What is QUIC synchronization in AgentDB?

It enables sub-millisecond latency synchronization between database instances using the QUIC protocol for fast, encrypted, and reliable cross-node communication in distributed AI systems.

Which distance metric should I use for my AI agent?

Use Cosine Similarity for general semantic search, Euclidean (L2) for image or spatial data, and Dot Product for pre-normalized vectors where speed is the primary concern.

How does AgentDB handle horizontal scaling?

AgentDB supports database sharding by domain or key, allowing you to route queries to specific shards and distribute data across multiple instances for production-grade performance.

When should I use MMR (Maximal Marginal Relevance)?

Use MMR when you need to retrieve a diverse set of results and avoid redundancy, which is crucial for recommendation systems and creating balanced context for LLMs.

Can AgentDB handle complex metadata filters?

Yes, AgentDB supports advanced hybrid search, allowing you to combine vector similarity scores with metadata filters for ranges, categories, tags, and custom boolean logic.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: ricable

byricable

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Ciencia de Datos y ML

Implements advanced vector database capabilities for distributed AI systems, multi-agent coordination, and high-performance hybrid search.

Acerca de

This skill empowers developers to master sophisticated database patterns using AgentDB within the SPARC framework. It provides expert guidance on low-latency QUIC synchronization for cross-node communication, advanced vector similarity metrics (Cosine, Euclidean, Dot Product), and hybrid search techniques that combine semantic vectors with complex metadata filtering. Designed for high-scale environments, the skill covers production-grade patterns like database sharding, connection pooling, and Maximal Marginal Relevance (MMR) for diverse result retrieval, making it essential for building resilient, distributed AI agent architectures.

Características Principales

2 GitHub stars
Sub-millisecond QUIC synchronization for distributed AI nodes
Support for Cosine, Euclidean, and Dot Product distance metrics
Advanced hybrid search combining vector similarity with metadata filters
Multi-database management and horizontal sharding strategies
Diversified result retrieval using Maximal Marginal Relevance (MMR)

Casos de Uso

Scaling AI applications through database sharding and production deployment patterns
Implementing high-performance semantic search with complex business logic filters
Building low-latency distributed multi-agent coordination systems

Acerca de

Características Principales

2 GitHub stars
Sub-millisecond QUIC synchronization for distributed AI nodes
Support for Cosine, Euclidean, and Dot Product distance metrics
Advanced hybrid search combining vector similarity with metadata filters
Multi-database management and horizontal sharding strategies
Diversified result retrieval using Maximal Marginal Relevance (MMR)

Casos de Uso

Scaling AI applications through database sharding and production deployment patterns
Implementing high-performance semantic search with complex business logic filters
Building low-latency distributed multi-agent coordination systems