Can I filter search results by metadata?

Absolutely. AgentDB supports hybrid search, which allows you to combine semantic vector proximity with specific metadata filters like categories, dates, or custom tags.

How do I use this with Claude Code tools?

The skill includes an MCP (Model Context Protocol) server. Once added to Claude Code, you can use tools like agentdb_query and agentdb_store directly within your conversation.

What is the main benefit of using AgentDB with Claude Code?

It provides sub-millisecond semantic search performance, making RAG (Retrieval-Augmented Generation) workflows significantly faster and more efficient compared to traditional vector databases.

How does quantization affect the database performance?

Quantization techniques like Binary or Product quantization reduce memory usage by 4x to 32x, allowing you to handle millions of vectors on standard hardware without compromising search speed.

Does this skill support different embedding models?

Yes, you can configure custom dimensions to support various models, including OpenAI ada-002 (1536), sentence-transformers (768), and all-MiniLM-L6-v2 (384).

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Microck

byMicrock

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データサイエンスとML

Implements ultra-high-performance semantic vector search and document retrieval for Claude-powered RAG systems and intelligent knowledge bases.

概要

AgentDB Vector Search empowers developers to build sophisticated retrieval-augmented generation (RAG) workflows by integrating a high-performance vector database directly into Claude Code. It offers industry-leading speeds—up to 12,500x faster than traditional solutions—using HNSW indexing and memory-efficient quantization techniques. This skill is essential for projects requiring sub-millisecond similarity matching, hybrid searching across vectors and metadata, and managing large-scale semantic datasets with minimal latency and memory footprint.

主な機能

81 GitHub stars
Advanced quantization options (Binary, Scalar, Product) for up to 32x memory reduction
Hybrid search capabilities combining semantic similarity with metadata filtering
Sub-millisecond HNSW indexing and retrieval with search times under 100µs
Full MCP server integration for seamless semantic tool usage within Claude Code
Maximal Marginal Relevance (MMR) support for diverse and non-redundant search results

ユースケース

Creating semantic search engines for large-scale technical documentation or knowledge bases
Building high-performance Retrieval-Augmented Generation (RAG) pipelines for AI agents
Implementing intelligent document matching and recommendation systems in development workflows

概要

主な機能

81 GitHub stars
Advanced quantization options (Binary, Scalar, Product) for up to 32x memory reduction
Hybrid search capabilities combining semantic similarity with metadata filtering
Sub-millisecond HNSW indexing and retrieval with search times under 100µs
Full MCP server integration for seamless semantic tool usage within Claude Code
Maximal Marginal Relevance (MMR) support for diverse and non-redundant search results

ユースケース

Creating semantic search engines for large-scale technical documentation or knowledge bases
Building high-performance Retrieval-Augmented Generation (RAG) pipelines for AI agents
Implementing intelligent document matching and recommendation systems in development workflows