triage/workflow/medical_workflow.py
iomgaa 399e4d4447 新增:实现医疗问诊工作流系统并优化Monitor智能体
- 新增完整的workflow模块,包含MedicalWorkflow、TaskManager、StepExecutor和WorkflowLogger四个核心组件
- 实现分诊、现病史、既往史三阶段任务管理和状态跟踪机制
- 优化Monitor智能体支持针对特定任务的精准评估,解决任务评价针对性问题
- 完善agent_system各模块的__init__.py文件,确保正确的模块导入
- 实现详细的jsonl格式日志记录系统,支持完整workflow执行追踪

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-08-11 20:40:33 +08:00

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from typing import Dict, Any, Optional
import time
from .task_manager import TaskManager, TaskPhase
from .step_executor import StepExecutor
from .workflow_logger import WorkflowLogger
class MedicalWorkflow:
"""
医疗问诊工作流主控制器
负责协调整个30步问诊过程的执行
"""
def __init__(self, case_data: Dict[str, Any], model_type: str = "gpt-oss:latest",
llm_config: Optional[Dict] = None, max_steps: int = 30, log_dir: str = "logs"):
"""
初始化医疗问诊工作流
Args:
case_data: 病例数据,包含病案介绍等信息
model_type: 使用的语言模型类型,默认为"gpt-oss:latest"
llm_config: 语言模型配置默认为None
max_steps: 最大执行步数默认为30
log_dir: 日志目录,默认为"logs"
"""
self.case_data = case_data
self.model_type = model_type
self.llm_config = llm_config or {}
self.max_steps = max_steps
# 初始化核心组件
self.task_manager = TaskManager()
self.step_executor = StepExecutor(model_type=model_type, llm_config=self.llm_config)
self.logger = WorkflowLogger(case_data=case_data, log_dir=log_dir)
# 初始化工作流状态
self.current_step = 0
self.conversation_history = ""
self.current_hpi = ""
self.current_ph = ""
self.current_chief_complaint = ""
self.workflow_completed = False
self.workflow_success = False
def run(self) -> str:
"""
执行完整的医疗问诊工作流
Returns:
str: 日志文件路径
"""
print(f"开始执行医疗问诊工作流,病例:{self.case_data.get('病案介绍', {}).get('主诉', '未知病例')}")
try:
# 执行工作流的主循环
for step in range(1, self.max_steps + 1):
self.current_step = step
# 检查是否所有任务都已完成
if self.task_manager.is_workflow_completed():
print(f"所有任务已完成,工作流在第 {step} 步结束")
self.workflow_completed = True
self.workflow_success = True
break
# 执行当前step
if not self._execute_single_step(step):
print(f"Step {step} 执行失败,工作流终止")
break
# 显示进度
self._print_step_progress(step)
# 如果达到最大步数但任务未完成
if not self.workflow_completed:
print(f"已达到最大步数 {self.max_steps},工作流结束")
self.workflow_success = False
except Exception as e:
print(f"工作流执行出现异常: {str(e)}")
self.logger.log_error(self.current_step, "workflow_error", str(e))
self.workflow_success = False
finally:
# 记录工作流完成信息
final_summary = self.task_manager.get_completion_summary()
self.logger.log_workflow_complete(
total_steps=self.current_step,
final_summary=final_summary,
success=self.workflow_success
)
print(f"工作流执行完成,日志文件:{self.logger.get_log_file_path()}")
return self.logger.get_log_file_path()
def _execute_single_step(self, step_num: int) -> bool:
"""
执行单个step
Args:
step_num: step编号
Returns:
bool: 是否执行成功
"""
try:
# 获取当前阶段和待完成任务
current_phase = self.task_manager.get_current_phase()
pending_tasks = self.task_manager.get_pending_tasks(current_phase)
# 记录step开始
self.logger.log_step_start(step_num, current_phase.value, pending_tasks)
# 确定是否为第一步
is_first_step = (step_num == 1)
# 准备医生问题(非首轮时使用上轮的结果)
doctor_question = getattr(self, '_last_doctor_question', "")
# 执行step
step_result = self.step_executor.execute_step(
step_num=step_num,
case_data=self.case_data,
task_manager=self.task_manager,
logger=self.logger,
conversation_history=self.conversation_history,
previous_hpi=self.current_hpi,
previous_ph=self.current_ph,
previous_chief_complaint=self.current_chief_complaint,
is_first_step=is_first_step,
doctor_question=doctor_question
)
# 检查执行结果
if not step_result["success"]:
print(f"Step {step_num} 执行失败: {step_result.get('errors', [])}")
return False
# 更新工作流状态
self._update_workflow_state(step_result)
# 记录step完成
self.logger.log_step_complete(
step_num=step_num,
doctor_question=step_result["doctor_question"],
conversation_history=step_result["conversation_history"],
task_completion_summary=step_result["task_completion_summary"]
)
return True
except Exception as e:
error_msg = f"Step {step_num} 执行异常: {str(e)}"
print(error_msg)
self.logger.log_error(step_num, "step_error", error_msg)
return False
def _update_workflow_state(self, step_result: Dict[str, Any]):
"""
根据step执行结果更新工作流状态
Args:
step_result: step执行结果
"""
self.conversation_history = step_result["conversation_history"]
self.current_hpi = step_result["updated_hpi"]
self.current_ph = step_result["updated_ph"]
self.current_chief_complaint = step_result["updated_chief_complaint"]
self._last_doctor_question = step_result["doctor_question"]
def _print_step_progress(self, step_num: int):
"""
打印step进度信息
Args:
step_num: step编号
"""
current_phase = self.task_manager.get_current_phase()
completion_summary = self.task_manager.get_completion_summary()
print(f"\n=== Step {step_num} 完成 ===")
print(f"当前阶段: {current_phase.value}")
# 显示各阶段完成情况
for phase_name, phase_info in completion_summary["phases"].items():
status = "" if phase_info["is_completed"] else ""
print(f"{status} {phase_name}: {phase_info['completed']}/{phase_info['total']} 任务完成 "
f"({phase_info['completion_rate']:.1%})")
print(f"对话轮次: {step_num}")
print(f"最新医生问题: {getattr(self, '_last_doctor_question', '暂无')[:50]}...")
print("-" * 60)
def get_current_status(self) -> Dict[str, Any]:
"""
获取当前工作流状态
Returns:
Dict: 工作流状态信息
"""
return {
"current_step": self.current_step,
"max_steps": self.max_steps,
"current_phase": self.task_manager.get_current_phase().value,
"workflow_completed": self.workflow_completed,
"workflow_success": self.workflow_success,
"completion_summary": self.task_manager.get_completion_summary(),
"conversation_length": len(self.conversation_history),
"log_file_path": self.logger.get_log_file_path()
}
def get_conversation_history(self) -> str:
"""
获取完整的对话历史
Returns:
str: 对话历史
"""
return self.conversation_history
def get_medical_summary(self) -> Dict[str, str]:
"""
获取当前医疗信息摘要
Returns:
Dict: 医疗信息摘要
"""
return {
"chief_complaint": self.current_chief_complaint,
"history_of_present_illness": self.current_hpi,
"past_history": self.current_ph
}