How do I tune hyperparameters for vector search?

Tuning hyperparameters for vector search involves adjusting parameters that control the balance between search accuracy, speed, and resource usage. Key hyperparameters typically include the number of clusters (in methods like IVF), the number of probes (for searching clusters), and parameters like the size of candidate lists or graph connections (in graph-based methods like HNSW). The goal is to optimize these settings based on your dataset size, query latency requirements, and acceptable error rates. For example, increasing the number of clusters in IVF reduces the search space per query, speeding up searches but potentially missing relevant results if clusters are too narrowly defined.

Start by benchmarking with default values and a validation set of known queries. Measure recall (percentage of true nearest neighbors found) and latency. For IVF, adjust the nlist (number of clusters) and nprobe (clusters searched per query): a higher nprobe improves recall but increases search time. If using HNSW, parameters like efConstruction (controls index quality during build) and efSearch (candidate list size during queries) affect results. For example, setting efSearch=100 might retrieve more accurate results than efSearch=20 but take longer. Experiment incrementally—increase nprobe or efSearch until recall plateaus, then check if latency is acceptable. Use tools like grid search or Bayesian optimization to systematically explore combinations.

Balance trade-offs based on your application. Real-time systems might prioritize latency over perfect recall, while batch processing could favor accuracy. For example, an e-commerce product search with 1M items might use IVF with nlist=1000 and nprobe=20 to achieve 90% recall in 5ms. Adjust memory constraints: methods like PQ (Product Quantization) reduce vector storage but introduce approximation errors. Test across hardware—parameters that work on a server with 64GB RAM may fail on edge devices. Document settings and re-evaluate when data changes (e.g., adding 50% more vectors). Open-source tools like FAISS’s autotune or ANN-Benchmarks can automate parts of this process.