Indexing

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. 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.

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

• How Each Index Works

Usage Pattern

Get started with:

from llama_index import VectorStoreIndex

index = VectorStoreIndex.from_documents(docs)

• Usage Pattern
  • Get Started
  • Configuring Document Parsing
  • Handling Document Update

Modules

• Module Guides
  • Vector Store Index
  • Summary Index
  • Tree Index
  • Keyword Table Index
  • Knowledge Graph Index
  • Custom Retriever combining KG Index and VectorStore Index
  • Knowledge Graph Query Engine
  • Knowledge Graph RAG Query Engine
  • REBEL + Knowledge Graph Index
  • REBEL + Wikipedia Filtering
  • SQL Index
  • SQL Query Engine with LlamaIndex + DuckDB
  • Document Summary Index
  • The ObjectIndex Class

Advanced Concepts

• Composability