deploy.stack/mcp_server-py/mcp_server.py

#!/usr/bin/env python3
# -*- coding: UTF-8 -*-

import os
import sys
import logging
import json
import toml
import traceback
from flask import Flask, request, jsonify
import openai

class MCPServer:
    def __init__(self):
        # 初始化应用
        self.app = Flask(__name__)
        # 加载配置
        self.config = self.load_config()
        # 配置日志
        self.setup_logging()
        # 配置OpenAI客户端
        self.setup_openai()
        # 注册路由
        self.register_routes()

    def load_config(self):
        """加载配置文件"""
        config_path = os.path.join(os.path.dirname(__file__), "config.toml")
        if not os.path.exists(config_path):
            print(f"错误：配置文件不存在: {config_path}")
            sys.exit(1)
        
        with open(config_path, "r", encoding="utf-8") as f:
            return toml.load(f)

    def setup_logging(self):
        """配置日志系统"""
        log_config = self.config.get("logging", {})
        log_level = getattr(logging, log_config.get("level", "info").upper())
        log_format = log_config.get("format", "text")
        log_file = log_config.get("file_path", "/var/log/mcp_server.log")
        
        # 确保日志目录存在
        log_dir = os.path.dirname(log_file)
        if log_dir and not os.path.exists(log_dir):
            os.makedirs(log_dir, exist_ok=True)
        
        # 配置日志格式
        if log_format == "json":
            formatter = logging.Formatter(
                '{"time":"%(asctime)s", "level":"%(levelname)s", "message":"%(message)s"}'
            )
        else:
            formatter = logging.Formatter(
                '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
            )
        
        # 配置文件日志
        file_handler = logging.FileHandler(log_file, encoding="utf-8")
        file_handler.setFormatter(formatter)
        
        # 配置控制台日志
        console_handler = logging.StreamHandler()
        console_handler.setFormatter(formatter)
        
        # 获取根logger并配置
        logger = logging.getLogger()
        logger.setLevel(log_level)
        logger.addHandler(file_handler)
        logger.addHandler(console_handler)
        
        self.logger = logging.getLogger("MCPServer")

    def setup_openai(self):
        """配置OpenAI客户端"""
        openai_config = self.config.get("openai", {})
        openai.api_key = openai_config.get("api_key")
        
        # 如果配置了自定义API基础URL
        if "base_url" in openai_config:
            openai.api_base = openai_config["base_url"]
        
        self.openai_config = openai_config

    def register_routes(self):
        """注册API路由"""
        @self.app.route("/mcp/v1/submit", methods=["POST"])
        def submit_data():
            """接收并处理MCP提交的数据"""
            try:
                # 获取请求数据
                data = request.get_json()
                if not data:
                    self.logger.warning("接收到无效的JSON数据")
                    return jsonify({"error": "无效的JSON数据"}), 400
                
                self.logger.info(f"接收到MCP提交请求，类型: {data.get('type')}")
                
                # 处理提交的数据
                result = self.process_submission(data)
                
                return jsonify({"status": "success", "result": result})
            except Exception as e:
                self.logger.error(f"处理提交请求时出错: {str(e)}")
                self.logger.debug(traceback.format_exc())
                return jsonify({"error": str(e)}), 500
        
        @self.app.route("/health", methods=["GET"])
        def health_check():
            """健康检查接口"""
            return jsonify({"status": "healthy"}), 200

    def process_submission(self, data):
        """处理提交的数据，调用OpenAI API进行分析"""
        submission_type = data.get("type", "")
        content = data.get("content", "")
        timestamp = data.get("timestamp", "")
        
        # 根据数据类型生成不同的提示词
        if submission_type == "disk_inspection_report":
            prompt = self.generate_disk_report_prompt(content)
        else:
            prompt = f"请分析以下数据（类型：{submission_type}）：\n{content}"
        
        # 调用OpenAI API
        response = self.call_openai_api(prompt)
        
        return {
            "analysis": response,
            "processed_at": self.get_current_timestamp(),
            "original_type": submission_type
        }

    def generate_disk_report_prompt(self, report_content):
        """为硬盘巡检报告生成特定的提示词"""
        return f"""你是一位专业的系统管理员，请分析以下硬盘巡检报告：

{report_content}

请提供详细的分析结果，包括：
1. 健康状态总结
2. 异常项分析（如果有）
3. 潜在风险评估
4. 维护建议
5. 需要特别关注的问题（如果有）"""

    def call_openai_api(self, prompt):
        """调用OpenAI API"""
        try:
            # 调用OpenAI ChatCompletion API
            response = openai.ChatCompletion.create(
                model=self.openai_config.get("model", "gpt-3.5-turbo"),
                messages=[
                    {"role": "system", "content": "你是一个专业的AI助手，帮助用户分析和处理各种数据。"},
                    {"role": "user", "content": prompt}
                ],
                max_tokens=self.openai_config.get("max_tokens", 2048),
                temperature=self.openai_config.get("temperature", 0.7)
            )
            
            # 提取回复内容
            return response["choices"][0]["message"]["content"].strip()
        except Exception as e:
            self.logger.error(f"调用OpenAI API时出错: {str(e)}")
            raise

    def get_current_timestamp(self):
        """获取当前时间戳"""
        import datetime
        return datetime.datetime.now().isoformat()

    def run(self):
        """启动服务器"""
        server_config = self.config.get("server", {})
        host = server_config.get("host", "0.0.0.0")
        port = server_config.get("port", 8080)
        debug = server_config.get("debug", False)
        
        self.logger.info(f"MCP服务器启动成功，监听地址: {host}:{port}")
        self.logger.info(f"OpenAI API配置: 模型={self.openai_config.get('model')}")
        
        # 启动Flask服务器
        self.app.run(host=host, port=port, debug=debug)

if __name__ == "__main__":
    # 创建并运行MCP服务器
    server = MCPServer()
    server.run()