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.
- 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
- 其他process-diagram.gv839 B
- 参考文档PROCESS.md1.3 KB
- 参考文档README.md1.1 KB
- 参考文档skill-report.json10.8 KB
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
- Full docs: SKILL.md
- AgentDB Optimization: https://agentdb.dev/docs/optimization