FLAT vs IVF

What's happening?

1. Build phase (already done): K-means partitioned the 200 points into 8 clusters. Each cluster has a centroid (the × marks).
2. Step 1 — evaluate centroids: IVF first computes the distance from the query to every centroid. That's only 8 comparisons, regardless of dataset size.
3. Step 2 — pick the closest nprobe clusters: these light up. All other clusters are skipped entirely.
4. Step 3 — scan points inside the chosen clusters: only ~50 of the 200 points.

The tradeoff: larger nprobe → more clusters scanned → higher recall but slower. Smaller nprobe → faster but you may miss true neighbors that live in unscanned clusters. Try setting nprobe = 1 and dragging the query near a cluster boundary to see recall drop.