notice

This is documentation for Rasa & Rasa Pro Documentation v2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (3.x).

Version: 2.x

rasa.model

Section Objects

class Section(NamedTuple)

Specifies which fingerprint keys decide whether this sub-model is retrained.

FingerprintComparisonResult Objects

class FingerprintComparisonResult()

Container for the results of a fingerprint comparison.

__init__

| __init__(nlu: bool = True, core: bool = True, nlg: bool = True, force_training: bool = False)

Creates a FingerprintComparisonResult instance.

Arguments:

  • nlu - True if the NLU model should be retrained.
  • core - True if the Core model should be retrained.
  • nlg - True if the responses in the domain should be updated.
  • force_training - True if a training of all parts is forced.

is_training_required

| is_training_required() -> bool

Check if anything has to be retrained.

should_retrain_core

| should_retrain_core() -> bool

Check if the Core model has to be updated.

should_retrain_nlg

| should_retrain_nlg() -> bool

Check if the responses have to be updated.

should_retrain_nlu

| should_retrain_nlu() -> bool

Check if the NLU model has to be updated.

get_local_model

get_local_model(model_path: Text = DEFAULT_MODELS_PATH) -> Text

Returns verified path to local model archive.

Arguments:

  • model_path - Path to the zipped model. If it's a directory, the latest trained model is returned.

Returns:

Path to the zipped model. If it's a directory, the latest trained model is returned.

Raises:

ModelNotFound Exception: When no model could be found at the provided path.

get_model

get_model(model_path: Text = DEFAULT_MODELS_PATH) -> TempDirectoryPath

Gets a model and unpacks it.

Arguments:

  • model_path - Path to the zipped model. If it's a directory, the latest trained model is returned.

Returns:

Path to the unpacked model.

Raises:

ModelNotFound Exception: When no model could be found at the provided path.

get_latest_model

get_latest_model(model_path: Text = DEFAULT_MODELS_PATH) -> Optional[Text]

Get the latest model from a path.

Arguments:

  • model_path - Path to a directory containing zipped models.

Returns:

Path to latest model in the given directory.

unpack_model

unpack_model(model_file: Text, working_directory: Optional[Union[Path, Text]] = None) -> TempDirectoryPath

Unpack a zipped Rasa model.

Arguments:

  • model_file - Path to zipped model.
  • working_directory - Location where the model should be unpacked to. If None a temporary directory will be created.

Returns:

Path to unpacked Rasa model.

get_model_subdirectories

get_model_subdirectories(unpacked_model_path: Text) -> Tuple[Optional[Text], Optional[Text]]

Return paths for Core and NLU model directories, if they exist. If neither directories exist, a ModelNotFound exception is raised.

Arguments:

  • unpacked_model_path - Path to unpacked Rasa model.

Returns:

Tuple (path to Core subdirectory if it exists or None otherwise, path to NLU subdirectory if it exists or None otherwise).

create_package_rasa

create_package_rasa(training_directory: Text, output_filename: Text, fingerprint: Optional[Fingerprint] = None) -> Text

Create a zipped Rasa model from trained model files.

Arguments:

  • training_directory - Path to the directory which contains the trained model files.
  • output_filename - Name of the zipped model file to be created.
  • fingerprint - A unique fingerprint to identify the model version.

Returns:

Path to zipped model.

project_fingerprint

project_fingerprint() -> Optional[Text]

Create a hash for the project in the current working directory.

Returns:

project hash

model_fingerprint

async model_fingerprint(file_importer: "TrainingDataImporter") -> Fingerprint

Create a model fingerprint from its used configuration and training data.

Arguments:

  • file_importer - File importer which provides the training data and model config.

Returns:

The fingerprint.

fingerprint_from_path

fingerprint_from_path(model_path: Text) -> Fingerprint

Load a persisted fingerprint.

Arguments:

  • model_path - Path to directory containing the fingerprint.

Returns:

The fingerprint or an empty dict if no fingerprint was found.

persist_fingerprint

persist_fingerprint(output_path: Text, fingerprint: Fingerprint) -> None

Persist a model fingerprint.

Arguments:

  • output_path - Directory in which the fingerprint should be saved.
  • fingerprint - The fingerprint to be persisted.

did_section_fingerprint_change

did_section_fingerprint_change(fingerprint1: Fingerprint, fingerprint2: Fingerprint, section: Section) -> bool

Check whether the fingerprint of a section has changed.

move_model

move_model(source: Text, target: Text) -> bool

Move two model directories.

Arguments:

  • source - The original folder which should be merged in another.
  • target - The destination folder where it should be moved to.

Returns:

True if the merge was successful, else False.

should_retrain

should_retrain(new_fingerprint: Fingerprint, old_model: Optional[Text], train_path: Text, has_e2e_examples: bool = False, force_training: bool = False) -> FingerprintComparisonResult

Check which components of a model should be retrained.

Arguments:

  • new_fingerprint - The fingerprint of the new model to be trained.
  • old_model - Path to the old zipped model file.
  • train_path - Path to the directory in which the new model will be trained.
  • has_e2e_examples - Whether the new training data contains e2e examples.
  • force_training - Indicates if the model needs to be retrained even if the data has not changed.

Returns:

A FingerprintComparisonResult object indicating whether Rasa Core and/or Rasa NLU needs to be retrained or not.

can_finetune

can_finetune(last_fingerprint: Fingerprint, new_fingerprint: Fingerprint, core: bool = False, nlu: bool = False) -> bool

Checks if components of a model can be finetuned with incremental training.

Arguments:

  • last_fingerprint - The fingerprint of the old model to potentially be fine-tuned.
  • new_fingerprint - The fingerprint of the new model.
  • core - Check sections for finetuning a core model.
  • nlu - Check sections for finetuning an nlu model.

Returns:

True if the old model can be finetuned, False otherwise.

package_model

package_model(fingerprint: Fingerprint, output_directory: Text, train_path: Text, fixed_model_name: Optional[Text] = None, model_prefix: Text = "") -> Text

Compress a trained model.

Arguments:

  • fingerprint - fingerprint of the model
  • output_directory - path to the directory in which the model should be stored
  • train_path - path to uncompressed model
  • fixed_model_name - name of the compressed model file
  • model_prefix - prefix of the compressed model file
  • Returns - path to 'tar.gz' model file

update_model_with_new_domain

async update_model_with_new_domain(importer: "TrainingDataImporter", unpacked_model_path: Union[Path, Text]) -> None

Overwrites the domain of an unpacked model with a new domain.

Arguments:

  • importer - Importer which provides the new domain.
  • unpacked_model_path - Path to the unpacked model.

get_model_for_finetuning

get_model_for_finetuning(previous_model_file: Optional[Union[Path, Text]]) -> Optional[Text]

Gets validated path for model to finetune.

Arguments:

  • previous_model_file - Path to model file which should be used for finetuning or a directory in case the latest trained model should be used.

Returns:

Path to model archive. None if there is no model.