Version Migration Guide

You are viewing documentation for our open source project which is maintained by the community. If you want to get started building assistants with Rasa please check out our latest documentation here.

This page contains information about changes between major versions and how you can migrate from one version to another.

Rasa 2.7 to 2.8

caution

This release breaks backward compatibility of machine learning models. It is not possible to load models trained with previous versions of Rasa Open Source. Please re-train your assistant before using this version.

Deprecations

Tracker Featurizers

training_states_actions_and_entities method of TrackerFeaturizer, FullDialogueTrackerFeaturizer and MaxHistoryTrackerFeaturizer classes is deprecated and will be removed in Rasa Open Source 3.0 . If you had a custom tracker featurizer which relied on this method from any of the above classes, please use training_states_labels_and_entities instead.
training_states_actions method of TrackerFeaturizer, FullDialogueTrackerFeaturizer and MaxHistoryTrackerFeaturizer classes is deprecated and will be removed in Rasa Open Source 3.0 . If you had a custom tracker featurizer which relied on this method from any of the above classes, please use training_states_labels instead.

State Featurizer

encode_all_actions method of SingleStateFeaturizer class is deprecated and will be removed in Rasa Open Source 3.0 . It is recommended to use the method encode_all_labels instead.

Incremental Training

Users don't need to specify an additional buffer size for sparse featurizers anymore during incremental training.

Space for new sparse features are created dynamically inside the downstream machine learning models - DIETClassifier, ResponseSelector. In other words, no extra buffer is created in advance for additional vocabulary items and space will be dynamically allocated for them inside the model.

This means there's no need to specify additional_vocabulary_size for CountVectorsFeaturizer or number_additional_patterns for RegexFeaturizer. These parameters are now deprecated.

Machine Learning Components

The option model_confidence=linear_norm is deprecated and will be removed in Rasa Open Source 3.0.0.

Rasa Open Source 2.3.0 introduced linear_norm as a possible value for model_confidence parameter in machine learning components such as DIETClassifier, ResponseSelector and TEDPolicy. Based on user feedback, we have identified multiple problems with this option. Therefore, model_confidence=linear_norm is now deprecated and will be removed in Rasa Open Source 3.0.0. If you were using model_confidence=linear_norm for any of the mentioned components, we recommend to revert it back to model_confidence=softmax and re-train the assistant. After re-training, we also recommend to re-tune the thresholds for fallback components.

Rasa 2.5 to 2.6

Forms

New ignored_intents parameter in Forms

There is a new parameter under Forms called ignored_intents. This parameter can be used to prevent any required slots in a form from being filled with the specified intent or intents. Please see the Forms documentation for examples and more information on how to use it in your domain.yml file.

Before, if a user did not want to fill any slots of a form with a specified intent they would have to define it under the not_intent parameter for every slot mapping as shown in the following example :

domain.yml
forms:
  restaurant_form:
    cuisine:
      - type: from_entity
        entity: cuisine
        not_intent: chitchat
    num_people:
      - type: from_entity
        entity: number
        intent: [inform, request_restaurant]
        not_intent: chitchat
    feedback:
      - type: from_entity
        entity: feedback
        not_intent: chitchat

By introducing the ignored_intents parameter, we now only need to define it in one place and it will affect all the slots of the form :

domain.yml
forms:
  restaurant_form:
    ignored_intents: chitchat
    required_slots:
      cuisine:
        - type: from_entity
          entity: cuisine
      num_people:
        - type: from_entity
          entity: number
          intent: [inform, request_restaurant]
      feedback:
        - type: from_entity
          entity: feedback
        - type: from_text

Rasa 2.4 to 2.5

Machine Learning Components

DIET, TED, and ResponseSelector

The former weight_sparsity parameter of the DIETClassifier, TEDPolicy, and the ResponseSelector, is now deprecated and superseded by the new connection_density parameter. The old weight_sparsity is roughly equivalent to 1 - connection_density, except at very low densities (high sparsities).

To avoid deprecation issues, you should set connection_density to 1 - your former weight_sparsity setting throughout the config file. (If you left weight_sparsity at its default setting, you don't need to do anything.)

SpaCy 3.0

Rasa now supports spaCy 3.0. This means that we can support more features for more languages but this also introduced a breaking change. SpaCy 3.0 deprecated the spacy link <language model> command. So from now on you need to use the the full model name in the config.yml file.

Before 

language: en
pipeline:
  - name: SpacyNLP

Now 

language: en
pipeline:
  - name: SpacyNLP
    model: en_core_web_md

Fallback
To make the transition easier, Rasa will try to fall back to a medium spaCy model whenever a compatible language is configured for the entire pipeline in config.yml, even if you don't specify a model. This fallback behavior is temporary and will be deprecated in Rasa Open Source 3.0.0.

We've updated our docs to reflect these changes. All examples now show a direct link to the correct spaCy model. We've also added a warning to the SpaCyNLP docs that explains the fallback behavior.

Rasa 2.3 to Rasa 2.4

Deprecating template for response

NLG Server

rasa.core.agent

rasa.core.nlg.generator

rasa.shared.core.domain

Response Selector

Rasa 2.3.3 to Rasa 2.3.4

caution

This is a release breaking backwards compatibility of machine learning models. It is not possible to load previously trained models if they were trained with model_confidence=cosine or model_confidence=inner setting. Please make sure to re-train the assistant before trying to use it with this improved version.