# Slots [**Warning:** This document is for an old version of Rasa. The latest version is 1.10.26.](https://legacy-docs-v1.rasa.com/1.10.26/core/slots/) ## [What are slots?](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id3) **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). Most of the time, you want slots to influence how the dialogue progresses. There are different slot types for different behaviors. For example, if your user has provided their home city, you might have a `text` slot called `home_city`. If the user asks for the weather, and you _don’t_ know their home city, you will have to ask them for it. A `text` slot only tells Rasa Core 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. If the value itself is important, use a `categorical` or a `bool` slot. There are also `float`, and `list` slots. If you just want to store some data, but don’t want it to affect the flow of the conversation, use an `unfeaturized` slot. ## [How Rasa Uses Slots](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id4) The `Policy` doesn’t have access to the value of your slots. It receives a featurized representation. As mentioned above, for a `text` slot the value is irrelevant. **You should choose your slot types carefully!** ## [How Slots Get Set](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id5) You can provide an initial value for a slot in your domain file: ```yaml slots: name: type: text initial_value: "human" ``` You can get the value of a slot using `.get_slot()` inside `actions.py` for example: ```python data = tracker.get_slot("slot-name") ``` There are multiple ways that slots are set during a conversation: ### [Slots Set from NLU](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id6) If your NLU model picks up an entity, and your domain contains a slot with the same name, the slot will be set automatically. For example: ```yaml # story_01 * greet{"name": "Ali"} - slot{"name": "Ali"} - utter_greet ``` In this case, you don’t have to include the `- slot{}` part in the story, because it is automatically picked up. To disable this behavior for a particular slot, you can set the `auto_fill` attribute to `False` in the domain file: ```yaml slots: name: type: text auto_fill: False ``` ### [Slots Set By Clicking Buttons](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id7) You can use buttons as a shortcut. Rasa Core will send messages starting with a `/` to the `RegexInterpreter`, which expects NLU input in the same format as in story files, e.g. `/choose{"color": "red"}`. You can specify this in your domain file like this: ```yaml utter_ask_color: - text: "what color would you like?" buttons: - title: "blue" payload: '/choose{"color": "blue"}' - title: "red" payload: '/choose{"color": "red"}' ``` ### [Slots Set by Actions](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id8) The second option is to set slots by returning events in [custom actions](https://legacy-docs-v1.rasa.com/1.10.22/core/actions/#custom-actions). In this case, your stories need to include the slots. For example: ```yaml slots: account_type: type: categorical values: - premium - basic ``` ```python from rasa_sdk.actions import Action from rasa_sdk.events import SlotSet import requests class FetchProfileAction(Action): def name(self): return "fetch_profile" def run(self, dispatcher, tracker, domain): url = "http://myprofileurl.com" data = requests.get(url).json() return [SlotSet("account_type", data["account_type"])] ``` ```yaml # story_01 * greet - action_fetch_profile - slot{"account_type" : "premium"} - utter_welcome_premium # story_02 * greet - action_fetch_profile - slot{"account_type" : "basic"} - utter_welcome_basic ``` In this case, you **do** have to include the `- slot{}` part in your stories. Rasa Core will learn to use this information to decide on the correct action to take (in this case, `utter_welcome_premium` or `utter_welcome_basic`). ***Note*** It is **very easy** to forget about slots if you are writing stories by hand. We strongly recommend that you build up these stories using [Interactive Learning with Forms](https://legacy-docs-v1.rasa.com/1.10.22/core/interactive-learning/#section-interactive-learning-forms) rather than writing them. ## [Slot Types](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id9) ### [Text Slot](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id10) `text` Use For: User preferences where you only care whether or not they’ve been specified. Example ```yaml slots: cuisine: type: text ``` Description: Results in the feature of the slot being set to `1` if any value is set. Otherwise the feature will be set to `0` (no value is set). ### [Boolean Slot](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id11) `bool` Use For: True or False Example ```yaml slots: is_authenticated: type: bool ``` Description: Checks if slot is set and if True ### [Categorical Slot](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id12) `categorical` Use For: Slots which can take one of N values Example ```yaml slots: risk_level: type: categorical values: - low - medium - high ``` Description: Creates a one-hot encoding describing which of the `values` matched. A default value `__other__` is automatically added to the user-defined values. All values encountered which are not explicitly defined in the domain are mapped to `__other__` for featurization. The value `__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. ### [Float Slot](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id13) `float` Use For: Continuous values Example ```yaml slots: temperature: type: float min_value: -100.0 max_value: 100.0 ``` Defaults: `max_value=1.0`, `min_value=0.0` Description: All values below `min_value` will be treated as `min_value`, the same happens for values above `max_value`. Hence, if `max_value` is set to `1`, there is no difference between the slot values `2` and `3.5` in terms of featurization (e.g. both values will influence the dialogue in the same way and the model cannot learn to differentiate between them). ### [List Slot](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id14) `list` Use For: Lists of values Example ```yaml slots: shopping_items: type: list ``` Description: The feature of this slot is set to `1` if a value with a list is set, where the list is not empty. If no value is set, or the empty list is the set value, the feature will be `0`. The **length of the list stored in** **the slot does not influence the dialogue**. ### [Unfeaturized Slot](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id15) `unfeaturized` Use For: Data you want to store which shouldn’t influence the dialogue flow Example ```yaml slots: internal_user_id: type: unfeaturized ``` Description: There will not be any featurization of this slot, hence its value does not influence the dialogue flow and is ignored when predicting the next action the bot should run. ## [Custom Slot Types](https://legacy-docs-v1.rasa.com/1.10.22/core/slots/#id16) 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. In the code below, we define a slot class called `NumberOfPeopleSlot`. The featurization defines how the value of this slot gets converted to a vector so that our machine learning model can deal with it. Our slot has three possible “values”, which we can represent with a vector of length `2`. | | | | --- | --- | | `(0,0)` | not yet set | | `(1,0)` | between 1 and 6 | | `(0,1)` | more than 6 | ```python from rasa.core.slots import Slot class NumberOfPeopleSlot(Slot): def feature_dimensionality(self): return 2 def as_feature(self): r = [0.0] * self.feature_dimensionality() if self.value: if self.value <= 6: r[0] = 1.0 else: r[1] = 1.0 return r ``` Now we also need some training stories, so that Rasa Core can learn from these how to handle the different situations: ```yaml # story1 ... * inform{"people": "3"} - action_book_table ... # story2 * inform{"people": "9"} - action_explain_table_limit ``` 👋 I can help you get started with Rasa and answer your technical questions.