Vectorless RAG: Build a Retrieval System Without Embeddings or Vector Databases

Building a Vectorless RAG System: Hierarchical Page Indexing Without Embeddings Core question this article answers: Can we build an effective retrieval-augmented generation system without vector embeddings, similarity search, or vector databases? Yes. By structuring documents as navigable trees and using LLM reasoning to traverse them, we can retrieve relevant context through hierarchical decision-making rather than mathematical similarity. This approach mirrors how humans actually search through documents—using tables of contents and section headings rather than comparing every paragraph’s semantic meaning. Why Consider a Vectorless Approach? Core question: What problems does traditional vector-based RAG create that motivate alternative architectures? Traditional RAG systems …