iomgaa
e24b161e54
- 构建完整的Recipient智能体系统，用于医疗对话信息整合 - 功能特性： * 根据完整对话记录更新现病史信息 * 根据完整对话记录更新既往史信息 * 从完整对话记录中提取患者主诉 * 输出顺序与生成顺序保持一致（现病史→既往史→主诉） - 包含完整的JSON格式示例输出和详细处理指令 - 遵循项目规范：中文注释、基于BaseAgent架构 - 支持同步和异步运行模式 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
from agent_system.base import BaseAgent
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from agent_system.recipient.prompt import RecipientPrompt
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from agent_system.recipient.response_model import RecipientResponseModel
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class RecipientAgent(BaseAgent):
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"""Recipient智能体：根据完整对话记录和上一轮医疗信息，更新现病史、既往史并提取主诉"""
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def __init__(self, model_type: str, llm_config: dict = {}):
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super().__init__(
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model_type=model_type,
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description=RecipientPrompt.description,
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instructions=RecipientPrompt.instructions,
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response_model=RecipientResponseModel,
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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(
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self,
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conversation_history: str,
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previous_HPI: str = None,
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previous_PH: str = None,
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previous_chief_complaint: str = None,
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**kwargs
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) -> RecipientResponseModel:
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"""运行Recipient智能体
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Args:
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conversation_history: 完整的对话记录
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previous_HPI: 上一轮的现病史
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previous_PH: 上一轮的既往史
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previous_chief_complaint: 上一轮的主诉（可选，用于参考）
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Returns:
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RecipientResponseModel: 包含更新后的主诉、现病史和既往史
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"""
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prompt = self.build_prompt(
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conversation_history,
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previous_HPI,
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previous_PH,
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previous_chief_complaint
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)
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return super().run(prompt, **kwargs)
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async def async_run(
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self,
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conversation_history: str,
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previous_HPI: str = None,
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previous_PH: str = None,
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previous_chief_complaint: str = None,
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**kwargs
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) -> RecipientResponseModel:
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"""异步运行Recipient智能体"""
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prompt = self.build_prompt(
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conversation_history,
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previous_HPI,
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previous_PH,
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previous_chief_complaint
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)
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return await super().async_run(prompt, **kwargs)
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def build_prompt(
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self,
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conversation_history: str,
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previous_HPI: str,
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previous_PH: str,
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previous_chief_complaint: str = None
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) -> str:
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"""构建处理提示"""
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prompt = f"完整对话记录：\n{conversation_history}\n\n"
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prompt += f"上一轮的现病史：\n{previous_HPI or '暂无现病史信息'}\n\n"
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prompt += f"上一轮的既往史：\n{previous_PH or '暂无既往史信息'}\n\n"
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if previous_chief_complaint:
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prompt += f"上一轮的主诉（参考）：\n{previous_chief_complaint}\n\n"
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prompt += f"请根据完整对话记录和上一轮的医疗信息，完成以下任务（按此顺序生成）：\n"
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prompt += f"1. 根据完整对话记录和上一轮现病史，更新并完善现病史（updated_HPI）\n"
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prompt += f"2. 根据完整对话记录和上一轮既往史，更新并完善既往史（updated_PH）\n"
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prompt += f"3. 从完整对话记录中提取患者的主诉（chief_complaint）"
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return prompt |