git clone https://github.com/PrettyKing/chalee-mcp-rag.git
cd chalee-mcp-rag

npm install

cp .env.example .env
# 编辑 .env 文件，设置你的 OpenAI API 密钥

npm run mcp-server

# 在另一个终端
npm run mcp-client

chalee-mcp-rag/
├── rag-agent.js           # RAG Agent 核心实现
├── mcp-rag-server.js      # MCP 服务器
├── mcp-client.js          # MCP 客户端示例
├── test.js                # RAG Agent 测试
├── package.json           # 项目配置
├── .env.example           # 环境变量示例
└── README.md              # 说明文档

{
  "mcpServers": {
    "chalee-rag-server": {
      "command": "node",
      "args": ["/path/to/your/chalee-mcp-rag/mcp-rag-server.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}

{
  "mcpServers": {
    "chalee-rag-server": {
      "command": "node",
      "args": ["C:\\path\\to\\your\\chalee-mcp-rag\\mcp-rag-server.js"],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here"
      }
    }
  }
}

const MCPRAGClient = require('./mcp-client');

async function example() {
  const client = new MCPRAGClient();
  
  // 连接服务器
  await client.connect();
  
  // 初始化 RAG
  await client.initializeRAG('your-openai-api-key');
  
  // 添加文档
  await client.addDocument('这是一个示例文档...', {
    category: '示例',
    source: 'demo'
  });
  
  // 提问
  const answer = await client.askQuestion('这个文档讲了什么？');
  console.log(answer.answer);
  
  // 断开连接
  await client.disconnect();
}

// 自定义 RAG 配置
await client.initializeRAG('your-api-key', {
  chunkSize: 800,        // 文档分块大小
  chunkOverlap: 100,     // 分块重叠大小
  maxRetrievedDocs: 5    // 最大检索文档数
});

# 运行 RAG Agent 测试
npm test

# 运行 MCP 客户端演示
npm run mcp-client

FROM node:16-alpine

WORKDIR /app
COPY package*.json ./
RUN npm install

COPY . .

EXPOSE 3000
CMD ["npm", "run", "mcp-server"]

# 使用 PM2 管理进程
npm install -g pm2
pm2 start mcp-rag-server.js --name "mcp-rag-server"
pm2 monitor

# 启用详细日志
DEBUG=mcp:* npm run mcp-server

// PDF 支持
const pdfParse = require('pdf-parse');

async function loadPDF(filePath) {
  const dataBuffer = fs.readFileSync(filePath);
  const data = await pdfParse(dataBuffer);
  return await agent.addDocument(data.text, { type: 'pdf', source: filePath });
}

// 使用 Pinecone 向量数据库
const { PineconeStore } = require('langchain/vectorstores/pinecone');

class PersistentRAGAgent extends RAGAgent {
  async initializePinecone() {
    this.vectorStore = await PineconeStore.fromExistingIndex(
      new OpenAIEmbeddings(),
      { pineconeIndex: this.index }
    );
  }
}

interface InitializeRAGParams {
  apiKey: string;
  config?: {
    chunkSize?: number;
    chunkOverlap?: number;
    maxRetrievedDocs?: number;
  };
}

interface AddDocumentParams {
  content: string;
  metadata?: Record;
}

interface AskQuestionParams {
  question: string;
}

interface AskQuestionResponse {
  question: string;
  answer: string;
  sources?: Array;
  }>;
  timestamp: string;
}