Rasa OSS 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.

[1.6.2] - 2020-01-28 ¶

Improvements ¶

• #4989: Switching back to a TensorFlow release which only includes CPU support to reduce the size of the dependencies. If you want to use the TensorFlow package with GPU support, please run tensorflow-gpu==1.15.0.

Bugfixes ¶

• #5111: Fixes Exception 'Loop' object has no attribute '_ready' error when running rasa init.

• #5126: Updated the end-to-end ValueError you recieve when you have a invalid story format to point to the updated doc link.

[1.6.1] - 2020-01-07 ¶

Bugfixes ¶

• #4989: Use an empty domain in case a model is loaded which has no domain (avoids errors when accessing agent.doman.<some attribute>).

• #4995: Replace error message with warning in tokenizers and featurizers if default parameter not set.

• #5019: Pin sanic patch version instead of minor version. Fixes sanic _run_request_middleware() error.

• #5032: Fix wrong calculation of additional conversation events when saving the conversation. This led to conversation events not being saved.

• #5032: Fix wrong order of conversation events when pushing events to conversations via POST /conversations/<conversation_id>/tracker/events.

[1.6.0] - 2019-12-18 ¶

Deprecations and Removals ¶

• #4935: Removed ner_features as a feature name from CRFEntityExtractor, use text_dense_features instead.

The following settings match the previous NGramFeaturizer:

- name: 'CountVectorsFeaturizer'
    analyzer: 'char_wb'
    min_ngram: 3
    max_ngram: 17
    max_features: 10
    min_df: 5

• #4957: To use custom features in the CRFEntityExtractor use text_dense_features instead of ner_features. If text_dense_features are present in the feature set, the CRFEntityExtractor will automatically make use of them. Just make sure to add a dense featurizer in front of the CRFEntityExtractor in your pipeline and set the flag return_sequence to True for that featurizer. See https://rasa.com/docs/rasa/nlu/entity-extraction/#passing-custom-features-to-crfentityextractor.

• #4990: Deprecated Agent.continue_training. Instead, a model should be retrained.

• #684: Specifying lookup tables directly in the NLU file is now deprecated. Please specify them in an external file.

Features ¶

• #4795: Replaced the warnings about missing templates, intents etc. in validator.py by debug messages.

• #4830: Added conversation sessions to trackers.

A conversation session represents the dialog between the assistant and a user. Conversation sessions can begin in three ways: 1. the user begins the conversation with the assistant, 2. the user sends their first message after a configurable period of inactivity, or 3. a manual session start is triggered with the /session_start intent message. The period of inactivity after which a new conversation session is triggered is defined in the domain using the session_expiration_time key in the session_config section. The introduction of conversation sessions comprises the following changes:

• Added a new event SessionStarted that marks the beginning of a new conversation session.

• Added a new default action ActionSessionStart. This action takes all SlotSet events from the previous session and applies it to the next session.

• Added a new default intent session_start which triggers the start of a new conversation session.

• SQLTrackerStore and MongoTrackerStore only retrieve events from the last session from the database.

Note

The session behaviour is disabled for existing projects, i.e. existing domains without session config section.

• #4935: Preparation for an upcoming change in the EmbeddingIntentClassifier:

Add option use_cls_token to all tokenizers. If it is set to True, the token __CLS__ will be added to the end of the list of tokens. Default is set to False. No need to change the default value for now.

Add option return_sequence to all featurizers. By default all featurizers return a matrix of size (1 x feature-dimension). If the option return_sequence is set to True, the corresponding featurizer will return a matrix of size (token-length x feature-dimension). See https://rasa.com/docs/rasa/nlu/components/#featurizers. Default value is set to False. However, you might want to set it to True if you want to use custom features in the CRFEntityExtractor. See https://rasa.com/docs/rasa/nlu/entity-extraction/#passing-custom-features-to-crfentityextractor.

Warning

These changes break model compatibility. You will need to retrain your old models!

Improvements ¶

• #3549: Added --no-plot option for rasa test command, which disables rendering of confusion matrix and histogram. By default plots will be rendered.

• #4086: If matplotlib couldn’t set up a default backend, it will be set automatically to TkAgg/Agg one

• #4647: Add the option random_seed to the rasa data split nlu command to generate reproducible train/test splits.

• #4734: Changed url``__init__() arguments for custom tracker stores to host to reflect the __init__ arguments of currently supported tracker stores. Note that in endpoints.yml, these are still declared as url.

• #4751: The kafka-python dependency has become as an “extra” dependency. To use the KafkaEventConsumer, rasa has to be installed with the [kafka] option, i.e.

$ pip install rasa[kafka]

• #4801: Allow creation of natural language interpreter and generator by classname reference in endpoints.yml.

• #4834: Made it explicit that interactive learning does not work with NLU-only models.

Interactive learning no longer trains NLU-only models if no model is provided and no core data is provided.

• #4899: The intent_report.json created by rasa test now creates an extra field confused_with for each intent. This is a dictionary containing the names of the most common false positives when this intent should be predicted, and the number of such false positives.

• #4976: rasa test nlu --cross-validation now also includes an evaluation of the response selector. As a result, the train and test F1-score, accuracy and precision is logged for the response selector. A report is also generated in the results folder by the name response_selection_report.json

Bugfixes ¶

• #4635: If a wait_time_between_pulls is configured for the model server in endpoints.yml, this will be used instead of the default one when running Rasa X.

• #4759: Training Luis data with luis_schema_version higher than 4.x.x will show a warning instead of throwing an exception.

• #4799: Running rasa interactive with no NLU data now works, with the functionality of rasa interactive core.

• #4917: When loading models from S3, namespaces (folders within a bucket) are now respected. Previously, this would result in an error upon loading the model.

• #4925: “rasa init” will ask if user wants to train a model

• #4942: Pin multidict dependency to 4.6.1 to prevent sanic from breaking, see https://github.com/huge-success/sanic/issues/1729

• #4985: Fix errors during training and testing of ResponseSelector.

[1.5.3] - 2019-12-11 ¶

Improvements ¶

• #4933: Improved error message that appears when an incorrect parameter is passed to a policy.

Bugfixes ¶

• #4914: Added rasa/nlu/schemas/config.yml to wheel package

[1.5.2] - 2019-12-09 ¶

Improvements ¶

• #3684: rasa interactive will skip the story visualization of training stories in case there are more than 200 stories. Stories created during interactive learning will be visualized as before.

• #4792: The log level for SocketIO loggers, including websockets.protocol, engineio.server, and socketio.server, is now handled by the LOG_LEVEL_LIBRARIES environment variable, where the default log level is ERROR.

• #4873: Updated all example bots and documentation to use the updated dispatcher.utter_message() method from rasa-sdk==1.5.0.

Bugfixes ¶

• #3684: rasa interactive will not load training stories in case the visualization is skipped.

• #4789: Fixed error where spacy models where not found in the docker images.

• #4802: Fixed unnecessary kwargs unpacking in rasa.test.test_core call in rasa.test.test function.

• #4898: Training data files now get loaded in the same order (especially relevant to subdirectories) each time to ensure training consistency when using a random seed.

• #4918: Locks for tickets in LockStore are immediately issued without a redundant check for their availability.

Improved Documentation ¶

• #4844: Added towncrier to automatically collect changelog entries.

• #4869: Document the pipeline for pretrained_embeddings_convert in the pre-configured pipelines section.

• #4894: Proactively Reaching Out to the User Using Actions now correctly links to the endpoint specification.

[1.5.1] - 2019-11-27 ¶

Improvements ¶

• When NLU training data is dumped as Markdown file the intents are not longer ordered alphabetically, but in the original order of given training data

Bugfixes ¶

• End to end stories now support literal payloads which specify entities, e.g. greet: /greet{"name": "John"}

• Slots will be correctly interpolated if there are lists in custom response templates.

• Fixed compatibility issues with rasa-sdk``1.5

• Updated /status endpoint to show correct path to model archive

[1.5.0] - 2019-11-26 ¶

Features ¶

• Added data validator that checks if domain object returned is empty. If so, exit early from the command rasa data validate.

• Added the KeywordIntentClassifier.

• Added documentation for AugmentedMemoizationPolicy.

• Fall back to InMemoryTrackerStore in case there is any problem with the current tracker store.

• Arbitrary metadata can now be attached to any Event subclass. The data must be stored under the metadata key when reading the event from a JSON object or dictionary.

• Add command line argument rasa x --config CONFIG, to specify path to the policy and NLU pipeline configuration of your bot (default: config.yml).

• Added a new NLU featurizer - ConveRTFeaturizer based on ConveRT model released by PolyAI.

• Added a new preconfigured pipeline - pretrained_embeddings_convert.

Improvements ¶

• Do not retrain the entire Core model if only the templates section of the domain is changed.

• Upgraded jsonschema version.

Deprecations and Removals ¶

• Remove duplicate messages when creating training data (issues/1446).

Bugfixes ¶

• MultiProjectImporter now imports files in the order of the import statements

• Fixed server hanging forever on leaving rasa shell before first message

• Fixed rasa init showing traceback error when user does Keyboard Interrupt before choosing a project path

• CountVectorsFeaturizer featurizes intents only if its analyzer is set to word

• Fixed bug where facebooks generic template was not rendered when buttons were None

• Fixed default intents unnecessarily raising undefined parsing error

[1.4.6] - 2019-11-22 ¶

Bugfixes ¶

• Fixed Rasa X not working when any tracker store was configured for Rasa.

• Use the matplotlib backend agg in case the tkinter package is not installed.

[1.4.5] - 2019-11-14 ¶

Bugfixes ¶

• NLU-only models no longer throw warnings about parsing features not defined in the domain

• Fixed bug that stopped Dockerfiles from building version 1.4.4.

• Fixed format guessing for e2e stories with intent restated as /intent

[1.4.4] - 2019-11-13 ¶

Features ¶

• PikaEventProducer adds the RabbitMQ App ID message property to published messages with the value of the RASA_ENVIRONMENT environment variable. The message property will not be assigned if this environment variable isn’t set.

Improvements ¶

• Updated Mattermost connector documentation to be more clear.

• Updated format strings to f-strings where appropriate.

• Updated tensorflow requirement to 1.15.0

• Dump domain using UTF-8 (to avoid \UXXXX sequences in the dumped files)

Bugfixes ¶

• Fixed exporting NLU training data in json format from rasa interactive

• Fixed numpy deprecation warnings

[1.4.3] - 2019-10-29 ¶

Bugfixes ¶

• Fixed Connection reset by peer errors and bot response delays when using the RabbitMQ event broker.

[1.4.2] - 2019-10-28 ¶

Deprecations and Removals ¶

• TensorFlow deprecation warnings are no longer shown when running rasa x

Bugfixes ¶

• Fixed 'Namespace' object has no attribute 'persist_nlu_data' error during interactive learning

• Pinned networkx~=2.3.0 to fix visualization in rasa interactive and Rasa X

• Fixed No model found error when using rasa run actions with “actions” as a directory.

[1.4.1] - 2019-10-22 ¶

Regression: changes from 1.2.12 were missing from 1.4.0, readded them

[1.4.0] - 2019-10-19 ¶

Features ¶

• add flag to CLI to persist NLU training data if needed

• log a warning if the Interpreter picks up an intent or an entity that does not exist in the domain file.

• added DynamoTrackerStore to support persistence of agents running on AWS

• added docstrings for TrackerStore classes

• added buttons and images to mattermost.

• CRFEntityExtractor updated to accept arbitrary token-level features like word vectors (issues/4214)

• SpacyFeaturizer updated to add ner_features for CRFEntityExtractor

• Sanitizing incoming messages from slack to remove slack formatting like < mailto:xyz@rasa.com|xyz@rasa.com> or < http://url.com|url.com> and substitute it with original content

• Added the ability to configure the number of Sanic worker processes in the HTTP server (rasa.server) and input channel server (rasa.core.agent.handle_channels()). The number of workers can be set using the environment variable SANIC_WORKERS (default: 1). A value of >1 is allowed only in combination with RedisLockStore as the lock store.

• Botframework channel can handle uploaded files in UserMessage metadata.

• Added data validator that checks there is no duplicated example data across multiples intents

Improvements ¶

• Unknown sections in markdown format (NLU data) are not ignored anymore, but instead an error is raised.

• It is now easier to add metadata to a UserMessage in existing channels. You can do so by overwriting the method get_metadata. The return value of this method will be passed to the UserMessage object.

• Tests can now be run in parallel

• Serialise DialogueStateTracker as json instead of pickle. DEPRECATION warning: Deserialisation of pickled trackers will be deprecated in version 2.0. For now, trackers are still loaded from pickle but will be dumped as json in any subsequent save operations.

• Event brokers are now also passed to custom tracker stores (using the event_broker parameter)

• Don’t run the Rasa Docker image as root.

• Use multi-stage builds to reduce the size of the Rasa Docker image.

• Updated the /status api route to use the actual model file location instead of the tmp location.

Deprecations and Removals ¶

• Removed Python 3.5 support

Bugfixes ¶

• fixed missing tkinter dependency for running tests on Ubuntu

• fixed issue with conversation JSON serialization

• fixed the hanging HTTP call with ner_duckling_http pipeline

• fixed Interactive Learning intent payload messages saving in nlu files

• fixed DucklingHTTPExtractor dimensions by actually applying to the request

[1.3.10] - 2019-10-18 ¶

Features ¶

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

Bugfixes ¶

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

[1.3.9] - 2019-10-10 ¶

Features ¶

• 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).

Bugfixes ¶

• 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 ¶

Improvements ¶

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

Bugfixes ¶

• 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 ¶

Bugfixes ¶

• 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 ¶

Features ¶

• 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 ¶

Bugfixes ¶

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

[1.3.4] - 2019-09-20 ¶

Features ¶

• 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

Bugfixes ¶

• 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.

Improvements ¶

• 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 ¶

Bugfixes ¶

• 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.

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

Deprecations and Removals ¶

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

[1.3.2] - 2019-09-10 ¶

Bugfixes ¶

• 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 ¶

Improvements ¶

• Pin gast to == 0.2.2

[1.3.0] - 2019-09-05 ¶

Features ¶

• 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 (eg. 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 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 flag 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

Improvements ¶

• 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 hold out 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 favour of nlu_as_markdown and nlu_as_json respectively.

• pin python-engineio >= 3.9.3

• update python-socketio req to >= 4.3.1

Bugfixes ¶

• 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

Deprecations and Removals ¶

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

[1.2.12] - 2019-10-16 ¶

Features ¶

• Support for transit encryption with Redis via use_ssl: True in the tracker store config in endpoints.yml

[1.2.11] - 2019-10-09 ¶

Features ¶

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

[1.2.10] - 2019-10-08 ¶

Features ¶

• 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 ¶

Bugfixes ¶

• Correctly pass SSL flag values to x CLI command (backport of

[1.2.8] - 2019-09-10 ¶

Bugfixes ¶

• 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 ¶

Bugfixes ¶

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

[1.2.6] - 2019-09-02 ¶

Bugfixes ¶

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

[1.2.5] - 2019-08-26 ¶

Features ¶

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

Bugfixes ¶

• 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 ¶

Bugfixes ¶

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

[1.2.3] - 2019-08-15 ¶

Improvements ¶

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

• data/test_evaluations/end_to_end_story.md was re-written in the restaurantbot domain

[1.2.3] - 2019-08-15 ¶

Improvements ¶

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

• data/test_evaluations/end_to_end_story.md was re-written in the restaurantbot domain

Bugfixes ¶

• Free text input was not allowed in the Rasa shell when the response template contained buttons, which has now been fixed.

[1.2.2] - 2019-08-07 ¶

Bugfixes ¶

• UserUttered events always got the same timestamp

[1.2.1] - 2019-08-06 ¶

Features ¶

• Docs now have an EDIT THIS PAGE button

Bugfixes ¶

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

[1.2.0] - 2019-08-01 ¶

Features ¶

• 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

Improvements ¶

• 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 not 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

Bugfixes ¶

• 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 ¶

Features ¶

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

• fill slots for custom templates

Improvements ¶

• 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

Bugfixes ¶

• 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

• rasa train core in comparison mode stores the model files compressed (tar.gz files)

• slot setting in interactive learning with the TwoStageFallbackPolicy

[1.1.7] - 2019-07-18 ¶

Features ¶

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

• case_sensitive option added to WhiteSpaceTokenizer with true as default.

Bugfixes ¶

• 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 ¶

Features ¶

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

Improvements ¶

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

Bugfixes ¶

• 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 ¶

Features ¶

• 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 developing 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

Improvements ¶

• 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

Deprecations and Removals ¶

• 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 Travis script

Bugfixes ¶

• 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 ¶

Features ¶

• unfeaturize single entities

• added agent readiness check to the /status resource

Improvements ¶

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

Bugfixes ¶

• 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 ¶

Bugfixes ¶

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

[1.1.2] - 2019-06-13 ¶

Bugfixes ¶

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

[1.1.1] - 2019-06-13 ¶

Bugfixes ¶

• fixed compatibility with Rasa SDK

• bot responses can contain custom messages besides other message types

[1.1.0] - 2019-06-13 ¶

Features ¶

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

Improvements ¶

• 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

Bugfixes ¶

• fixed creation of RasaNLUHttpInterpreter

• form actions are included in domain warnings

• default actions, which are overriden 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 ¶

Improvements ¶

• 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 ¶

Features ¶

• 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)

Improvements ¶

• default PikaProducer and KafkaProducer queues to rasa_production_events

• exclude unfeaturized slots from domain warnings

Bugfixes ¶

• loading of additional training data with the SkillSelector

• strip trailing slashes in endpoint URLs

[1.0.7] - 2019-06-06 ¶

Features ¶

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

Bugfixes ¶

• 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 ¶

Bugfixes ¶

• fixed backwards incompatible utils changes

[1.0.5] - 2019-06-03 ¶

Bugfixes ¶

• fixed spacy being a required dependency (regression)

[1.0.4] - 2019-06-03 ¶

Features ¶

• 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);.

Improvements ¶

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

[1.0.3] - 2019-05-30 ¶

Bugfixes ¶

• 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 ¶

Features ¶

• 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

Bugfixes ¶

• 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 ¶

Bugfixes ¶

• updated installation command in docs for Rasa X

[1.0.0] - 2019-05-21 ¶

Features ¶

• 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 current user

• Rasa HTTP intent evaluation endpoint at POST /intentEvaluation. This endpoints 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 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

Improvements ¶

• 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)

Deprecations and Removals ¶

• 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.

Bugfixes ¶

• 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

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