Deprecations and Removals
-
#6410:
Domain.random_template_for
is deprecated and will be removed in Rasa Open Source
3.0.0. You can alternatively use theTemplatedNaturalLanguageGenerator
.Domain.action_names
is deprecated and will be removed in Rasa Open Source
3.0.0. Please useDomain.action_names_or_texts
instead. -
#7458: Interfaces for
Policy.__init__
andPolicy.load
have changed.
See migration guide for details. -
#7495: Deprecate training and test data in Markdown format. This includes:
- reading and writing of story files in Markdown format
- reading and writing of NLU data in Markdown format
- reading and writing of retrieval intent data in Markdown format
Support for Markdown data will be removed entirely in Rasa Open Source 3.0.0.
Please convert your existing Markdown data by using the commands
from the migration guide:rasa data convert nlu -f yaml --data={SOURCE_DIR} --out={TARGET_DIR} rasa data convert nlg -f yaml --data={SOURCE_DIR} --out={TARGET_DIR} rasa data convert core -f yaml --data={SOURCE_DIR} --out={TARGET_DIR}
-
#7529:
Domain.add_categorical_slot_default_value
,Domain.add_requested_slot
andDomain.add_knowledge_base_slots
are deprecated and will be removed in Rasa Open
Source 3.0.0. Their internal versions are now called during the Domain creation.
Calling them manually is no longer required.
Features
-
#6971: Incremental training of models in a pipeline is now supported.
If you have added new NLU training examples or new stories/rules for
dialogue manager, you don't need to train the pipeline from scratch.
Instead, you can initialize the pipeline with a previously trained model
and continue finetuning the model on the complete dataset consisting of
new training examples. To do so, userasa train --finetune
. For more
detailed explanation of the command, check out the docs on incremental
training.Added a configuration parameter
additional_vocabulary_size
to
CountVectorsFeaturizer
andnumber_additional_patterns
toRegexFeaturizer
.
These parameters are useful to configure when using incremental training for your pipelines. -
#7408: Add the option to use cross-validation to the
POST /model/test/intents
endpoint.
To use cross-validation specify the query parametercross_validation_folds
in addition
to the training data in YAML format.Add option to run NLU evaluation
(POST /model/test/intents
) and
model training (POST /model/train
)
asynchronously.
To trigger asynchronous processing specify
a callback URL in the query parametercallback_url
which Rasa Open Source should send
the results to. This URL will also be called in case of errors. -
#7496: Make TED Policy an end-to-end policy. Namely, make it possible to train TED on stories that contain
intent and entities or user text and bot actions or bot text.
If you don't have text in your stories, TED will behave the same way as before.
Add possibility to predict entities using TED.Here's an example of a dialogue in the Rasa story format:
stories: - story: collect restaurant booking info # name of the story - just for debugging steps: - intent: greet # user message with no entities - action: utter_ask_howcanhelp # action that the bot should execute - intent: inform # user message with entities entities: - location: "rome" - price: "cheap" - bot: On it # actual text that bot can output - action: utter_ask_cuisine - user: I would like [spanish](cuisine). # actual text that user input - action: utter_ask_num_people
Some model options for
TEDPolicy
got renamed.
Please update your configuration files using the following mapping:Old model option New model option transformer_size dictionary “transformer_size” with keys “text”, “action_text”, “label_action_text”, “dialogue” number_of_transformer_layers dictionary “number_of_transformer_layers” with keys “text”, “action_text”, “label_action_text”, “dialogue” dense_dimension dictionary “dense_dimension” with keys “text”, “action_text”, “label_action_text”, “intent”, “action_name”, “label_action_name”, “entities”, “slots”, “active_loop”
Improvements
-
#3998: Added a message showing the location where the failed stories file was saved.
-
#7232: Add support for the top-level response keys
quick_replies
,attachment
andelements
refered to inrasa.core.channels.OutputChannel.send_reponse
, as well asmetadata
. -
#7257: Changed the format of the histogram of confidence values for both correct and incorrect predictions produced by running
rasa test
. -
#7284: Run
bandit
checks on pull requests.
Introducemake static-checks
command to run all static checks locally. -
#7397: Add
rasa train --dry-run
command that allows to check if training needs to be performed
and what exactly needs to be retrained. -
#7408:
POST /model/test/intents
now returns
thereport
field forintent_evaluation
,entity_evaluation
and
response_selection_evaluation
as machine-readable JSON payload instead of string. -
#7436: Make
rasa data validate stories
work for end-to-end.The
rasa data validate stories
function now considers the tokenized user text instead of the plain text that is part of a state.
This is closer to what Rasa Core actually uses to distinguish states and thus captures more story structure problems.
Bugfixes
- #6804: Rename
language_list
tosupported_language_list
forJiebaTokenizer
. - #7244: A
float
slot returns unambiguous values -[1.0, <value>]
if successfully converted,[0.0, 0.0]
if not.
This makes it possible to distinguish an empty float slot from a slot set to0.0
.
:::caution
This change is model-breaking. Please retrain your models.
::: - #7306: Fix an erroneous attribute for Redis key prefix in
rasa.core.tracker_store.RedisTrackerStore
: 'RedisTrackerStore' object has no attribute 'prefix'. - #7407: Remove token when its text (for example, whitespace) can't be tokenized by LM tokenizer (from
LanguageModelFeaturizer
). - #7408: Temporary directories which were created during requests to the HTTP API
are now cleaned up correctly once the request was processed. - #7422: Add option
use_word_boundaries
forRegexFeaturizer
andRegexEntityExtractor
. To correctly process languages such as Chinese that don't use whitespace for word separation, the user needs to add theuse_word_boundaries: False
option to those two components. - #7529: Correctly fingerprint the default domain slots. Previously this led to the issue
thatrasa train core
would always retrain the model even if the training data hasn't
changed.
Improved Documentation
- #7313: Return the "Migrate from" entry to the docs sidebar.