A Vectorless RAG System for Smarter Document Intelligence

Modern AI applications rely heavily on Retrieval-Augmented Generation (RAG) to analyze documents and answer questions. Most implementations follow a familiar approach of Split documents into chunks Generate embeddings Store them in a vector database Retrieve the most similar chunks While this architecture works well for small documents, it begins to break down when dealing with long, complex documents such as research papers, legal contracts, financial reports, or technical manuals. Important context gets fragmented Sections lose their relationships Retrieval becomes noisy To solve this problem, PageIndex introduces a fundamentally different approach to document retrieval. Instead of relying on vector similarity search, PageIndex transforms documents into a hierarchical tree structure and allows large language models to reason over that structure directly. The result is a vectorless, reasoning-based RAG system that more closely resembles how human experts read and navigate documents. T