ReddRadar
Agent skill
AgentDB Vector Search Optimization
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/when-optimizing-vector-search-use-agentdb-optimization
Metadata
Additional technical details for this skill
- tags
-
agentdb optimization quantization hnsw-indexing performance
- author
- claude-flow
- created
- 1761782400
SKILL.md
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
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