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vector-index-tuning

Optimize vector indexes for latency, recall, and memory efficiency.

Updated Mar 18, 2026
Search Tags:#indexing#recall#memory#latency#vector#quantization#hnsw
One-click install
npx skills add https://github.com/ekremmkasap/jarvis --skill vector-index-tuning-ekremmkasap
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vector-index-tuning
Source: https://github.com/ekremmkasap/jarvis/tree/main/server/agent_prompts/wshobson/plugins/llm-application-dev/skills/vector-index-tuning
Command: npx skills add https://github.com/ekremmkasap/jarvis --skill vector-index-tuning-ekremmkasap

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Optimize vector indexes to improve latency, recall, and memory efficiency.

Core Features & Use Cases

  • Tuning HNSW parameters for recall vs latency trade-offs.
  • Selecting and applying quantization strategies to reduce memory footprint.
  • Estimating memory usage and providing practical templates for common vector search stacks (e.g., HNSW, IVF, or Qdrant).

Quick Start

Run the tuning workflow to adjust index type, M, ef_construction, and quantization to meet target latency and recall.

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 vector search recall and latency?▼

Reduce vector index memory usage by selecting and applying quantization strategies. This Skill provides memory usage estimates and practical templates to help scale vector storage while maintaining acceptable recall.

What is the best way to estimate memory usage for a production vector search deployment?▼

Estimate vector search memory usage by evaluating index type, vector dimensions, and quantization schemes. This Skill provides memory calculation templates and configuration examples for common stacks like HNSW, IVF, and Qdrant.

Does this vector index tuning workflow support Qdrant and IVF configurations?▼

Yes, vector index tuning supports Qdrant and IVF configurations. It provides practical templates and configuration examples to optimize latency, recall, and memory efficiency across common vector search stacks.

How do I choose the right quantization strategy to scale vector storage?▼

Choose a vector quantization strategy by evaluating the trade-off between memory footprint reduction and recall degradation. This Skill guides selecting and applying quantization schemes tailored to your production deployment targets.

Why does my vector search latency spike when increasing ef_construction?▼

Vector search latency may spike when increasing ef_construction because the HNSW algorithm explores more neighbors during index building and querying. Tuning this parameter requires balancing recall improvements against higher computational overhead.

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