# LlamaIndex

***

[LlamaIndex](https://www.llamaindex.ai/) is a data framework for LLM applications to ingest, structure, and access private or domain-specific data. It has advanced retrieval techniques for designing RAG (Retrieval Augmented Generation) apps.

Flowise complements LlamaIndex by offering a visual interface. Here, nodes are organized into distinct sections, making it easier to build workflows.

### LlamaIndex Sections:

* [Agents](https://docs.flowiseai.com/integrations/llamaindex/agents)
* [Chat Models](https://docs.flowiseai.com/integrations/llamaindex/chat-models)
* [Embeddings](https://docs.flowiseai.com/integrations/llamaindex/embeddings)
* [Engine](https://docs.flowiseai.com/integrations/llamaindex/engine)
* [Response Synthesizer](https://docs.flowiseai.com/integrations/llamaindex/response-synthesizer)
* [Tools](https://docs.flowiseai.com/integrations/llamaindex/tools)
* [Vector Stores](https://docs.flowiseai.com/integrations/llamaindex/vector-stores)


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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.flowiseai.com/integrations/llamaindex.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.