agentdb-vector-search-optimization

skill_id when-optimizing-vector-search-use-agentdb-optimization name agentdb-vector-search-optimization 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. version 1.0.0 category agentdb subcategory performance-optimization trigger_pattern when-optimizing-vector-search agents ["performance-analyzer","ml-developer","backend-dev"] complexity intermediate estimated_duration 5-7 hours prerequisites ["AgentDB basics","Vector search concepts","Performance profiling skills"] outputs ["Optimized vector database","4-32x memory reduction","150x faster search","Performance benchmarks"] 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"] metadata {"author":"claude-flow","created":"2025-10-30T00:00:00.000Z","tags":["agentdb","optimization","quantization","hnsw-indexing","performance"]} 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