Can I use this with OpenAI embeddings?

Yes, it is fully compatible with OpenAI embeddings (like ada-002 with 1536 dimensions) as well as open-source models like sentence-transformers or MiniLM.

How do I integrate this with Claude Code?

You can integrate it using the built-in MCP server support by running 'npx agentdb@latest mcp' and adding it to Claude Code via the MCP add command.

How much faster is AgentDB Vector Search than traditional solutions?

AgentDB Vector Search is designed for extreme performance, offering operations that are 150x to 12,500x faster than traditional vector database solutions, with search times often under 100 microseconds.

What quantization methods does this skill support?

It supports Binary quantization (32x memory reduction), Scalar quantization (4x reduction), and Product quantization (8-16x reduction) to optimize memory usage for large datasets.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: nbossn

bynbossn

0•

データサイエンスとML

Implements ultra-fast semantic vector search and intelligent document retrieval using high-performance AgentDB indexing.

AgentDB Vector Search is a specialized capability designed to integrate high-performance semantic similarity matching into Claude Code workflows. By leveraging Hierarchical Navigable Small World (HNSW) indexing and advanced quantization techniques, it achieves sub-millisecond search speeds that are significantly faster than traditional solutions. This skill is essential for developers building Retrieval-Augmented Generation (RAG) systems, intelligent knowledge bases, or hybrid search engines that require memory-efficient storage and context-aware querying within their AI-driven applications.

主な機能

01Hybrid search capabilities combining vector similarity with metadata filtering

020 GitHub stars

03High-speed HNSW indexing with sub-millisecond retrieval

04Advanced quantization options (Binary, Scalar, Product) for up to 32x memory reduction

05Native Claude Code MCP server integration for seamless tool access

06Maximal Marginal Relevance (MMR) support for diverse result sets

ユースケース

01Implementing semantic search engines for large-scale documentation

02Developing intelligent knowledge bases with similarity-based discovery

03Building high-performance Retrieval-Augmented Generation (RAG) pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agentdb-vector-search

For use in Claude.ai and ChatGPT

主な機能

01Hybrid search capabilities combining vector similarity with metadata filtering

020 GitHub stars

03High-speed HNSW indexing with sub-millisecond retrieval

04Advanced quantization options (Binary, Scalar, Product) for up to 32x memory reduction

05Native Claude Code MCP server integration for seamless tool access

06Maximal Marginal Relevance (MMR) support for diverse result sets

ユースケース

01Implementing semantic search engines for large-scale documentation

02Developing intelligent knowledge bases with similarity-based discovery

03Building high-performance Retrieval-Augmented Generation (RAG) pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agentdb-vector-search

For use in Claude.ai and ChatGPT