Simone Salerno

Vector databases might be the wrong abstraction for document retrieval. A new open-source approach called PageIndex just hit 98.7% accuracy on a financial benchmark, beating traditional RAG by 30+ points. No embeddings. No chunking. No vector DB. The insight: when a 10-K says “see Note 15 for debt details,” vector search has no idea what that means. It retrieves whatever text looks similar to your query, not whatever text actually answers it. Similarity and relevance are different things. PageIndex builds a hierarchical tree from document structure, then uses LLM reasoning to traverse it. The model asks “where would an expert look?” instead of “what text looks similar?” The math is stark. Traditional RAG systems hover around 60-70% on FinanceBench. That 30-point gap represents every time vector search found semantically similar text but missed the actual answer buried in an appendix or cross-referenced table. What makes this interesting: the infrastructure is simpler, not more complex. No vector DB to maintain. No embedding pipeline. No chunking decisions. Just a tree and reasoning. Vector search was the best we had when LLMs couldn’t reason well enough to navigate document structure. Now they can. The techniques we built around their limitations are becoming the bottleneck. For simple use cases, vector RAG still wins on speed and simplicity. But for professional documents requiring multi-step reasoning, treating structure as signal instead of noise changes everything.