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, Indices 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, Indices store data in Node objects (which represent chunks of the original documents), and expose a Retriever interface that supports additional configuration and automation.

For a more in-depth explanation, check out our guide below:

Usage Pattern

Get started with:

from llama_index import VectorStoreIndex

index = VectorStoreIndex.from_documents(docs)

Advanced Concepts