刘金宝
77 lines
3.6 KiB
Python
77 lines
3.6 KiB
Python
from collections.abc import AsyncIterator
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from app.agents.llm_adapter import DeepSeekClient, LLMResponse, LLMStreamChunk
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from app.core.config import settings
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from app.models.source_case import CaseBase
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class PatientAgent:
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"""AI 病人：根据病例资料、隐藏信息和短期 memory 回复医生问诊。"""
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def __init__(self, llm: DeepSeekClient | None = None) -> None:
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self.llm = llm or DeepSeekClient()
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async def reply(self, case: CaseBase, memory_messages: list[dict], user_message: str, mode: str) -> LLMResponse:
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"""问诊回复：拼接病例上下文、短期记忆和用户输入后调用 Patient Agent。"""
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messages = self._build_messages(case, memory_messages, user_message, mode)
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return await self.llm.chat(
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messages,
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settings.llm_fast_model,
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thinking_enabled=settings.llm_fast_thinking_enabled,
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max_tokens=settings.llm_fast_max_tokens,
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)
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async def stream_reply(
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self,
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case: CaseBase,
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memory_messages: list[dict],
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user_message: str,
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mode: str,
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) -> AsyncIterator[LLMStreamChunk]:
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"""流式问诊：以 SSE 方式返回 AI 病人增量回复。"""
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messages = self._build_messages(case, memory_messages, user_message, mode)
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async for chunk in self.llm.stream_chat(
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messages,
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settings.llm_fast_model,
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thinking_enabled=settings.llm_fast_thinking_enabled,
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max_tokens=settings.llm_fast_max_tokens,
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):
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yield chunk
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def _build_messages(self, case: CaseBase, memory_messages: list[dict], user_message: str, mode: str) -> list[dict]:
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"""提示词拼接：构造 AI 病人的系统提示词和对话历史。"""
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profile = case.ai_patient_profile or {}
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hidden_info = case.hidden_patient_info or {}
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mode_rule = {
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"novice": "新手模式：回答清楚，必要时可提示医生继续追问症状、既往史或检查。",
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"practice": "练习模式：只回答被问到的信息，不主动给诊断建议。",
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"teaching": "教学模式：保持患者身份，允许在回答后补充简短学习提示。",
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}.get(mode, "只回答被问到的信息。")
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system = f"""
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你是一名标准化 AI 病人或患儿家属，只能基于病例资料回答。
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病例主诉：{case.chief_complaint}
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患者人设：{profile}
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隐藏信息：{hidden_info}
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回答规则：
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1. 不主动透露未被问到的隐藏信息。
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2. 不替医生做诊断，不提供治疗方案。
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3. 不编造病例外检查检验结果。
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4. 每次回答控制在1到3句话，使用患儿家属口吻，不输出分析过程。
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5. 只输出给医生看的家属回答纯文本，不输出 JSON、Markdown、标题、解释或思考过程。
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6. 如果医生一次问多个问题，按问题顺序简短回答，不扩展病例外信息。
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7. {mode_rule}
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"""
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messages = [{"role": "system", "content": system.strip()}]
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messages.extend(self._to_llm_history(memory_messages[-12:]))
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messages.append({"role": "user", "content": user_message})
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return messages
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def _to_llm_history(self, memory_messages: list[dict]) -> list[dict]:
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"""历史转换：把业务角色 doctor/patient 转换为 LLM role。"""
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role_map = {"doctor": "user", "patient": "assistant", "system": "system", "tool": "assistant"}
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return [
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{"role": role_map.get(item.get("role"), "user"), "content": item.get("content", "")}
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for item in memory_messages
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if item.get("content")
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]
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