How do I use this within the Claude Code interface?

You can integrate it using the built-in MCP server support. Simply run 'claude mcp add agentdb' to provide Claude with tools for querying, storing, and analyzing vector data.

How does quantization benefit my vector database?

Quantization reduces the memory footprint of your vectors significantly—up to 32x with binary quantization—allowing you to store millions of vectors on standard hardware.

What makes AgentDB Vector Search faster than traditional solutions?

It utilizes HNSW (Hierarchical Navigable Small World) indexing and optimized C++ backends to achieve search speeds between 150x and 12,500x faster than standard databases.

Can I use this with any embedding model?

Yes, it supports customizable dimensions, making it compatible with OpenAI (1536), sentence-transformers (768), all-MiniLM-L6-v2 (384), and other custom models.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: while-basic

bywhile-basic

0•

Data Science & ML

Implements high-performance semantic vector search and intelligent document retrieval for RAG systems using HNSW indexing and quantization.

AgentDB Vector Search is a high-performance skill for Claude Code that enables ultra-fast semantic similarity matching and context-aware querying. By leveraging AgentDB's optimized vector engine, it provides search speeds up to 12,500x faster than traditional solutions with sub-millisecond latency. This skill is essential for developers building Retrieval-Augmented Generation (RAG) pipelines, intelligent knowledge bases, or semantic search engines, offering advanced features like HNSW indexing, multiple quantization strategies for 32x memory reduction, and seamless MCP server integration for direct interaction within Claude.

Key Features

01Sub-millisecond semantic search with HNSW indexing (<100µs latency)

02Advanced quantization (Binary, Scalar, Product) for up to 32x memory efficiency

03Hybrid search capabilities combining vector similarity with metadata filtering

04Native MCP server integration for direct usage within Claude Code

05Built-in CLI for rapid database initialization, querying, and benchmarking

060 GitHub stars

Use Cases

01Building production-grade Retrieval-Augmented Generation (RAG) systems

02Developing intelligent documentation search engines with semantic understanding

03Managing large-scale embedding datasets for AI agent memory and reasoning

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add while-basic/c-flow agentdb-vector-search

For use in Claude.ai and ChatGPT

Download Skill