xiamuceer
69e3e46c96
- 静默检查MCP工具可用性，支持提示词注入调用mcp - 集成UniversalMCPAdapter，支持自动API能力检测和智能降级 - 新增MCP适配器配置项，增强系统兼容性和健壮性
353 lines
12 KiB
Python
353 lines
12 KiB
Python
"""通用MCP适配器 - 自动检测API能力并选择最佳适配器"""
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import time
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import asyncio
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from typing import Dict, Any, List, Optional
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from datetime import datetime, timedelta
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from dataclasses import dataclass
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from app.mcp.adapters.base import BaseMCPAdapter, AdapterType, ToolCallResult
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from app.mcp.adapters.prompt_injection import PromptInjectionAdapter
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from app.mcp.adapters.function_calling import FunctionCallingAdapter
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from app.logger import get_logger
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logger = get_logger(__name__)
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@dataclass
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class APICapability:
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"""API能力检测结果"""
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supports_function_calling: bool
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tested_at: datetime
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test_duration_ms: float
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error_message: Optional[str] = None
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class UniversalMCPAdapter:
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"""
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通用MCP适配器管理器
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功能：
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1. 自动检测API是否支持Function Calling
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2. 缓存检测结果
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3. 自动降级策略：FC失败时切换到提示词注入
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4. 提供统一接口
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"""
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def __init__(
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self,
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cache_ttl_hours: int = 24,
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enable_auto_fallback: bool = True
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):
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"""
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初始化通用适配器
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Args:
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cache_ttl_hours: 能力检测缓存时长（小时）
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enable_auto_fallback: 是否启用自动降级
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"""
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# 适配器实例
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self.adapters = {
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AdapterType.FUNCTION_CALLING: FunctionCallingAdapter(),
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AdapterType.PROMPT_INJECTION: PromptInjectionAdapter()
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}
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# API能力缓存: {api_identifier: APICapability}
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self._capability_cache: Dict[str, APICapability] = {}
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self._cache_ttl = timedelta(hours=cache_ttl_hours)
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self._cache_lock = asyncio.Lock()
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# 配置
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self._enable_auto_fallback = enable_auto_fallback
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logger.info(
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f"✅ UniversalMCPAdapter初始化完成 "
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f"(缓存TTL={cache_ttl_hours}小时, 自动降级={'开启' if enable_auto_fallback else '关闭'})"
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)
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async def get_adapter(
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self,
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api_identifier: str,
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test_function: Optional[callable] = None
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) -> BaseMCPAdapter:
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"""
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获取适合当前API的适配器
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Args:
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api_identifier: API标识符（如"openai_official", "azure_openai"等）
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test_function: 可选的测试函数，用于检测API能力
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Returns:
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最适合的适配器实例
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"""
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# 检查缓存
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capability = await self._get_cached_capability(api_identifier)
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if capability is None and test_function:
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# 缓存未命中，执行检测
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capability = await self._detect_capability(api_identifier, test_function)
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# 选择适配器
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if capability and capability.supports_function_calling:
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logger.info(f"🎯 使用Function Calling适配器: {api_identifier}")
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return self.adapters[AdapterType.FUNCTION_CALLING]
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else:
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logger.info(f"🎯 使用提示词注入适配器: {api_identifier}")
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return self.adapters[AdapterType.PROMPT_INJECTION]
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async def _get_cached_capability(
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self,
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api_identifier: str
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) -> Optional[APICapability]:
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"""获取缓存的能力检测结果"""
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async with self._cache_lock:
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if api_identifier not in self._capability_cache:
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return None
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capability = self._capability_cache[api_identifier]
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# 检查是否过期
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if datetime.now() - capability.tested_at > self._cache_ttl:
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logger.info(f"⏰ API能力缓存过期: {api_identifier}")
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del self._capability_cache[api_identifier]
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return None
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logger.debug(f"🎯 API能力缓存命中: {api_identifier}")
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return capability
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async def _detect_capability(
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self,
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api_identifier: str,
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test_function: callable
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) -> APICapability:
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"""
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检测API能力
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Args:
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api_identifier: API标识符
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test_function: 测试函数，应该尝试使用Function Calling
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Returns:
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能力检测结果
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"""
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logger.info(f"🔍 开始检测API能力: {api_identifier}")
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start_time = time.time()
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try:
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# 调用测试函数
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result = await test_function()
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# 判断是否成功
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supports_fc = self._is_function_calling_response(result)
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duration_ms = (time.time() - start_time) * 1000
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capability = APICapability(
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supports_function_calling=supports_fc,
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tested_at=datetime.now(),
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test_duration_ms=duration_ms
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)
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# 缓存结果
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async with self._cache_lock:
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self._capability_cache[api_identifier] = capability
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status = "✅ 支持" if supports_fc else "❌ 不支持"
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logger.info(
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f"{status} Function Calling: {api_identifier} "
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f"(耗时: {duration_ms:.2f}ms)"
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)
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return capability
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except Exception as e:
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duration_ms = (time.time() - start_time) * 1000
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logger.warning(
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f"⚠️ API能力检测失败: {api_identifier}, 错误: {e}, "
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f"将使用提示词注入模式"
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)
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capability = APICapability(
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supports_function_calling=False,
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tested_at=datetime.now(),
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test_duration_ms=duration_ms,
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error_message=str(e)
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)
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# 缓存失败结果（避免重复测试）
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async with self._cache_lock:
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self._capability_cache[api_identifier] = capability
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return capability
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def _is_function_calling_response(self, response: Any) -> bool:
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"""
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判断响应是否是Function Calling格式
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Args:
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response: API响应
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Returns:
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是否支持Function Calling
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"""
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try:
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# 检查字典格式
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if isinstance(response, dict):
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message = response.get("choices", [{}])[0].get("message", {})
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return "tool_calls" in message or "function_call" in message
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# 检查对象格式（OpenAI SDK）
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if hasattr(response, "choices"):
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message = response.choices[0].message
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return hasattr(message, "tool_calls") or hasattr(message, "function_call")
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return False
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except Exception:
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return False
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async def call_with_fallback(
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self,
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api_identifier: str,
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tools: List[Dict[str, Any]],
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user_message: str,
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call_function: callable,
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test_function: Optional[callable] = None
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) -> ToolCallResult:
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"""
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带降级策略的工具调用
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Args:
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api_identifier: API标识符
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tools: MCP工具列表
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user_message: 用户消息
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call_function: 实际调用API的函数
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test_function: 可选的测试函数
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Returns:
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工具调用结果
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"""
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# 获取适配器
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adapter = await self.get_adapter(api_identifier, test_function)
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# 首次尝试
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try:
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if adapter.supports_native_tools():
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# Function Calling模式
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logger.info("🚀 尝试使用Function Calling模式")
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result = await self._try_function_calling(
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tools, user_message, call_function, adapter
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)
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else:
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# 提示词注入模式
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logger.info("🚀 使用提示词注入模式")
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result = await self._try_prompt_injection(
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tools, user_message, call_function, adapter
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)
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return result
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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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# 自动降级
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if self._enable_auto_fallback and adapter.supports_native_tools():
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logger.warning("⚠️ Function Calling失败，降级到提示词注入模式")
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# 更新缓存，标记为不支持
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async with self._cache_lock:
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self._capability_cache[api_identifier] = APICapability(
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supports_function_calling=False,
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tested_at=datetime.now(),
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test_duration_ms=0,
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error_message=str(e)
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)
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# 使用提示词注入重试
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fallback_adapter = self.adapters[AdapterType.PROMPT_INJECTION]
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return await self._try_prompt_injection(
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tools, user_message, call_function, fallback_adapter
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)
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raise
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async def _try_function_calling(
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self,
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tools: List[Dict[str, Any]],
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user_message: str,
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call_function: callable,
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adapter: FunctionCallingAdapter
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) -> ToolCallResult:
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"""尝试Function Calling模式"""
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# Function Calling不需要修改提示词
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response = await call_function(
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message=user_message,
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tools_param=tools,
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tool_choice_param="auto"
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)
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return adapter.parse_tool_calls(response)
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async def _try_prompt_injection(
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self,
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tools: List[Dict[str, Any]],
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user_message: str,
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call_function: callable,
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adapter: PromptInjectionAdapter
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) -> ToolCallResult:
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"""尝试提示词注入模式"""
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# 注入工具到提示词
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enhanced_prompt = adapter.format_tools_for_prompt(tools, user_message)
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# 调用API（不传tools参数）
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response = await call_function(
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message=enhanced_prompt,
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tools_param=None,
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tool_choice_param=None
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)
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# 从文本响应中解析工具调用
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return adapter.parse_tool_calls(response)
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def clear_cache(self, api_identifier: Optional[str] = None):
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"""
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清理能力缓存
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Args:
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api_identifier: 可选，只清理特定API的缓存
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"""
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if api_identifier:
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if api_identifier in self._capability_cache:
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del self._capability_cache[api_identifier]
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logger.info(f"🧹 已清理API能力缓存: {api_identifier}")
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else:
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self._capability_cache.clear()
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logger.info("🧹 已清理所有API能力缓存")
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def get_cache_stats(self) -> Dict[str, Any]:
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"""获取缓存统计信息"""
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return {
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"total_cached": len(self._capability_cache),
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"cache_ttl_hours": self._cache_ttl.total_seconds() / 3600,
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"cached_apis": [
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{
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"api_identifier": api_id,
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"supports_fc": cap.supports_function_calling,
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"tested_at": cap.tested_at.isoformat(),
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"test_duration_ms": cap.test_duration_ms
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}
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for api_id, cap in self._capability_cache.items()
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]
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}
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# 全局单例
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universal_mcp_adapter = UniversalMCPAdapter() |