@Morgan-coded suggested this update to the article for the passage beginning withtxtai's Faiss backend uses exact search through 5,000 rows to clarify what's being discussed here.
txtai's Faiss backend uses exact search through 5,000 rows and switches to IVF above that threshold. At 5,183 rows on scifact, it selects `IVF132,Flat` with `nprobe=8`. Default IVF reduced NDCG at 10 by 41% for LEMUR-2048 (0.54910 → 0.32346), 34% for MUVERA-2048 (0.36757 → 0.24111), and 25% for MUVERA-10240 (0.50021 → 0.37341).
Raising `nprobe` to 64 recovered LEMUR-2048 to 0.5085, 7.4% below exact while remaining approximate. Tuned against tuned, LEMUR-2048 at 0.5085 still edged MUVERA-10240 at 0.48939 with one-fifth the storage. Collection-mean centering narrowed LEMUR's IVF gap from 41% to 30%, but hurt MUVERA under IVF, so it is not a general substitute for index tuning. These are single-dataset scifact/ColBERTv2 numbers from the same harness as the article's tables. Pin `faiss.components` to `IDMap,Flat` when exact search is practical; otherwise raise `nprobe` rather than accepting the default.
