agentdb-vector-search-optimization

AgentDB Vector Search Optimization

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LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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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
}));