What makes AgentDB Vector Search faster than other databases?

AgentDB uses optimized HNSW indexing and specialized retrieval patterns to achieve search times under 100µs, outperforming traditional solutions by 150x to 12,500x depending on the scale.

Can I use this with different embedding models?

Yes, the skill supports customizable dimensions, allowing seamless integration with OpenAI (1536), sentence-transformers (768), and all-MiniLM (384) models.

Does it support filtering along with vector search?

Yes, it supports hybrid search, allowing you to combine semantic vector similarity with standard metadata filters like categories, dates, or custom tags.

How does it handle large datasets in memory?

It provides several quantization options—Binary, Scalar, and Product—that can reduce memory footprints by 4x to 32x, enabling the handling of millions of vectors on standard hardware.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Microck

byMicrock

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数据科学与机器学习

Implement high-performance semantic search and vector storage for intelligent document retrieval and RAG systems.

This skill integrates AgentDB into Claude Code, providing a high-performance vector database optimized for extreme speed and memory efficiency. It enables developers to build sophisticated Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and intelligent knowledge bases with sub-millisecond query times. By utilizing advanced features like HNSW indexing and multiple quantization levels, it offers significantly faster operations than traditional vector databases while maintaining a small footprint, making it ideal for agentic workflows and local AI development.

主要功能

01Maximal Marginal Relevance (MMR) support for diverse and non-redundant search results.

0281 GitHub stars

03Native MCP server integration for direct tool-calling within Claude Code environments.

04Sub-millisecond semantic search using HNSW indexing with O(log n) complexity.

05Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.

06Hybrid search capabilities combining vector similarity with complex metadata filtering.

使用场景

01Implementing semantic search engines for large-scale enterprise knowledge bases.

02Creating similarity-based recommendation systems using metadata-filtered vector results.

03Building efficient RAG pipelines for context-aware AI document querying.

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