Research

PageIndex is a vectorless, reasoning-based retrieval framework that simulates how human experts extract knowledge from complex documents. Instead of relying on vector similarity search, it builds a tree-structured index from documents and enables LLMs to perform agentic reasoning over that structure for context-aware retrieval. The retrieval process is traceable and interpretable, and requires no vector DBs or chunking.

We benchmarked PageIndex Chat against ChatGPT 5.1 on real-world long documents. PageIndex achieved 100% accuracy compared to ChatGPT 5.1's 59-82%, with faster response times and page-level traceability.

How PageIndex’s vectorless, reasoning-based RAG overcomes the challenges of traditional vector RAG in long, complex technical manuals.