AgentDB Vector Search

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AgentDB Vector Search provides a specialized toolkit for building advanced RAG systems and semantic search engines with extreme performance. By leveraging HNSW indexing and advanced quantization techniques, it enables sub-millisecond similarity matching while significantly reducing memory overhead by up to 32x. This skill facilitates efficient document storage, hybrid search, and context-aware querying, ensuring Claude can retrieve the most relevant information with speed and precision for complex reasoning tasks.

Características Principales

• Sub-millisecond vector search using HNSW indexing
• Hybrid search combining semantic similarity with metadata filters
• Seamless MCP server integration for Claude Code workflows
• Binary, scalar, and product quantization for memory efficiency
• Diverse result sets through Maximal Marginal Relevance (MMR)
• 0 GitHub stars

Casos de Uso

• Managing memory-efficient vector storage for AI agents
• Building RAG pipelines for intelligent document retrieval
• Developing semantic search engines with high-speed indexing