feat: 重构MCP功能和AI服务提供者架构
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xiamuceer-j
+253
-7
@@ -22,7 +22,7 @@ from app.schemas.settings import (
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from app.user_manager import User
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from app.logger import get_logger
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from app.config import settings as app_settings, PROJECT_ROOT
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from app.services.ai_service import AIService, create_user_ai_service
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from app.services.ai_service import AIService, create_user_ai_service, create_user_ai_service_with_mcp
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logger = get_logger(__name__)
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@@ -53,9 +53,14 @@ async def get_user_ai_service(
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db: AsyncSession = Depends(get_db)
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) -> AIService:
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"""
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依赖：获取当前用户的AI服务实例
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从数据库读取用户设置并创建对应的AI服务
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依赖：获取当前用户的AI服务实例（支持MCP工具自动加载）
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从数据库读取用户设置并创建对应的AI服务。
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自动传递 user_id 和 db_session，使得 AIService 能够加载用户配置的MCP工具。
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根据用户的所有MCP插件状态决定是否启用MCP：如果有启用的插件则启用，否则禁用。
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"""
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from app.models.mcp_plugin import MCPPlugin
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result = await db.execute(
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select(Settings).where(Settings.user_id == user.user_id)
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)
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@@ -73,15 +78,34 @@ async def get_user_ai_service(
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await db.refresh(settings)
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logger.info(f"用户 {user.user_id} 首次使用AI服务，已从.env同步设置到数据库")
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# 使用用户设置创建AI服务实例（包括系统提示词）
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return create_user_ai_service(
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# 查询用户的所有MCP插件状态
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mcp_result = await db.execute(
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select(MCPPlugin).where(MCPPlugin.user_id == user.user_id)
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)
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mcp_plugins = mcp_result.scalars().all()
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# 检查是否有启用的MCP插件
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enable_mcp = any(plugin.enabled for plugin in mcp_plugins) if mcp_plugins else False
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if mcp_plugins:
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enabled_count = sum(1 for p in mcp_plugins if p.enabled)
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logger.info(f"用户 {user.user_id} 有 {len(mcp_plugins)} 个MCP插件，{enabled_count} 个启用，{enable_mcp} 决定使用MCP")
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else:
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logger.debug(f"用户 {user.user_id} 没有配置MCP插件，禁用MCP")
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# ✅ 使用支持MCP的工厂函数创建AI服务实例
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# 传递 user_id 和 db_session，使得 AIService 能够自动加载用户配置的MCP工具
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return create_user_ai_service_with_mcp(
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api_provider=settings.api_provider,
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api_key=settings.api_key,
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api_base_url=settings.api_base_url or "",
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model_name=settings.llm_model,
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temperature=settings.temperature,
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max_tokens=settings.max_tokens,
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system_prompt=settings.system_prompt # 传递系统提示词
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user_id=user.user_id, # ✅ 传递 user_id
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db_session=db, # ✅ 传递 db_session
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system_prompt=settings.system_prompt,
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enable_mcp=enable_mcp, # 根据MCP插件状态动态决定
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)
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@@ -327,6 +351,227 @@ class ApiTestRequest(BaseModel):
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llm_model: str
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@router.post("/check-function-calling")
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async def check_function_calling_support(data: ApiTestRequest):
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"""
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检查模型是否支持 Function Calling（工具调用）
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基于业界最佳实践的测试方法：
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1. 发送包含工具定义的请求
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2. 检查响应的 finish_reason 是否为 "tool_calls"
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3. 验证响应中是否包含有效的 tool_calls 数据
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Args:
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data: 包含 API 配置的请求数据
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Returns:
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检测结果包含支持状态、详细信息和建议
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"""
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api_key = data.api_key
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api_base_url = data.api_base_url
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provider = data.provider
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llm_model = data.llm_model
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try:
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start_time = time.time()
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# 定义一个简单的测试工具（天气查询）
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test_tools = [{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "获取指定城市的当前天气信息",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "城市名称，例如：北京、上海、深圳"
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "温度单位"
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}
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},
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"required": ["city"]
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}
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}
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}]
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# 测试提示：故意设计一个需要调用工具的问题
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test_prompt = "请告诉我北京现在的天气情况如何？"
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logger.info(f"🧪 开始检测 Function Calling 支持")
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logger.info(f" - 提供商: {provider}")
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logger.info(f" - 模型: {llm_model}")
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logger.info(f" - 测试工具: get_weather")
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# 创建临时 AI 服务实例进行测试
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test_service = AIService(
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api_provider=provider,
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api_key=api_key,
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api_base_url=api_base_url,
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default_model=llm_model,
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default_temperature=0.3, # 使用较低温度以获得更确定的行为
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default_max_tokens=200
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)
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# 发送带工具的测试请求
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response = await test_service.generate_text(
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prompt=test_prompt,
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provider=provider,
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model=llm_model,
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temperature=0.3,
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max_tokens=200,
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tools=test_tools,
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tool_choice="auto", # 让模型自动决定是否使用工具
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auto_mcp=False # 禁用 MCP 自动加载
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)
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end_time = time.time()
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response_time = round((end_time - start_time) * 1000, 2)
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# 分析响应以确定是否支持 Function Calling
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supported = False
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finish_reason = None
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tool_calls = None
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response_content = None
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if isinstance(response, dict):
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# 检查 finish_reason（OpenAI 标准）
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finish_reason = response.get("finish_reason")
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# 检查是否有 tool_calls
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if "tool_calls" in response and response["tool_calls"]:
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supported = True
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tool_calls = response["tool_calls"]
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logger.info(f"✅ 检测到工具调用: {len(tool_calls)} 个")
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# 记录返回的内容（如果有）
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if "content" in response:
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response_content = response["content"]
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elif isinstance(response, str):
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# 如果只返回字符串，说明不支持工具调用
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response_content = response
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logger.info(f" - 响应时间: {response_time}ms")
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logger.info(f" - finish_reason: {finish_reason}")
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logger.info(f" - 支持状态: {'✅ 支持' if supported else '❌ 不支持'}")
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# 构建详细的返回信息
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result = {
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"success": True,
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"supported": supported,
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"message": "✅ 模型支持 Function Calling" if supported else "❌ 模型不支持 Function Calling",
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"response_time_ms": response_time,
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"provider": provider,
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"model": llm_model,
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"details": {
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"finish_reason": finish_reason,
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"has_tool_calls": bool(tool_calls),
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"tool_call_count": len(tool_calls) if tool_calls else 0,
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"test_tool": "get_weather",
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"test_prompt": test_prompt,
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"response_type": "tool_calls" if supported else "text"
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}
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}
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# 添加工具调用详情
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if tool_calls:
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result["tool_calls"] = tool_calls
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result["suggestions"] = [
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"✅ 该模型支持 Function Calling，可以正常使用 MCP 插件",
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"建议：启用需要的 MCP 插件以扩展 AI 能力",
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"提示：测试成功检测到工具调用，模型能够正确解析和使用外部工具"
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]
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else:
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result["response_preview"] = response_content[:200] if response_content else None
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result["suggestions"] = [
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"❌ 该模型不支持 Function Calling，无法使用 MCP 插件功能",
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"建议：更换支持工具调用的模型",
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"推荐模型：GPT-4 系列、GPT-4-turbo、Claude 3 Opus/Sonnet、Gemini 1.5 Pro 等",
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"说明：模型返回了文本回复而非工具调用，表明不支持该功能"
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]
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return result
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except ValueError as e:
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error_msg = str(e)
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logger.error(f"❌ Function Calling 检测配置错误: {error_msg}")
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return {
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"success": False,
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"supported": False,
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"message": "配置错误",
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"error": error_msg,
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"error_type": "ConfigurationError",
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"suggestions": [
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"请检查 API Key 是否正确",
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"请确认 API Base URL 格式是否正确",
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"请验证所选提供商与配置是否匹配"
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]
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}
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except TimeoutError as e:
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error_msg = str(e)
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logger.error(f"❌ Function Calling 检测超时: {error_msg}")
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return {
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"success": False,
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"supported": None,
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"message": "检测超时",
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"error": error_msg,
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"error_type": "TimeoutError",
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"suggestions": [
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"请检查网络连接是否正常",
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"请确认 API 服务是否可访问",
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"建议：稍后重试或使用其他网络环境"
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]
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}
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except Exception as e:
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error_msg = str(e)
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error_type = type(e).__name__
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logger.error(f"❌ Function Calling 检测失败: {error_msg}")
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logger.error(f" - 错误类型: {error_type}")
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# 智能分析错误原因
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suggestions = []
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if "tool" in error_msg.lower() or "function" in error_msg.lower():
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suggestions = [
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"该模型可能不支持 Function Calling 功能",
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"API 返回了与工具调用相关的错误",
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"建议：更换支持工具调用的模型或联系 API 提供商"
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]
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elif "unauthorized" in error_msg.lower() or "401" in error_msg:
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suggestions = [
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"API Key 认证失败",
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"请检查 API Key 是否正确且有效",
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"请确认 API Key 是否有足够的权限"
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]
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elif "not found" in error_msg.lower() or "404" in error_msg:
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suggestions = [
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"模型不存在或不可用",
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"请检查模型名称是否正确",
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"请确认该模型在当前 API 中是否可用"
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]
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else:
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suggestions = [
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"检测过程中遇到未知错误",
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"建议：检查所有配置参数是否正确",
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"提示：查看详细错误信息以获取更多线索"
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]
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return {
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"success": False,
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"supported": False,
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"message": "Function Calling 检测失败",
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"error": error_msg,
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"error_type": error_type,
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"suggestions": suggestions
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}
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@router.post("/test")
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async def test_api_connection(data: ApiTestRequest):
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"""
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@@ -370,7 +615,8 @@ async def test_api_connection(data: ApiTestRequest):
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provider=provider,
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model=llm_model,
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temperature=0.7,
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max_tokens=8000
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max_tokens=8000,
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auto_mcp=False # 测试时不加载MCP工具
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)
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end_time = time.time()
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