Migration Guide

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

Rasa 1.7 to Rasa 1.8

General

The TED Policy replaced the Keras Policy as recommended machine learning policy. New projects generated with rasa init will automatically use this policy. In case you want to change your existing model configuration to use the TED Policy add this to the policies section in your config.yml and remove potentially existing KerasPolicy entries:
```
policies:
# - ... other policies
- name: TEDPolicy
  max_history: 5
  epochs: 100
```
All pre-defined pipeline templates are deprecated. Any templates you use will be mapped to the new configuration, but the underlying architecture is the same. Take a look at Choosing a Pipeline to decide on what components you should use in your configuration file.
The Embedding Policy was renamed to TED Policy. The functionality of the policy stayed the same. Please update your configuration files to use TEDPolicy instead of EmbeddingPolicy.
Most of the model options for EmbeddingPolicy, EmbeddingIntentClassifier, and ResponseSelector got renamed. Please update your configuration files using the following mapping:

Old model option	New model option
hidden_layers_sizes_a	dictionary “hidden_layers_sizes” with key “text”
hidden_layers_sizes_b	dictionary “hidden_layers_sizes” with key “label”
hidden_layers_sizes_pre_dial	dictionary “hidden_layers_sizes” with key “dialogue”
hidden_layers_sizes_bot	dictionary “hidden_layers_sizes” with key “label”
num_transformer_layers	number_of_transformer_layers
num_heads	number_of_attention_heads
max_seq_length	maximum_sequence_length
dense_dim	dense_dimension
embed_dim	embedding_dimension
num_neg	number_of_negative_examples
mu_pos	maximum_positive_similarity
mu_neg	maximum_negative_similarity
use_max_sim_neg	use_maximum_negative_similarity
C2	regularization_constant
C_emb	negative_margin_scale
droprate_a	droprate_dialogue
droprate_b	droprate_label
evaluate_every_num_epochs	evaluate_every_number_of_epochs
evaluate_on_num_examples	evaluate_on_number_of_examples

Old configuration options will be mapped to the new names, and a warning will be thrown. However, these will be deprecated in a future release.

EmbeddingIntentClassifier is now deprecated and will be replaced by DIETClassifier in the future. DIETClassfier performs intent classification as well as entity recognition. If you want to get the same model behavior as the current EmbeddingIntentClassifier, you can use the following configuration of DIETClassifier:

    pipeline:
    # - ... other components
    - name: DIETClassifier
      hidden_layers_sizes:
          text: [256, 128]
      number_of_transformer_layers: 0
      weight_sparsity: 0
      intent_classification: True
      entity_recognition: False
      use_masked_language_model: False
      BILOU_flag: False
      # ... any other parameters
    ```

See [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.21/nlu/components/#diet-classifier) for more information about the new component. Specifying `EmbeddingIntentClassifier` in the configuration maps to the above component definition, the behavior is unchanged from previous versions.

- `CRFEntityExtractor` is now deprecated and will be replaced by `DIETClassifier` in the future. If you want to get the same model behavior as the current `CRFEntityExtractor`, you can use the following configuration:

pipeline:
# - ... other components
- name: LexicalSyntacticFeaturizer
  features: [
      ["low", "title", "upper"],
      [
        "BOS",
        "EOS",
        "low",
        "prefix5",
        "prefix2",
        "suffix5",
        "suffix3",
        "suffix2",
        "upper",
        "title",
        "digit",
      ],
      ["low", "title", "upper"],
  ]
- name: DIETClassifier
  intent_classification: False
  entity_recognition: True
  use_masked_language_model: False
  number_of_transformer_layers: 0
  # ... any other parameters
```

CRFEntityExtractor featurizes user messages on its own, it does not depend on any featurizer. We extracted the featurization from the component into the new featurizer LexicalSyntacticFeaturizer. Thus, in order to obtain the same results as before, you need to add this featurizer to your pipeline before the DIETClassifier. Specifying CRFEntityExtractor in the configuration maps to the above component definition, the behavior is unchanged from previous versions.

If your pipeline contains CRFEntityExtractor and EmbeddingIntentClassifier you can substitute both components with DIETClassifier. You can use the following pipeline for that:

    pipeline:
    # - ... other components
    - name: LexicalSyntacticFeaturizer
      features: [
          ["low", "title", "upper"],
          [
            "BOS",
            "EOS",
            "low",
            "prefix5",
            "prefix2",
            "suffix5",
            "suffix3",
            "suffix2",
            "upper",
            "title",
            "digit",
          ],
          ["low", "title", "upper"],
      ]
    - name: DIETClassifier
      number_of_transformer_layers: 0
      # ... any other parameters
    ```

## Rasa 1.6 to Rasa 1.7

### General  
- By default, the `EmbeddingIntentClassifier`, `EmbeddingPolicy`, and `ResponseSelector` will now normalize the top 10 confidence results if the `loss_type` is "softmax" (which has been default since 1.3, see [Rasa 1.2 to Rasa 1.3](https://legacy-docs-v1.rasa.com/1.10.21/migration-guide/#migration-to-rasa-1-3)). This is configurable via the `ranking_length` configuration parameter; to turn off normalization to match the previous behavior, set `ranking_length: 0`.

## Rasa 1.2 to Rasa 1.3

### General  
- Default parameters of `EmbeddingIntentClassifier` are changed. See [Components](https://legacy-docs-v1.rasa.com/1.10.21/nlu/components/#components) for details. Architecture implementation is changed as well, so **old trained models cannot be loaded**. Default parameters and architecture for `EmbeddingPolicy` are changed. See [Policies](https://legacy-docs-v1.rasa.com/1.10.21/core/policies/#policies) for details. It uses transformer instead of lstm. **Old trained models cannot be loaded**. They use `inner` similarity and `softmax` loss by default instead of `cosine` similarity and `margin` loss (can be set in config file). They use `balanced` batching strategy by default to counteract class imbalance problem. The meaning of `evaluate_on_num_examples` is changed. If it is non zero, random examples will be picked by stratified split and used as **hold out** validation set, so they will be excluded from training data. We suggest to set it to zero (default) if data set contains a lot of unique examples of dialogue turns. Removed `label_tokenization_flag` and `label_split_symbol` from component. Instead moved intent splitting to `Tokenizer` components via `intent_tokenization_flag` and `intent_split_symbol` flag.

- Default `max_history` for `EmbeddingPolicy` is `None` which means it’ll use the `FullDialogueTrackerFeaturizer`. We recommend to set `max_history` to some finite value in order to use `MaxHistoryTrackerFeaturizer` for **faster training**. See [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.10.21/api/core-featurization/#featurization-conversations) for details. We recommend to increase `batch_size` for `MaxHistoryTrackerFeaturizer` (e.g. "batch_size": [32, 64])

- **Compare** mode of `rasa train core` allows the whole core config comparison. Therefore, we changed the naming of trained models. They are named by config file name instead of policy name. Old naming style will not be read correctly when creating **compare** plots (`rasa test core`). Please remove old trained models in comparison folder and retrain. Normal core training is unaffected.

- We updated the **evaluation metric** for our **NER**. We report the weighted precision and f1-score. So far we included `no-entity` in this report. However, as most of the tokens actually don’t have an entity set, this will influence the weighted precision and f1-score quite a bit. From now on we exclude `no-entity` from the evaluation. The overall metrics now only include proper entities. You might see a drop in the performance scores when running the evaluation again.

- `/` is reserved as a delimiter token to distinguish between retrieval intent and the corresponding response text identifier. Make sure you don’t include `/` symbol in the name of your intents.

## Rasa NLU 0.14.x and Rasa Core 0.13.x to Rasa 1.0

### General  
- The scripts in `rasa.core` and `rasa.nlu` can no longer be executed. To train, test, run, … an NLU or Core model, you should now use the command line interface `rasa`. The functionality is, for the most part, the same as before. Some changes in commands reflect the combined training and running of NLU and Core models, but NLU and Core can still be trained and used individually. If you attempt to run one of the old scripts in `rasa.core` or `rasa.nlu`, an error is thrown that points you to the command you should use instead. See all the new commands at [Command Line Interface](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/command-line-interface/#command-line-interface).

- If you have written a custom output channel, all `send_` methods subclassed from the `OutputChannel` class need to take an additional `**kwargs` argument. You can use these keyword args from your custom action code or the templates in your domain file to send any extra parameters used in your channel’s send methods.

- If you were previously importing the `Button` or `Element` classes from `rasa_core.dispatcher`, these are now to be imported from `rasa_sdk.utils`.

- Rasa NLU and Core previously used [separate configuration files](https://legacy-docs.rasa.com/docs/nlu/0.15.1/migrations/?&_ga=2.218966814.608734414.1560704810-314462423.1543594887#id1). These two files should be merged into a single file either named `config.yml`, or passed via the `--config` parameter.

### HTTP API  
- There are numerous HTTP API endpoint changes which can be found [here](/content/docs/rasa/api/http-api/index.html).