Can I use this with OpenAI embeddings?

Yes, it is fully compatible with OpenAI's 1536-dimension vectors as well as custom models like sentence-transformers (768) or MiniLM (384).

What makes AgentDB Vector Search faster than other solutions?

It uses high-performance HNSW indexing and optimized quantization techniques, achieving search speeds of less than 100 microseconds, which is up to 12,500x faster than traditional vector search methods.

Does it support metadata filtering?

Yes, it features hybrid search that allows you to combine vector similarity with traditional metadata filters (like category or date) for more precise querying.

How do I integrate this with Claude Code?

The skill includes a built-in MCP (Model Context Protocol) server. You can add it by running 'claude mcp add agentdb npx agentdb@latest mcp' to use its tools directly in your Claude session.

What is the benefit of quantization in this skill?

Quantization (Binary, Scalar, or Product) reduces the memory footprint of your vector database by 4x to 32x, allowing for efficient storage of millions of vectors on standard hardware.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

데이터 과학 및 ML

Implements high-performance semantic vector search and document retrieval using AgentDB's ultra-fast HNSW indexing and quantization.

소개

AgentDB Vector Search is a specialized Claude Code skill designed to integrate high-performance vector database capabilities into your development workflow. It enables developers to build sophisticated Retrieval-Augmented Generation (RAG) systems, semantic search engines, and intelligent knowledge bases with sub-millisecond retrieval times. By leveraging HNSW indexing and various quantization strategies (Binary, Scalar, Product), it offers significant performance gains—up to 12,500x faster than traditional solutions—while maintaining a small memory footprint, making it ideal for managing large-scale vector datasets within Claude Code environments.

주요 기능

Support for Maximal Marginal Relevance (MMR) to ensure diverse search results.
0 GitHub stars
Seamless MCP server integration for direct use within Claude Code tools.
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.
Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector search.
Hybrid search capabilities combining vector similarity with metadata filtering.

사용 사례

Implementing semantic search for large-scale documentation or knowledge bases.
Building production-grade Retrieval-Augmented Generation (RAG) pipelines.
Creating recommendation engines based on embedding similarity matching.

소개

주요 기능

Support for Maximal Marginal Relevance (MMR) to ensure diverse search results.
0 GitHub stars
Seamless MCP server integration for direct use within Claude Code tools.
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.
Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector search.
Hybrid search capabilities combining vector similarity with metadata filtering.

사용 사례

Implementing semantic search for large-scale documentation or knowledge bases.
Building production-grade Retrieval-Augmented Generation (RAG) pipelines.
Creating recommendation engines based on embedding similarity matching.