How does MMR improve search results?

Maximal Marginal Relevance (MMR) ensures search results are diverse and non-redundant by balancing semantic relevance with the novelty of the information.

What distance metrics are supported?

It covers Cosine Similarity for semantic search, Euclidean Distance for spatial data, Dot Product for normalized vectors, and patterns for custom metric implementation.

What is QUIC synchronization in AgentDB?

It enables sub-millisecond latency synchronization between database instances across networks using encrypted UDP connections with automatic retry and recovery.

Can I use custom metadata filters with vector search?

Yes, this skill provides patterns for hybrid search, allowing you to combine semantic similarity with advanced filters like range queries, inclusions, and tag containment.

Does this skill help with database sharding?

Yes, it includes implementation patterns for horizontal scaling by sharding databases by domain or shard key to handle large-scale datasets.

Advanced AgentDB Management

Name: Advanced AgentDB Management
Author: zellycloud

byzellycloud

数据库管理

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

关于

Enhances Claude Code with the ability to architect and maintain high-performance, distributed AI memory systems using AgentDB. This skill enables sub-millisecond cross-node synchronization via QUIC, sophisticated hybrid searches combining vector similarity with complex metadata filters, and diverse result retrieval through Maximal Marginal Relevance (MMR). It is an essential toolkit for developers building multi-agent systems, scalable knowledge bases, or production-grade AI applications requiring robust data sharding and custom distance metrics.

主要功能

0 GitHub stars
Hybrid search combining vector embeddings with advanced metadata filtering
Horizontal scaling strategies through database sharding and multi-DB management
Context synthesis and MMR for diverse, non-redundant search results
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product
Sub-millisecond QUIC synchronization for distributed node communication

使用场景

Building low-latency distributed AI agent swarms with shared memory
Architecting production RAG systems with diverse context and automated synchronization
Implementing high-performance semantic search for large-scale research repositories

关于

主要功能

0 GitHub stars
Hybrid search combining vector embeddings with advanced metadata filtering
Horizontal scaling strategies through database sharding and multi-DB management
Context synthesis and MMR for diverse, non-redundant search results
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product
Sub-millisecond QUIC synchronization for distributed node communication

使用场景

Building low-latency distributed AI agent swarms with shared memory
Architecting production RAG systems with diverse context and automated synchronization
Implementing high-performance semantic search for large-scale research repositories