You are viewing documentation for our open source project which is maintained by the community. If you want to get started building assistants with Rasa please check out our latest [documentation here](/content/docs/index.html). Conversational assistants often support user goals that involve collecting required information from the user before doing something for them. For example, a restaurant search bot would need to gather a few pieces of information about the user's preferences to find them a suitable restaurant: User: Help me find a restaurant Bot: What cuisine? User: I'm looking for Tuscan food Bot: How many people? User: 5 Bot: Do you want to sit outside? User: Yes Bot: All done! Bot: I am going to run a restaurant search using the following parameters: Bot: - cuisine: Tuscan - num_people: 5 - outdoor_seating: True Finding a restaurant This page is a guide on handling the **business logic** of collecting user information to fulfill a request. In the example above, business logic includes needing to know the user's preferred cuisine, party size, and seating preference. The examples on this page come from the [formbot example bot](https://github.com/RasaHQ/rasa/tree/main/examples/formbot). ## Step-by-step Guide on Using Forms to Handle Business Logic [Forms](https://legacy-docs-oss.rasa.com/docs/rasa/forms) work by prompting the user for information until it has gathered all required information. The information is stored in [slots](https://legacy-docs-oss.rasa.com/docs/rasa/domain#slots). Once all the required slots are filled, the bot fulfills the user's original request. ### 1. Defining the form To define a form, you will need to define: - Slot mappings: The required info to collect - Responses: How your bot should ask for each piece of information #### Slot Mappings For the restaurant search example, we want to collect the following information from the user: - cuisine - number of people - whether they want to sit outside or not You define a form in your domain by specifying a list of required slots under the form name: ```yaml domain.yml forms: restaurant_form: required_slots: - cuisine - num_people - outdoor_seating ``` These slots need to be added to your domain's `slots` section, along with [slot mappings](https://legacy-docs-oss.rasa.com/docs/rasa/domain#slot-mappings) which define how the slots can be filled. For any slot filled `from_entity`, the entity also needs to be added to the domain. Slots filled by forms should usually not influence the conversation, so set `influence_conversation` to `false: ```yaml domain.yml entities: - cuisine - number slots: cuisine: type: text mappings: - type: from_entity entity: cuisine num_people: type: float mappings: - type: from_entity entity: number outdoor_seating: type: bool mappings: - type: from_intent intent: affirm value: true conditions: - active_loop: restaurant_form requested_slot: outdoor_seating - type: from_intent intent: deny value: false conditions: - active_loop: restaurant_form requested_slot: outdoor_seating ``` The `number` slot is filled from an entity. Entities like numbers can be extracted by [DucklingEntityExtractor](https://legacy-docs-oss.rasa.com/docs/rasa/components#ducklingentityextractor). To use it, add DucklingEntityExtractor to your NLU pipeline: ```yaml config.yml language: en pipeline: # other components - name: DucklingEntityExtractor dimensions: ["number"] ``` ##### Slot Mappings with Conditions The `outdoor_seating` slot is filled based on the user's intent: If it is `affirm`, it'll be `true`, if it is `deny`, it'll be `false`. However, the slot should only be set to `true` or `false` if the user was responding to the question, `Do you want to sit outside?`. To enforce this condition, the `conditions` for the `outdoor_seating` slot requires that `restaurant_form` is active and that the requested slot is `outdoor_seating`. If there were no conditions and the user had sent a message with the `affirm` or `deny` intent earlier in the conversation, the `outdoor_seating` slot would already be filled when the form was activated. Therefore the form would not prompt the user for their outdoor seating preference. See [mapping conditions](https://legacy-docs-oss.rasa.com/docs/rasa/domain#mapping-conditions) for more information. #### Validating Slots Often, you'll want to validate the user's input before accepting it, for example by checking if the given cuisine is in your assistant's database of available cuisines. See the docs on [validating form input](https://legacy-docs-oss.rasa.com/docs/rasa/forms#validating-form-input) for more information about validation actions. #### Requesting Slots To specify how the bot should ask for the required information, you define [`responses`](https://legacy-docs-oss.rasa.com/docs/rasa/domain#responses) called `utter_ask_{slotname}` in your domain: ```yaml domain.yml responses: utter_ask_cuisine: text: "What cuisine?" utter_ask_num_people: text: "How many people?" utter_ask_outdoor_seating: text: "Do you want to sit outside?" ``` ### 2. Updating the configuration A form's [happy path](https://legacy-docs-oss.rasa.com/docs/rasa/glossary#happy--unhappy-paths) should be defined as a [rule](https://legacy-docs-oss.rasa.com/docs/rasa/rules) which means you'll need to add the [RulePolicy](https://legacy-docs-oss.rasa.com/docs/rasa/policies#rule-policy) to your policies: ```yaml config.yml policies: - name: RulePolicy ``` ### 3. Creating rules The form itself takes care of the logic around asking the user for all the required information, so you need only two rules for a form's happy path: One that defines when it starts, and one that defines what happens when it has been filled. For the restaurant search example, in real life the assistant would look up a restaurant based on the user's preferences. In this case, the bot will utter a response with the details that would be used for a search. ```yaml rules.yml rules: - rule: activate restaurant form steps: - intent: request_restaurant # intent that triggers form activation - action: restaurant_form # run the form active_loop: restaurant_form # this form is active - rule: submit form condition: - active_loop: restaurant_form # this form must be active steps: - action: restaurant_form # run the form active_loop: null # the form is no longer active because it has been filled - action: utter_submit # action to take after the form is complete - action: utter_slots_values # action to take after the form is complete ``` By splitting up the activation and submission of the form, the rules will still apply if the user provides [unexpected input](https://legacy-docs-oss.rasa.com/docs/rasa/unexpected-input) or interrupts the form with [chitchat](https://legacy-docs-oss.rasa.com/docs/rasa/chitchat-faqs). ### 4. Updating the NLU training data You'll need to add examples for the intent that should activate the form, as well as examples for how the user will provide the required information. #### Form Activation Intent(s) You need to provide training examples for the intent(s) that should activate the form. Add examples for the intent `request_restaurant`: ```yaml nlu.yml nlu: - intent: request_restaurant examples: | - im looking for a restaurant - can i get [swedish](cuisine) food in any area - a restaurant that serves [caribbean](cuisine) food - id like a restaurant - im looking for a restaurant that serves [mediterranean](cuisine) food - can i find a restaurant that serves [chinese](cuisine) ``` Slots filled with `from_entity` can by default be filled by any user utterance, regardless of the intent, as long as the correct entity is extracted. That means that if the user provides the `cuisine` entity as part of their first message, the slot will be filled at the beginning of the form and the bot will not ask them for the cuisine again. #### Form Filling Intent(s) While the form is filling slots, it will not pay attention to which intent was predicted unless a slot mapping explicitly requires or excludes an intent. For the restaurant search example, the `outdoor_seating` slot is mapped to two intents, so you need to add training data for these intents. For the `cuisine` and `number` slots, no intent is specified, so you can add examples to a generic `inform` intent. You need to annotate the `cuisine` entity so that DIETClassifier can learn to extract it. You don't need to annotate the `number` entity since DucklingEntityExtractor is a rule-based extractor that isn't trained on your training data. Only a few examples are shown for each intent; for your bot to work well, you should add more training data than is shown here: ```yaml nlu.yml nlu: - intent: affirm examples: | - Yes - yes, please - yup - intent: deny examples: | - no don't - no - no I don't want that - intent: inform examples: | - [afghan](cuisine) food - how bout [asian oriental](cuisine) - what about [indian](cuisine) food - uh how about [turkish](cuisine) type of food - um [english](cuisine) - im looking for [tuscan](cuisine) food - id like [moroccan](cuisine) food - for ten people - 2 people - for three people - just one person - book for seven people - 2 please - nine people ``` Update your domain to include these intents: ```yaml domain.yml intents: - request_restaurant - affirm - deny - inform ``` ### 5. Defining the responses Add the responses that are sent after the form has been submitted: ```yaml domain.yml responses: utter_submit: text: "All done!" utter_slots_values: text: "I am going to run a restaurant search using the following parameters:\n - cuisine: {cuisine}\n - num_people: {num_people}\n - outdoor_seating: {outdoor_seating}" ``` ## Summary Forms can simplify the logic of collecting user information. To define a minimal form like the restaurant search example above, this is a summary of what you'll need to do: - [ ] Add the RulePolicy to `config.yml` - [ ] Define the form with required slots in the domain - [ ] Add slot mappings for all required slots in the domain - [ ] Add rules for activating and submitting the form - [ ] Add examples for the intent(s) to activate your form - [ ] Add examples for the intent(s) to fill the required slots - [ ] Define an action or response for the bot to take when the form is completed - [ ] Update your domain with new intents and actions you've defined To try out your newly defined form, retrain the bot's model by running `rasa train` and start `rasa shell`. Because the DucklingEntityExtractor is being used to extract entities, you'll need to start Duckling in the background as well (see the [instructions for running Duckling](https://legacy-docs-oss.rasa.com/docs/rasa/components#DucklingEntityExtractor)).