PageIndex FAQs

Question 1

What is PageIndex and how does it work?

Accepted Answer

PageIndex is a vectorless, reasoning-based Retrieval-Augmented Generation (RAG) system. It represents documents as hierarchical tree structures, enabling LLMs to navigate and retrieve information using multi-step reasoning and structure, much like a human expert uses a book's index.

Question 2

What are the main benefits of using PageIndex?

Accepted Answer

Key benefits include higher accuracy in retrieval, transparent reasoning trajectories, no infrastructure overhead from vector databases, preservation of full document context without chunking, and the ability to process long PDFs seamlessly for human-like conversations with LLMs.

Question 3

Does PageIndex require me to chunk documents or use a vector database?

Accepted Answer

No, a core advantage of PageIndex is that it completely eliminates the need for both document chunking and vector databases. It processes documents as whole entities, maintaining their full context and structure within its hierarchical tree representation.

Question 4

Can I integrate PageIndex with my existing LLMs or agents?

Accepted Answer

Yes, PageIndex MCP provides a direct, LLM-native, in-context tree index that can be leveraged by Claude, Cursor, and other MCP-compatible agents and LLMs, allowing them to reason over document structures directly.

Question 5

How does PageIndex differ from traditional RAG systems?

Accepted Answer

Unlike traditional RAG systems, PageIndex eliminates the need for vector databases and document chunking. It focuses on reasoning and document structure (hierarchical trees) for retrieval, providing higher accuracy, better transparency, and human-like information access, rather than relying on vector similarity.

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