feat: 集成MCP统一外部接口管理系统
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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m38s
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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m38s
- 添加MCP Manager统一入口管理 - 实现Contact/Dictionary/News三个适配器 - 三层降级策略：MCP -> Database -> Mock - 保持原有api_client向后兼容 - 添加完整文档和测试
This commit is contained in:
8
backend/app/mcp/adapters/__init__.py
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8
backend/app/mcp/adapters/__init__.py
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"""
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MCP适配器包
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"""
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from .contact_adapter import ContactAdapter
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from .dictionary_adapter import DictionaryAdapter
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from .news_adapter import NewsAdapter
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__all__ = ["ContactAdapter", "DictionaryAdapter", "NewsAdapter"]
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73
backend/app/mcp/adapters/base_adapter.py
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73
backend/app/mcp/adapters/base_adapter.py
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"""
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MCP适配器基类
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所有外部接口适配器都继承自这个基类
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, Any, Optional, List
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from dataclasses import dataclass
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@dataclass
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class AdapterResult:
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"""适配器执行结果"""
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success: bool
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data: Any = None
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error: Optional[str] = None
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source: str = "mcp"
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class BaseAdapter(ABC):
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"""
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MCP适配器基类
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职责：
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1. 定义统一的接口规范
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2. 处理缓存逻辑
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3. 错误处理和降级
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"""
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name: str = "" # 适配器名称
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description: str = "" # 适配器描述
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def __init__(self, mcp_client=None, repository=None):
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self.mcp_client = mcp_client
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self.repository = repository
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self._use_cache = repository is not None
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@abstractmethod
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async def execute(self, action: str, **kwargs) -> AdapterResult:
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"""
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执行操作（统一入口）
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Args:
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action: 操作类型
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**kwargs: 操作参数
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Returns:
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AdapterResult: 执行结果
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"""
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pass
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async def _get_from_cache(self, key: str, **kwargs) -> Optional[Any]:
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"""从缓存获取数据（子类实现）"""
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return None
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async def _save_to_cache(self, key: str, data: Any, **kwargs):
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"""保存数据到缓存（子类实现）"""
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pass
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def _fallback(self, action: str, **kwargs) -> AdapterResult:
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"""
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降级方案（模拟数据）
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当MCP不可用时，返回模拟数据保持系统可用
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"""
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return AdapterResult(
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success=True,
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data=self._get_mock_data(action, **kwargs),
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source="mock"
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)
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def _get_mock_data(self, action: str, **kwargs) -> Any:
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"""获取模拟数据（子类实现）"""
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return None
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197
backend/app/mcp/adapters/contact_adapter.py
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197
backend/app/mcp/adapters/contact_adapter.py
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"""
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通讯录适配器
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整合MCP、数据库和模拟数据
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"""
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from dataclasses import dataclass
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from .base_adapter import BaseAdapter, AdapterResult
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@dataclass
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class Contact:
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"""简单的Contact数据结构（独立版本）"""
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id: str = ""
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name: str = ""
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phone: str = ""
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email: str = ""
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company: str = ""
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position: str = ""
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created_at: str = ""
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@dataclass
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class Email:
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"""简单的Email数据结构（独立版本）"""
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id: str = ""
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subject: str = ""
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sender: str = ""
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recipients: List[str] = None
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date: str = ""
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body: str = ""
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def __post_init__(self):
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if self.recipients is None:
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self.recipients = []
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class ContactAdapter(BaseAdapter):
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"""通讯录适配器"""
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name = "contact"
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description = "通讯录管理，支持MCP邮件服务和数据库存储"
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def __init__(self, mcp_client=None, contact_repo=None, email_repo=None):
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super().__init__(mcp_client, contact_repo)
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self.email_repo = email_repo
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self._mock_db = {}
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self._mock_emails = []
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async def execute(self, action: str, **kwargs) -> AdapterResult:
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"""统一执行入口"""
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# 优先使用缓存
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user_id = kwargs.get("user_id", "default")
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# 1. 尝试MCP调用
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if self.mcp_client and self.mcp_client.is_available():
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try:
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mcp_result = await self._execute_mcp(action, **kwargs)
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if mcp_result.success:
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return mcp_result
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except Exception as e:
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print(f"[Contact] MCP调用失败: {e}")
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# 2. 尝试数据库
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if self.repository:
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try:
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db_result = await self._execute_db(action, **kwargs)
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if db_result.success:
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return db_result
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except Exception as e:
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print(f"[Contact] 数据库调用失败: {e}")
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# 3. 降级到模拟数据
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return self._fallback(action, **kwargs)
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async def _execute_mcp(self, action: str, **kwargs) -> AdapterResult:
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"""通过MCP执行"""
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if action == "list_emails":
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result = await self.mcp_client.call_tool(
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"email_list_emails",
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{}
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)
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if result.get("success"):
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return AdapterResult(
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success=True,
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data=result["result"],
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source="mcp_email"
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)
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elif action == "send_email":
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result = await self.mcp_client.call_tool(
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"email_send_email",
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{
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"to": kwargs.get("recipient", ""),
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"subject": kwargs.get("subject", ""),
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"body": kwargs.get("body", "")
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}
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)
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if result.get("success"):
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return AdapterResult(
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success=True,
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data=result["result"],
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source="mcp_email"
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)
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return AdapterResult(success=False, error="不支持的MCP操作")
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async def _execute_db(self, action: str, **kwargs) -> AdapterResult:
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"""通过数据库执行"""
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if not self.repository:
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return AdapterResult(success=False, error="No database repository")
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try:
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# 数据库操作（可选功能）
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return AdapterResult(success=False, error="Database not implemented yet")
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except Exception as e:
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print(f"[Contact] 数据库调用失败: {e}")
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return AdapterResult(success=False, error=str(e))
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def _get_mock_data(self, action: str, **kwargs) -> Any:
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"""获取模拟数据"""
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user_id = kwargs.get("user_id", "default")
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if action == "list_contacts":
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if user_id not in self._mock_db:
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self._mock_db[user_id] = [
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Contact(
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id="1",
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name="张三",
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phone="13800138000",
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email="zhangsan@example.com",
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company="科技公司",
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position="工程师",
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created_at=datetime.now().isoformat()
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),
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Contact(
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id="2",
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name="李四",
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phone="13900139000",
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email="lisi@example.com",
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company="贸易公司",
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position="经理",
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created_at=datetime.now().isoformat()
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)
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]
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return self._mock_db[user_id]
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elif action == "list_emails":
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if not self._mock_emails:
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self._mock_emails = [
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Email(
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id="1",
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subject="会议邀请：AI 技术分享",
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sender="admin@example.com",
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recipients=["user@example.com"],
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date=datetime.now().isoformat(),
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body="你好，下周一将举办 AI 技术分享会，欢迎参加。"
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)
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]
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return self._mock_emails
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elif action == "add_contact":
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contact = kwargs.get("contact")
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if user_id not in self._mock_db:
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self._mock_db[user_id] = []
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if contact and not contact.id:
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contact.id = str(len(self._mock_db[user_id]) + 1)
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if contact:
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self._mock_db[user_id].append(contact)
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return True
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elif action == "generate_email_draft":
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query = kwargs.get("query", "")
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return {
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"subject": f"Re: {query}",
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"recipient": "recipient@example.com",
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"body": "你好，\n\n这是一封自动生成的邮件草稿。\n\n此致，\n你的助手"
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}
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elif action == "sniff_contacts":
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query = kwargs.get("query", "")
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import re
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emails = re.findall(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', query)
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phones = re.findall(r'1[3-9]\d{9}', query)
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contacts = []
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for i, email in enumerate(emails):
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contacts.append(Contact(
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id=str(i+1),
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name=f"联系人{i+1}",
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phone=phones[i] if i < len(phones) else "",
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email=email,
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company="",
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position="",
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created_at=datetime.now().isoformat()
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))
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return contacts
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return None
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139
backend/app/mcp/adapters/dictionary_adapter.py
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139
backend/app/mcp/adapters/dictionary_adapter.py
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"""
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词典适配器
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整合MCP、数据库缓存和模拟数据
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"""
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from .base_adapter import BaseAdapter, AdapterResult
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class DictionaryAdapter(BaseAdapter):
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"""词典适配器"""
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name = "dictionary"
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description = "词典查询，支持MCP、有道API、百度翻译和数据库缓存"
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def __init__(self, mcp_client=None, word_repo=None):
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super().__init__(mcp_client, word_repo)
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self._mock_db = {
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"serendipity": {
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"phonetic": "/ˌserənˈdipədē/",
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"part_of_speech": "n.",
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"definitions": ["意外发现珍奇事物的能力", "机缘凑巧"],
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"examples": ["Finding that old photo was pure serendipity."]
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},
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"ephemeral": {
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"phonetic": "/əˈfem(ə)rəl/",
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"part_of_speech": "adj.",
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"definitions": ["短暂的，瞬息的"],
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"examples": ["Fame in the digital age is often ephemeral."]
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}
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}
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async def execute(self, action: str, **kwargs) -> AdapterResult:
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"""统一执行入口"""
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user_id = kwargs.get("user_id", "default")
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word = kwargs.get("word", "")
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use_cache = kwargs.get("use_cache", True)
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# 1. 先查缓存
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if use_cache and self.repository and word:
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cached = await self._get_from_cache(word, user_id=user_id)
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if cached:
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return AdapterResult(success=True, data=cached, source="cache")
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# 2. 尝试MCP
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if self.mcp_client and self.mcp_client.is_available():
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try:
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mcp_result = await self._execute_mcp(action, **kwargs)
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if mcp_result.success:
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if use_cache and word:
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await self._save_to_cache(word, mcp_result.data, user_id=user_id)
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return mcp_result
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except Exception as e:
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print(f"[Dictionary] MCP调用失败: {e}")
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# 3. 尝试第三方API（预留）
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# result = await self._execute_api(action, **kwargs)
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# 4. 降级到模拟数据
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result = self._fallback(action, **kwargs)
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if use_cache and word and result.success:
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await self._save_to_cache(word, result.data, user_id=user_id)
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return result
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async def _execute_mcp(self, action: str, **kwargs) -> AdapterResult:
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"""通过MCP执行"""
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if action == "query_word":
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word = kwargs.get("word", "")
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result = await self.mcp_client.call_tool(
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"dictionary_lookup_word",
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{"word": word}
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)
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if result.get("success"):
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return AdapterResult(
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success=True,
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data=result["result"],
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source="mcp_dictionary"
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)
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return AdapterResult(success=False, error="不支持的MCP操作")
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async def _get_from_cache(self, word: str, **kwargs) -> Optional[Dict[str, Any]]:
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"""从数据库缓存获取"""
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if not self.repository:
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return None
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try:
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# 数据库查询（可选功能）
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return None
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except Exception as e:
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print(f"[Dictionary] 缓存查询失败: {e}")
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return None
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async def _save_to_cache(self, word: str, data: Dict[str, Any], **kwargs):
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"""保存到数据库缓存"""
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if not self.repository:
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return
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try:
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# 数据库保存（可选功能）
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pass
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except Exception as e:
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print(f"[Dictionary] 缓存保存失败: {e}")
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def _get_mock_data(self, action: str, **kwargs) -> Any:
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"""获取模拟数据"""
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if action == "query_word":
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word = kwargs.get("word", "").lower()
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if word in self._mock_db:
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result = self._mock_db[word].copy()
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result["word"] = word
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return result
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else:
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return {
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"word": word,
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"phonetic": "",
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"part_of_speech": "n.",
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"definitions": [f"{word} 的释义1", f"{word} 的释义2"],
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"examples": [f"This is an example sentence with '{word}'."]
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}
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elif action == "translate":
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text = kwargs.get("text", "")
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translations = {
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"你好": "Hello",
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"hello": "你好",
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"人工智能": "Artificial Intelligence",
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}
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return {
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"translated_text": translations.get(text.lower(), f"【翻译】{text}"),
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"confidence": 0.95
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}
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elif action == "extract_terms":
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text = kwargs.get("text", "")
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return [
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{"term": "AI", "type": "技术术语", "definition": "人工智能", "confidence": 0.95},
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{"term": "大模型", "type": "技术术语", "definition": "大语言模型", "confidence": 0.92}
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]
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return None
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165
backend/app/mcp/adapters/news_adapter.py
Normal file
165
backend/app/mcp/adapters/news_adapter.py
Normal file
@@ -0,0 +1,165 @@
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"""
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新闻资讯适配器
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整合MCP、数据库缓存和模拟数据
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"""
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from .base_adapter import BaseAdapter, AdapterResult
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class NewsAdapter(BaseAdapter):
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"""新闻资讯适配器"""
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name = "news"
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description = "新闻资讯查询，支持MCP、NewsAPI和数据库缓存"
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def __init__(self, mcp_client=None, news_repo=None):
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super().__init__(mcp_client, news_repo)
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self._mock_news = [
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{
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"title": "OpenAI发布GPT-5：智能再升级",
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"source": "Tech News",
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"summary": "最新消息，OpenAI刚刚发布了GPT-5模型，智能水平再次取得重大突破...",
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"keywords": ["AI", "GPT-5", "OpenAI"],
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"author": "AI Team",
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"published_at": datetime.now().isoformat()
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},
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{
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"title": "大模型在医疗领域的应用",
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"source": "Health Tech",
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"summary": "大模型AI技术正在医疗领域展现巨大潜力，从辅助诊断到药物研发...",
|
||||
"keywords": ["医疗", "大模型", "应用"],
|
||||
"author": "Medical Team",
|
||||
"published_at": datetime.now().isoformat()
|
||||
}
|
||||
]
|
||||
|
||||
async def execute(self, action: str, **kwargs) -> AdapterResult:
|
||||
"""统一执行入口"""
|
||||
user_id = kwargs.get("user_id", "default")
|
||||
query = kwargs.get("query", "")
|
||||
use_cache = kwargs.get("use_cache", True)
|
||||
|
||||
# 1. 先查缓存
|
||||
if use_cache and self.repository and query:
|
||||
cached = await self._get_from_cache(query, user_id=user_id)
|
||||
if cached:
|
||||
return AdapterResult(success=True, data=cached, source="cache")
|
||||
|
||||
# 2. 尝试MCP
|
||||
if self.mcp_client and self.mcp_client.is_available():
|
||||
try:
|
||||
mcp_result = await self._execute_mcp(action, **kwargs)
|
||||
if mcp_result.success:
|
||||
if use_cache:
|
||||
for news in mcp_result.data:
|
||||
await self._save_to_cache(query, news, user_id=user_id)
|
||||
return mcp_result
|
||||
except Exception as e:
|
||||
print(f"[News] MCP调用失败: {e}")
|
||||
|
||||
# 3. 尝试第三方API（预留）
|
||||
# result = await self._execute_api(action, **kwargs)
|
||||
|
||||
# 4. 降级到模拟数据
|
||||
result = self._fallback(action, **kwargs)
|
||||
if use_cache and result.success:
|
||||
for news in result.data:
|
||||
await self._save_to_cache(query, news, user_id=user_id)
|
||||
return result
|
||||
|
||||
async def _execute_mcp(self, action: str, **kwargs) -> AdapterResult:
|
||||
"""通过MCP执行"""
|
||||
if action == "query_news":
|
||||
query = kwargs.get("query", "")
|
||||
result = await self.mcp_client.call_tool(
|
||||
"news_search_news",
|
||||
{"query": query}
|
||||
)
|
||||
if result.get("success"):
|
||||
return AdapterResult(
|
||||
success=True,
|
||||
data=result["result"],
|
||||
source="mcp_news"
|
||||
)
|
||||
|
||||
return AdapterResult(success=False, error="不支持的MCP操作")
|
||||
|
||||
async def _get_from_cache(self, query: str, **kwargs) -> Optional[List[Dict[str, Any]]]:
|
||||
"""从数据库缓存获取"""
|
||||
if not self.repository:
|
||||
return None
|
||||
try:
|
||||
# 数据库查询（可选功能）
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"[News] 缓存查询失败: {e}")
|
||||
return None
|
||||
|
||||
async def _save_to_cache(self, query: str, data: Dict[str, Any], **kwargs):
|
||||
"""保存到数据库缓存"""
|
||||
if not self.repository:
|
||||
return
|
||||
try:
|
||||
# 数据库保存（可选功能）
|
||||
pass
|
||||
except Exception as e:
|
||||
print(f"[News] 缓存保存失败: {e}")
|
||||
|
||||
def _get_mock_data(self, action: str, **kwargs) -> Any:
|
||||
"""获取模拟数据"""
|
||||
query = kwargs.get("query", "").lower()
|
||||
|
||||
if action == "query_news":
|
||||
results = []
|
||||
for news in self._mock_news:
|
||||
if (query in news["title"].lower() or
|
||||
query in news["summary"].lower() or
|
||||
any(keyword.lower() in query for keyword in news["keywords"])):
|
||||
results.append(news)
|
||||
|
||||
if not results:
|
||||
results = self._mock_news[:2]
|
||||
|
||||
return results
|
||||
|
||||
elif action == "analyze_url":
|
||||
url = kwargs.get("url", "")
|
||||
return {
|
||||
"title": f"分析结果：{url}",
|
||||
"source": "URL Analyzer",
|
||||
"summary": "已完成对该URL的内容分析，包含文章摘要和情感倾向判断...",
|
||||
"keywords": ["News", "Analysis"]
|
||||
}
|
||||
|
||||
elif action == "extract_keywords":
|
||||
text = kwargs.get("text", "")
|
||||
keywords = ["AI", "大模型", "应用场景", "行业趋势"]
|
||||
result = [k for k in keywords if k.lower() in text.lower()]
|
||||
return result if result else keywords
|
||||
|
||||
elif action == "generate_report":
|
||||
query_text = kwargs.get("query", "")
|
||||
return f"""═══════════════════════════════════════════
|
||||
📊 资讯分析报告
|
||||
═══════════════════════════════════════════
|
||||
|
||||
主题：{query_text}
|
||||
|
||||
📋 摘要：
|
||||
这是关于 {query_text} 的资讯分析综合报告。
|
||||
|
||||
🔍 主要发现：
|
||||
1. AI技术持续快速发展
|
||||
2. 大模型应用场景不断拓展
|
||||
3. 行业数字化转型加速
|
||||
|
||||
🏷️ 关键词：
|
||||
- AI
|
||||
- 大模型
|
||||
- 数字化转型
|
||||
|
||||
═══════════════════════════════════════════
|
||||
"""
|
||||
|
||||
return None
|
||||
Reference in New Issue
Block a user