# Knowledge Base Actions

Warning

This feature is experimental. We introduce experimental features to get feedback from our community, so we encourage you to try it out! However, the functionality might be changed or removed in the future. If you have feedback (positive or negative) please share it with us on the [forum](https://forum.rasa.com/).

## Using `ActionQueryKnowledgeBase`

### Create a Knowledge Base

The data used to answer the user’s requests will be stored in a knowledge base. A knowledge base can be used to store complex data structures. We suggest you get started by using the `InMemoryKnowledgeBase`. Once you want to start working with a large amount of data, you can switch to a custom knowledge base.

To initialize an `InMemoryKnowledgeBase`, you need to provide the data in a json file. The following example contains data about restaurants and hotels.

```json
{
    "restaurant": [
        {
            "id": 0,
            "name": "Donath",
            "cuisine": "Italian",
            "outside-seating": true,
            "price-range": "mid-range"
        },
        {
            "id": 1,
            "name": "Berlin Burrito Company",
            "cuisine": "Mexican",
            "outside-seating": false,
            "price-range": "cheap"
        },
        {
            "id": 2,
            "name": "I due forni",
            "cuisine": "Italian",
            "outside-seating": true,
            "price-range": "mid-range"
        }
    ],
    "hotel": [
        {
            "id": 0,
            "name": "Hilton",
            "price-range": "expensive",
            "breakfast-included": true,
            "city": "Berlin",
            "free-wifi": true,
            "star-rating": 5,
            "swimming-pool": true
        },
        {
            "id": 1,
            "name": "Hilton",
            "price-range": "expensive",
            "breakfast-included": true,
            "city": "Frankfurt am Main",
            "free-wifi": true,
            "star-rating": 4,
            "swimming-pool": false
        },
        {
            "id": 2,
            "name": "B&B",
            "price-range": "mid-range",
            "breakfast-included": false,
            "city": "Berlin",
            "free-wifi": false,
            "star-rating": 1,
            "swimming-pool": false
        }
    ]
}
```

## Define the NLU Data

In this section:

- we will introduce a new intent, `query_knowledge_base`
- we will annotate `mention` entities so that our model detects indirect mentions of objects like “the first one”
- we will use [synonyms](https://legacy-docs-v1.rasa.com/1.10.12/nlu/training-data-format/#entity-synonyms) extensively

For the bot to understand that the user wants to retrieve information from the knowledge base, you need to define a new intent. We will call it `query_knowledge_base`.

## Create an Action to Query your Knowledge Base

To create your own knowledge base action, you need to inherit `ActionQueryKnowledgeBase` and pass the knowledge base to the constructor of `ActionQueryKnowledgeBase`.

```python
from rasa_sdk.knowledge_base.storage import InMemoryKnowledgeBase
from rasa_sdk.knowledge_base.actions import ActionQueryKnowledgeBase

class MyKnowledgeBaseAction(ActionQueryKnowledgeBase):
    def __init__(self):
        knowledge_base = InMemoryKnowledgeBase("data.json")
        super().__init__(knowledge_base)
```

Whenever you create an `ActionQueryKnowledgeBase`, you need to pass a `KnowledgeBase` to the constructor. This is the entirety of the code for this action! The name of the action is `action_query_knowledge_base`. Don’t forget to add it to your domain file:

```yaml
actions:
- action_query_knowledge_base
```

## How It Works

`ActionQueryKnowledgeBase` looks at both the entities that were picked up in the request as well as the previously set slots to decide what to query for.