Addon Domain và Sub-Domain là gì?
Addon Domain là tên miền thêm vào trên host, tên miền ở dạng này sẽ hoạt động độc lập và như một website riêng. Ví dụ bạn chạy website AAA.COM trên host là tên miền chính, và bạn muốn có thêm website BBB.COM chạy cùng trên host này nhưng dữ liệu độc lập với nhau, thì lúc này bạn cần thêm BBB.COM như một addon domain.
Sub Domain là tên miền con của một tên miền chính, nó sẽ có dạng là tên.tên-miền.ltd, ví dụ my.azdigi.com là sub domain của tên miền azdigi.com. Nếu bạn cần tạo tên miền con như thế này thì cần tạo sub domain.
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🔥 Proxy is a high performance HTTP(S) proxies, SOCKS5 proxies,WEBSOCKET, TCP, UDP proxy server implemented by golang. Now, it supports chain-style proxies,nat forwarding in different lan,TCP/UDP port forwarding, SSH forwarding.Proxy是golang实现的高性能http,https,websocket,tcp,socks5代理服务器,支持内网穿透,链式代理,通讯加密,智能HTTP,SOCKS5代理,黑白名单,限速,限流量,限连接数,跨平台,KCP支持,认证API。
Chào các bạn, trong quá trình sử dụng hosting nếu bạn muốn phát triển thêm một website mới thì bắt buộc bạn phải addon một tên miền (domain) mới vào hosting để cài website mới.
Trước khi tiến hành thêm tên miền Addon Domain và Sub-Domain vào host, chúng ta nên phân biệt rõ giữa hai khái niệm này nhé.
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
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.
Here is a full example of a domain, taken from the concertbot example:
influence_conversation: false
influence_conversation: false
influence_conversation: true
- text: "Sorry, I didn't get that, can you rephrase?"
- text: "You're very welcome."
- text: "I am a bot, powered by Rasa."
- text: "I can help you find concerts and venues. Do you like music?"
- text: "Awesome! You can ask me things like \"Find me some concerts\" or \"What's a good venue\""
- action_search_concerts
- action_search_venues
- action_show_concert_reviews
- action_show_venue_reviews
- action_set_music_preference
session_expiration_time: 60 # value in minutes
carry_over_slots_to_new_session: true
Slots and Conversation Behavior#
You can specify whether or not a slot influences the conversation with the influence_conversation property.
If you want to store information in a slot without it influencing the conversation, set influence_conversation: false when defining your slot.
The following example defines a slot age which will store information about the user's age, but which will not influence the flow of the conversation. This means that the assistant will ignore the value of the slot each time it predicts the next action.
influence_conversation: false
When defining a slot, if you leave out influence_conversation or set it to true, that slot will influence the next action prediction, unless it has slot type any. The way the slot influences the conversation will depend on its slot type.
The following example defines a slot home_city that influences the conversation. A text slot will influence the assistant's behavior depending on whether the slot has a value. The specific value of a text slot (e.g. Bangalore or New York or Hong Kong) doesn't make any difference.
influence_conversation: true
As an example, consider the two inputs "What is the weather like?" and "What is the weather like in Bangalore?" The conversation should diverge based on whether the home_city slot was set automatically by the NLU. If the slot is already set, the bot can predict the action_forecast action. If the slot is not set, it needs to get the home_city information before it is able to predict the weather.
Storing true or false values.
If influence_conversation is set to true, the assistant's behavior will change depending on whether the slot is empty, set to true or set to false. Note that an empty bool slot influences the conversation differently than if the slot was set to false.
Storing slots which can take one of N values.
If influence_conversation is set to true, the assistant's behavior will change depending on the concrete value of the slot. This means the assistant's behavior is different depending on whether the slot in the above example has the value low, medium, or high.
A default value __other__ is automatically added to the user-defined values. All values encountered which are not explicitly defined in the slot's values are mapped to __other__. __other__ should not be used as a user-defined value; if it is, it will still behave as the default to which all unseen values are mapped.
Storing real numbers.
max_value=1.0, min_value=0.0
If influence_conversation is set to true, the assistant's behavior will change depending on the value of the slot. If the value is between min_value and max_value, the specific value of the number is used. All values below min_value will be treated as min_value, and all values above max_value will be treated as max_value. Hence, if max_value is set to 1, there is no difference between the slot values 2 and 3.5.
Storing arbitrary values (they can be of any type, such as dictionaries or lists).
Slots of type any are always ignored during conversations. The property influence_conversation cannot be set to true for this slot type. If you want to store a custom data structure which should influence the conversation, use a custom slot type.
Maybe your restaurant booking system can only handle bookings for up to 6 people. In this case you want the value of the slot to influence the next selected action (and not just whether it's been specified). You can do this by defining a custom slot class.
The code below defines a custom slot class called NumberOfPeopleSlot. The featurization defines how the value of this slot gets converted to a vector so Rasa machine learning model can deal with it. The NumberOfPeopleSlot has three possible “values”, which can be represented with a vector of length 2.
from rasa.shared.core.slots import Slot
class NumberOfPeopleSlot(Slot):
def feature_dimensionality(self):
def as_feature(self):
r = [0.0] * self.feature_dimensionality()
You can implement a custom slot class as an independent python module, separate from custom action code. Save the code for your custom slot in a directory alongside an empty file called "__init__.py" so that it will be recognized as a python module. You can then refer to the custom slot class by it's module path.
For example, say you have saved the code above in "addons/my_custom_slots.py", a directory relative to your bot project:
│ └── my_custom_slots.py
Your custom slot type's module path is then addons.my_custom_slots.NumberOfPeopleSlot. Use the module path to refer to the custom slot type in your domain file:
type: addons.my_custom_slots.NumberOfPeopleSlot
influence_conversation: true
Now that your custom slot class can be used by Rasa, add training stories that diverge based on the value of the people slot. You could write one story for the case where people has a value between 1 and 6, and one for a value greater than six. You can choose any value within these ranges to put in your stories, since they are all featurized the same way (see the featurization table above).
- story: collecting table info
# ... other story steps
- action: action_book_table
- story: too many people at the table
# ... other story steps
- action: action_explain_table_limit
As of 3.0, slot mappings are defined in the slots section of the domain. This change removes the implicit mechanism of setting slots via auto-fill and replaces it with a new explicit mechanism of setting slots after every user message. You will need to explicitly define slot mappings for each slot in the slots section of domain.yml. If you are migrating from an earlier version, please read through the migration guide to update your assistant.
Rasa comes with four predefined mappings to fill slots based on the latest user message.
In addition to the predefined mappings, you can define custom slot mappings. All custom slot mappings should contain a mapping of type custom.
Slot mappings are specified as a YAML list of dictionaries under the key mappings in the domain file. Slot mappings are prioritized in the order they are listed in the domain. The first slot mapping found to apply will be used to fill the slot.
The default behavior is for slot mappings to apply after every user message, regardless of the dialogue context. To make a slot mapping apply only within the context of a form see Mapping Conditions. There is one additional default limitation on applying from_entity slot mappings in the context of a form; see unique from_entity mapping matching for details.
Note that you can also define lists of intents for the optional parameters intent and not_intent.
The from_entity slot mapping fills slots based on extracted entities. The following parameters are required:
The following parameters are optional and can be used to further specify when the mapping applies:
not_intent: excluded_intent
There is an intentional limitation on applying from_entity slot mappings in the context of a form. When a form is active, a from_entity slot mapping will be applied only if one or more of the following conditions are met:
This limitation exists to prevent a form from filling multiple required slots with the same extracted entity value.
For example, in the example below, an entity date uniquely sets the slot arrival_date, an entity city with a role from uniquely sets the slot departure_city and an entity city with a role to uniquely sets the slot arrival_city, therefore they can be used to fit corresponding slots even if these slots were not requested. However, entity city without a role can fill both departure_city and arrival_city slots, depending which one is requested, so if an entity city is extracted when slot arrival_date is requested, it'll be ignored by the form.
Note that the unique from_entity mapping constraint will not prevent filling slots which are not in the active form's required_slots; those mappings will apply as usual, regardless of the uniqueness of the mapping. To limit applicability of a slot mapping to a specific form, see Mapping Conditions.
The from_text mapping will use the text of the last user utterance to fill the slot slot_name. If intent_name is None, the slot will be filled regardless of intent name. Otherwise, the slot will only be filled if the user's intent is intent_name.
The slot mapping will not apply if the intent of the message is excluded_intent.
not_intent: excluded_intent
To maintain the 2.x form behavior when using from_text slot mappings, you must use mapping conditions, where both active_loop and requested_slot keys are defined.
The from_intent mapping will fill slot slot_name with value my_value if user intent is intent_name. If you choose not to specify the parameter intent, the slot mapping will apply regardless of the intent of the message as long as the intent is not listed under not_intent parameter.
The following parameter is required:
The following parameters are optional and can be used to further specify when the mapping applies:
Note that if you choose not to define the parameter intent, the slot mapping will apply regardless of the intent of the message as long as the intent is not listed under the not_intent parameter.
not_intent: excluded_intent
The from_trigger_intent mapping will fill slot slot_name with value my_value if a form is activated by a user message with intent intent_name. The slot mapping will not apply if the intent of the message is excluded_intent.
- type: from_trigger_intent
not_intent: excluded_intent
To apply a slot mapping only within the context of a form, specify the name of the form in the conditions key of a slot mapping. Conditions list the form name(s) for which the mapping is applicable in the active_loop key.
Slot mappings can now specify null as the value of active_loop to indicate that the slot should only be filled when no form is active. Note that requested_slot cannot be used in conjunction with active_loop: null.
Conditions can also include the name of the requested_slot. If requested_slot is not mentioned, then the slot will be set if relevant information is extracted, regardless of which slot is being requested by the form.
- active_loop: your_form
requested_slot: slot_name
- active_loop: another_form
If conditions are not included in a slot mapping, the slot mapping will be applicable regardless of whether any form is active. As long as a slot is listed in a form's required_slots, the form will prompt for the slot if it is empty when the form is activated.
Hướng dẫn tạo subdomain
Để tạo một sub domain bạn cũng truy cập vào Domains
Giao diện hiển thị bạn hãy nhập subdomain cần thêm vào.
Cuối cùng nhấn Submit để tạo sub và chờ 10-20s để hoàn tất
Sau khi tạo hoàn tất bạn sẽ nhận được kết quả như bên dưới.
Như vậy với thao tác trên, bạn đã hoàn tất thêm addon domain và sub domain vào hosting sử dụng cPanel rồi đó. Chúc các bạn thành công.
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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. You can also manage your responses, slots, custom actions in Rasa Studio.
Using the command line interface, you can train a model with split domain files by running:
rasa train --domain path_to_domain_directory
Responses are templated messages that your assistant can send to your user. Responses can contain rich content like buttons, images, and custom json payloads. Every response is also an action, meaning that it can be used directly in an action step in a flow. Responses can be defined directly in the domain file under the responses key. For more information on responses and how to define them, see Responses.
Actions are the things your bot can do. For example, an action could:
All custom actions should be listed in your domain.
Rasa also has default actions which you do not need to list in your domain.
Slots are your assistant'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 default value. Different slot types exist to restrict the possible values a slot can take.
If you decide to fill slots through response buttons where the payload syntax issues SetSlot command(s), note that the slot name must not include certain characters such as (, ), = or ,.
A text slot can take on any string value.
A boolean slot can only take on the values true or false. This is useful when you want to store a binary value.
A categorical slot can only take on values from a predefined set. This is useful when you want to restrict the possible values a slot can take.
If the user provides a value where the casing does not match the casing of the values defined in the domain, the value will be coerced to the correct casing. For example, if the user provides the value LOW for a slot with values low, medium, high, the value will be converted to low and stored in the slot.
If you define a categorical slot with a list of values, where multiple of the values coerce to the same value, a warning will be issued and you should remove one of the values from the set in the domain. For example, if you define a categorical slot with values low, medium, high, and Low, the value Low will be coerced to low and a warning will be issued.
A float slot can only take on floating point values. This is useful when you want to store a number with a decimal point.
This slot type can take on any value. This is useful when you want to store any type of information, including structured data like dictionaries.
A list slot can take on a list of values. Note that the list slot type is only supported in custom actions when building an assistant with CALM. List slots cannot be filled with flows in either the collect or set_slots flow step types.
When building an assistant with CALM, you can configure slot filling to either use nlu-based predefined slot mappings or the newly introduced from_llm slot mapping type.
You can continue using the nlu-based predefined slot mappings such as from_entity or from_intent when building an assistant with CALM. In addition to including tokenizers, featurizers, intent classifiers, and entity extractors to your pipeline, you must also add the NLUCommandAdapter to the config.yml file. The NLUCommandAdapter will match the output of the NLU pipeline (intents and entities) against the slot mappings defined in the domain file. If the slot mappings are satisfied, the NLUCommandAdapter will issue set slot commands to fill the slots.
If during message processing, the NLUCommandAdapter issues commands, then the following command generators in the pipeline such as LLM-based command generators will be entirely bypassed. As a consequence, LLM-based command generators will not be able to fill slots by issuing set slot commands at any point in the conversation flow. If the LLM-based command generator issues commands to fill slots with nlu-based predefined mappings, these set slot commands from LLM-based command generator are ignored. If no other commands were predicted for the same turn, then the assistant will trigger the cannot_handle conversation repair pattern.
Sometimes the user message may contain intentions that go beyond setting a slot. For example, the user message may contain an entity that fills a slot but also starts a digression that must be handled. In such cases, we recommend using NLU triggers to handle those specific intents within flows. Please refer to the Impact of slot mappings in different scenarios section for more details.
In a CALM assistant built with flows and using NLU components to process the message, the default action action_extract_slots will not run, because the slot set events are applied to the dialogue tracker during command execution. This ensures that this default action does not overwrite CALM set slot(./dialogue-understanding.mdx#set-slot) commands and does not duplicate SlotSet events that were already applied to the dialogue tracker.
In the case of coexistence, the action_extract_slots action will be executed only when the NLU-based system is active.
You can use the from_llm slot mapping type to fill slots with values generated by LLM-based command generators. This is the default slot mapping type if the mappings are not explicitly defined in the domain file.
In this example, the user_name slot will be filled with the value generated by the LLM-based command generator. The LLM-based command generator is allowed to fill this slot at any point in the conversation flow, not just at the corresponding collect step for this slot.
If you have defined additional NLU-based components in the config.yml pipeline, these components will continue to process the user message however they will not be able to fill slots. The NLUCommandAdapter will skip any slots with from_llm mappings and will not issue set slot commands to fill these slots. Please refer to the Impact of slot mappings in different scenarios section for more details.
Note that a slot must not have both from_llm and NLU-based predefined mappings or custom slot mappings. If you define a slot with from_llm mapping, you cannot define any other mapping types for that slot.
You can define conditions for slot mappings to be satisfied before the slot is filled. The conditions are defined as a list of conditions under the conditions key. Each condition can specify the flow id that must be active to the active_flow property.
This is particularly useful if you define several slots mapped to the same entity, but you do not want to fill all of them when the entity is extracted.
- active_flow: greet_user
- active_flow: issue_invoice
You can use the custom mapping type to define custom slot mappings for slots that should be filled by a custom action. The custom action must be specified in the action property of the slot mapping. You must also list the action in the domain file under the actions key.
- action_fill_user_name
action: action_fill_user_name
In this example, the user_name slot will be filled by the action_fill_user_name custom action. The custom action must return a SlotSet event with the slot name and value to fill the slot.
Note that if you're using the action_ask_
If you are using custom validation actions (using the validate_
If you are training with the --skip-validation flag and you have defined slots with custom slot mappings that do not
specify the action property in the domain file, nor do they have corresponding action_ask_
You can also run this check via the rasa data validate command.
This section clarifies which components in a CALM assistant built with flows and a NLU pipeline are responsible for filling slots in different scenarios when the flow is at either the collect step for slot name or at any other step.
Main takeaway is that the NLUCommandAdapter cannot fill slots with from_llm mappings at any point in the conversation.
You can provide an initial value for any slot in your domain file:
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.
