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vector-index-tuning

Tune HNSW parameters and quantization strategies for vector index performance.

2|1|Updated Feb 27, 2026
Search Tags:#recall#benchmarking#memory#latency#quantization#hnsw#vector-index
One-click install
npx skills add https://github.com/spideynolove/claude-code-in-action --skill vector-index-tuning-spideynolove
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vector-index-tuning
Source: https://github.com/spideynolove/claude-code-in-action/tree/main/27-tdd-conductor-llmdev/.claude/skills/vector-index-tuning
Command: npx skills add https://github.com/spideynolove/claude-code-in-action --skill vector-index-tuning-spideynolove

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Optimize vector index performance.

Core Features & Use Cases

  • HNSW parameter tuning for recall vs latency
  • Quantization strategies to reduce memory footprint
  • Memory usage estimation and deployment scaling for billions of vectors
  • Practical templates and Python examples for benchmarking and configuration

Quick Start

Run the included templates to benchmark HNSW parameters on your vector dataset.

Frequently Asked Questions about vector-index-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I tune HNSW parameters for better recall and lower latency?▼

Tuning HNSW parameters involves adjusting graph connectivity and search depth to balance recall and latency. This Skill provides practical templates and configuration presets to optimize HNSW index performance on your vector dataset.

What's the best way to reduce vector index memory usage?▼

To reduce vector index memory usage, apply quantization strategies like IVF with PQ. This Skill offers memory estimators and quantization configurations to minimize footprint while maintaining search performance.

Can I scale a vector index to handle billions of vectors?▼

Scaling a vector index to billions of vectors requires memory estimation and deployment tuning. This Skill provides configuration presets for large-scale setups, including Qdrant-like deployments, to manage billions of vectors efficiently.

How do I benchmark vector index performance on my dataset?▼

Benchmarking vector index performance requires running tests against your dataset to measure recall, latency, and memory. This Skill includes Python examples and benchmarking templates to evaluate HNSW and quantization setups.

Does IVF with PQ work well for reducing vector search latency?▼

IVF with PQ reduces vector search latency and memory by clustering and compressing vectors. This Skill provides specific configuration presets for IVF with PQ to optimize your index for lower latency and efficient memory usage.

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