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

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Database Management

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

About

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.

Key Features

Hybrid search combining vector similarity with complex metadata filtering
Advanced database sharding and connection pooling for production scaling
1 GitHub stars
Maximal Marginal Relevance (MMR) for diverse, non-redundant results
Sub-millisecond QUIC synchronization for distributed node coordination
Support for Cosine, Euclidean, and Dot Product distance metrics

Use Cases

Implementing highly filtered vector search for specialized research and documentation databases
Building low-latency distributed AI agent swarms across multiple network nodes
Developing advanced recommendation systems requiring diverse search results

About

Key Features

Hybrid search combining vector similarity with complex metadata filtering
Advanced database sharding and connection pooling for production scaling
1 GitHub stars
Maximal Marginal Relevance (MMR) for diverse, non-redundant results
Sub-millisecond QUIC synchronization for distributed node coordination
Support for Cosine, Euclidean, and Dot Product distance metrics

Use Cases

Implementing highly filtered vector search for specialized research and documentation databases
Building low-latency distributed AI agent swarms across multiple network nodes
Developing advanced recommendation systems requiring diverse search results