What is AgentDB QUIC synchronization?

QUIC synchronization enables sub-millisecond latency for data sharing between multiple AgentDB instances across network boundaries with automatic retry, multiplexing, and built-in TLS 1.3 encryption.

Does this skill support different distance metrics?

Absolutely. It covers Cosine Similarity for semantic search, Euclidean Distance for spatial/image data, and Dot Product for fast computation with pre-normalized vectors.

What is MMR and why is it used?

Maximal Marginal Relevance (MMR) is a technique used to retrieve diverse results. It helps avoid redundancy in search outcomes, which is crucial for recommendation systems and exploratory search.

Can I manage multiple databases or shards?

Yes, this skill provides patterns for multi-database coordination and horizontal sharding by domain, allowing for efficient scaling of AI memory and knowledge bases.

How does hybrid search work in this skill?

It combines semantic vector similarity with traditional metadata filters (like price, category, or date) and weighted scoring to provide highly relevant and specifically targeted search results.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: ricable

byricable

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Ciencia de Datos y ML

Enables high-performance distributed vector search and multi-agent coordination using QUIC synchronization and hybrid search.

Acerca de

This skill provides Claude with advanced expertise in managing AgentDB, a specialized database designed for AI agents. It covers sophisticated implementation patterns including sub-millisecond QUIC synchronization for distributed systems, hybrid search that combines vector embeddings with complex metadata filters, and Maximal Marginal Relevance (MMR) for diverse result retrieval. It is an essential capability for developers building complex multi-agent architectures, scalable AI systems, or high-performance search applications requiring fine-grained control over distance metrics, database sharding, and context synthesis.

Características Principales

Multi-database management and horizontal sharding for large-scale deployments
Maximal Marginal Relevance (MMR) for diverse, non-redundant search results
Sub-millisecond QUIC synchronization for distributed AI nodes
Advanced distance metrics including Cosine, Euclidean, and Dot Product
1 GitHub stars
Hybrid search combining vector similarity with complex metadata filtering

Casos de Uso

Implementing enterprise-grade semantic search with strict metadata constraints
Building high-availability distributed AI agent swarms across network boundaries
Scaling AI memory systems using multi-database sharding and optimization

Acerca de

Características Principales

Multi-database management and horizontal sharding for large-scale deployments
Maximal Marginal Relevance (MMR) for diverse, non-redundant search results
Sub-millisecond QUIC synchronization for distributed AI nodes
Advanced distance metrics including Cosine, Euclidean, and Dot Product
1 GitHub stars
Hybrid search combining vector similarity with complex metadata filtering

Casos de Uso

Implementing enterprise-grade semantic search with strict metadata constraints
Building high-availability distributed AI agent swarms across network boundaries
Scaling AI memory systems using multi-database sharding and optimization