AgentDB Performance Optimization

What problem does it solve?

This Skill addresses the critical need to enhance the performance and reduce the memory footprint of AgentDB vector databases, making them more efficient and scalable for demanding applications.

Core Features & Use Cases

• Quantization: Achieve 4x-32x memory reduction with various quantization strategies (binary, scalar, product) while maintaining high accuracy.
• HNSW Indexing: Accelerate search times by up to 150x with optimized Hierarchical Navigable Small World indexing.
• Caching & Batch Operations: Improve retrieval speeds with in-memory caching and significantly speed up data ingestion using batch inserts.
• Use Case: Optimize a large-scale AgentDB instance storing millions of vectors by implementing binary quantization and HNSW indexing to reduce memory usage from 3GB to 96MB and speed up searches from seconds to microseconds.

Quick Start

Run the AgentDB performance benchmarks to see the impact of optimizations.