# Internal Search

## Overview

Drafter AI's Internal Search allows you to search your organization's internal content and knowledge for information. It helps you find the information you need quickly and easily, without having to spend hours searching through multiple sources.

You can search for documents, presentations, media files, spreadsheets, and other types of content, as well as information stored in knowledge bases and support systems. Internal Search is highly customizable, allowing you to refine your search criteria and filter the results to meet your specific needs.

## How to Setup

Provide the next mandatory info:

1. Data input (text) - variables from other blocks or provided by a user as manual input
2. Data output - search result variable, that the block produces. Change its name for ease of further use.

![](/files/mXdtOwuNyvuyBxwQB2Kz)

## Inputs and Outputs

<table><thead><tr><th width="188">Input</th><th width="163.33333333333331">Output</th><th>Output Description</th></tr></thead><tbody><tr><td>Search query (text)</td><td>Search Result (Text)</td><td>Generates texts using the prompt template and input data variables.</td></tr><tr><td></td><td>Document Name (Text)</td><td>Displayed name of the document in the storage</td></tr><tr><td></td><td>Path (Text)</td><td><p>Path to a specific element, where relevant search result was detected.</p><p>Can be a title, paragraph, reference, table of content element, list element, or table element.</p></td></tr><tr><td></td><td>Page (number)</td><td>Number of the page where search result was located</td></tr><tr><td></td><td>Document URL (url)</td><td>Link to the original document as PDF</td></tr></tbody></table>


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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.drafter.ai/building-blocks/actions/internal-search.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.