Enterprise AI

Most RAG systems split documents into fixed chunks and use vector similarity to retrieve context. PageIndex uses a hierarchical document tree and LLM reasoning instead. This article explains its architecture, quickstart, costs, and limits, and shows why it is not a universal replacement for vector databases.

Many teams can call an LLM, yet still get stuck on document parsing, chunking, retrieval, citations, and agent workflows. This article explains how LlamaIndex connects private data to LLM applications, how to start with a verifiable document question-answering flow, and which production limits require careful evaluation.