AGENT SKILL

agentdb-vector-search-optimization

Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors.

适用职业开发工程师
使用场景后端与接口
License 许可
MIT
最近更新
2026-06-24
Stars
★ 67
Forks
24

01 前言 Front Matter

name
agentdb-vector-search-optimization
agents
[ "performance-analyzer", "ml-developer", "backend-dev" ]
outputs
[ "Optimized vector database", "4-32x memory reduction", "150x faster search", "Performance benchmarks" ]
version
1.0.0
category
agentdb
metadata
{ "tags": [ "agentdb", "optimization", "quantization", "hnsw-indexing", "performance" ], "author": "claude-flow", "created": 1761782400000 }
skill_id
when-optimizing-vector-search-use-agentdb-optimization
complexity
intermediate
description
Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors.
subcategory
performance-optimization
prerequisites
[ "AgentDB basics", "Vector search concepts", "Performance profiling skills" ]
trigger_pattern
when-optimizing-vector-search
estimated_duration
5-7 hours
validation_criteria
[ "Memory usage reduced by 4x minimum", "Search latency < 10ms (p95)", "Throughput > 50K ops/sec", "Accuracy maintained > 95%" ]
evidence_based_techniques
[ "Quantitative benchmarking", "A/B comparison testing", "Performance profiling" ]

02 资源文件 4

03 SKILL.md 正文

AgentDB Vector Search Optimization

Overview

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.

SOP Framework: 5-Phase Optimization

Phase 1: Baseline Performance (1 hour)

  • Measure current metrics (latency, throughput, memory)
  • Identify bottlenecks
  • Set optimization targets

Phase 2: Apply Quantization (1-2 hours)

  • Configure product quantization
  • Train codebooks
  • Apply compression
  • Validate accuracy

Phase 3: Implement HNSW Indexing (1-2 hours)

  • Build HNSW index
  • Tune parameters (M, efConstruction, efSearch)
  • Benchmark speedup

Phase 4: Configure Caching (1 hour)

  • Implement query cache
  • Set TTL and eviction policies
  • Monitor hit rates

Phase 5: Benchmark Results (1-2 hours)

  • Run comprehensive benchmarks
  • Compare before/after
  • Validate improvements

Quick Start

import { AgentDB, Quantization, QueryCache } from 'agentdb-optimization';

const db = new AgentDB({ name: 'optimized-db', dimensions: 1536 });

// Quantization (4x memory reduction)
const quantizer = new Quantization({
  method: 'product-quantization',
  compressionRatio: 4
});
await db.applyQuantization(quantizer);

// HNSW indexing (150x speedup)
await db.createIndex({
  type: 'hnsw',
  params: { M: 16, efConstruction: 200 }
});

// Caching
db.setCache(new QueryCache({
  maxSize: 10000,
  ttl: 3600000
}));

Optimization Techniques

Quantization

  • Product Quantization: 4-8x compression
  • Scalar Quantization: 2-4x compression
  • Binary Quantization: 32x compression

Indexing

  • HNSW: 150x faster, high accuracy
  • IVF: Fast, partitioned search
  • LSH: Approximate search

Caching

  • Query Cache: LRU eviction
  • Result Cache: TTL-based
  • Embedding Cache: Reuse embeddings

Success Metrics

  • Memory reduction: 4-32x
  • Search speedup: 150x
  • Accuracy maintained: > 95%
  • Cache hit rate: > 70%

Additional Resources

04 同仓库更多 Skills