PageIndex Agent工作流

简介

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📑 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 — 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. Generate a “Table-of-Contents” tree structure index of documents
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. - 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”). - Context-Aware Retrieval: Retrieval depends on your full context (e.g., conversation history and domain knowledge), and easily incorporates new context. - Human-like Retrieval: Simulates how human experts navigate and extract knowledge from complex documents.

PageIndex powers a reasoning-based RAG system that achieved state-of-the-art 98.7% accuracy on FinanceBench, demonstrating superior performance over vector-based RAG solutions in professional document analysis. See our blog post for details.