# Knowledge Base

In Knowledge Base you can provide the virtual being with all the important information and content that is specific for your use case and needs. The virtual being will use this content to provide the most truthful and relevant responses based on the user's input as well as the context of the conversation.

{% hint style="info" %}
(If you're into AI tech like we are, you might find plenty of online resources regarding the core philosophy behind creating functional knowledge bases under the term "RAG", or Retrieval Augmented Generation, for example [by LangChain here)](https://python.langchain.com/docs/use_cases/question_answering/).
{% endhint %}

<figure><img src="/files/kqCgQztoAdfan1hJIEVP" alt=""><figcaption><p>Knowledge organized into Collections and Documents</p></figcaption></figure>

### Building your Knowledge Base

Knowledge Base in AI Editor is made of Collections and Docs.

**Collection** is a group of Docs, most likely related to each other. This organization will allow you to manage the content easily by dividing your information into smaller content pieces, Docs, grouped into corresponding categories – Collections.

**Document**, Doc for short, is a content item in the Collection that **contains text as well as images.** The text field accepts up to 10,000 characters. When adding images, there's an additional text field for the description of the image.&#x20;

<figure><img src="/files/vnkGzBl5rORawwjoPu6X" alt=""><figcaption><p>Example: A Document in the "Snowboards" collection</p></figcaption></figure>

{% hint style="info" %}
It's important to include information on what is depicted in the image – this will allow the virtual being to **select the most relevant images during the conversation and display them contextually.**
{% endhint %}

<figure><img src="/files/8p97laixQgvf40ZUHIee" alt=""><figcaption><p>Adding an image to a Document</p></figcaption></figure>

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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.virbe.ai/dashboard-management/knowledge-base-1.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.