agentdb-reinforcement-learning-training

This skill enables developers to build semantic vector search systems using AgentDB for intelligent document retrieval and RAG applications. It provides embedding-based similarity matching to create knowledge bases and query APIs. Use it when implementing search functionality that requires understanding semantic meaning rather than just keyword matching.

This skill optimizes AgentDB vector search by implementing quantization for memory reduction and HNSW indexing for faster queries. Use it when scaling to millions of vectors to achieve 4-32x lower memory usage and 150x faster search speeds. It provides a complete optimization workflow including caching strategies and batch operations.

This skill teaches developers to implement advanced AgentDB vector search features for distributed AI systems. It covers QUIC synchronization, multi-database management, and custom hybrid search with custom distance metrics. Use it when you need to build high-performance, synchronized vector search clusters that significantly outperform baseline implementations.

This skill implements persistent memory patterns for AI agents using AgentDB, enabling session memory, long-term storage, and pattern learning. It's designed for building stateful agents, chat systems, and intelligent assistants that require context management across sessions. Use it when you need agents to maintain memory, learn from interactions, and manage complex conversational contexts.