Can I use this skill for mobile or edge AI development?

Yes, the memory optimization recipes (specifically binary quantization) are designed for mobile and edge environments where RAM is limited but fast vector search is required.

What are batch operations in AgentDB?

Batch operations allow you to insert or retrieve multiple vectors in a single transaction. This is up to 500x faster than individual operations, processing 100 vectors in roughly 2ms instead of 1 second.

How much faster is HNSW search compared to linear scanning?

HNSW indexing provides logarithmic search complexity. In benchmarks, it is approximately 150x faster for 10k vectors and up to 12,500x faster when searching across 1 million vectors.

What is the primary benefit of AgentDB quantization?

Quantization reduces the memory footprint of vector embeddings by 4x to 32x. For example, binary quantization can reduce 3GB of vector data down to just 96MB, which is critical for scaling to millions of vectors.

Does optimizing for speed reduce search accuracy?

It depends on the method. Scalar quantization maintains 98-99% accuracy, while Binary quantization offers maximum speed and compression with a small 2-5% trade-off in accuracy.

AgentDB Optimization Claude Code Skill

소개

This skill provides a comprehensive suite of optimization techniques for AgentDB, the vector database engine used within the ultimate-ai-agent framework. It empowers developers to scale AI agents to millions of vectors by implementing quantization strategies (Binary, Scalar, and Product) that reduce memory footprints by up to 32x and HNSW indexing for logarithmic search complexity. By utilizing the provided caching mechanisms and batch operations, users can achieve near-instantaneous search results and significantly faster data ingestion, making it an essential tool for building production-grade, memory-efficient RAG systems and autonomous agents.

주요 기능

HNSW indexing configuration for O(log n) search and 150x speed improvements
Advanced in-memory caching for sub-millisecond pattern retrieval
Automatic pattern consolidation and pruning to maintain database health
Multi-tier quantization (Binary, Scalar, Product) for up to 32x memory reduction
High-performance batch operations to accelerate data ingestion by up to 500x
2 GitHub stars

사용 사례

Reducing search latency for real-time AI agent reasoning and context retrieval
Scaling vector databases to millions of records while maintaining a low memory footprint
Optimizing vector-based applications for deployment on resource-constrained edge or mobile devices

소개

주요 기능

HNSW indexing configuration for O(log n) search and 150x speed improvements
Advanced in-memory caching for sub-millisecond pattern retrieval
Automatic pattern consolidation and pruning to maintain database health
Multi-tier quantization (Binary, Scalar, Product) for up to 32x memory reduction
High-performance batch operations to accelerate data ingestion by up to 500x
2 GitHub stars

사용 사례

Reducing search latency for real-time AI agent reasoning and context retrieval
Scaling vector databases to millions of records while maintaining a low memory footprint
Optimizing vector-based applications for deployment on resource-constrained edge or mobile devices

AgentDB Performance Optimization

AgentDB Performance Optimization

소개

주요 기능

사용 사례

소개

주요 기능

사용 사례