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database","testing","batch","database","benchmarking","performance","memory","2026-11-10 10:12:30",false,"---\nskill_id: when-optimizing-vector-search-use-agentdb-optimization\nname: agentdb-vector-search-optimization\ndescription: 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.\nversion: 1.0.0\ncategory: agentdb\nsubcategory: performance-optimization\ntrigger_pattern: \"when-optimizing-vector-search\"\nagents:\n  - performance-analyzer\n  - ml-developer\n  - backend-dev\ncomplexity: intermediate\nestimated_duration: 5-7 hours\nprerequisites:\n  - AgentDB basics\n  - Vector search concepts\n  - Performance profiling skills\noutputs:\n  - Optimized vector database\n  - 4-32x memory reduction\n  - 150x faster search\n  - Performance benchmarks\nvalidation_criteria:\n  - Memory usage reduced by 4x minimum\n  - Search latency \u003C 10ms (p95)\n  - Throughput > 50K ops\u002Fsec\n  - Accuracy maintained > 95%\nevidence_based_techniques:\n  - Quantitative benchmarking\n  - A\u002FB comparison testing\n  - Performance profiling\nmetadata:\n  author: claude-flow\n  created: 2025-10-30\n  tags:\n    - agentdb\n    - optimization\n    - quantization\n    - hnsw-indexing\n    - performance\n---\n\n# AgentDB Vector Search Optimization\n\n## Overview\n\nOptimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.\n\n## SOP Framework: 5-Phase Optimization\n\n### Phase 1: Baseline Performance (1 hour)\n- Measure current metrics (latency, throughput, memory)\n- Identify bottlenecks\n- Set optimization targets\n\n### Phase 2: Apply Quantization (1-2 hours)\n- Configure product quantization\n- Train codebooks\n- Apply compression\n- Validate accuracy\n\n### Phase 3: Implement HNSW Indexing (1-2 hours)\n- Build HNSW index\n- Tune parameters (M, efConstruction, efSearch)\n- Benchmark speedup\n\n### Phase 4: Configure Caching (1 hour)\n- Implement query cache\n- Set TTL and eviction policies\n- Monitor hit rates\n\n### Phase 5: Benchmark Results (1-2 hours)\n- Run comprehensive benchmarks\n- Compare before\u002Fafter\n- Validate improvements\n\n## Quick Start\n\n```typescript\nimport { AgentDB, Quantization, QueryCache } from 'agentdb-optimization';\n\nconst db = new AgentDB({ name: 'optimized-db', dimensions: 1536 });\n\n\u002F\u002F Quantization (4x memory reduction)\nconst quantizer = new Quantization({\n  method: 'product-quantization',\n  compressionRatio: 4\n});\nawait db.applyQuantization(quantizer);\n\n\u002F\u002F HNSW indexing (150x speedup)\nawait db.createIndex({\n  type: 'hnsw',\n  params: { M: 16, efConstruction: 200 }\n});\n\n\u002F\u002F Caching\ndb.setCache(new QueryCache({\n  maxSize: 10000,\n  ttl: 3600000\n}));\n```\n\n## Optimization Techniques\n\n### Quantization\n- **Product Quantization**: 4-8x compression\n- **Scalar Quantization**: 2-4x compression\n- **Binary Quantization**: 32x compression\n\n### Indexing\n- **HNSW**: 150x faster, high accuracy\n- **IVF**: Fast, partitioned search\n- **LSH**: Approximate search\n\n### Caching\n- **Query Cache**: LRU eviction\n- **Result Cache**: TTL-based\n- **Embedding Cache**: Reuse embeddings\n\n## Success Metrics\n\n- Memory reduction: 4-32x\n- Search speedup: 150x\n- Accuracy maintained: > 95%\n- Cache hit rate: > 70%\n\n## Additional Resources\n\n- Full docs: SKILL.md\n- AgentDB Optimization: https:\u002F\u002Fagentdb.dev\u002Fdocs\u002Foptimization\n","# AgentDB Vector Search Optimization\n\n## Overview\n\nOptimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.\n\n## SOP Framework: 5-Phase Optimization\n\n### Phase 1: Baseline Performance (1 hour)\n- Measure current metrics (latency, throughput, memory)\n- Identify bottlenecks\n- Set optimization targets\n\n### Phase 2: Apply Quantization (1-2 hours)\n- Configure product quantization\n- Train codebooks\n- Apply compression\n- Validate accuracy\n\n### Phase 3: Implement HNSW Indexing (1-2 hours)\n- Build HNSW index\n- Tune parameters (M, efConstruction, efSearch)\n- Benchmark speedup\n\n### Phase 4: Configure Caching (1 hour)\n- Implement query cache\n- Set TTL and eviction policies\n- Monitor hit rates\n\n### Phase 5: Benchmark Results (1-2 hours)\n- Run comprehensive benchmarks\n- Compare before\u002Fafter\n- Validate improvements\n\n## Quick Start\n\n```typescript\nimport { AgentDB, Quantization, QueryCache } from 'agentdb-optimization';\n\nconst db = new AgentDB({ name: 'optimized-db', dimensions: 1536 });\n\n\u002F\u002F Quantization (4x memory reduction)\nconst quantizer = new Quantization({\n  method: 'product-quantization',\n  compressionRatio: 4\n});\nawait db.applyQuantization(quantizer);\n\n\u002F\u002F HNSW indexing (150x speedup)\nawait db.createIndex({\n  type: 'hnsw',\n  params: { M: 16, efConstruction: 200 }\n});\n\n\u002F\u002F Caching\ndb.setCache(new QueryCache({\n  maxSize: 10000,\n  ttl: 3600000\n}));\n```\n\n## Optimization Techniques\n\n### Quantization\n- **Product Quantization**: 4-8x compression\n- **Scalar Quantization**: 2-4x compression\n- **Binary Quantization**: 32x compression\n\n### Indexing\n- **HNSW**: 150x faster, high accuracy\n- **IVF**: Fast, partitioned search\n- **LSH**: Approximate search\n\n### Caching\n- **Query Cache**: LRU eviction\n- **Result Cache**: TTL-based\n- **Embedding Cache**: Reuse embeddings\n\n## Success Metrics\n\n- Memory reduction: 4-32x\n- Search speedup: 150x\n- Accuracy maintained: > 95%\n- Cache hit rate: > 70%\n\n## Additional Resources\n\n- Full docs: SKILL.md\n- AgentDB Optimization: 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