PageIndex Developer
Vectorless, Reasoning-based RAG
Traceable, explainable, context-aware retrieval.
No vector databases or chunking.
Key Features
01
Higher Accuracy
Relevance beyond similarity
Higher Accuracy
Similarity ≠ relevance. PageIndex delivers precise, context-aware retrieval by reasoning over document structure to find what's truly relevant, achieving state-of-the-art accuracy on domain benchmarks.
02
Traceable & Explainable
Reasoning-driven retrieval with references
Traceable & Explainable
Retrieval is reasoning-driven and grounded in explicit page and section references, making every result traceable and interpretable, ensuring transparency, auditability, and trust — no more "vibe retrieval" with opaque, approximate vector search.
03
No Chunking
Preserves document structure
No Chunking
Avoids breaking documents into artificial chunks and prevents context fragmentation, preserving semantic integrity and the full hierarchical structure, enabling structure-driven retrieval.
04
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 vector search systems.
05
No Top-K
Retrieves all relevant passages
No Top-K
Retrieves relevant passages based on reasoning, without arbitrary top-K thresholds or manual parameter tuning.
06
Context-aware Retrieval
Retrieval depends on full context
Context-aware Retrieval
Retrieval adapts dynamically to full context, from conversational history, user preference, to domain and enterprise knowledge — ensuring retrieval sees the full contextual picture, not isolated single-query lookup.
07
Human-like Retrieval
Mirrors how humans read
Human-like Retrieval
Mirrors the human reasoning process of reading and retrieval — the LLM navigates a table-of-contents-like structure to reason and extract information as a human reader would.
Key Features
01
Higher Accuracy
Relevance beyond similarity
02
Traceable & Explainable
Reasoning-driven retrieval with references
03
No Chunking
Preserves document structure
04
No Vector DB
No extra infra overhead
05
No Top-K
Retrieves all relevant passages
06
Context-aware Retrieval
Retrieval depends on full context
07
Human-like Retrieval
Mirrors how humans read
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 to determine relevance rather than similarity, ideal for domain-specific data.
No Time-to-First-Token Delay
Retrieval happens during generation time, allowing responses to stream immediately without waiting for a separate retrieval phase.
Context-Aware Retrieval
Retrieval depends on full context (e.g., conversational history and domain or enterprise knowledge), enabling holistic retrieval with seamless integration of new context.
Explainable & Traceable Retrieval
Explainable and traceable reasoning process, with each retrieved result containing exact page or section references.
Lightweight Infra
Requires only a lightweight tree index (JSON) that integrates with mainstream databases. No extra infra needed.
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 similarity does not imply relevance.
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
Embedding models lack the capacity to effectively incorporate chat context or specialized knowledge into retrieval, requiring fine-tuning to adapt to new context.
Black-box Retrieval without Traceability
Often lacks clear traceability to source documents, difficult to verify information or understand retrieval decisions.
Extra Infra Overhead
Requires a separate embedding pipeline, vector database, and additional infra, with sync and maintenance overhead.
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 delivers state-of-the-art 98.7% accuracy on the financial document QA benchmark (FinanceBench), significantly outperforming traditional vector RAG systems, and still the highest to date.
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.
