Llama Index

LlamaIndex is the framework that treats your data as the center of the LLM application. Where LangChain starts from chains of model calls, LlamaIndex starts from indexes: it ingests documents, chunks them, embeds them, and exposes them through query engines and retrievers so a model can answer questions grounded in your content. It’s available in both Python and TypeScript.

If your problem is fundamentally “make an LLM smart about these documents,” LlamaIndex is usually the shortest path — it’s the most RAG-native of the major frameworks. Its ingestion pipeline handles loaders for PDFs, Notion, databases, and hundreds of other sources (via LlamaHub), and its retrieval layer goes well beyond naive top-k: sentence-window retrieval, auto-merging, rerankers, and structured metadata filtering. It has since grown full agent abstractions and multi-step Workflows, so document agents that plan, retrieve, and synthesize live in one framework rather than being bolted together.

You’ll pip install llama-index, point a SimpleDirectoryReader at a folder, and build a VectorStoreIndex from the documents in about four lines. From the index you derive a query_engine for one-shot Q&A or a chat_engine for conversational memory, then graduate to wrapping query engines as tools inside an agent. Most of your real work happens in tuning the ingestion: chunk sizes, embedding models, metadata, and which retriever strategy fits your corpus — the code stays small while retrieval quality does the heavy lifting.