---

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: 2025-10-30 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

Copyimport { 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