Vectorless Retrieval

Vectorless retrieval is an emerging approach to information retrieval that bypasses the traditional pipeline of chunking documents, generating vector embeddings, and performing similarity search. Instead of converting text into mathematical representations and finding nearest neighbors in embedding space, vectorless systems use a language model to reason through document structure directly, navigating from general to specific to locate the precise information needed.

The most prominent example is tree-based document navigation, where a document is converted into a hierarchical structure - similar to an intelligent table of contents - with summaries and relationships preserved at each level. At query time, an LLM traverses the tree by reasoning about which branches are most likely to contain the answer, following cross-references and comparing sections along the way. This mirrors how a human expert would navigate a document rather than how a search engine indexes one.

Vectorless retrieval addresses several fundamental limitations of vector-based RAG. It avoids artificial relevance, where semantically similar but contextually wrong results are returned. It preserves document hierarchy that chunking destroys. It maintains cross-references and relationships between sections. And it produces full audit trails showing exactly how an answer was derived. The tradeoff is latency and cost - reasoning through a document tree at query time is slower and more expensive than a vector lookup. However, on benchmarks like FinanceBench, vectorless approaches have achieved 98.7% accuracy compared to roughly 50% for traditional vector-based RAG, making the tradeoff compelling for high-stakes applications in finance, law, and healthcare where accuracy outweighs speed.