AgentDB Vector Search

소개

AgentDB Vector Search is a high-performance skill designed to integrate lightning-fast semantic retrieval into Claude Code workflows. It leverages HNSW indexing and multiple quantization strategies—including binary, scalar, and product quantization—to achieve sub-millisecond search times while reducing memory overhead by up to 32x. This skill is essential for developers building robust Retrieval Augmented Generation (RAG) systems, semantic search engines, or large-scale knowledge bases that require efficient similarity matching and hybrid metadata filtering at scale.

주요 기능

• High-speed HNSW indexing for sub-millisecond search latency
• Hybrid search capabilities combining vector similarity with metadata filters
• Full MCP integration for native use within the Claude Code environment
• Advanced retrieval patterns including Maximal Marginal Relevance (MMR)
• Multiple quantization modes for up to 32x memory reduction
• 81 GitHub stars

사용 사례

• Building RAG pipelines to provide AI models with relevant document context
• Implementing semantic search engines for large unstructured datasets
• Creating intelligent knowledge bases with efficient hybrid querying