vector-index-tuning
Optimize vector index performance across latency, recall, and memory tradeoffs.
- Covers HNSW parameter tuning (M, efConstruction, efSearch) with benchmarking templates and automated recommendation logic based on vector count and target recall
- Includes quantization strategies: scalar INT8, product quantization, binary quantization, and FP16 compression with memory estimation tools
- Provides Qdrant collection configuration templates optimized for three scenarios: recall-focused, speed-focused, balanced, and memory-constrained deployments
- Includes search performance monitoring, latency profiling (p50/p95/p99), and recall measurement against ground truth
Vector Index Tuning
Guide to optimizing vector indexes for production performance.
When to Use This Skill
- Tuning HNSW parameters
- Implementing quantization
- Optimizing memory usage
- Reducing search latency
- Balancing recall vs speed
- Scaling to billions of vectors
Core Concepts
1. Index Type Selection
Data Size Recommended Index
More from wshobson/agents
tailwind-design-system
Build scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.
41.0Ktypescript-advanced-types
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.
40.4Knodejs-backend-patterns
Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
31.8Kpython-performance-optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
22.1Kapi-design-principles
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
20.3Kpython-testing-patterns
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
19.7K