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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.

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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