"""多 Agent 控制器""" import uuid from fastapi import APIRouter, Depends, Query, Path from sqlalchemy.orm import Session from app.db import get_db from app.dependencies import get_current_user from app.core.response_utils import success from app.core.logging_config import get_business_logger from app.schemas import multi_agent_schema from app.schemas.response_schema import PageData, PageMeta from app.services.multi_agent_service import MultiAgentService from app.models import User router = APIRouter(prefix="/apps", tags=["Multi-Agent"]) logger = get_business_logger() # ==================== 多 Agent 配置管理 ==================== @router.post( "/{app_id}/multi-agent", summary="创建多 Agent 配置" ) def create_multi_agent_config( app_id: uuid.UUID = Path(..., description="应用 ID"), data: multi_agent_schema.MultiAgentConfigCreate = ..., current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """创建多 Agent 配置 支持四种编排模式： - sequential: 顺序执行 - parallel: 并行执行 - conditional: 条件路由 - loop: 循环执行 """ service = MultiAgentService(db) config = service.create_config( app_id=app_id, data=data, created_by=current_user.id ) return success( data=multi_agent_schema.MultiAgentConfigSchema.model_validate(config), msg="多 Agent 配置创建成功" ) @router.get( "/{app_id}/multi-agent", summary="获取当前应用的最新有效多 Agent 配置" ) def get_multi_agent_configs( app_id: uuid.UUID = Path(..., description="应用 ID"), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """获取指定应用的最新有效多 Agent 配置，如果不存在则返回默认模板""" service = MultiAgentService(db) # 通过 app_id 获取最新有效配置（已转换 agent_id 为 app_id） config = service.get_multi_agent_configs(app_id) if not config: # 返回默认模板 default_template = { "app_id": str(app_id), "master_agent_id": None, "master_agent_name": None, "orchestration_mode": "conditional", "sub_agents": [], "routing_rules": [], "execution_config": { "max_iterations": 10, "timeout": 300, "enable_parallel": False, "error_handling": "stop" }, "aggregation_strategy": "merge", } return success( data=default_template, msg="该应用暂无配置，返回默认模板" ) # config 已经是字典格式，直接返回 return success(data=config) @router.put( "/{app_id}/multi-agent", summary="更新多 Agent 配置" ) def update_multi_agent_config( app_id: uuid.UUID = Path(..., description="应用 ID"), data: multi_agent_schema.MultiAgentConfigUpdate = ..., current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """更新多 Agent 配置""" service = MultiAgentService(db) config = service.update_config(app_id, data) return success( data=multi_agent_schema.MultiAgentConfigSchema.model_validate(config), msg="多 Agent 配置更新成功" ) @router.delete( "/{app_id}/multi-agent", summary="删除多 Agent 配置" ) def delete_multi_agent_config( app_id: uuid.UUID = Path(..., description="应用 ID"), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """删除多 Agent 配置""" service = MultiAgentService(db) service.delete_config(app_id) return success(msg="多 Agent 配置删除成功") # ==================== 多 Agent 运行 ==================== @router.post( "/{app_id}/multi-agent/run", summary="运行多 Agent 任务" ) async def run_multi_agent( app_id: uuid.UUID = Path(..., description="应用 ID"), request: multi_agent_schema.MultiAgentRunRequest = ..., current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """运行多 Agent 任务 根据配置的编排模式执行多个 Agent： - sequential: 按优先级顺序执行 - parallel: 并行执行所有 Agent - conditional: 根据条件选择 Agent - loop: 循环执行直到满足条件 """ service = MultiAgentService(db) result = await service.run(app_id, request) return success( data=multi_agent_schema.MultiAgentRunResponse(**result), msg="多 Agent 任务执行成功" ) # ==================== 智能路由测试 ==================== @router.post( "/{app_id}/multi-agent/test-routing", summary="测试智能路由" ) async def test_routing( app_id: uuid.UUID = Path(..., description="应用 ID"), request: multi_agent_schema.RoutingTestRequest = ..., current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """测试智能路由功能 支持三种路由模式： - keyword: 仅使用关键词路由 - llm: 使用 LLM 路由（需要提供 routing_model_id） - hybrid: 混合路由（关键词 + LLM） 参数： - message: 测试消息 - conversation_id: 会话 ID（可选） - routing_model_id: 路由模型 ID（可选，用于 LLM 路由） - use_llm: 是否启用 LLM（默认 False） - keyword_threshold: 关键词置信度阈值（默认 0.8） """ from app.services.conversation_state_manager import ConversationStateManager from app.services.llm_router import LLMRouter from app.models import ModelConfig # 1. 获取多 Agent 配置 service = MultiAgentService(db) config = service.get_config(app_id) if not config: return success( data=None, msg="应用未配置多 Agent，无法测试路由" ) # 2. 准备子 Agent 信息 sub_agents = {} for sub_agent_info in config.sub_agents: agent_id = sub_agent_info["agent_id"] sub_agents[agent_id] = { "name": sub_agent_info.get("name", agent_id), "role": sub_agent_info.get("role", "") } # 3. 获取路由模型（如果指定） routing_model = None if request.routing_model_id: routing_model = db.get(ModelConfig, request.routing_model_id) if not routing_model: return success( data=None, msg=f"路由模型不存在: {request.routing_model_id}" ) # 4. 初始化路由器 state_manager = ConversationStateManager() router = LLMRouter( db=db, state_manager=state_manager, routing_rules=config.routing_rules or [], sub_agents=sub_agents, routing_model_config=routing_model, use_llm=request.use_llm and routing_model is not None ) # 5. 设置阈值 if request.keyword_threshold: router.keyword_high_confidence_threshold = request.keyword_threshold # 6. 执行路由 try: routing_result = await router.route( message=request.message, conversation_id=str(request.conversation_id) if request.conversation_id else None, force_new=request.force_new ) # 7. 获取 Agent 信息 agent_id = routing_result["agent_id"] agent_info = sub_agents.get(agent_id, {}) # 8. 构建响应 response_data = { "message": request.message, "routing_result": { "agent_id": agent_id, "agent_name": agent_info.get("name", agent_id), "agent_role": agent_info.get("role", ""), "confidence": routing_result["confidence"], "strategy": routing_result["strategy"], "topic": routing_result["topic"], "topic_changed": routing_result["topic_changed"], "reason": routing_result["reason"], "routing_method": routing_result["routing_method"] }, "cmulti-agent/batch-test-routingonfig_info": { "use_llm": request.use_llm and routing_model is not None, "routing_model": routing_model.name if routing_model else None, "keyword_threshold": router.keyword_high_confidence_threshold, "total_sub_agents": len(sub_agents) } } return success( data=response_data, msg="路由测试成功" ) except Exception as e: logger.error(f"路由测试失败: {str(e)}") return success( data=None, msg=f"路由测试失败: {str(e)}" ) @router.post( "/{app_id}/", summary="批量测试智能路由" ) async def batch_test_routing( app_id: uuid.UUID = Path(..., description="应用 ID"), request: multi_agent_schema.BatchRoutingTestRequest = ..., current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): """批量测试智能路由功能 用于测试多条消息的路由效果，并统计准确率 参数： - test_cases: 测试用例列表 - routing_model_id: 路由模型 ID（可选） - use_llm: 是否启用 LLM - keyword_threshold: 关键词置信度阈值 """ from app.services.conversation_state_manager import ConversationStateManager from app.services.llm_router import LLMRouter from app.models import ModelConfig # 1. 获取多 Agent 配置 service = MultiAgentService(db) config = service.get_config(app_id) if not config: return success( data=None, msg="应用未配置多 Agent，无法测试路由" ) # 2. 准备子 Agent 信息 sub_agents = {} for sub_agent_info in config.sub_agents: agent_id = sub_agent_info["agent_id"] sub_agents[agent_id] = { "name": sub_agent_info.get("name", agent_id), "role": sub_agent_info.get("role", "") } # 3. 获取路由模型 routing_model = None if request.routing_model_id: routing_model = db.get(ModelConfig, request.routing_model_id) # 4. 初始化路由器 state_manager = ConversationStateManager() router = LLMRouter( db=db, state_manager=state_manager, routing_rules=config.routing_rules or [], sub_agents=sub_agents, routing_model_config=routing_model, use_llm=request.use_llm and routing_model is not None ) if request.keyword_threshold: router.keyword_high_confidence_threshold = request.keyword_threshold # 5. 批量测试 results = [] correct_count = 0 total_count = len(request.test_cases) for test_case in request.test_cases: try: routing_result = await router.route( message=test_case.message, conversation_id=str(uuid.uuid4()) # 每个测试用例使用独立会话 ) agent_id = routing_result["agent_id"] agent_info = sub_agents.get(agent_id, {}) # 判断是否正确 is_correct = None if test_case.expected_agent_id: is_correct = (agent_id == str(test_case.expected_agent_id)) if is_correct: correct_count += 1 results.append({ "message": test_case.message, "description": test_case.description, "routed_agent_id": agent_id, "routed_agent_name": agent_info.get("name"), "expected_agent_id": str(test_case.expected_agent_id) if test_case.expected_agent_id else None, "is_correct": is_correct, "confidence": routing_result["confidence"], "routing_method": routing_result["routing_method"], "strategy": routing_result["strategy"] }) except Exception as e: logger.error(f"测试用例失败: {test_case.message}, 错误: {str(e)}") results.append({ "message": test_case.message, "description": test_case.description, "error": str(e) }) # 6. 统计 accuracy = None if correct_count > 0: total_with_expected = sum(1 for r in results if r.get("expected_agent_id")) if total_with_expected > 0: accuracy = correct_count / total_with_expected * 100 response_data = { "total_count": total_count, "correct_count": correct_count, "accuracy": accuracy, "results": results, "config_info": { "use_llm": request.use_llm and routing_model is not None, "routing_model": routing_model.name if routing_model else None, "keyword_threshold": router.keyword_high_confidence_threshold } } return success( data=response_data, msg=f"批量测试完成，准确率: {accuracy:.1f}%" if accuracy else "批量测试完成" )