This skill provides a comprehensive toolkit for scaling AgentDB vector databases to support millions of vectors with sub-millisecond latency. It automates the implementation of advanced performance techniques including multiple quantization levels (Binary, Scalar, Product), Hierarchical Navigable Small World (HNSW) indexing, and intelligent LRU caching. By applying these patterns, developers can achieve up to 12,500x faster search speeds and 32x memory reduction, making it an essential utility for high-scale AI applications, edge deployments, and resource-constrained environments.
Características Principales
01LRU-based in-memory pattern caching for <1ms retrieval times
02High-efficiency batch insert and retrieval operations
03Automated database health maintenance via pattern consolidation and pruning
040 GitHub stars
05Multi-tier quantization strategies for 4x to 32x memory footprint reduction
06High-speed HNSW indexing for O(log n) vector search complexity
