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…
RAG, No Vectors launched on April 11, 2025 with 11 votes, #2,830 of 7,818 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
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
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG - VectifyAI/PageIndex
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…
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PageIndex – Vectorless RAG2025 · github.com · ▲192Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning…
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As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
A Vectorless LLM-Native Document Index Method2025 · github.com · ▲14The word "index" originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents. Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans. PageIndex is a new step that "looks back to move forward".…
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