What is the maximum memory reduction I can achieve with this skill?

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

Is this skill compatible with standard Node.js environments?

Yes, it requires Node.js 18+ and the AgentDB package (often used via agentic-flow) to implement the optimization techniques.

How much does HNSW indexing improve search speed?

HNSW indexing provides O(log n) search complexity. Benchmarks show improvements from 150x for small datasets to over 12,500x for large-scale databases with 1 million vectors.

Does quantization significantly impact search accuracy?

It depends on the method: Scalar quantization maintains 98-99% accuracy with 4x compression, while Binary quantization offers 95-98% accuracy with 32x compression.

Can I automate the cleaning of my vector database?

Yes, the skill includes strategies for automatic consolidation of similar patterns and pruning of low-confidence or stale data to maintain database health.

AgentDB Performance Optimization

Name: AgentDB Performance Optimization
Author: ricable

byricable

•

データベース管理

Optimizes AgentDB vector database performance through quantization, HNSW indexing, and efficient batch operations.

概要

This skill empowers Claude to implement high-performance vector storage and retrieval using AgentDB within the SPARC framework. It provides comprehensive patterns for memory optimization via quantization (Binary, Scalar, Product), search acceleration through HNSW indexing, and throughput improvements with batch operations. Ideal for developers building large-scale AI agents or RAG systems, it ensures low-latency responses and efficient resource utilization across environments ranging from edge devices to massive cloud deployments.

主な機能

Intelligent pattern caching to achieve high hit rates and minimal search latency
2 GitHub stars
HNSW indexing configuration for O(log n) search complexity and sub-millisecond retrieval
High-speed batch operations providing up to 500x faster data ingestion
Advanced quantization strategies (Binary, Scalar, Product) for up to 32x memory reduction
Automated database maintenance including pattern consolidation and confidence-based pruning

ユースケース

Scaling AI agent memory to handle millions of vector embeddings efficiently
Deploying vector databases on memory-constrained mobile or edge devices
Optimizing RAG (Retrieval-Augmented Generation) systems for real-time performance

概要

主な機能

Intelligent pattern caching to achieve high hit rates and minimal search latency
2 GitHub stars
HNSW indexing configuration for O(log n) search complexity and sub-millisecond retrieval
High-speed batch operations providing up to 500x faster data ingestion
Advanced quantization strategies (Binary, Scalar, Product) for up to 32x memory reduction
Automated database maintenance including pattern consolidation and confidence-based pruning

ユースケース

Scaling AI agent memory to handle millions of vector embeddings efficiently
Deploying vector databases on memory-constrained mobile or edge devices
Optimizing RAG (Retrieval-Augmented Generation) systems for real-time performance