in case the trailing slash is actually wanted

• starter packs were removed from Github and are therefore no longer tested by the Travis script

Fixed

• all temporal model files are now deleted after stopping the Rasa server

• rasa shell nlu now outputs unicode characters instead of \uxxxx codes

• fixed PUT /model with model_server by deserializing the model_server to EndpointConfig.

• x in AnySlotDict is now True for any x, which fixes empty slot warnings in interactive learning

• rasa train now also includes NLU files in other formats than the Rasa format

• rasa train core no longer crashes without a --domain arg

• rasa interactive now looks for endpoints in endpoints.yml if no --endpoints arg is passed

• custom files, e.g. custom components and channels, load correctly when using the command line interface

• MappingPolicy now works correctly when used as part of a PolicyEnsemble

[1.1.4] - 2019-06-18

Added

• unfeaturize single entities

• added agent readiness check to the /status resource

Changed

• removed leading underscore from name of ‘_create_initial_project’ function.

Fixed

• fixed bug where facebook quick replies were not rendering

• take FB quick reply payload rather than text as input

• fixed bug where training_data path in metadata.json was an absolute path

[1.1.3] - 2019-06-14

Fixed

• fixed any inconsistent type annotations in code and some bugs revealed by type checker

[1.1.2] - 2019-06-13

Fixed

• fixed duplicate events appearing in tracker when using a PostgreSQL tracker store

[1.1.1] - 2019-06-13

Fixed

• fixed compatibility with Rasa SDK

• bot responses can contain custom messages besides other message types

[1.1.0] - 2019-06-13

Added

• nlu configs can now be directly compared for performance on a dataset in rasa test nlu

Changed

• update the tracker in interactive learning through reverting and appending events instead of replacing the tracker

• POST /conversations/{conversation_id}/tracker/events supports a list of events

Fixed

• fixed creation of RasaNLUHttpInterpreter

• form actions are included in domain warnings

• default actions, which are overridden by custom actions and are listed in the domain are excluded from domain warnings

• SQL data column type to Text for compatibility with MySQL

• non-featurizer training parameters don’t break SklearnPolicy anymore

[1.0.9] - 2019-06-10

Changed

• revert PR #3739 (as this is a breaking change): set PikaProducer and KafkaProducer default queues back to rasa_core_events

[1.0.8] - 2019-06-10

Added

• 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)

Changed

• default PikaProducer and KafkaProducer queues to rasa_production_events

• exclude unfeaturized slots from domain warnings

Fixed

• loading of additional training data with the SkillSelector

• strip trailing slashes in endpoint URLs

[1.0.7] - 2019-06-06

Added

• added argument --rasa-x-port to specify the port of Rasa X when running Rasa X locally via rasa x

Fixed

• slack notifications from bots correctly render text

• fixed usage of --log-file argument for rasa run and rasa shell

• check if correct tracker store is configured in local mode

[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 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 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 the current user

• Rasa HTTP intent evaluation endpoint at POST /intentEvaluation. This endpoint performs an intent evaluation of a Rasa 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 the 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.