When should I use Euclidean distance over Cosine similarity?

Use Euclidean distance (L2) for spatial data or computer vision where vector magnitude is important; use Cosine similarity for general semantic text search and NLP applications.

How does MMR improve search results?

Maximal Marginal Relevance (MMR) helps retrieve diverse, non-redundant results, which is essential for recommendation systems and avoiding information redundancy in AI responses.

What is QUIC synchronization in AgentDB?

QUIC synchronization enables sub-millisecond latency when syncing data between multiple AgentDB instances across network boundaries with automatic retry and encryption.

Does this skill support database sharding?

Yes, it includes implementation guides for sharding by domain or key to help scale your AgentDB implementation horizontally for production-grade performance.

Can I filter search results by metadata?

Yes, this skill provides patterns for hybrid search, allowing you to combine vector similarity scores with specific metadata filters like date ranges, categories, or custom tags.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: ellisapotheosis

byellisapotheosis

Database Management

Optimizes AgentDB implementations with advanced capabilities like QUIC synchronization, hybrid search, and distributed database management.

About

This skill empowers developers to build sophisticated, distributed AI systems using AgentDB's most advanced features. It provides comprehensive implementation patterns for sub-millisecond cross-node communication via QUIC, complex hybrid search combining vector similarity with metadata filtering, and multi-database sharding strategies for horizontal scaling. By integrating these capabilities into your Claude Code workflow, you can achieve high-performance vector retrieval, robust data synchronization across multi-agent environments, and diverse search results using Maximal Marginal Relevance (MMR).

Key Features

Multiple distance metrics including Cosine, Euclidean, and Dot Product
Sub-millisecond QUIC synchronization for distributed nodes
Advanced multi-database management and sharding patterns
Hybrid search combining vector similarity and complex metadata filters
Maximal Marginal Relevance (MMR) for diverse, non-redundant search results
0 GitHub stars

Use Cases

Scaling vector databases horizontally for high-traffic AI applications
Implementing production-grade RAG with weighted hybrid search and metadata filtering
Building low-latency distributed multi-agent coordination systems

About

Key Features

Multiple distance metrics including Cosine, Euclidean, and Dot Product
Sub-millisecond QUIC synchronization for distributed nodes
Advanced multi-database management and sharding patterns
Hybrid search combining vector similarity and complex metadata filters
Maximal Marginal Relevance (MMR) for diverse, non-redundant search results
0 GitHub stars

Use Cases

Scaling vector databases horizontally for high-traffic AI applications
Implementing production-grade RAG with weighted hybrid search and metadata filtering
Building low-latency distributed multi-agent coordination systems