AgentDB Vector Search

概要

AgentDB Vector Search enables ultra-fast semantic search capabilities for autonomous AI workflows, delivering search operations up to 12,500x faster than traditional solutions. By leveraging HNSW indexing and multiple quantization strategies, it achieves sub-millisecond latency while maintaining high memory efficiency. This skill is essential for developers building Retrieval-Augmented Generation (RAG) systems, intelligent knowledge bases, or applications requiring precise similarity matching across large-scale vector datasets.

主な機能

• Advanced quantization strategies including Binary, Scalar, and Product quantization for up to 32x memory reduction
• Maximal Marginal Relevance (MMR) support to ensure diverse and non-redundant search results
• 10,769 GitHub stars
• Sub-millisecond semantic retrieval using HNSW indexing and optimized vector storage
• Hybrid search capabilities that combine vector similarity with complex metadata filtering
• Native MCP server integration for seamless document querying and storage directly within Claude Code

ユースケース

• Building enterprise-grade Retrieval-Augmented Generation (RAG) pipelines
• Implementing intelligent knowledge bases with context-aware querying
• Developing semantic search engines for large-scale document collections