What is the benefit of quantization in this skill?

Quantization significantly reduces memory usage (up to 32x for binary quantization), allowing you to handle massive vector datasets on local machines or smaller server instances.

Does it support different embedding models?

Yes, it is compatible with OpenAI embeddings (1536 dimensions), sentence-transformers (768 dimensions), and other custom models by setting specific dimensions during initialization.

What is AgentDB Vector Search?

It is a high-performance vector database skill that enables Claude to store and retrieve data based on semantic similarity, allowing for more intelligent context-aware responses.

How fast is the search performance?

AgentDB offers sub-millisecond retrieval (under 100µs) and is up to 12,500x faster than traditional vector solutions due to its optimized HNSW indexing.

Can I use this for building RAG systems?

Absolutely. This skill includes specialized features like MMR for diverse result sets and context synthesis, making it ideal for building robust Retrieval-Augmented Generation pipelines.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

Ciencia de Datos y ML

Implements high-performance semantic vector search for RAG systems, knowledge bases, and intelligent similarity matching.

Acerca de

AgentDB Vector Search provides an ultra-fast, local vector database capability for Claude Code, enabling intelligent document retrieval and semantic querying. It leverages HNSW indexing and multiple quantization strategies—Binary, Scalar, and Product—to deliver sub-millisecond search results even with datasets exceeding one million vectors. This skill is specifically designed for developers building Retrieval-Augmented Generation (RAG) pipelines and context-aware AI applications where efficiency, low latency, and memory optimization are critical requirements.

Características Principales

Maximal Marginal Relevance (MMR) for diverse, non-redundant retrieval
0 GitHub stars
Comprehensive CLI and API for seamless Node.js integration
High-speed HNSW indexing for sub-millisecond search performance (<100µs)
Hybrid search combining vector similarity with complex metadata filtering
Advanced quantization options for up to 32x memory reduction

Casos de Uso

Building efficient RAG pipelines for AI-powered document retrieval
Implementing similarity matching for recommendation systems or duplicate detection
Creating semantic search engines for large-scale knowledge bases

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills agentdb-vector-search

For use in Claude.ai and ChatGPT

Download Skill

GitHub