Will quantization significantly affect search accuracy?

Accuracy trade-offs are minimal. Scalar quantization typically maintains 98-99% accuracy, while Binary quantization offers 95% accuracy with maximum memory savings.

What is HNSW and why is it used?

HNSW (Hierarchical Navigable Small World) is a graph-based indexing algorithm that reduces search complexity to O(log n), enabling sub-millisecond searches even as your dataset grows to millions of vectors.

How much memory can I save with this skill?

You can achieve a 4x to 32x reduction in memory usage depending on the quantization strategy selected (Binary, Scalar, or Product).

Does this skill support bulk data operations?

Yes, it includes batch insert optimizations that are up to 500x faster than individual record insertions, making it ideal for large-scale data ingestion.

Can I use this for mobile or edge deployment?

Absolutely. The skill includes specific recipes for memory-constrained environments, reducing the footprint to as little as 10MB for 100K vectors using binary quantization.

AgentDB Performance Optimizer

Name: AgentDB Performance Optimizer
Author: ellisapotheosis

byellisapotheosis

Database Management

Optimizes AgentDB vector databases using quantization, HNSW indexing, and caching to significantly improve search speed and memory efficiency.

About

This skill provides a comprehensive suite of optimization tools for AgentDB, the vector database used in Agentic Flow. It enables developers to scale their AI applications by implementing advanced techniques like Binary, Scalar, and Product quantization, along with Hierarchical Navigable Small World (HNSW) indexing for O(log n) search complexity. By reducing memory overhead by up to 32x and accelerating search latency to sub-millisecond levels, it allows Claude to efficiently handle millions of vectors even in memory-constrained or edge environments while maintaining high retrieval accuracy.

Key Features

Automated memory consolidation and low-confidence pattern pruning
Automatic HNSW indexing for up to 150x faster vector search performance
Intelligent LRU in-memory caching for pattern retrieval under 1ms
0 GitHub stars
Quantization strategies (Binary, Scalar, Product) for up to 32x memory reduction
High-performance batch operations providing 500x faster data insertion

Use Cases

Deploying AI agent memory systems to edge devices or resource-constrained environments
Scaling vector search to millions of embeddings without excessive RAM requirements
Improving real-time agent response times by optimizing retrieval latency

About

Key Features

Automated memory consolidation and low-confidence pattern pruning
Automatic HNSW indexing for up to 150x faster vector search performance
Intelligent LRU in-memory caching for pattern retrieval under 1ms
0 GitHub stars
Quantization strategies (Binary, Scalar, Product) for up to 32x memory reduction
High-performance batch operations providing 500x faster data insertion

Use Cases

Deploying AI agent memory systems to edge devices or resource-constrained environments
Scaling vector search to millions of embeddings without excessive RAM requirements
Improving real-time agent response times by optimizing retrieval latency