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llamaindex-pro

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LlamaIndex guidance — data ingestion, indexing, query engines, agents, evaluation, and RAG optimization.

skills/llamaindex-pro/SKILL.md

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## Overview

LlamaIndex is the data framework for LLM applications: connectors that ingest your documents, indexes that structure them for retrieval, query engines that reason over them, and agents that act on them. Where LangChain is the general LLM app framework, LlamaIndex goes deep on the data side — ingestion, indexing strategies, and retrieval quality.

This skill covers the LlamaIndex workflow: loading and transforming documents, choosing index structures, building query engines and routers, adding agents, and evaluating retrieval quality.

## When to use

- Building RAG over documents, databases, or APIs.
- Ingesting messy real-world data (PDFs, wikis, tickets).
- Choosing index structures (vector, tree, keyword, knowledge graph).
- Composing query engines (routers, sub-question, multi-step).
- Evaluating retrieval and answer quality.
- Choosing between LlamaIndex and LangChain.
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