As production datasets grow from thousands to millions of vectors, simple nearest-neighbor searches quickly become a latency bottleneck. Scaling your search infrastructure requires adjusting key indexing parameters to balance speed against accuracy.
Choosing the Right Index Type
Hierarchical Navigable Small World graphs offer rapid query times but carry a heavy memory footprint. For memory-constrained environments, turning to inverted file indexing with product quantization can yield massive infrastructure savings.
The Impact of Distance Metrics
The choice between cosine similarity, dot product, and Euclidean distance is not arbitrary. Matching your distance metric exactly to the training loss of your embedding model prevents subtle retrieval errors and ranking mismatches.
Actionable Index Tuning
Benchmark your search latency under peak load using different quantization levels to find the exact point where memory usage drops without degrading user experience.