Is there a significant trade-off in accuracy when optimizing?

The skill provides balanced 'recipes' ranging from No Quantization (100% accuracy) to Binary Quantization (95-98% accuracy), allowing you to choose the right trade-off for your specific use case.

Does this skill help with bulk data loading?

Yes, it provides implementation patterns for batch inserts that are up to 500x faster than individual operations, processing 100 vectors in about 2ms.

How much memory can I save using this skill?

By using Binary Quantization, you can achieve up to a 32x reduction in memory usage, allowing 1 million vectors to fit in approximately 96MB instead of 3GB.

What is the speed advantage of HNSW indexing in AgentDB?

HNSW indexing provides massive performance gains, ranging from 150x faster searches on small datasets to over 12,000x faster on large-scale databases with 1 million vectors.

How does the caching mechanism improve performance?

It implements an in-memory LRU (Least Recently Used) cache that stores frequently accessed patterns, bringing retrieval times down from ~2ms to under 1ms for cache hits.

AgentDB Performance Optimization

Name: AgentDB Performance Optimization
Author: whrit

bywhrit

데이터베이스 관리

Optimizes AgentDB vector database performance through quantization, HNSW indexing, and advanced caching strategies to maximize search speed and minimize memory usage.

소개

This skill provides a comprehensive suite of optimization techniques for AgentDB, allowing developers to scale vector databases to millions of records with extreme efficiency. It enables Claude to implement various quantization levels—Binary, Scalar, and Product—to reduce memory footprints by up to 32x, while utilizing HNSW indexing to achieve search speeds up to 12,500x faster than linear scans. Ideal for production-grade AI applications, the skill also covers advanced caching, high-speed batch operations, and automatic pattern consolidation to ensure high accuracy is maintained even under heavy loads or on resource-constrained edge devices.

주요 기능

Multi-level quantization strategies for up to 32x memory reduction
High-throughput batch operations for inserts and queries
In-memory LRU pattern caching for sub-1ms retrieval
HNSW indexing implementation for O(log n) search complexity
0 GitHub stars
Automatic pattern consolidation and database pruning

사용 사례

Scaling vector storage to millions of records while maintaining low latency
Deploying efficient AI memory systems on memory-constrained mobile or edge devices
Optimizing real-time similarity search for high-traffic production applications

소개

주요 기능

Multi-level quantization strategies for up to 32x memory reduction
High-throughput batch operations for inserts and queries
In-memory LRU pattern caching for sub-1ms retrieval
HNSW indexing implementation for O(log n) search complexity
0 GitHub stars
Automatic pattern consolidation and database pruning

사용 사례

Scaling vector storage to millions of records while maintaining low latency
Deploying efficient AI memory systems on memory-constrained mobile or edge devices
Optimizing real-time similarity search for high-traffic production applications