PageIndex: Next-Generation Vectorless, Reasoning-based RAG

PageIndex is a vectorless, reasoning-based retrieval framework that simulates how human experts extract knowledge from complex documents. Instead of relying on vector similarity search, it builds a tree-structured index from documents and enables LLMs to perform agentic reasoning over that structure for context-aware retrieval. The retrieval process is traceable and interpretable, and requires no vector DBs or chunking.

Product

May 3, 2026

PageIndex File System: Massive-Scale Document Search

PageIndex File System is a file-level tree layer that sits above your documents and scales the same PageIndex tree search from a single document to millions of documents in one index. It synthesizes a semantic hierarchy with virtual nodes when no usable folder structure exists, builds the tree on demand for each query, and adapts how it searches each node to stay efficient at scale.

Insights

February 2, 2026

Context Blindness: A Fundamental Limitation of Vector RAG

We argue that context blindness — the inability of vector-based retrieval to condition on full conversational and reasoning context — is a fundamental limitation of vector RAG, and outline a paradigm shift from semantic similarity to context-dependent relevance classification. In this view, retrieval becomes a relevance decision made by an LLM with full context, scaled efficiently through hierarchical tree search.

Insights

October 27, 2025

Do We Still Need OCR?

We examine the inherent limitations of OCR from an information-theoretic perspective and show why a direct, vision-based approach with PageIndex is more effective. Because flattening a 2D page into a 1D text sequence is inherently lossy, PageIndex acts as a vectorless retrieval layer that selects the relevant pages of a long document, which a VLM then reads directly as images.

Insights

September 1, 2025

From Claude Code to Agentic RAG

The same bet behind both PageIndex and Claude Code: skip the vector DB and let the LLM itself drive retrieval. We explore the rise of agentic retrieval over vector indexing and PageIndex's agentic, vectorless RAG framework. Where Claude Code retrieves over a codebase with simple bash tools instead of a vector database, PageIndex gives long documents an in-context tree index that an LLM agent navigates by reasoning — no chunking, embeddings, or vector store.