diff --git a/README.md b/README.md index fede65240..96044b4a3 100644 --- a/README.md +++ b/README.md @@ -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