When should I use MMR (Maximal Marginal Relevance)?

Use MMR when you need search results to be diverse and non-redundant, which is particularly useful for avoiding 'echo chambers' in AI memory or building recommendation systems.

Which distance metrics are supported for vector search?

It supports Cosine Similarity (default for semantic search), Euclidean Distance (spatial/geometric data), and Dot Product (fast computation for normalized vectors).

What is QUIC synchronization in AgentDB?

QUIC sync is a low-latency UDP-based synchronization method that allows multiple AgentDB instances to share data in under 1ms with built-in encryption and automatic retries.

Does AgentDB support hybrid search?

Yes, AgentDB supports hybrid search which combines vector similarity scores with metadata filters (like price, date, or category) for highly precise data retrieval.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: pacphi

bypacphi

•

데이터베이스 관리

Powers distributed AI systems with low-latency QUIC synchronization, hybrid vector search, and multi-database management.

This skill equips Claude with advanced capabilities for managing AgentDB in production-grade AI environments. It enables the implementation of high-performance distributed systems through sub-millisecond QUIC-based peer synchronization and sophisticated data retrieval strategies like hybrid vector-metadata search and Maximal Marginal Relevance (MMR). Developers can utilize this skill to orchestrate multi-agent coordination, manage database sharding for horizontal scaling, and implement custom distance metrics for specialized vector search applications within the agentic-flow ecosystem.

주요 기능

01Hybrid search combining vector similarity with complex metadata filtering

02Advanced database sharding and connection pooling for production scaling

031 GitHub stars

04Maximal Marginal Relevance (MMR) for diverse, non-redundant results

05Sub-millisecond QUIC synchronization for distributed node coordination

06Support for Cosine, Euclidean, and Dot Product distance metrics

사용 사례

01Implementing highly filtered vector search for specialized research and documentation databases

02Building low-latency distributed AI agent swarms across multiple network nodes

03Developing advanced recommendation systems requiring diverse search results

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pacphi/ampel agentdb-advanced

For use in Claude.ai and ChatGPT

Download Skill