How much faster is AgentDB compared to traditional solutions?

AgentDB offers operations 150x to 12,500x faster than traditional vector stores, with search times often under 100 microseconds thanks to optimized HNSW indexing.

Does this skill support different embedding models?

Yes, it supports customizable dimensions, making it compatible with OpenAI (1536), sentence-transformers (768), and all-MiniLM (384) models among others.

Can I use this directly within Claude Code?

Yes, this skill includes an MCP (Model Context Protocol) server integration that allows Claude to query, store, and manage vectors directly through its tool-calling interface.

What is quantization and why should I use it?

Quantization compresses vector data to reduce memory usage. AgentDB supports binary (32x reduction), scalar (4x), and product quantization to save resources while maintaining search accuracy.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Microck

byMicrock

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Database Management

Implements high-performance semantic vector search with AgentDB for lightning-fast document retrieval and context-aware querying.

This skill integrates AgentDB into Claude Code, enabling developers to build sophisticated Retrieval-Augmented Generation (RAG) systems and semantic search engines with extreme performance. It leverages HNSW indexing and multiple quantization strategies—including Binary, Scalar, and Product quantization—to achieve sub-millisecond search results and significant memory efficiency. By bridging Claude's reasoning capabilities with a high-speed local vector store, it facilitates the creation of intelligent knowledge bases that are both highly scalable and resource-efficient.

Key Features

01Sub-millisecond semantic search using optimized HNSW indexing

0289 GitHub stars

03Hybrid search capabilities combining vector similarity with metadata filtering

04Advanced quantization options for up to 32x memory reduction

05Seamless MCP server integration for direct usage within Claude Code

06Support for multiple distance metrics including Cosine, Euclidean, and Dot Product

Use Cases

01Building efficient RAG pipelines for AI-powered chatbots and assistants

02Creating recommendation engines based on vector similarity and pattern matching

03Implementing semantic document search for large-scale enterprise knowledge bases

What are Skills?·How to Install

Install with 🐟 Skill.Fish

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

For use in Claude.ai and ChatGPT

Download Skill