Vectorless RAG Uses Document Structure Instead of Embeddings for Smarter Retrieval

Traditional Retrieval-Augmented Generation (RAG) splits documents into chunks, converts them into vector embeddings, and retrieves the most semantically similar chunk to answer a query. However, this approach has known limitations, including fragmented context from chunking, semantic similarity not always equating to relevance, and opaque retrieval that lacks traceability. Vectorless RAG addresses these issues by treating a document as a structured hierarchy — similar to a table of contents — rather than a flat collection of chunks. An LLM navigates this document tree using reasoning to identify the most relevant section, enabling answers to be traced back to their exact source. The two approaches are not mutually exclusive; depending on the use case and data type, traditional vector search and Vectorless RAG can be used together.