When should I use Euclidean distance over Cosine similarity?

Use Euclidean distance for spatial or image data where vector magnitude matters; use Cosine similarity for text embeddings and semantic search applications.

What is QUIC synchronization in AgentDB?

QUIC synchronization allows multiple AgentDB instances to stay in sync with sub-millisecond latency using encrypted UDP-based connections, ideal for distributed AI nodes.

Can I shard AgentDB for large datasets?

Yes, this skill provides implementation patterns for horizontal scaling by sharding data across multiple database instances based on domain or shard keys.

How does MMR improve search results?

Maximal Marginal Relevance (MMR) reduces redundancy by selecting results that are both relevant to the query and diverse from each other, preventing 'echo chambers'.

What are the benefits of Hybrid Search?

Hybrid Search combines vector similarity (semantic meaning) with traditional metadata filters like dates or categories, providing more precise and relevant retrieval.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: whrit

bywhrit

데이터베이스 관리

Implements high-performance vector search, distributed QUIC synchronization, and multi-database management for advanced AI systems.

소개

This skill empowers Claude to architect and implement sophisticated vector database solutions using AgentDB's advanced feature set. It provides specialized knowledge for building distributed AI systems with sub-millisecond QUIC synchronization, complex hybrid search filtering, and multi-node coordination. Whether you are managing database shards, optimizing search relevance with Maximal Marginal Relevance (MMR), or synthesizing context from multiple memories, this skill offers the production-grade implementation patterns and best practices required for high-scale agentic workflows.

주요 기능

Advanced distance metrics including Cosine, Euclidean, and Dot Product
Hybrid search combining vector similarity with complex metadata filtering
0 GitHub stars
Sub-millisecond QUIC synchronization for multi-node deployments
Context synthesis and Maximal Marginal Relevance (MMR) for result diversity
Horizontal scaling through database sharding and multi-DB management

사용 사례

Implementing high-performance research search engines with metadata filtering
Architecting scalable AI backends using database sharding and connection pooling
Building distributed multi-agent systems with real-time memory synchronization

소개

주요 기능

Advanced distance metrics including Cosine, Euclidean, and Dot Product
Hybrid search combining vector similarity with complex metadata filtering
0 GitHub stars
Sub-millisecond QUIC synchronization for multi-node deployments
Context synthesis and Maximal Marginal Relevance (MMR) for result diversity
Horizontal scaling through database sharding and multi-DB management

사용 사례

Implementing high-performance research search engines with metadata filtering
Architecting scalable AI backends using database sharding and connection pooling
Building distributed multi-agent systems with real-time memory synchronization