Key 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
Context Preservation
Retrieval depends on the full context
Context Preservation
Enables retrieval decisions that dynamically adapt to conversational context, ensuring that information is retrieved based on the full understanding of the ongoing conversation rather than isolated queries.
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
Context Preservation
Retrieval depends on the full context
RAG Comparison
RAG Comparison
PageIndex vs Vector DB
Choose the right RAG technique for your task
PageIndex
Logical Reasoning
High Retrieval Accuracy
Relies on logical reasoning, ideal for domain-specific data where semantics are similar.
No Time-to-First-Token Delay
Retrieval happens during generation time, allowing immediate streaming of responses without waiting for a separate retrieval phase.
Context-Preserved Retrieval
Leverages full chat history for relevance classification with LLM, enabling retrieval decisions that adapt to conversational context.
Efficient Context-level Knowledge Integration
Easily integrates with expert knowledge and user preferences during the tree search process.
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
Vector DB
Semantic Similarity
Low Retrieval Accuracy
Relies on semantic similarity, unreliable for domain-specific data where all content has similar semantics.
Time-to-First-Token Delay
Retrieval is separate from generation, requiring users to wait for the entire retrieval phase to complete before the response begins streaming.
Context-Independent Retrieval
Limited by embedding model input length, unable to incorporate full chat history into retrieval decisions, resulting in context-agnostic search.
Knowledge Integration Requires Fine-Tuning
Requires fine-tuning embedding models to incorporate new knowledge or preferences.
Best for Generic & Exploratory Applications
Vibe retrieval
Semantic recommendation systems
Creative writing and ideation tools
Short news/email retrieval
Generic knowledge question answering
Case Study
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.