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

数据科学与机器学习

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

关于

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.

主要功能

Hybrid search combining vector similarity with complex metadata filtering
Seamless MCP server integration for direct usage within Claude Code
Advanced quantization options for up to 32x memory usage reduction
0 GitHub stars
Sub-millisecond semantic search using high-performance HNSW indexing
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product

使用场景

Implementing semantic document matching across massive knowledge bases
Building low-latency RAG systems for real-time AI context retrieval
Developing intelligent search engines with hybrid metadata and vector filtering

关于

主要功能

Hybrid search combining vector similarity with complex metadata filtering
Seamless MCP server integration for direct usage within Claude Code
Advanced quantization options for up to 32x memory usage reduction
0 GitHub stars
Sub-millisecond semantic search using high-performance HNSW indexing
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product

使用场景

Implementing semantic document matching across massive knowledge bases
Building low-latency RAG systems for real-time AI context retrieval
Developing intelligent search engines with hybrid metadata and vector filtering