Agentdb vector search optimization

Skill aiskillstore/marketplace/skills/dnyoussef/agentdb-vector-search-optimization

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npx -y skills add aiskillstore/marketplace --skill agentdb-vector-search-optimization

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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.

SKILL.md

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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