> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tokenfactory.nebius.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Integration of Nebius Token Factory with LlamaIndex.

You can use the Nebius Token Factory third-party integration with [LlamaIndex](https://www.llamaindex.ai/). This is a Python framework that provides high-level libraries and enables you to create applications and agents based on AI models. If you have already created a LlamaIndex-based application, you can embed requests to Nebius Token Factory into your application.

[LlamaIndex documentation](https://docs.llamaindex.ai/en/stable/)\
[LlamaIndex + Nebius Token Factory doccumentation](https://developers.llamaindex.ai/python/examples/llm/nebius/)

The integration supports the following types of models:

<Columns cols={2}>
  <Card title="Text-to-text" icon="text" href="/integrations/frameworks/llama-index/text-to-text">
    Text-to-text models
  </Card>

  <Card title="Vision" icon="eye" href="/integrations/frameworks/llama-index/vision">
    Image-text-to-text models
  </Card>

  <Card title="Embeding" icon="table" href="/integrations/frameworks/llama-index/embedding">
    Feature extraction
  </Card>
</Columns>

## More Examples

<Columns cols={3}>
  <Card title="RAG example 1" icon="file-pdf" href="https://github.com/nebius/token-factory-cookbook/tree/main/rag/rag-pdf-llama-index">
    Query PDF documents
  </Card>

  <Card title="RAG example 2" icon="file-pdf" href="https://github.com/nebius/token-factory-cookbook/tree/main/rag/rag-milvus-1">
    RAG example with vector database
  </Card>
</Columns>