PageIndex

Description

PageIndex is VectifyAI's MIT-licensed open-source retrieval engine for long professional documents (financial filings, contracts, regulatory manuals, technical specifications). It is deliberately vectorless: at index time it parses a document into a hierarchical table-of-contents tree with a summary at each node and keeps leaf text intact rather than splitting it into fixed-size chunks; it computes no embeddings and builds no vector store. At query time it presents the tree to an LLM, which performs a tree search — judging which branch is most likely to hold the answer, descending, and repeating — and returns the leaf sections it reaches together with their page and section identifiers, so every retrieval is traceable to a named location in the source. The project ships a Python library (run_pageindex.py), an MCP server that exposes the tree navigator to MCP clients, and an example built on the OpenAI Agents SDK that swaps a vector-search tool for the tree-navigation tool. It positions itself as an alternative to embedding-based retrieval-augmented generation for documents where vocabulary overlap misleads similarity search and where auditable, citation-grounded retrieval matters.

Solution

Index time: parse document -> table-of-contents tree with per-node summaries (no embeddings, no chunking). Query time: the LLM reasons over the tree, chooses a branch, descends, and repeats until it reaches the relevant leaf sections; retrieval returns those sections with page and section references for the generator to answer over.

Primary use cases

• retrieval and question answering over long structured documents (financial filings, contracts, manuals)
• auditable retrieval where every result must point to a named page and section
• agentic document analysis where a tree-navigation tool replaces a vector-search tool