---
title: Indexing | LlamaIndex OSS Documentation
---

## Concept

An `Index` is a data structure that allows us to quickly retrieve relevant context for a user query. For LlamaIndex, it’s the core foundation for retrieval-augmented generation (RAG) use-cases.

At a high-level, `Indexes` are built from [Documents](/python/framework/module_guides/loading/documents_and_nodes/index.md). They are used to build [Query Engines](/python/framework/module_guides/deploying/query_engine/index.md) and [Chat Engines](/python/framework/module_guides/deploying/chat_engines/index.md) which enables question & answer and chat over your data.

Under the hood, `Indexes` store data in `Node` objects (which represent chunks of the original documents), and expose a [Retriever](/python/framework/module_guides/querying/retriever/index.md) interface that supports additional configuration and automation.

The most common index by far is the `VectorStoreIndex`; the best place to start is the [VectorStoreIndex usage guide](/python/framework/module_guides/indexing/vector_store_index/index.md).

For other indexes, check out our guide to [how each index works](/python/framework/module_guides/indexing/index_guide/index.md) to help you decide which one matches your use-case.

## Other Index resources

See the [modules guide](/python/framework/module_guides/indexing/modules/index.md).