PageIndex: Document Index for Vectorless, Reasoning-based RAG

Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic similarity rather than true relevance. But similarity ≠ relevance — what we truly need in retrieval is relevance, and that requires reasoning. When working with professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search often falls short — missing what's relevant but not similar, and returning what's similar yet not relevant.

Inspired by AlphaGo, we propose PageIndex — a vectorless, reasoning-based RAG system that builds a hierarchical tree index from long documents, and uses LLMs to reason over that index for agentic, context-aware retrieval. The retrieval is traceable and explainable, with no vector DBs or chunking. PageIndex simulates how human experts navigate and extract knowledge from complex documents through tree search, enabling LLMs to think and reason their way to the most relevant document sections. It performs retrieval in two steps: