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README: concrete description of indexing mechanism #78

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656a74d
refine implementation summary
cameron Jan 24, 2026
29b78c0
Revise indexing and querying steps in README
cameron Jan 24, 2026
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -51,8 +51,8 @@ Are you frustrated with vector database retrieval accuracy for long professional
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. Generate a “Table-of-Contents” **tree structure index** of documents
2. Perform reasoning-based retrieval through **tree search**
1. Indexing: LLM turns the document into a tree of semantically-delineated sections (e.g. doc -> chapter -> section), with each node having a summary of its subtree
2. Querying: LLM traverses the tree of summaries in the same way agent harnesses unpack skills—e.g., by having read the TOC into the context window, and then loading chapters' (and so on) children as necessary

<div align="center">
<a href="https://pageindex.ai/blog/pageindex-intro" target="_blank" title="The PageIndex Framework">
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