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PageIndex

Vectorless, reasoning-based RAG — builds a hierarchical tree index from long documents so an LLM retrieves by relevance instead of similarity. No chunking, no embeddings, no vector DB.

34,634 3,035 Python MITupdated 2 days ago
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Curator's take

Reach for it on long, structured professional documents (contracts, filings, manuals) where similarity search returns 'similar but irrelevant' passages and you need reasoning over document structure. Tradeoff is per-query LLM reasoning cost/latency versus a cheap vector lookup, and you still need clean parsed text upstream. If your corpus is huge, homogeneous and similarity is good enough, a vector DB (chroma, supavec) is cheaper.

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VectifyAI%2FPageIndex | Trendshift

PageIndex: Vectorless, Reasoning-based RAG

Reasoning-based RAG  ◦  No Vector DB, No Chunking  ◦  Context-Aware Retrieval  ◦  Reads Like a Human

🌐 Website  •   🖥️ Chat Platform  •   🔌 MCP & API  •   📖 Docs  •   💬 Discord  •   ✉️ Contact 

📢 Updates

  • 🔥 Agentic Vectorless RAG — A simple agentic, vectorless RAG example with self-hosted PageIndex, using OpenAI Agents SDK.
  • Scale PageIndex to Millions of Documents — PageIndex File System is a file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document, enabling massive-scale document search.
  • PageIndex Chat — Human-like document analysis agent platform for professional long documents. Also available via MCP or API.
  • PageIndex Framework — Deep dive into PageIndex: an agentic, in-context tree index that enables LLMs to perform reasoning-based, context-aware retrieval over long documents.

📑 Introduction to PageIndex

Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic similarity rather than true relevance. But similarity ≠ relevance — what we truly need in retrieval is relevance, and that requires reasoning. When working with professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search often falls short — missing what's relevant but not similar, and returning what's similar yet not relevant.

Inspired by AlphaGo, we propose PageIndex — a vectorless, reasoning-based RAG system that builds a hierarchical tree index from long documents, and uses LLMs to reason over that index for agentic, context-aware retrieval. The retrieval is traceable and explainable, with no vector DBs or chunking. PageIndex simulates how human experts navigate and extract knowledge from complex documents through tree search, enabling LLMs to think and reason their way to the most relevant document sections. It performs retrieval in two steps:

  1. Generate a “Table-of-Contents” tree structure index of documents
  2. Perform (agentic) reasoning-based retrieval through

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