Documentation

Vector Index Tuning

Guide to optimizing vector indexes for production performance.

●Tuning HNSW parameters

●Implementing quantization

●Optimizing memory usage

●Reducing search latency

●Balancing recall vs speed

●Scaling to billions of vectors

●You only need exact search on small datasets (use a flat index)

●You lack workload metrics or ground truth to validate recall

●You need end-to-end retrieval system design beyond index tuning

1.Gather workload targets (latency, recall, QPS), data size, and memory budget.

2.Choose an index type and establish a baseline with default parameters.

3.Benchmark parameter sweeps using real queries and track recall, latency, and memory.

4.Validate changes on a staging dataset before rolling out to production.

Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.

●Avoid reindexing in production without a rollback plan.

●Validate changes under realistic load before applying globally.

●Track recall regressions and revert if quality drops.

●resources/implementation-playbook.md for detailed patterns, checklists, and templates.

vector-index-tuning