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

Data Science & ML

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

About

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

Sub-millisecond semantic search with HNSW indexing (<100µs latency)
Advanced quantization (Binary, Scalar, Product) for up to 32x memory efficiency
Hybrid search capabilities combining vector similarity with metadata filtering
Native MCP server integration for direct usage within Claude Code
Built-in CLI for rapid database initialization, querying, and benchmarking
0 GitHub stars

Use Cases

Building production-grade Retrieval-Augmented Generation (RAG) systems
Developing intelligent documentation search engines with semantic understanding
Managing large-scale embedding datasets for AI agent memory and reasoning

About

Key Features

Sub-millisecond semantic search with HNSW indexing (<100µs latency)
Advanced quantization (Binary, Scalar, Product) for up to 32x memory efficiency
Hybrid search capabilities combining vector similarity with metadata filtering
Native MCP server integration for direct usage within Claude Code
Built-in CLI for rapid database initialization, querying, and benchmarking
0 GitHub stars

Use Cases

Building production-grade Retrieval-Augmented Generation (RAG) systems
Developing intelligent documentation search engines with semantic understanding
Managing large-scale embedding datasets for AI agent memory and reasoning