Vector RAG Is Dead. PageIndex Just Proved It.

2026-03-21 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

PageIndex, an open-source project by VectifyAI, has achieved 98.7% accuracy on the FinanceBench benchmark, significantly outperforming traditional vector RAG systems which typically score around 50%. This 49-percentage-point difference challenges the established $2.2 billion vector database market. Inspired by AlphaGo's tree-search logic, PageIndex operates without vector databases or document chunking. Instead, it employs an iterative retrieval loop that navigates documents similarly to a human analyst, focusing on reasoning-based, tree-search framework to locate relevant information. This approach represents a departure from the common industry practice of embedding document chunks into vectors and using cosine similarity for retrieval.

Key takeaway

For AI/ML Directors evaluating retrieval-augmented generation (RAG) architectures, PageIndex's 98.7% accuracy on FinanceBench suggests a critical re-evaluation of vector database reliance. Your teams should investigate reasoning-based, tree-search frameworks as a potentially superior alternative, especially for high-accuracy, document-heavy applications, to avoid the limitations of traditional chunking and vector similarity.

Key insights

PageIndex's tree-search framework achieves 98.7% accuracy on FinanceBench, bypassing vector RAG's limitations.

Principles

• Reasoning-based search outperforms vector similarity.
• Iterative navigation mimics human analysis.

Method

PageIndex uses an iterative retrieval loop and a tree-search framework to navigate documents, eliminating the need for vector databases and document chunking for information retrieval.

In practice

• Explore tree-search for document retrieval.
• Evaluate alternatives to vector RAG.

Topics

• PageIndex
• Retrieval-Augmented Generation
• Vector Databases
• FinanceBench
• Tree Search Algorithms

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Machine Learning Engineer, AI Engineer, Data Scientist

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• I Stopped Using Libraries and Built TF-IDF From Scratch to Truly Understand Retrieval — Building TF-IDF from scratch clarifies the vector math underlying modern retrieval and RAG systems.
• RAG is dead, right?? — Kuba Rogut, Turbopuffer — Hybrid tool-rich retrieval and iterative agentic search are replacing simple RAG for advanced context understanding and performance gains.
• RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora — Existing RAG benchmarks misrepresent real-world performance due to high document redundancy and similarity in enterprise corpora.
• RIP Chunking? Meet Reasoning-Based, Vectorless RAG. — PageIndex offers a vectorless RAG alternative that navigates document structure like a human expert, avoiding fixed-chunking issues.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.