How To Use Llamaindex
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What it is
LlamaIndex is the data framework for LLMs, purpose-built for ingesting documents and powering RAG over your private knowledge.
Install
Where LangChain tries to be everything, LlamaIndex stays narrower and deeper: loaders for 150+ data sources, chunking and metadata extraction pipelines, a VectorStoreIndex abstraction over every vector DB that matters, and query engines that combine retrieval with re-ranking and response synthesis. A newer Workflows API adds event-driven orchestration for when you outgrow simple query pipelines.
First run
LlamaIndex is MIT-licensed and maintained by LlamaIndex Inc. (Jerry Liu and team). The Python package llama-index-core is the base; integrations live in separate packages like llama-index-vector-stores-qdrant. A TypeScript port (llamaindex on npm) covers the essentials. LlamaParse, a paid managed service, handles complex PDFs and tables the OSS parser struggles with.
Everyday workflows
Index a folder of documents and ask a question about them:
Gotchas and tips
RAG quality lives and dies by chunking. Default settings are generic; tune chunk_size and chunk_overlap to your content — contracts, forum posts, and code all want different values. Measure recall with a small labeled set before declaring victory.
Who it’s for
Persistence is a common foot-gun. Calling from_documents every run re-embeds everything and bills you twice. Use StorageContext.persist() and load_index_from_storage, or push to a real vector DB that keeps state for you.