To tune IVF index parameters (like nlist and nprobe) for a target recall with optimal query speed, start by understanding their roles. nlist determines how many clusters the dataset is divided into during index creation, while nprobe controls how many clusters are searched during a query. Higher nprobe improves recall by searching more clusters but slows queries. Larger nlist spreads data into finer clusters, which can reduce the points searched per probe but requires balancing with nprobe to avoid missing relevant clusters. The goal is to find the smallest nprobe and optimal nlist that together achieve the desired recall while minimizing query time.
Begin by fixing nlist to a reasonable default (e.g., 1,000–10,000 clusters, depending on dataset size) and incrementally adjust nprobe. Measure recall and query speed at each step. For example, if nlist=4096 and nprobe=16 yields 85% recall but your target is 90%, increase nprobe to 32 and retest. If this hits 90% but query time is too slow, try increasing nlist (e.g., to 8192) and reduce nprobe (e.g., to 24). Larger nlist reduces cluster size, so fewer points are scanned per probe, potentially maintaining recall with lower nprobe. However, avoid extreme nlist values, as too many clusters can fragment the data, requiring higher nprobe to compensate, negating speed gains.
Automate testing using grid search or Bayesian optimization to explore parameter combinations efficiently. For instance, tools like FAISS’s autotune functionality can test nlist/nprobe pairs against a validation set. Dataset characteristics matter: high-dimensional data may need larger nlist to keep clusters manageable, while smaller datasets might perform best with lower nlist and moderate nprobe. Always validate on a holdout set to ensure generalization. For example, a 1M-vector dataset might achieve 90% recall with nlist=4096 and nprobe=32, but switching to nlist=8192 and nprobe=24 could maintain recall while cutting query time by 20% due to smaller cluster sizes. Balance index build time (which grows with nlist) against query speed gains.