vector-index-tuning

Tune HNSW parameters by benchmarking M, efConstruction, and efSearch on your vector dataset to achieve target recall with acceptable latency. Adjusting M controls graph connectivity, while efConstruction and efSearch govern search depth during index building and querying respectively.

Reduce memory footprint for vector search by selecting an appropriate quantization strategy. Options include FP32 or FP16 for precision, INT8, product quantization (PQ), or binary quantization, each trading recall and latency for lower memory consumption across billions of vectors.

Choose vector index types based on data size: Flat for small datasets needing exact search, HNSW for high-recall low-latency requirements, HNSW with quantization for memory constraints, or IVF+PQ for large-scale clustering to optimize production performance.

Estimate vector storage and index overhead using memory and latency estimation tooling to guide deployment decisions. This planning step calculates memory requirements and expected latency trade-offs across different quantization settings and HNSW configurations.

Vector search latency spikes when scaling to billions of vectors due to unoptimized HNSW parameters or excessive memory usage. Benchmarking M, efConstruction, and efSearch values alongside quantization strategies like PQ or INT8 ensures production performance is maintained at scale.