Reasoning-based RAG • No Vector DB • No Chunking • Human-like Retrieval
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. Inspired by AlphaGo, PageIndex is 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. It simulates how human experts navigate and extract knowledge from complex documents through tree search.Quick Start
Get PageIndex running on your first PDF in under 5 minutes
Installation
Install PageIndex and set up your environment
Tree Structure
Understand the hierarchical tree index that powers PageIndex
API Reference
Explore configuration options and advanced parameters
How PageIndex Works
PageIndex performs retrieval in two steps:- Generate a hierarchical tree index - Similar to a “Table of Contents” but optimized for LLMs
- Perform reasoning-based retrieval - Navigate the tree through LLM-powered tree search
Core Features
Compared to traditional vector-based RAG, PageIndex features:No Vector DB
Uses document structure and LLM reasoning for retrieval, instead of vector similarity search
No Chunking
Documents are organized into natural sections, not artificial chunks
Human-like Retrieval
Simulates how human experts navigate and extract knowledge from complex documents
Better Explainability
Retrieval is based on reasoning — traceable and interpretable with page and section references
State-of-the-Art Performance
PageIndex powers a reasoning-based RAG system that achieved 98.7% accuracy on FinanceBench, demonstrating superior performance over vector-based RAG solutions in professional document analysis.See the Mafin 2.5 benchmark results for detailed comparisons and performance metrics.
Deployment Options
Self-Host
Run locally with the open-source package
Cloud Service
Try instantly with the Chat Platform or integrate via MCP/API
Enterprise
Private or on-prem deployment with dedicated support
Next Steps
Quick Start Guide
Generate your first PageIndex tree structure in minutes
Vectorless RAG Cookbook
See a minimal, hands-on example of reasoning-based RAG