This is documentation for Rasa & Rasa Pro Documentation v2.x, which is no longer actively maintained.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).
async extract_story_graph(resource_name: Text, domain: "Domain", use_e2e: bool = False, exclusion_percentage: Optional[int] = None) -> "StoryGraph"
Loads training stories / rules from file or directory.
resource_name- Path to file or directory.
domain- The model domain.
Trueif Markdown files should be parsed as conversation test files.
exclusion_percentage- Percentage of stories which should be dropped.
Noneif all training data should be used.
The loaded training data as graph.
async load_data(resource_name: Union[Text, "TrainingDataImporter"], domain: "Domain", remove_duplicates: bool = True, unique_last_num_states: Optional[int] = None, augmentation_factor: int = 50, tracker_limit: Optional[int] = None, use_story_concatenation: bool = True, debug_plots: bool = False, exclusion_percentage: Optional[int] = None) -> List["TrackerWithCachedStates"]
Load training data from a resource.
resource_name- resource to load the data from. either a path or an importer
domain- domain used for loading
remove_duplicates- should duplicated training examples be removed?
unique_last_num_states- number of states in a conversation that make the a tracker unique (this is used to identify duplicates) augmentation_factor: by how much should the story training data be augmented tracker_limit: maximum number of trackers to generate during augmentation use_story_concatenation: should stories be concatenated when doing data augmentation debug_plots: generate debug plots during loading exclusion_percentage: how much data to exclude
list of loaded trackers