agentdb-vector-search-optimization

Optimize AgentDB performance with quantization, HNSW indexing, and caching for 4-32x memory reduction and 150x faster search.

When to Use

• Reduce memory usage (4-32x)
• Accelerate search speed (150x)
• Scale to millions of vectors
• Improve throughput and latency

Quick Start

npm install agentdb-optimization
npx ts-node optimize-db.ts

5-Phase Workflow

1. Baseline Performance (1 hr) - Measure current metrics
2. Apply Quantization (1-2 hrs) - 4-32x memory reduction
3. Implement HNSW Indexing (1-2 hrs) - 150x speedup
4. Configure Caching (1 hr) - Query and result caching
5. Benchmark Results (1-2 hrs) - Validate improvements

Techniques

• Product/Scalar/Binary Quantization
• HNSW/IVF/LSH Indexing
• LRU/TTL Caching
• Batch Operations

Success Metrics

• Memory: 4-32x reduction
• Speed: 150x faster
• Accuracy: > 95%
• Cache: > 70% hit rate

Agents

• performance-analyzer: Benchmarking
• ml-developer: Quantization and indexing
• backend-dev: Implementation

Duration

5-7 hours