vector-index-tuning

Tune HNSW parameters by adjusting M, efConstruction, and efSearch to balance vector search recall against query latency. Increasing M and efConstruction improves graph connectivity and recall during indexing, while efSearch controls the recall-latency trade-off at query time.

Reduce memory footprint for large-scale vector search by applying quantization strategies such as INT8, PQ, or binary quantization. These techniques compress vector representations, lowering memory usage and improving throughput without drastically degrading recall.

Choose between Flat, HNSW, and IVF indexes based on your dataset size and memory constraints. Flat indexes suit small datasets requiring exact search, while HNSW and IVF handle large-scale, latency-constrained workloads with approximate nearest neighbor search.

You can benchmark different vector index configurations using provided benchmarking templates. These templates help reproduce experiments to compare HNSW and quantization setups, tracking recall, latency, and memory metrics across configurations.

Vector search latency spikes under memory constraints when index configurations exceed available RAM, causing disk swapping or inefficient graph traversal. Tuning HNSW parameters and applying quantization like PQ or INT8 reduces the memory footprint to stabilize latency.

Vector index tuning works for production-scale recommendation and similarity workloads with billions of vectors. It addresses latency targets and memory budgets by selecting appropriate index types, HNSW parameters, and quantization strategies for large-scale deployments.