Stop Drowning in Vectors: How I Built a Graph-Powered RAG That Actually Scales

The Problem with Traditional RAG Let's be honest - vector-based RAG has a scaling problem. You chunk documents, embed everything, store it in a vector database, and hope semantic similarity finds the right context. But when you're dealing with: Hundreds of technical documents Cross-referenced content (citations, related sections) Hierarchical information (chapters → sections → subsections) Vector search starts to feel like finding a needle in a haystack of needles. You either blow up your context window or miss critical relationships between documents. Enter Vectorless RAG The PageIndex architecture introduced a brilliant alternative: parse documents into hierarchical JSON trees and let the LLM navigate the structure directly. No embeddings. No similarity search. Just pure structural reasoning. But the original approach had a limitation - it kept everything in memory. Try loading hundreds of document trees simultaneously, and you'll watch your RAM wave goodbye. Why I Put It on Neo4j I