Does this skill support OpenAI embeddings?

Yes, it is pre-configured for OpenAI's 1536-dimension embeddings but can be customized for any model like sentence-transformers or MiniLM.

What makes AgentDB Vector Search faster than other solutions?

It utilizes HNSW indexing and specialized quantization techniques to achieve search speeds up to 12,500x faster than traditional vector databases during large-scale queries.

What kind of memory savings can I expect?

Through binary quantization, you can achieve up to a 32x reduction in memory usage, allowing large datasets to fit in significantly smaller footprints.

Can I use this with Claude Code directly?

Yes, it includes a built-in MCP (Model Context Protocol) server, allowing Claude Code to store and query vectors natively using natural language commands.

Is hybrid search supported?

Absolutely. You can combine vector similarity search with specific metadata filters such as categories, dates, or custom JSON attributes for highly precise results.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

0•

Data Science & ML

Powers Claude with high-performance vector search and semantic retrieval for RAG systems and intelligent document indexing.

AgentDB Vector Search enables Claude to manage and query vector databases with extreme efficiency, utilizing HNSW indexing and advanced quantization techniques to achieve sub-millisecond retrieval speeds. This skill is optimized for developers building Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and intelligent knowledge bases, offering 150x to 12,500x faster performance than traditional database solutions. It provides a seamless bridge between raw document storage and AI-driven context synthesis through its integrated MCP server for Claude Code.

Key Features

01Hybrid search combining vector similarity with complex metadata filtering

02Seamless MCP server integration for direct usage within Claude Code

03Advanced quantization options for up to 32x memory usage reduction

040 GitHub stars

05Sub-millisecond semantic search using high-performance HNSW indexing

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

Use Cases

01Implementing semantic document matching across massive knowledge bases

02Building low-latency RAG systems for real-time AI context retrieval

03Developing intelligent search engines with hybrid metadata and vector filtering

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