Inverted File (IVF) indexes accelerate vector similarity searches by organizing high-dimensional vectors into clusters. During index creation, the dataset is partitioned into clusters using a clustering algorithm like k-means, where each cluster is represented by a centroid (a vector at the cluster’s center). Each vector in the dataset is assigned to the cluster whose centroid is closest to it. The IVF structure then creates an "inverted list" for each centroid, which stores references to all vectors belonging to that cluster. This setup allows the database to narrow down the search scope during queries, avoiding a brute-force scan of the entire dataset.
Clustering centroids act as proxies for narrowing the search space. When a query vector is received, the system first compares it to all centroids to identify the nearest clusters (e.g., the top 10 closest centroids). Only the vectors in these selected clusters are then scanned for similarity against the query. For example, if the dataset has 1 million vectors divided into 1,000 clusters (nlist=1,000), and the query checks the 10 closest clusters (nprobe=10), the search reduces from 1 million comparisons to roughly 10,000 (10 clusters × 1,000 vectors per cluster). This trade-off between speed and accuracy is controlled by parameters like nprobe: higher values improve recall but increase latency.
The effectiveness of IVF depends on clustering quality and parameter tuning. Poorly chosen centroids or an insufficient nprobe value can lead to missed relevant vectors, especially near cluster boundaries. Libraries like FAISS or databases like Milvus use IVF with optimizations such as product quantization to further compress vectors and reduce memory usage. For developers, key considerations include balancing nlist (more clusters mean smaller per-cluster searches but higher centroid comparison overhead) and nprobe (which directly impacts latency and recall). IVF is particularly useful for large datasets where approximate nearest neighbor (ANN) searches are acceptable, trading exact results for significantly faster queries.