The requested_slot slot#
The slot requested_slot is automatically added to the domain as a slot of type text. The value of the requested_slot will be ignored during conversations. If you want to change this behavior, you need to add the requested_slot to your domain file as a categorical slot with influence_conversation set to true. You might want to do this if you want to handle your unhappy paths differently, depending on what slot is currently being asked from the user. For example, if your users respond to one of the bot's questions with another question, like why do you need to know that? The response to this explain intent depends on where we are in the story. In the restaurant case, your stories would look something like this:
- story: explain cuisine slot
- intent: request_restaurant
- action: restaurant_form
- active_loop: restaurant
- requested_slot: cuisine
- action: utter_explain_cuisine
- action: restaurant_form
- story: explain num_people slot
- intent: request_restaurant
- action: restaurant_form
- active_loop: restaurant
- requested_slot: cuisine
- requested_slot: num_people
- action: utter_explain_num_people
- action: restaurant_form
Again, it is strongly recommended that you use interactive learning to build these stories.
Ignoring Entities for Certain Intents#
To ignore all entities for certain intents, you can add the use_entities: [] parameter to the intent in your domain file like this:
To ignore some entities or explicitly take only certain entities into account you can use this syntax:
You can only use_entities or ignore_entities for any single intent.
Excluded entities for those intents will be unfeaturized and therefore will not impact the next action predictions. This is useful when you have an intent where you don't care about the entities being picked up.
If you list your intents without a use_entities or ignore_entities parameter, the entities will be featurized as normal.
It is also possible to ignore an entity for all intents by setting the influence_conversation flag to false for the entity itself. See the entities section for details.
Excluded entities for intents will be unfeaturized and therefore will not impact the next action predictions. This is useful when you have an intent where you don't care about the entities being picked up.
If you list your intents without this parameter, and without setting influence_conversation to false for any entities, all entities will be featurized as normal.
If you want these entities not to influence action prediction via slots either, set the influence_conversation: false parameter for slots with the same name.
As of 3.1, you can use the influence_conversation flag under entities. The flag can be set to false to declare that an entity should not be featurized for any intents. It is a shorthand syntax for adding an entity to the ignore_entities list of every intent in the domain. The flag is optional and default behaviour remains unchanged.
The entities section lists all entities that can be extracted by any entity extractor in your NLU pipeline.
- PERSON # entity extracted by SpacyEntityExtractor
- time # entity extracted by DucklingEntityExtractor
- membership_type # custom entity extracted by DIETClassifier
- priority # custom entity extracted by DIETClassifier
When using multiple domain files, entities can be specified in any domain file, and can be used or ignored by any intent in any domain file.
If you are using the feature Entity Roles and Groups you also need to list the roles and groups of an entity in this section.
- city: # custom entity extracted by DIETClassifier
- topping: # custom entity extracted by DIETClassifier
- size: # custom entity extracted by DIETClassifier
By default, entities influence action prediction. To prevent extracted entities from influencing the conversation for specific intents you can ignore entities for certain intents. To ignore an entity for all intents, without having to list it under the ignore_entities flag of each intent, you can set the flag influence_conversation to false under the entity:
influence_conversation: false
This syntax has the same effect as adding the entity to the ignore_entities list for every intent in the domain.
Explicitly setting influence_conversation: true does not change any behaviour. This is the default setting.
Slots are your bot's memory. They act as a key-value store which can be used to store information the user provided (e.g their home city) as well as information gathered about the outside world (e.g. the result of a database query).
Slots are defined in the slots section of your domain with their name, type and if and how they should influence the assistant's behavior. The following example defines a slot with name "slot_name", type text and predefined slot mapping from_entity.
Jam Operasional dan Pengalaman Pengunjung
Kafe ini buka dari Rabu hingga Sabtu pukul 09.30-19.30, dan pada hari Selasa dari pukul 14.00-19.30. Pengunjung yang datang biasanya untuk nongkrong atau bersama keluarga, dan saat long weekend atau liburan, kafe ini seringkali sampai close order karena penuh.
"Kami tutup pada Minggu dan Senin," jelas Ahmad.
Dengan keindahan alamnya, suasana yang nyaman, dan berbagai fasilitas yang ditawarkan, Rumah Rasa Kaliurang benar-benar menghadirkan pengalaman seolah berada di negeri dongeng.
"Kami ingin pengunjung merasa seperti di rumah sendiri, tapi dengan sentuhan magis dari alam," kata Ahmad.
Thêm bài hát vào playlist thành công
Channel-Specific Response Variations#
To specify different response variations depending on which channel the user is connected to, use channel-specific response variations.
In the following example, the channel key makes the first response variation channel-specific for the slack channel while the second variation is not channel-specific:
- text: "Which game would you like to play on Slack?"
- text: "Which game would you like to play?"
Make sure the value of the channel key matches the value returned by the name() method of your input channel. If you are using a built-in channel, this value will also match the channel name used in your credentials.yml file.
When your assistant looks for suitable response variations under a given response name, it will first try to choose from channel-specific variations for the current channel. If there are no such variations, the assistant will choose from any response variations which are not channel-specific.
In the above example, the second response variation has no channel specified and can be used by your assistant for all channels other than slack.
For each response, try to have at least one response variation without the channel key. This allows your assistant to properly respond in all environments, such as in new channels, in the shell and in interactive learning.
Session configuration#
A conversation session represents the dialogue between the assistant and the user. Conversation sessions can begin in three ways:
the user begins the conversation with the assistant,
the user sends their first message after a configurable period of inactivity, or
a manual session start is triggered with the /session_start intent message.
You can define the period of inactivity after which a new conversation session is triggered in the domain under the session_config key.
Available parameters are:
The default session configuration looks as follows:
session_expiration_time: 60 # value in minutes, 0 means infinitely long
carry_over_slots_to_new_session: true # set to false to forget slots between sessions
This means that if a user sends their first message after 60 minutes of inactivity, a new conversation session is triggered, and that any existing slots are carried over into the new session. Setting the value of session_expiration_time to 0 means that sessions will not end (note that the action_session_start action will still be triggered at the very beginning of conversations).
A session start triggers the default action action_session_start. Its default implementation moves all existing slots into the new session. Note that all conversations begin with an action_session_start. Overriding this action could for instance be used to initialize the tracker with slots from an external API call, or to start the conversation with a bot message. The docs on Customizing the session start action shows you how to do that.
The requested_slot slot#
The slot requested_slot is automatically added to the domain as a slot of type text. The value of the requested_slot will be ignored during conversations. If you want to change this behavior, you need to add the requested_slot to your domain file as a categorical slot with influence_conversation set to true. You might want to do this if you want to handle your unhappy paths differently, depending on what slot is currently being asked from the user. For example, if your users respond to one of the bot's questions with another question, like why do you need to know that? The response to this explain intent depends on where we are in the story. In the restaurant case, your stories would look something like this:
- story: explain cuisine slot
- intent: request_restaurant
- action: restaurant_form
- active_loop: restaurant
- requested_slot: cuisine
- action: utter_explain_cuisine
- action: restaurant_form
- story: explain num_people slot
- intent: request_restaurant
- action: restaurant_form
- active_loop: restaurant
- requested_slot: cuisine
- requested_slot: num_people
- action: utter_explain_num_people
- action: restaurant_form
Again, it is strongly recommended that you use interactive learning to build these stories.
Select which actions should receive domain#
You can control if an action should receive a domain or not.
To do this you must first enable selective domain in you endpoint configuration for action_endpoint in endpoints.yml.
url: "http://localhost:5055/webhook" # URL to your action server
enable_selective_domain: true
After selective domain for custom actions is enabled, domain will be sent only to those custom actions which have specifically stated that they need it. Custom actions inheriting from rasa-sdk FormValidationAction parent class are an exception to this rule as they will always have the domain sent to them. To specify if an action needs the domain add {send_domain: true} to custom action in the list of actions in domain.yml:
- action_hello_world: {send_domain: True} # will receive domain
- action_calculate_mass_of_sun # will not receive domain
- validate_my_form # will receive domain
Menu dan Spesialisasi
Menu di Rumah Rasa didominasi oleh Western food, dengan andalan utama pizza. Ahmad menyebut Pizza lah yang paling dipesan oleh pengunjung. Keunikan lainnya adalah produksi keju sendiri bernama Keju Montkaas yang bisa dibeli untuk dibawa pulang
"Susunya kami ambil dari peternak sekitar," tambahnya.
Meskipun harga makanan di sini sedikit tidak ramah kantong pelajar, pengalaman yang dirasakan akan luar biasa, dengan setiap sudut yang estetik dan cocok untuk berfoto.
Selain kafe, Rumah Rasa juga memiliki villa khusus untuk staycation bernama Kanestayi Villa, yang berarsitektur gaya peninggalan zaman Belanda. Villa ini menawarkan pengalaman menginap yang unik seperti di rumah.
Lokasi villanya tepat di samping kafe membuatnya menjadi pilihan yang tepat bagi pengunjung yang ingin menghabiskan lebih banyak waktu di lingkungan yang tenang dan indah.
Multiple Domain Files#
The domain can be defined as a single YAML file or split across multiple files in a directory. When split across multiple files, the domain contents will be read and automatically merged together.
Using the command line interface, you can train a model with split domain files by running:
rasa train --domain path_to_domain_directory
The intents key in your domain file lists all intents used in your NLU data and conversation training data.
Dynamic Form Behavior#
By default Rasa Open Source will ask for the next empty slot from the slots listed for your form in the domain file. If you use custom slot mappings and the FormValidationAction, it will ask for the first empty slot returned by the required_slots method. If all slots in required_slots are filled the form will be be deactivated.
If needed, you can update the required slots of your form dynamically. This is, for example, useful when you need further details based on how a previous slot was filled or you want to change the order in which slots are requested.
If you are using the Rasa SDK, we recommend you to use the FormValidationAction and
override required_slots to fit your dynamic behavior. You should implement
a method extract_
from typing import Text, List, Optional
from rasa_sdk.forms import FormValidationAction
class ValidateRestaurantForm(FormValidationAction):
def name(self) -> Text:
return "validate_restaurant_form"
async def required_slots(
slots_mapped_in_domain: List[Text],
dispatcher: "CollectingDispatcher",
domain: "DomainDict",
) -> Optional[List[Text]]:
additional_slots = ["outdoor_seating"]
if tracker.slots.get("outdoor_seating") is True:
additional_slots.append("shade_or_sun")
return additional_slots + slots_mapped_in_domain
