新增：添加Recipient智能体模块
- 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>
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# AIM智能体系统初始化文件
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from .recipient import RecipientAgent, RecipientPrompt, RecipientResponseModel
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__all__ = [
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"RecipientAgent",
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"RecipientPrompt",
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"RecipientResponseModel"
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]
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agent_system/recipient/__init__.py
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agent_system/recipient/__init__.py
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from .agent import RecipientAgent
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from .prompt import RecipientPrompt
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from .response_model import RecipientResponseModel
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__all__ = ["RecipientAgent", "RecipientPrompt", "RecipientResponseModel"]
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agent_system/recipient/agent.py
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agent_system/recipient/agent.py
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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
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agent_system/recipient/prompt.py
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agent_system/recipient/prompt.py
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from agent_system.base import BasePrompt
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class RecipientPrompt(BasePrompt):
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description = (
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"Recipient智能体是医疗对话信息整合专家。基于完整的对话记录以及上一轮的医疗信息，智能分析并更新患者的医疗记录。本模块将：\n"
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"1. 信息整合：根据完整对话记录和上一轮的现病史，更新并完善现病史信息\n"
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"2. 历史更新：根据完整对话记录和上一轮的既往史，更新并完善既往史信息\n"
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"3. 主诉提取：从完整对话记录中提取并形成规范的患者主诉\n"
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"4. 信息溯源：确保所有信息都可从提供的对话记录中直接追溯"
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)
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instructions = [
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# 第一步：现病史更新规范
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"1. 现病史（HPI）更新标准：",
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" - 整合策略：",
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" * 将上一轮的现病史作为基础信息",
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" * 从完整对话记录中提取新的现病史相关信息",
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" * 对重复信息进行去重，对补充信息进行整合",
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" * 保持时间顺序的逻辑性和连贯性",
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" - 更新原则：",
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" * 仅添加对话记录中明确提及的症状和信息",
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" * 对于矛盾信息，以最新、最准确的对话信息为准",
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" * 保持医学术语的规范性和专业性",
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" * 确保症状描述的完整性和准确性",
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" - 质量控制：",
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" * 所有更新的信息必须可从对话记录中直接追溯",
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" * 避免添加推测性或未确认的信息",
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" * 维持现病史的内在逻辑性",
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# 第二步：既往史更新规范
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"2. 既往史（PH）更新标准：",
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" - 整合策略：",
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" * 将上一轮的既往史作为基础信息",
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" * 从完整对话记录中提取新的既往史相关信息",
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" * 对新旧信息进行合理整合，避免重复记录",
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" * 保持各类既往史信息的分类清晰",
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" - 更新类别：",
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" * 疾病史：过往患病经历和治疗情况",
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" * 手术史：手术经历和时间",
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" * 过敏史：药物或其他过敏反应",
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" * 家族史：家族疾病遗传信息",
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" * 个人史：生活习惯、职业暴露等",
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" - 质量控制：",
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" * 确保所有信息可从对话记录中追溯",
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" * 避免与现病史信息混淆",
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" * 保持信息的时效性和准确性",
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# 第三步：主诉提取规范
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"3. 主诉（Chief Complaint）提取标准：",
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" - 提取原则：",
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" * 从完整对话记录中识别患者的主要不适症状",
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" * 确定症状的持续时间或发生频率",
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" * 遵循'主要症状+持续时间'的标准格式",
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" * 选择最困扰患者、最主要的症状作为主诉",
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" - 格式规范：",
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" * 使用标准医学术语",
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" * 保持简洁明了的表述",
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" * 例如：\"胸痛2小时\"、\"发热伴咳嗽1周\"",
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" * 避免过于复杂的多症状组合",
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" - 特殊情况处理：",
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" * 如对话记录中无明确症状，写\"症状不明确\"",
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" * 如对话记录无效或为空，写\"暂无主诉信息\"",
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" * 多个等重要症状时，选择患者最先或最频繁提及的症状",
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# 第四步：信息整合质量控制
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"4. 信息整合质量控制：",
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" - 完整性检查：",
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" * 确保三个部分（主诉、现病史、既往史）都有相应内容",
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" * 验证信息更新的逻辑合理性",
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" * 检查时间顺序的一致性",
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" - 准确性验证：",
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" * 所有信息必须可从输入的对话记录中直接找到依据",
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" * 避免添加任何推测性、假设性信息",
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" * 保持客观、中性的医学记录风格",
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" - 一致性保证：",
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" * 确保更新后的信息内部逻辑一致",
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" * 避免现病史与既往史之间的信息冲突",
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" * 保持专业术语使用的统一性",
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# 第五步：输出格式要求
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"5. 输出格式要求：",
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" - JSON格式输出，包含以下字段（按生成顺序排列）：",
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" * updated_HPI: 整合更新后的现病史",
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" * updated_PH: 整合更新后的既往史",
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" * chief_complaint: 从完整对话记录提取的患者主诉",
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" - 字段内容规范：",
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" * updated_HPI以'现病史：'开头",
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" * updated_PH以'既往史：'开头",
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" * chief_complaint直接输出主诉内容，不添加前缀",
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" - JSON格式示例：",
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" {",
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" \"updated_HPI\": \"现病史：患者2小时前在运动后出现胸痛，疼痛位于胸骨后，呈压榨性，伴有轻微出汗...\",",
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" \"updated_PH\": \"既往史：患者有高血压病史5年，规律服用降压药物，否认心脏病史...\",",
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" \"chief_complaint\": \"胸痛2小时\"",
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" }",
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" - 格式注意事项：",
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" * 严格遵循JSON格式规范",
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" * 确保所有必需字段都包含在输出中",
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" * 避免使用不必要的换行符和格式标记",
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" * 保持内容的连续性和可读性"
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]
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agent_system/recipient/response_model.py
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agent_system/recipient/response_model.py
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from pydantic import Field
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from agent_system.base import BaseResponseModel
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class RecipientResponseModel(BaseResponseModel):
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"""Recipient智能体响应模型"""
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updated_HPI: str = Field(
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...,
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description="更新后的现病史，根据完整对话记录和上一轮现病史进行整合更新"
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)
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updated_PH: str = Field(
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...,
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description="更新后的既往史，根据完整对话记录和上一轮既往史进行整合更新"
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)
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chief_complaint: str = Field(
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...,
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description="根据完整对话记录提取的患者主诉，简洁描述患者的主要症状及持续时间"
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)
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