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This is unreleased documentation for Rasa Documentation Main/Unreleased version.
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Version: Main/Unreleased

rasa.engine.recipes.default_recipe

DefaultV1RecipeRegisterException Objects

class DefaultV1RecipeRegisterException(RasaException)

If you register a class which is not of type GraphComponent.

DefaultV1Recipe Objects

class DefaultV1Recipe(Recipe)

Recipe which converts the normal model config to train and predict graph.

ComponentType Objects

@enum.unique
class ComponentType(Enum)

Enum to categorize and place custom components correctly in the graph.

__init__

def __init__() -> None

Creates recipe.

RegisteredComponent Objects

@dataclasses.dataclass()
class RegisteredComponent()

Describes a graph component which was registered with the decorator.

register

@classmethod
def register(
cls,
component_types: Union[ComponentType, List[ComponentType]],
is_trainable: bool,
model_from: Optional[Text] = None
) -> Callable[[Type[GraphComponent]], Type[GraphComponent]]

This decorator can be used to register classes with the recipe.

Arguments:

  • component_types - Describes the types of a component which are then used to place the component in the graph.
  • is_trainable - True if the component requires training.
  • model_from - Can be used if this component requires a pre-loaded model such as SpacyNLP or MitieNLP.

Returns:

The registered class.

graph_config_for_recipe

def graph_config_for_recipe(
config: Dict,
cli_parameters: Dict[Text, Any],
training_type: TrainingType = TrainingType.BOTH,
is_finetuning: bool = False) -> GraphModelConfiguration

Converts the default config to graphs (see interface for full docstring).

auto_configure

@staticmethod
def auto_configure(
config_file_path: Optional[Text],
config: Dict,
training_type: Optional[TrainingType] = TrainingType.BOTH
) -> Tuple[Dict[Text, Any], Set[str], Set[str]]

Determine configuration from auto-filled configuration file.

Keys that are provided and have a value in the file are kept. Keys that are not provided are configured automatically.

Note that this needs to be called explicitly; ie. we cannot auto-configure automatically from importers because importers are not allowed to access code outside of rasa.shared.

Arguments:

  • config_file_path - The path to the configuration file.
  • config - Configuration in dictionary format.
  • training_type - Optional training type to auto-configure. By default both core and NLU will be auto-configured.

complete_config

@staticmethod
def complete_config(config: Dict[Text, Any],
keys_to_configure: Set[Text]) -> Dict[Text, Any]

Complete a config by adding automatic configuration for the specified keys.

Arguments:

  • config - The provided configuration.
  • keys_to_configure - Keys to be configured automatically (e.g. policies).

Returns:

The resulting configuration including both the provided and the automatically configured keys.