# Introduction

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## What is Deeptrain?

Deeptrain is a multi-modal data connector for LLMs and AI agents. We help you source and integrate data that is not directly available and understandable by transformer models and AI.

## Multi-modal Capabilities

Deeptrain helps you build truly mulimodal applications where your AI agents can learn and interact with content that is beyond the processing capabilities of currently existing LLMs.&#x20;

### Text&#x20;

Train and customise your AI agents beyond the pre-existing context window size using localized embedding database which retrieves content in real-time from live data sources to back your AI's answers. &#x20;

### Images

Easily turn any non-vision supported model into a computer-vision integrated LLM using Deeptrain.

### Flowchart, graphs

Help AI understand flowcharts, graphs, diagrams and more using Deeptrain integration with your AI agents and models.

### Audio

Proces any audio content and use it for training and processing for your AI agents.

### Videos

NEW : Deeptrain now supports multi-dimensional video processing. You can now use an video, hosted locally, self-hosted, or on supporting platform like Vimeo or YouTube to process them and use it as your AI's agent knowledge. You can take videos from your users as input filed using [Deeptrain's Transcribe API.    ](/documentation/getting-started/transcribe-api.md)

## Multi-dimensional Capabilities

Deeptrain is not limited to one particular language model. In fact, you ca equip 200+ private or open-source models with Deeptrain's tech & services.&#x20;

Deeptrain will soon be able to process custom models too.&#x20;


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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://deeptrain.gitbook.io/documentation/getting-started/introduction.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.