Traditional RAG cuts documents into chunks and retrieves the ones that look most similar to your question. But similar isn't always relevant. PageIndex takes a different approach: it builds a table-of-contents tree of a document and lets an LLM reason its way to the right section. It needs no vector database and no chunking, and every answer comes with page and section references.