# Slots

## What are slots?

**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

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. The policy just sees a `1` or `0` depending on whether it is set.

**You should choose your slot types carefully!**

## How Slots Get Set

You can provide an initial value for a slot in your domain file:

```yaml
slots:
  name:
    type: text
    initial_value: "human"
```

There are multiple ways that slots are set during a conversation:

### Slots Set from NLU

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
```

### Slots Set By Clicking Buttons

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. `/intent{entities}`. For example:

```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

The second option is to set slots by returning events in [custom actions](https://legacy-docs-v1.rasa.com/1.7.4/core/actions/#custom-actions). In this case, your stories need to include the slots.

```yaml
slots:
   account_type:
      type: categorical
      values:
      - premium
      - basic
```

## Slot Types

### Text Slot

`text`

**Use For**
User preferences where you only care whether or not they’ve been specified.

**Example**
```yaml
slots:
   cuisine:
      type: text
```

### Boolean Slot

`bool`

**Use For**
True or False

**Example**
```yaml
slots:
   is_authenticated:
      type: bool
```

### Categorical Slot

`categorical`

**Use For**
Slots which can take one of N values

**Example**
```yaml
slots:
   risk_level:
      type: categorical
      values:
      - low
      - medium
      - high
```

### Float Slot

`float`

**Use For**
Continuous values

**Example**
```yaml
slots:
   temperature:
      type: float
      min_value: -100.0
      max_value:  100.0
```

### List Slot

`list`

**Use For**
Lists of values

**Example**
```yaml
slots:
   shopping_items:
      type: list
```

### Unfeaturized Slot

`unfeaturized`

**Use For**
Data you want to store which shouldn’t influence the dialogue flow

**Example**
```yaml
slots:
   internal_user_id:
      type: unfeaturized
```

## Custom Slot Types

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`:

```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
```

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