AgentDB Performance Optimization

概要

AgentDB Performance Optimization provides a comprehensive set of techniques to scale and tune AgentDB vector databases for production-grade performance. It enables massive memory reductions of up to 32x through various quantization strategies (Binary, Scalar, Product) and achieves significant search speed increases using Hierarchical Navigable Small World (HNSW) indexing. This skill is ideal for developers managing large-scale vector datasets or deploying on memory-constrained edge devices, offering fine-grained control over the balance between accuracy, speed, and resource efficiency.

主な機能

• Intelligent in-memory caching and LRU eviction for sub-millisecond pattern retrieval
• High-performance batch operations that increase insert speeds by up to 500x
• Hierarchical Navigable Small World (HNSW) indexing for O(log n) search complexity
• Automated memory consolidation and pruning of low-confidence patterns
• Multiple quantization methods (Binary, Scalar, Product) for up to 32x memory reduction
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ユースケース

• Scaling vector databases to millions of records without proportional memory growth
• Deploying vector search capabilities on mobile or edge devices with limited RAM
• Improving real-time search latency for high-traffic AI applications