What is a sparse vector, and how does it differ structurally from the dense vectors used in semantic search?

A sparse vector represents an item in a very high-dimensional feature space where most dimensions are zero and only a relatively small set of dimensions have explicit weights. In information retrieval, those dimensions often correspond to terms or learned token features, with the non-zero values representing their relevance. A dense embedding is different: it usually has a fixed, much smaller dimensionality such as 384, 768, or 1536 and nearly every component contains a value. Structurally, Qdrant represents sparse vectors as indexed values paired with their non-zero weights rather than a full array of mostly-zero numbers. Sparse retrieval is especially good at exact lexical signals such as product names, IDs, rare terms, and technical terminology, while dense vectors capture semantic similarity. A common misconception is that sparse means low-dimensional; sparse vectors are typically high-dimensional but contain few non-zero entries.