AgentDB Advanced Features

소개

This skill provides specialized guidance for implementing high-performance vector database operations with AgentDB, focusing on distributed systems and sophisticated search patterns. It covers sub-millisecond cross-node communication via the QUIC protocol, hybrid search techniques that merge vector similarity with complex metadata filters, and retrieval diversification using Maximal Marginal Relevance (MMR). Developers can leverage this skill to manage database sharding, optimize search results with custom distance metrics, and deploy production-grade AI memory systems that scale across multiple instances with minimal latency.

주요 기능

• 0 GitHub stars
• Support for multiple distance metrics including Cosine, Euclidean, and Dot Product
• Horizontal scaling strategies through database sharding and connection pooling
• Hybrid search combining vector similarity with advanced metadata filtering
• Maximal Marginal Relevance (MMR) for retrieving diverse, non-redundant result sets
• Sub-millisecond QUIC synchronization for multi-node deployments

사용 사례

• Developing distributed multi-agent systems requiring real-time state synchronization
• Optimizing large-scale AI memory storage through sharding and production deployment patterns
• Building complex RAG pipelines that require high-precision, filtered search results