forked from xiaohei/taiji-AI-PAD
xiaohei
ff14abe1de
✅ 主要功能: - 实现函数注册表 (function_registry.py) - 16个内置安全函数（数学、字符串、日期、JSON、哈希等） - 函数元数据管理 - 函数查询和列表 - 实现沙箱执行器 (sandbox_executor.py) - 超时控制 (默认5秒) - 参数验证和资源限制 - 安全的执行环境 - 实现 _execute_function_tool 方法 - 函数白名单验证 - 参数类型验证 - 沙箱执行 - 完善的错误处理 🔒 安全特性: - 只允许执行注册表中的函数 - 禁止网络和文件系统访问 - 超时和资源限制 - 参数验证和类型检查 📚 文档: - 添加实现方案文档 - 添加实现完成文档 完成度: 95% (核心功能完成，单元测试待完成) 版本: v1.2.0
689 lines
25 KiB
Python
689 lines
25 KiB
Python
"""
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MCP (Model Context Protocol) 协议处理器
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实现MCP协议的核心功能，包括工具管理、资源管理和代理通信
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"""
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import json
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import uuid
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from datetime import datetime
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from typing import Any, Dict, List, Optional, Union
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import asyncio
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import logging
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import traceback
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import redis.asyncio as redis
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import nats
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import httpx
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from schemas import (
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MCPRequest, MCPResponse, MCPError,
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ToolDefinition, ToolResult, ExecutionResult
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)
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from function_registry import get_function_registry
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from sandbox_executor import get_sandbox_executor
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logger = logging.getLogger(__name__)
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class MCPProtocolHandler:
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"""MCP协议处理器"""
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def __init__(
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self,
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redis_client: redis.Redis,
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nats_client: nats.NATS,
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litellm_url: str = "http://litellm-gateway:4000"
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):
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self.redis = redis_client
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self.nats = nats_client
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self.litellm_url = litellm_url
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self.http_client = httpx.AsyncClient(timeout=30.0)
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# MCP协议版本
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self.protocol_version = "2024-11-05"
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# 函数注册表和沙箱执行器
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self.function_registry = get_function_registry()
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self.sandbox_executor = get_sandbox_executor(
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timeout=5.0,
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max_memory_mb=100,
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allow_network=False,
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allow_filesystem=False
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)
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# 支持的MCP方法
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self.supported_methods = {
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"initialize",
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"tools/list",
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"tools/call",
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"resources/list",
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"resources/read",
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"prompts/list",
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"prompts/get",
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"completion/complete",
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"logging/setLevel"
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}
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# 工具注册表
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self._tools_registry: Dict[str, ToolDefinition] = {}
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# 资源注册表
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self._resources_registry: Dict[str, Dict[str, Any]] = {}
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# 会话管理
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self._sessions: Dict[str, Dict[str, Any]] = {}
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async def execute_request(self, agent_id: str, request: MCPRequest) -> ExecutionResult:
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"""执行MCP请求"""
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execution_id = str(uuid.uuid4())
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started_at = datetime.utcnow()
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logger.info(f"开始执行MCP请求: {execution_id}, method: {request.method}")
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try:
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# 验证方法是否支持
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if request.method not in self.supported_methods:
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raise ValueError(f"不支持的MCP方法: {request.method}")
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# 发布执行开始事件
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await self._publish_execution_event(
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"execution.started",
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{
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"execution_id": execution_id,
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"agent_id": agent_id,
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"method": request.method,
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"timestamp": started_at.isoformat()
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}
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)
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# 执行具体方法
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result = await self._dispatch_method(agent_id, request)
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completed_at = datetime.utcnow()
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execution_time = (completed_at - started_at).total_seconds() * 1000
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# 发布执行完成事件
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await self._publish_execution_event(
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"execution.completed",
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{
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"execution_id": execution_id,
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"agent_id": agent_id,
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"method": request.method,
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"execution_time": execution_time,
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"success": True,
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"timestamp": completed_at.isoformat()
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}
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)
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return ExecutionResult(
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execution_id=execution_id,
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success=True,
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result=result,
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execution_time=execution_time,
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started_at=started_at,
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completed_at=completed_at
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)
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except Exception as e:
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completed_at = datetime.utcnow()
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execution_time = (completed_at - started_at).total_seconds() * 1000
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error_msg = str(e)
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logger.error(f"MCP请求执行失败: {execution_id}, error: {error_msg}")
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logger.error(traceback.format_exc())
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# 发布执行失败事件
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await self._publish_execution_event(
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"execution.failed",
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{
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"execution_id": execution_id,
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"agent_id": agent_id,
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"method": request.method,
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"execution_time": execution_time,
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"error": error_msg,
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"timestamp": completed_at.isoformat()
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}
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)
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return ExecutionResult(
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execution_id=execution_id,
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success=False,
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error=error_msg,
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execution_time=execution_time,
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started_at=started_at,
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completed_at=completed_at
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)
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async def _dispatch_method(self, agent_id: str, request: MCPRequest) -> Any:
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"""分发MCP方法调用"""
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method = request.method
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params = request.params or {}
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if method == "initialize":
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return await self._handle_initialize(params)
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elif method == "tools/list":
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return await self._handle_tools_list(agent_id, params)
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elif method == "tools/call":
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return await self._handle_tools_call(agent_id, params)
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elif method == "resources/list":
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return await self._handle_resources_list(agent_id, params)
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elif method == "resources/read":
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return await self._handle_resources_read(agent_id, params)
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elif method == "prompts/list":
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return await self._handle_prompts_list(agent_id, params)
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elif method == "prompts/get":
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return await self._handle_prompts_get(agent_id, params)
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elif method == "completion/complete":
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return await self._handle_completion_complete(agent_id, params)
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elif method == "logging/setLevel":
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return await self._handle_logging_set_level(params)
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else:
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raise ValueError(f"未实现的方法: {method}")
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async def _handle_initialize(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理初始化请求"""
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client_info = params.get("clientInfo", {})
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protocol_version = params.get("protocolVersion")
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logger.info(f"MCP客户端初始化: {client_info}")
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return {
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"protocolVersion": self.protocol_version,
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"capabilities": {
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"tools": {
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"listChanged": True
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},
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"resources": {
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"subscribe": True,
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"listChanged": True
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},
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"prompts": {
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"listChanged": True
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},
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"completion": {
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"argument": True
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},
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"logging": {}
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},
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"serverInfo": {
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"name": "taiji-AI-PAD MCP Server",
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"version": "1.0.0"
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}
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}
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async def _handle_tools_list(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理工具列表请求"""
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try:
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# 从Redis获取Agent的工具列表
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agent_tools_key = f"agent:{agent_id}:tools"
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tool_names = await self.redis.smembers(agent_tools_key)
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tools = []
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for tool_name in tool_names:
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tool_info = await self._get_tool_info(tool_name)
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if tool_info:
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tools.append({
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"name": tool_info["name"],
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"description": tool_info["description"],
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"inputSchema": tool_info.get("schema", {})
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})
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return {"tools": tools}
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except Exception as e:
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logger.error(f"获取工具列表失败: {e}")
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return {"tools": []}
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async def _handle_tools_call(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理工具调用请求"""
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tool_name = params.get("name")
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arguments = params.get("arguments", {})
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if not tool_name:
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raise ValueError("工具名称不能为空")
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logger.info(f"调用工具: {tool_name}, arguments: {arguments}")
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try:
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# 验证Agent是否有权限使用该工具
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agent_tools_key = f"agent:{agent_id}:tools"
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if not await self.redis.sismember(agent_tools_key, tool_name):
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raise ValueError(f"Agent {agent_id} 无权限使用工具 {tool_name}")
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# 获取工具信息
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tool_info = await self._get_tool_info(tool_name)
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if not tool_info:
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raise ValueError(f"工具 {tool_name} 不存在")
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# 执行工具调用
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result = await self._execute_tool(tool_name, tool_info, arguments)
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return {
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"content": [
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{
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"type": "text",
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"text": json.dumps(result.result) if result.success else f"错误: {result.error}"
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}
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],
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"isError": not result.success
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}
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except Exception as e:
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logger.error(f"工具调用失败: {e}")
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return {
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"content": [
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{
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"type": "text",
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"text": f"工具调用失败: {str(e)}"
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}
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],
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"isError": True
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}
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async def _handle_resources_list(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理资源列表请求"""
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try:
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# 从Redis获取Agent的资源列表
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agent_resources_key = f"agent:{agent_id}:resources"
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resource_names = await self.redis.smembers(agent_resources_key)
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resources = []
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for resource_name in resource_names:
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resource_info = await self._get_resource_info(resource_name)
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if resource_info:
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resources.append({
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"uri": resource_info["uri"],
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"name": resource_info["name"],
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"description": resource_info.get("description"),
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"mimeType": resource_info.get("mimeType", "application/json")
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})
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return {"resources": resources}
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except Exception as e:
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logger.error(f"获取资源列表失败: {e}")
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return {"resources": []}
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async def _handle_resources_read(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理资源读取请求"""
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uri = params.get("uri")
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if not uri:
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raise ValueError("资源URI不能为空")
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try:
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# 验证权限
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agent_resources_key = f"agent:{agent_id}:resources"
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# 这里应该根据URI找到资源名称
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resource_name = uri.split("/")[-1] # 简化处理
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if not await self.redis.sismember(agent_resources_key, resource_name):
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raise ValueError(f"Agent {agent_id} 无权限访问资源 {uri}")
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# 读取资源内容
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content = await self._read_resource_content(uri)
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return {
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"contents": [
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{
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"uri": uri,
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"mimeType": "application/json",
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"text": json.dumps(content)
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}
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]
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}
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except Exception as e:
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logger.error(f"读取资源失败: {e}")
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raise ValueError(f"无法读取资源 {uri}: {str(e)}")
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async def _handle_prompts_list(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理提示词列表请求"""
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# 获取Agent相关的提示词
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prompts = [
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{
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"name": "system_prompt",
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"description": "系统提示词",
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"arguments": [
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{
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"name": "context",
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"description": "上下文信息",
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"required": False
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}
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]
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}
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]
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return {"prompts": prompts}
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async def _handle_prompts_get(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理获取提示词请求"""
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name = params.get("name")
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arguments = params.get("arguments", {})
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if name == "system_prompt":
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# 构建系统提示词
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agent_info = await self._get_agent_info(agent_id)
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prompt = f"""你是 {agent_info.get('name', 'AI助手')}。
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角色定义: {agent_info.get('role', '通用助手')}
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目标: {agent_info.get('goal', '帮助用户完成任务')}
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可用工具: {', '.join(agent_info.get('tools', []))}
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请根据用户的请求，选择合适的工具来完成任务。"""
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return {
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"description": "Agent系统提示词",
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"messages": [
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{
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"role": "system",
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"content": {
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"type": "text",
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"text": prompt
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}
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}
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]
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}
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raise ValueError(f"未知的提示词: {name}")
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async def _handle_completion_complete(self, agent_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理补全请求"""
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ref = params.get("ref", {})
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argument = params.get("argument", {})
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# 根据参考信息生成补全建议
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completions = []
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if ref.get("type") == "tool":
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tool_name = ref.get("name")
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if tool_name:
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tool_info = await self._get_tool_info(tool_name)
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if tool_info and "schema" in tool_info:
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# 基于工具schema生成参数建议
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schema = tool_info["schema"]
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properties = schema.get("properties", {})
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for prop_name, prop_info in properties.items():
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completions.append({
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"type": "text",
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"text": prop_name,
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"insertText": f'"{prop_name}": ""'
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})
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return {"completion": {"values": completions}}
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async def _handle_logging_set_level(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""处理设置日志级别请求"""
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level = params.get("level", "info")
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# 设置日志级别
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numeric_level = getattr(logging, level.upper(), logging.INFO)
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logging.getLogger().setLevel(numeric_level)
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logger.info(f"日志级别已设置为: {level}")
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return {"success": True}
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async def _execute_tool(
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self,
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tool_name: str,
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tool_info: Dict[str, Any],
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arguments: Dict[str, Any]
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) -> ToolResult:
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"""执行工具调用"""
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start_time = datetime.utcnow()
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try:
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# 根据工具类型执行不同的逻辑
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category = tool_info.get("category", "api")
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if category == "api":
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result = await self._execute_api_tool(tool_info, arguments)
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elif category == "function":
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result = await self._execute_function_tool(tool_info, arguments)
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elif category == "llm":
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result = await self._execute_llm_tool(tool_info, arguments)
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else:
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raise ValueError(f"不支持的工具类型: {category}")
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execution_time = (datetime.utcnow() - start_time).total_seconds() * 1000
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return ToolResult(
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success=True,
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result=result,
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execution_time=execution_time,
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cost=tool_info.get("cost_per_call", 0.0)
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)
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except Exception as e:
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execution_time = (datetime.utcnow() - start_time).total_seconds() * 1000
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return ToolResult(
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success=False,
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error=str(e),
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execution_time=execution_time,
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cost=tool_info.get("cost_per_call", 0.0)
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)
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async def _execute_api_tool(self, tool_info: Dict[str, Any], arguments: Dict[str, Any]) -> Any:
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"""执行API工具调用"""
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endpoint = tool_info.get("endpoint")
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method = tool_info.get("method", "POST")
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headers = tool_info.get("headers", {})
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timeout = tool_info.get("timeout", 30)
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if not endpoint:
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raise ValueError("API端点不能为空")
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# 发送HTTP请求
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response = await self.http_client.request(
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method=method,
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url=endpoint,
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json=arguments,
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headers=headers,
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timeout=timeout
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)
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response.raise_for_status()
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return response.json()
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async def _execute_function_tool(self, tool_info: Dict[str, Any], arguments: Dict[str, Any]) -> Any:
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"""
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执行函数工具调用
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安全机制:
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1. 只允许执行注册表中的函数
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2. 在沙箱环境中执行
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3. 参数验证和资源限制
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4. 超时控制
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"""
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function_name = tool_info.get("name") or tool_info.get("function_name")
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if not function_name:
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raise ValueError("函数名称不能为空")
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# 检查函数是否在注册表中
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function_info = self.function_registry.get(function_name)
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if not function_info:
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raise ValueError(
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f"函数 {function_name} 未在注册表中。"
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f"可用函数: {', '.join(self.function_registry.list_all()[:10])}"
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)
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# 验证参数
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self._validate_function_arguments(function_info, arguments)
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# 在沙箱中执行函数
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try:
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result = await self.sandbox_executor.execute(
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func=function_info["func"],
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arguments=arguments,
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function_name=function_name
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)
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logger.info(f"函数 {function_name} 执行成功")
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return result
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except TimeoutError as e:
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logger.error(f"函数 {function_name} 执行超时: {e}")
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raise
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except Exception as e:
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logger.error(f"函数 {function_name} 执行失败: {e}")
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raise RuntimeError(f"函数执行失败: {str(e)}")
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def _validate_function_arguments(
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self,
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function_info: Dict[str, Any],
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arguments: Dict[str, Any]
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):
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"""验证函数参数"""
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expected_params = {
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param["name"]: param
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for param in function_info.get("parameters", [])
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}
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# 检查必需参数
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for param_name, param_info in expected_params.items():
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if param_info.get("required", True):
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if param_name not in arguments:
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raise ValueError(f"缺少必需参数: {param_name}")
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# 检查参数类型（基本验证）
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for param_name, param_value in arguments.items():
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if param_name not in expected_params:
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logger.warning(f"未知参数: {param_name}，将被忽略")
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continue
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param_info = expected_params[param_name]
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expected_type = param_info.get("type", "any")
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# 基本类型检查
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if expected_type == "number" and not isinstance(param_value, (int, float)):
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try:
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float(param_value) # 尝试转换
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except (ValueError, TypeError):
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raise ValueError(
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f"参数 {param_name} 应该是数字类型，但得到 {type(param_value).__name__}"
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)
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elif expected_type == "string" and not isinstance(param_value, str):
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raise ValueError(
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f"参数 {param_name} 应该是字符串类型，但得到 {type(param_value).__name__}"
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)
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elif expected_type == "object" and not isinstance(param_value, (dict, list)):
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raise ValueError(
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f"参数 {param_name} 应该是对象类型，但得到 {type(param_value).__name__}"
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)
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async def _execute_llm_tool(self, tool_info: Dict[str, Any], arguments: Dict[str, Any]) -> Any:
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"""执行LLM工具调用"""
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# 调用LiteLLM网关
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payload = {
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"model": arguments.get("model", "gpt-3.5-turbo"),
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"messages": arguments.get("messages", []),
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"temperature": arguments.get("temperature", 0.7),
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"max_tokens": arguments.get("max_tokens", 150)
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}
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response = await self.http_client.post(
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f"{self.litellm_url}/chat/completions",
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json=payload,
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headers={"Authorization": "Bearer sk-taiji-master-key"}
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)
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response.raise_for_status()
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return response.json()
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async def _get_tool_info(self, tool_name: str) -> Optional[Dict[str, Any]]:
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"""获取工具信息"""
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tool_key = f"tool:{tool_name}"
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tool_data = await self.redis.get(tool_key)
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if tool_data:
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return json.loads(tool_data)
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return None
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async def _get_resource_info(self, resource_name: str) -> Optional[Dict[str, Any]]:
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"""获取资源信息"""
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resource_key = f"resource:{resource_name}"
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resource_data = await self.redis.get(resource_key)
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if resource_data:
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return json.loads(resource_data)
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return None
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async def _get_agent_info(self, agent_id: str) -> Dict[str, Any]:
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"""获取Agent信息"""
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agent_key = f"agent:{agent_id}"
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agent_data = await self.redis.get(agent_key)
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if agent_data:
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return json.loads(agent_data)
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return {}
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async def _read_resource_content(self, uri: str) -> Any:
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"""读取资源内容"""
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# 这里可以根据URI类型读取不同的资源
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# 例如：文件、数据库、API等
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if uri.startswith("file://"):
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# 读取文件
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file_path = uri[7:] # 移除file://前缀
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with open(file_path, 'r') as f:
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return f.read()
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elif uri.startswith("http://") or uri.startswith("https://"):
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# 读取HTTP资源
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response = await self.http_client.get(uri)
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response.raise_for_status()
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return response.json()
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else:
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raise ValueError(f"不支持的资源类型: {uri}")
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async def _publish_execution_event(self, event_type: str, data: Dict[str, Any]):
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"""发布执行事件"""
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try:
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if self.nats:
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await self.nats.publish(
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f"mcp.{event_type}",
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json.dumps(data).encode()
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)
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except Exception as e:
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logger.error(f"发布事件失败: {e}")
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async def register_tool(self, tool_definition: ToolDefinition) -> bool:
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"""注册工具"""
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try:
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tool_key = f"tool:{tool_definition.name}"
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tool_data = tool_definition.dict()
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await self.redis.setex(
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tool_key,
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3600, # 1小时过期
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json.dumps(tool_data)
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)
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logger.info(f"工具注册成功: {tool_definition.name}")
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return True
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except Exception as e:
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logger.error(f"工具注册失败: {e}")
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return False
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async def register_agent_tool(self, agent_id: str, tool_name: str) -> bool:
|
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"""为Agent注册工具"""
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try:
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agent_tools_key = f"agent:{agent_id}:tools"
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await self.redis.sadd(agent_tools_key, tool_name)
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await self.redis.expire(agent_tools_key, 3600)
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logger.info(f"Agent {agent_id} 工具注册成功: {tool_name}")
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return True
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|
except Exception as e:
|
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logger.error(f"Agent工具注册失败: {e}")
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return False
|
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|
async def close(self):
|
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"""清理资源"""
|
|
try:
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|
await self.http_client.aclose()
|
|
except Exception as e:
|
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logger.error(f"资源清理失败: {e}")
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|