## Format A story is a representation of a conversation between a user and an AI assistant, converted into a specific format where user inputs are expressed as intents (and entities when necessary), while the assistant's responses and actions are expressed as action names. Here's an example of a dialogue in the Rasa story format: ``` stories: - story: collect restaurant booking info # name of the story - just for debugging steps: - intent: greet # user message with no entities - action: utter_ask_howcanhelp - intent: inform # user message with entities entities: - location: "rome" - price: "cheap" - action: utter_on_it # action that the bot should execute - action: utter_ask_cuisine - intent: inform entities: - cuisine: "spanish" - action: utter_ask_num_people ``` ### User Messages While writing stories, you do not have to deal with the specific contents of the messages that the users send. Instead, you can take advantage of the output from the NLU pipeline, which lets you use just the combination of an intent and entities to refer to all the possible messages the users can send to mean the same thing. It is important to include the entities here as well because the policies learn to predict the next action based on a _combination_ of both the intent and entities (you can, however, change this behavior using the [use_entities](https://legacy-docs-oss.rasa.com/docs/rasa/domain#ignoring-entities-for-certain-intents) attribute). ### Actions All actions executed by the bot, including [responses](https://legacy-docs-oss.rasa.com/docs/rasa/responses) are listed in stories under the `action` key. You can use a response from your domain as an action by listing it as one in a story. Similarly, you can indicate that a story should call a custom action by including the name of the custom action from the `actions` list in your domain. ### Events During training, Rasa does not call the action server. This means that your assistant's dialogue management model doesn't know which events a custom action will return. Because of this, events such as setting a slot or activating/deactivating a form have to be explicitly written out as part of the stories. For more info, see the documentation on [Events](https://legacy-docs-oss.rasa.com/docs/rasa/action-server/events). #### Slot Events Slot events are written under `slot_was_set` in a story. If this slot is set inside a custom action, add the `slot_was_set` event immediately following the custom action call. If your custom action resets a slot value to `None`, the corresponding event for that would look like this: ``` stories: - story: set slot to none steps: # ... other story steps - action: my_custom_action - slot_was_set: - my_slot: null ``` #### Form Events There are three kinds of events that need to be kept in mind while dealing with forms in stories. - A form action event (e.g. `- action: restaurant_form`) is used in the beginning when first starting a form, and also while resuming the form action when the form is already active. - A form activation event (e.g. `- active_loop: restaurant_form`) is used right after the first form action event. - A form deactivation event (e.g. `- active_loop: null`), which is used to deactivate the form. ##### Writing Form Stories In order to get around the pitfall of forgetting to add events, the recommended way to write these stories is to use [interactive learning](https://legacy-docs-oss.rasa.com/docs/rasa/writing-stories#using-interactive-learning). ## Checkpoints and OR Statements Checkpoints and OR statements should be used with caution, if at all. There is usually a better way to achieve what you want by using [Rules](https://legacy-docs-oss.rasa.com/docs/rasa/rules) or the [ResponseSelector](https://legacy-docs-oss.rasa.com/docs/rasa/components#responseselector). ### Checkpoints You can use checkpoints to modularize and simplify your training data. Checkpoints can be useful, but **do not overuse them**. Using lots of checkpoints can quickly make your example stories hard to understand and will slow down training. Here is an example of stories that contain checkpoints: ``` stories: - story: beginning of flow steps: - intent: greet - action: action_ask_user_question - checkpoint: check_asked_question - story: handle user affirm steps: - checkpoint: check_asked_question - intent: affirm - action: action_handle_affirmation - checkpoint: check_flow_finished - story: handle user deny steps: - checkpoint: check_asked_question - intent: deny - action: action_handle_denial - checkpoint: check_flow_finished - story: finish flow steps: - checkpoint: check_flow_finished - intent: goodbye - action: utter_goodbye ``` ##### Note Unlike regular stories, checkpoints are not restricted to starting with user input. As long as the checkpoint is inserted at the right points in the main stories, the first event can be a custom action or a response as well. ### Or Statements Another way to write shorter stories, or to handle multiple intents or slot events the same way, is to use an `or` statement. For example, if you ask the user to confirm something, and you want to treat the `affirm` and `thankyou` intents in the same way. The story below will be converted into two stories at training time: ``` stories: - story: steps: # ... previous steps - action: utter_ask_confirm - or: - intent: affirm - intent: thankyou - action: action_handle_affirmation ``` You can also use `or` statements with slot events. The following means the story requires that the current value for the `name` slot is set and is either `joe` or `bob`: ``` stories: - story: steps: - intent: greet - action: utter_greet - intent: tell_name - or: - slot_was_set: - name: joe - slot_was_set: - name: bob # ... next actions ``` `or` statements can be useful, but if you are using a lot of them, it is probably better to restructure your domain and/or intents. Overusing OR statements will slow down training. ## Test Conversation Format The test conversation format is a format that combines both NLU data and stories into a single file for evaluation. Read more about this format in [Testing Your Assistant](https://legacy-docs-oss.rasa.com/docs/rasa/testing-your-assistant). ##### Testing Only This format is only used for testing and cannot be used for training. ## End-to-end Training ##### New in 2.2 End-to-end training is an experimental feature. We introduce experimental features to get feedback from our community, so we encourage you to try it out! However, the functionality might be changed or removed in the future. If you have feedback (positive or negative) please share it with us on the [Rasa Forum](https://forum.rasa.com/). With end-to-end training, you do not have to deal with the specific intents of the messages that are extracted by the NLU pipeline or with separate `utter_` responses in the domain file. Instead, you can include the text of the user messages and/or bot responses directly in your stories. See the [training data format](https://legacy-docs-oss.rasa.com/docs/rasa/training-data-format#end-to-end-training) for detailed description of how to write end-to-end stories. You can mix training data in the end-to-end format with labeled training data which has `intent`s and `action`s specified: Stories can have some steps defined by intents/actions and other steps defined directly by user or bot utterances. We call it end-to-end training because policies can consume and predict actual text. The only difference to creating `utter_` response is how `TEDPolicy` featurizes bot utterances. In case of an `utter_` action, `TEDPolicy` sees only the name of the action, while if you provide actual utterance using `bot` key, `TEDPolicy` will featurize it as textual input depending on the NLU configuration. This can help in case of similar utterances in slightly different situations. However, this can also make things harder to learn because the fact that different utterances have similar texts make it easier for `TEDPolicy` to confuse these utterances. End-to-end training requires significantly more parameters in `TEDPolicy`. Therefore, training an end-to-end model might require significant computational resources depending on how many end-to-end turns you have in your stories.