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What If RAG Didn’t Need Vector Databases?

https://github.com/VectifyAI/PageIndex Most RAG systems rely on vector databases and arbitrary text chunking. PageIndex takes a completely different approach. In this video, we do a deep code walk-through of PageIndex, a reasoning-based RAG framework that removes vector databases entirely and instead reconstructs a document’s hierarchical Table of Contents. By reverse-engineering the semantic structure of long documents, PageIndex allows AI agents to navigate information the way a human researcher would. This isn’t a surface-level demo. We go inside the code to understand: • Why chunking often breaks retrieval accuracy • How PageIndex rebuilds document structure • How agents traverse content using a TOC-style hierarchy • Where this approach fits (and doesn’t) in real-world RAG systems If you’re building AI agents, RAG pipelines, or working with long-document retrieval, this architecture is worth understanding.