# PageIndex Emerges as Vectorless RAG Alternative for AI Retrieval > Source: > Published: 2026-09-29 10:29:01+00:00 PageIndex Emerges as Vectorless RAG Alternative for AI Retrieval Last updated Sep 28, 2026 PageIndex builds a hierarchical tree from a document's natural structure, skipping embeddings, chunking, and vector searches. An LLM then reasons through the tree using summaries and titles for precise retrieval. On FinanceBench, the Mafin 2.5 system hit 98.7% accuracy—well above vector-based tools like Perplexity at 45% or GPT-4o at 31%—while preserving context and explainability for financial filings and technical manuals. Developers praise its potential for structured docs but debate scalability, latency, and fit for broader searches. This story is a summary of posts on X and may evolve over time. Grok can make mistakes, verify its outputs. The entire RAG industry is about to get cooked. Researchers developed a new RAG approach that bypasses almost everything traditional RAG depends on. - No vector DB - No data embeddings - No chunking - No similarity search It's called PageIndex. Instead of splitting yourShow more The entire RAG industry is about to get cooked. Researchers developed a new RAG approach that bypasses almost everything traditional RAG depends on. - No vector DB - No data embeddings - No chunking - No similarity search It's called PageIndex. Instead of splitting yourShow more Obviously you can replace a reranker with Jev But I don't see how you replace embeddings (i.e. first-cut search) unless your dataset is so small that you never needed embeddings in the first place 98.7% is a result for Mafin 2.5, a full system built on PageIndex. Even its authors say FinanceBench mainly tests questions answered from a single document. Show me an ablation proving the tree index caused the gain, plus a matched comparison against strong hybrid RAG onShow more 55 1.1K1.1K Been wanting people to use Jev for this for a while now :) Semantic hierarchical search! just like a human would. check out our hierarchical classification cookbook The entire RAG industry is about to get cooked. Researchers developed a new RAG approach that bypasses almost everything traditional RAG depends on. - No vector DB - No data embeddings - No chunking - No similarity search It's called PageIndex. Instead of splitting yourShow more