PageIndex: Vectorless, Reasoning-based RAG

Reasoning-based RAG  ◦  No Vector DB  ◦  No Chunking  ◦  Human-like Retrieval

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📢 Updates

• 🔥 **Agentic Vectorless RAG** — A simple agentic, vectorless RAG example with self-hosted PageIndex, using OpenAI Agents SDK.
• 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, human-like 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 domain expertise and multi-step reasoning, similarity search often falls short.

Inspired by AlphaGo, we propose [PageIndex](https://vectify.ai/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. It 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. PageIndex performs retrieval in two steps:

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

🎯 Core Features

Compared to traditional vector-based RAG, PageIndex features:

• No Vector DB: Uses document structure and LLM reasoning for retrieval, instead of vector similarity search.
• No Chunking: Documents are organized into natural sections, not artificial chunks.
• Human-like Retrieval: Simulates how human experts navigate and extract knowledge from complex documents.
• Better Explainability and Traceability: Retrieval is based on reasoning — traceable and interpretable, with page and section references. No more opaque, approximate vector search (“vibe retrieval”).