Document retrieval forthe reasoning era.
No chunking. No embeddings. No vector DB. Vectorless parses documents into structured maps any LLM can navigate — precision retrieval with citations you can trust.
// 1. Install
npm i @vectorless/sdk
// 2. Ask with citations
const { answer, citations } = await vl.ask(doc, "what changed?")One document, parsed into structure, walked to the node.
We don't guess similarity. We reason through your document's actual hierarchy to find the exact branch where the truth lives.
Retrieval, rebuilt around reasoning
Tree, not chunks
Vectorless parses a document into a hierarchical tree that preserves its real structure — sections, sub-sections, tables. No fixed-size chunking, no lost context.
treewalk navigation
An LLM agent walks the tree node by node, reasoning about where the answer lives. Retrieval becomes a navigation problem, not a nearest-neighbor lottery.
Citations by construction
Every answer traces back to the exact nodes it came from. Path-correct citations are a property of the engine, not a bolt-on afterthought.
No vector DB to run
No embeddings to compute, no index to maintain, no similarity threshold to tune. Point Vectorless at a document and ask.
Retrieval stopped being a search problem. It became a reasoning problem.