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Vector Index Tuning

Guide to optimizing vector indexes for production performance.

Use this skill when

• Tuning HNSW parameters
• Implementing quantization
• Optimizing memory usage
• Reducing search latency
• Balancing recall vs speed
• Scaling to billions of vectors

Do not use this skill when

• 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

Instructions

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.

Safety

• 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

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

Weekly Installs

90

Repository

sickn33/antigra…e-skills

GitHub Stars

27.4K

First Seen

Jan 28, 2026

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Installed on

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