将任何数据源在5分钟内转化为MCP服务器。

建立知识库，让像Claude和Cursor这样的AI助手可以直接查询。不需要基础设施。

---

这是什么？

该SDK允许您从任何数据源创建MCP（模型上下文协议）服务器。您的文档、PDF、网站或任何文本都可以成为AI助手可直接访问的可查询知识库。

使用场景：

• 📚 让你的文档可通过光标/Claude搜索
• 🔍 构建RAG（检索增强生成）流水线
• 🤖 创建具备领域知识的定制AI助手
• 📖 索引研究论文、指南或任何文本内容

---

快速启动

安装

基本用法

连接光标

在你的.cursor/mcp.json中添加：

连接Claude桌面

加到~/Library/Application Support/Claude/claude_desktop_config.json：

---

特色

📁 多源摄取

🔍 聪明的块

文本会自动被拆分为最优嵌入区块：

🧠 柔性嵌入

默认使用 OpenAI，但你可以自带：

💾 向量商店

内存中（默认）

非常适合开发和小型数据集：

Qdrant

akyn-ai

Turn any data source into an MCP server in 5 minutes.

Build knowledge bases that AI assistants like Claude and Cursor can query directly. No infrastructure needed.

---

What is this?

This SDK lets you create MCP (Model Context Protocol) servers from any data source. Your docs, PDFs, websites, or any text can become a queryable knowledge base that AI assistants can access directly.

Use cases:

• 📚 Make your documentation searchable by Cursor/Claude
• 🔍 Build RAG (Retrieval-Augmented Generation) pipelines
• 🤖 Create custom AI assistants with domain knowledge
• 📖 Index research papers, guides, or any text content

---

Quick Start

Install

npm install akyn-ai

Basic Usage

import { KnowledgeBase } from 'akyn-ai'

// Create a knowledge base
const kb = new KnowledgeBase({
  name: 'my-docs',
  description: 'My project documentation',
})

// Add your content
await kb.addDirectory('./docs')           // Add all docs from a folder
await kb.addFile('./README.md')           // Add a specific file
await kb.addURL('https://docs.example.com') // Scrape a URL
await kb.addText('Important info here')   // Add raw text

// Serve as MCP server
kb.serveStdio()  // For Cursor/Claude Desktop

Connect to Cursor

Add to your .cursor/mcp.json:

{
  "mcpServers": {
    "my-docs": {
      "command": "npx",
      "args": ["ts-node", "./my-kb.ts"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Connect to Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "my-docs": {
      "command": "npx",
      "args": ["ts-node", "/path/to/my-kb.ts"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

---

Features

📁 Multi-Source Ingestion

// Files (PDF, DOCX, TXT, Markdown)
await kb.addFile('./guide.pdf')
await kb.addFile('./manual.docx')

// Directories (recursive)
await kb.addDirectory('./docs', {
  recursive: true,
  extensions: ['.md', '.txt', '.pdf'],
})

// URLs
await kb.addURL('https://docs.example.com')
await kb.addURLs([
  'https://example.com/page1',
  'https://example.com/page2',
])

// Raw text
await kb.addText('Custom content here', 'My Notes')

🔍 Smart Chunking

Text is automatically split into optimal chunks for embedding:

const kb = new KnowledgeBase({
  name: 'my-kb',
  chunking: {
    maxSize: 1000,    // Max characters per chunk
    overlap: 200,     // Overlap between chunks for context
  },
})

🧠 Flexible Embeddings

Uses OpenAI by default, but you can bring your own:

import { KnowledgeBase, type EmbeddingsProvider } from 'akyn-ai'