RAG, No Vectors
We built PageIndex, a document indexing system that turns documents into hierarchical search trees to support reasoning-based RAG. Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we really need in retrieval is relevance — which requires reasoning. When working with professional documents that demand domain expertise and multi-step reasoning, vector-based RAG and similarity search often fall short. So we started exploring a more reasoning-driven approach to RAG. Reasoning-based RAG enables LLMs to think and…
In plain words
RAG, No Vectors is a document indexing system called PageIndex that structures long documents into hierarchical search trees to improve retrieval accuracy in AI applications. Instead of relying on vector similarity, it uses reasoning-based retrieval to find relevant document sections for language models working with professional documents that require domain expertise and multi-step analysis. The system applies tree search techniques inspired by AlphaGo to enable more accurate document retrieval and is available as open-source software.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We built PageIndex, a document indexing system that turns documents into hierarchical search trees to support reasoning-based RAG. Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we really need in retrieval is relevance — which requires reasoning. When working with professional documents that demand domain expertise and multi-step reasoning, vector-based RAG and similarity search often fall short. So we started exploring a more reasoning-driven approach to RAG. Reasoning-based RAG enables LLMs to think and reason their way to the most relevant document sections. Inspired by AlphaGo, we use tree search to perform structured document retrieval. We open-sourced one of the key components: PageIndex. PageIndex is a hierarchical indexing system that builds search tree structures from long documents (like financial reports, regulatory documents, or textbooks), making them ready for reasoning-based RAG. Some highlights: - Hierarchical Structure: Organizes lengthy PDFs into LLM-friendly trees — like a smart table of contents. - Precise Referencing: Each node includes a summary and exact physical page numbers. - Natural Segmentation: Nodes align with document sections, preserving context — no arbitrary chunking. We've used PageIndex for financial document analysis with reasoning-based RAG and saw significant improvements in retrieval accuracy compared to vector-based systems. Would love any feedback — especially thoughts on reasoning-based RAG, or ideas for where PageIndex could be applied!
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