Written by Jakub Rusinowski · Last updated July 30, 2026
PageIndex throws out vector databases, embeddings, and chunking. Instead it builds a table-of-contents tree from your documents and lets an LLM reason its way to the right section — the way a human expert would. Here's how vectorless, reasoning-based RAG works, how it hit 98.7% on FinanceBench, when it beats classic RAG, and how to run it (even against a local model).
For three years, "do RAG" has meant one recipe: chop your documents into chunks, turn each chunk into a vector with an embedding model, stuff those vectors into a database, and at question time retrieve the chunks whose vectors sit closest to the question's vector. It works. It also loses to a surprisingly simple idea. PageIndex, an open-source project from VectifyAI, deletes the entire vector stack. No embeddings, no chunking, no Pinecone or Chroma. Instead it turns each document into a hierarchical tree — essentially a machine-readable table of contents — and lets an LLM reason its way down …