PageIndex
Human-like Document AI
PageIndex is a vectorless, reasoning-based RAG engine designed for long documents, delivering higher accuracy and explainability, without vector DBs or chunking.
Best for:
Technical Manuals
Legal Documents
Medical Records
Financial Reports
Research Papers
Technical Manuals
Legal Documents
Medical Records
Financial Reports
Research Papers
Technical Manuals
Legal Documents
Medical Records
Financial Reports
Research Papers
01
Better Explainability
Traceable reasoning steps and references
Better Explainability
Provides traceable and interpretable reasoning steps in retrieval, with page and section level references, ensuring clarity, auditability, and trust.
02
Higher Accuracy
True relevance beyond similarity
Higher Accuracy
Delivers precise, context-aware answers, achieving leading accuracy on industry benchmarks.
03
No Chunking
Preserves full context
No Chunking
Avoids breaking documents into artificial chunks, preserving the full hierarchical and semantic structure of the document for better context retention and structure-aware retrieval.
04
No Top-K
Retrieves all relevant passages
No Top-K
Retrieves all relevant passages without manual parameter tuning or limiting results to arbitrary top‑K thresholds.
05
No Vector DB
No extra infra overhead
No Vector DB
Eliminates the overhead, cost, and opacity of vector databases. No extra infra, no external similarity search, no embeddings pipeline.
06
Like a Human
Retrieves like a human expert
Like a Human
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
Better Explainability
Traceable reasoning steps and references
02
Higher Accuracy
True 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
Like a Human
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 LLMs to search the tree 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.
Fully Traceable Retrieval Process
Tree search provides a traceable reasoning process, each retrieved node also contains an exact page reference.
Compromised Efficiency for Accuracy
Tree search prioritizes accuracy over speed, delivering precise results for domain-specific analysis.
Efficient Prompt-Level Knowledge
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 Powers Leading Industry Models
PageIndex forms the foundation of Mafin 2.5, a leading RAG model 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.
