PageIndex
Human-like Document AI
PageIndex is a reasoning-based RAG engine for long documents that mirrors how humans read, delivering traceable, explainable and accurate retrieval, without vector databases or chunking.
Best for:
Textbooks
Financial Reports
Legal Documents
Technical Manuals
Medical Files
Research Papers
Business Plans
Textbooks
Financial Reports
Legal Documents
Technical Manuals
Medical Files
Research Papers
Business Plans
Textbooks
Financial Reports
Legal Documents
Technical Manuals
Medical Files
Research Papers
Business Plans
Features
01
Traceable & Explainable
Reasoning-driven retrieval with references
Traceable & Explainable
Provides traceable and interpretable reasoning steps in retrieval, with clear page and section level references, ensuring transparency, auditability, and trust.
02
Higher Accuracy
Context relevance beyond similarity
Higher Accuracy
Delivers precise, context-aware answers by reasoning over document structure, achieving leading accuracy on domain benchmarks.
03
No Chunking
Preserves full context
No Chunking
Avoids breaking documents into artificial chunks and prevents context fragmentation, preserving the full hierarchical structure so retrieval is context-aware and structure-driven.
04
No Top-K
Retrieves all relevant passages
No Top-K
Retrieves relevant passages based on reasoning, without setting arbitrary top-K thresholds and manual parameter tuning.
05
No Vector DB
No extra infra overhead
No Vector DB
Eliminates the cost and complexity of vector databases — minimal infra overhead, no embeddings pipeline, no external similarity search.
06
Human-like Retrieval
Retrieves like a human expert
Human-like Retrieval
Mimics the human reasoning process of reading and retrieval, allowing the LLM to navigate a table-of-contents-like hierarchical structure to reason and extract information as a human reader would.
Key Features
01
Traceable & Explainable
Reasoning-driven retrieval with references
02
Higher Accuracy
Context relevance beyond similarity
03
No Chunking
Preserves full context
04
No Top-K
Retrieves all relevant passages
05
No Vector DB
No extra infra overhead
06
Human-like Retrieval
Retrieves like a human expert
Want to integrate PageIndex to your LLMs or AI agents?
Introduction
PageIndex Building Blocks
PageIndex simulates how human experts extract knowledge from long documents. It transforms documents into a tree-structured index and uses LLM reasoning to search the tree index for relevant information.
01
PageIndex Tree Generation
Generate hierarchical tree-structure index optimized for retrieval
02
PageIndex Retrieval
Reasoning-based retrieval by document tree search
RAG Comparison
PageIndex vs Vector DB
Choose the right RAG technique for your task
PageIndex
Logical Reasoning
Best for Domain-Specific Document Analysis
Financial reports and SEC filings
Regulatory and compliance documents
Healthcare and medical reports
Legal contracts and case law
Technical manuals and scientific documentation
High Retrieval Accuracy
Relies on logical reasoning, ideal for domain-specific data where semantics are similar.
Explainable & Traceable Retrieval Process
Provides an explainable and traceable reasoning process, with each retrieved node containing an exact page reference.
Compromised Efficiency for Accuracy
Prioritizes accuracy over speed, delivering precise results for domain-specific analysis.
Efficient Context-level Knowledge Integration
Easily integrates with expert knowledge and user preferences during the tree search process.
Vector DB
Semantic Similarity
Best for Generic & Exploratory Applications
Vibe retrieval
Semantic recommendation systems
Creative writing and ideation tools
Short news/email retrieval
Generic knowledge question answering
Low Retrieval Accuracy
Relies on semantic similarity, unreliable for domain-specific data where all content has similar semantics.
Black-box Retrieval without Traceability
Often lacks clear traceability to source documents, difficult to verify information or understand retrieval decisions.
Speed-Optimized Vector Search
Prioritizes efficiency and speed, making it ideal for applications where quick responses are critical.
Knowledge Integration Requires Fine-Tuning
Requires fine-tuning embedding models to incorporate new knowledge or preferences.
Case Study
PageIndex Leads Industry Benchmarks
PageIndex forms the foundation of Mafin 2.5, a leading RAG system for financial report analysis, achieving 98.7% accuracy on FinanceBench — the highest in the market.
30%
RAG with Vector DB
One vector index for all the documents.
50%
RAG with Vector DB
One vector index for each document.
98.7%
RAG with PageIndex
Query-to-SQL for document-level retrieval, PageIndex for node-level retrieval.