59 lines
3.1 KiB
Python
59 lines
3.1 KiB
Python
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from collections.abc import AsyncIterator
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from app.agents.llm_adapter import LLMResponse, LLMStreamChunk, OpenAICompatibleLLMClient
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from app.core.config import settings
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from app.schemas.learning_assistant import LearningAssistantSource
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class LearningAssistantAgent:
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"""AI学习助手 Agent：根据 RAG 来源生成带循证出处的医学学习回答。"""
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def __init__(self, llm_client: OpenAICompatibleLLMClient | None = None) -> None:
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self.llm_client = llm_client or OpenAICompatibleLLMClient()
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async def answer(self, question: str, sources: list[LearningAssistantSource]) -> LLMResponse:
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"""非流式回答：把问题和检索来源拼接后调用快速模型生成标准回答。"""
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return await self.llm_client.chat(
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self._messages(question, sources),
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model=settings.llm_fast_model,
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thinking_enabled=settings.llm_fast_thinking_enabled,
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max_tokens=1200,
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)
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async def stream_answer(self, question: str, sources: list[LearningAssistantSource]) -> AsyncIterator[LLMStreamChunk]:
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"""流式回答：输出 AI 学习助手增量文本，前端可直接渲染。"""
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async for chunk in self.llm_client.stream_chat(
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self._messages(question, sources),
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model=settings.llm_fast_model,
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thinking_enabled=settings.llm_fast_thinking_enabled,
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max_tokens=1200,
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):
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yield chunk
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def _messages(self, question: str, sources: list[LearningAssistantSource]) -> list[dict]:
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"""提示词拼接：命中知识库时必须引用来源，未命中时必须声明未找到参考。"""
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if sources:
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context = "\n\n".join(
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(
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f"[来源{index}] 文档：{source.document_title or source.file_name}；"
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f"页码：{source.page_start}-{source.page_end}；chunk_uid：{source.chunk_uid}\n"
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f"{source.quote}"
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)
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for index, source in enumerate(sources, start=1)
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)
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system = (
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"你是医学学习助手，只用于医学教育学习，不替代临床诊疗。"
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"请优先依据给定知识库片段回答，回答要清晰、准确、分点。"
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"每个关键结论后标注对应来源编号，例如【来源1】。"
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"不得编造不存在的PDF、页码或指南来源。"
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)
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user = f"用户问题：{question}\n\n可用知识库片段：\n{context}\n\n请给出带来源的学习回答。"
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else:
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system = (
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"你是医学学习助手，只用于医学教育学习，不替代临床诊疗。"
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"当前没有检索到机构知识库参考，回答开头必须写：未检索到本机构知识库参考，以下为大模型通用学习回答。"
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"不得伪造PDF来源、页码或指南名称。"
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)
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user = f"用户问题：{question}\n\n请给出通用学习回答，并提醒用户以课程教材和临床规范为准。"
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return [{"role": "system", "content": system}, {"role": "user", "content": user}]
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