100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
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from typing import Optional
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from agent_system.base import BaseAgent
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from agent_system.monitor.prompt import MonitorPrompt
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from agent_system.monitor.response_model import MonitorResult
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class Monitor(BaseAgent):
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"""
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Monitor智能体
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监控和评估现病史、既往史和主诉的质量，提供完成度评分
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"""
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def __init__(self, model_type: str = "gpt-oss:latest", llm_config: dict = {}):
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super().__init__(
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model_type=model_type,
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description=MonitorPrompt.description,
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instructions=MonitorPrompt.instructions,
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response_model=MonitorResult,
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llm_config=llm_config,
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structured_outputs=True,
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markdown=False,
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use_cache=False
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)
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def run(self, hpi_content: str, ph_content: str, chief_complaint: str) -> MonitorResult:
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"""
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监控病史质量
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Args:
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hpi_content: 现病史内容
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ph_content: 既往史内容
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chief_complaint: 主诉
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Returns:
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MonitorResult: 包含完成度评分和评分理由
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"""
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# 构建评估提示
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prompt = self.build_prompt(hpi_content, ph_content, chief_complaint)
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# 调用LLM进行评估
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result = super().run(prompt)
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# 确保返回正确的类型
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if isinstance(result, MonitorResult):
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return result
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elif isinstance(result, dict):
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return MonitorResult(**result)
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else:
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# 解析失败，返回默认结果
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return MonitorResult(
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completion_score=0.0,
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reason="监控评估失败：无法解析LLM响应"
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)
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def build_prompt(self, hpi_content: str, ph_content: str, chief_complaint: str) -> str:
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"""
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构建监控评估的提示语
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Args:
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hpi_content: 现病史内容
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ph_content: 既往史内容
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chief_complaint: 主诉
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Returns:
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str: 构建好的提示语
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"""
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prompt = f"""请对以下病史信息进行质量监控和评估：
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**主诉**：
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{chief_complaint}
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**现病史**：
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{hpi_content}
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**既往史**：
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{ph_content}
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**评估要求**：
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1. 综合评估现病史、既往史和主诉的信息完整性
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2. 考虑信息之间的逻辑一致性和相互关联性
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3. 基于医学标准评估信息的临床价值
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4. **必须先给出详细的评分理由，再基于理由给出0.0-1.0范围内的完成度评分**
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5. 评分必须与理由保持逻辑一致
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**输出格式**：
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严格按照以下JSON格式输出：
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{{
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"completion_score": 浮点数（0.0-1.0），
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"reason": "详细的评分理由"
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}}
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**评分指导**：
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- 0.9-1.0: 信息非常完整，逻辑清晰，临床价值高
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- 0.8-0.9: 信息较完整，有少量缺失，整体质量良好
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- 0.7-0.8: 信息基本完整，存在一些不足
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- 0.6-0.7: 信息不够完整，有明显缺失
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- 0.5-0.6: 信息缺失较多，质量有待提高
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- 0.0-0.5: 信息严重不足，需要大幅改善
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请基于上述标准进行客观评估。"""
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return prompt
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