What is the IDF modifier on a sparse vector configuration, and what problem does it address?

The IDF modifier applies inverse-document-frequency-style weighting to sparse retrieval. The underlying idea is that a term appearing in many documents is less informative than a term appearing in relatively few documents. Without this correction, very common tokens can contribute too much to relevance. Conceptually, the weighting behaves like the IDF component of traditional information retrieval, where a common term receives a smaller contribution and a rare term receives a larger one. In Qdrant, the IDF modifier is configured on a sparse vector's collection configuration so the sparse index can account for document-frequency information. The trade-off is that IDF statistics need to be maintained and evolve with the collection, adding complexity. A common misconception is that IDF replaces the sparse encoder; it modifies the weighting used for sparse retrieval rather than creating semantic embeddings.