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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. They are used to build Query Engines and Chat Engines 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 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.

For other indexes, check out our guide to how each index works to help you decide which one matches your use-case.

Other Index resources#

See the modules guide.