def export_stories(self):
from rasa_core.training_utils.dsl import StoryStep, Story
story_step = StoryStep()
for event in self._applied_events():
story_step.add_event(event)
story = Story([story_step])
return story.as_story_string(flat=True)
def build_stories(self, domain, max_number_of_trackers=2000):
# type: (Domain, NaturalLanguageInterpreter, bool, int) -> List[Story]
"""Build the stories of a graph."""
from rasa_core.training_utils.dsl import STORY_START, Story
active_trackers = {STORY_START: [Story()]}
rand = random.Random(42)
for step in self.ordered_steps():
if step.start_checkpoint_name() in active_trackers:
# these are the trackers that reached this story step
# and that need to handle all events of the step
incoming_trackers = active_trackers[
step.start_checkpoint_name()]
# TODO: we can't use tracker filter here to filter for
# checkpoint conditions since we don't have trackers.
# this code should rather use the code from the dsl.
if max_number_of_trackers is not None:
incoming_trackers = utils.subsample_array(
incoming_trackers, max_number_of_trackers, rand)
events = step.explicit_events(domain)
# need to copy the tracker as multiple story steps might
# start with the same checkpoint and all of them
# will use the same set of incoming trackers
if events:
trackers = [
Story(tracker.story_steps + [step])
for tracker in incoming_trackers
]
else:
trackers = [] # small optimization
# update our tracker dictionary with the trackers that handled
# the events of the step and that can now be used for further
# story steps that start with the checkpoint this step ended on
if step.end_checkpoint_name() not in active_trackers:
active_trackers[step.end_checkpoint_name()] = []
active_trackers[step.end_checkpoint_name()].extend(trackers)
return active_trackers[None]
def test_persist_and_read_test_story(tmpdir, default_domain):
graph = extract_story_graph_from_file("data/dsl_stories/stories.md",
default_domain)
out_path = tmpdir.join("persisted_story.md")
Story(graph.story_steps).dump_to_file(out_path.strpath)
recovered_stories = extract_stories_from_file(out_path.strpath,
default_domain)
existing_stories = {
s.as_story_string()
for s in graph.build_stories(default_domain)
}
for r in recovered_stories:
story_str = r.as_story_string()
assert story_str in existing_stories
existing_stories.discard(story_str)