chore: finalize backend feature scope

2026-06-11 16:19:07 +08:00
parent d855ecab82
commit ec515d5453
43 changed files with 680 additions and 712 deletions
@@ -9,11 +9,11 @@ from app.models.training import SessionOrder, TrainingSession


 class HintAgent:
-    """新手提示 Agent：基于病例、对话和检查结果调用快速模型生成结构化提示。"""
+    """练习提示 Agent：基于病例、对话和检查结果调用快速模型生成结构化提示。"""

    def __init__(self, llm: DeepSeekClient | None = None) -> None:
        self.llm = llm or DeepSeekClient()
-        self.template_path = Path(__file__).resolve().parents[1] / "prompts" / "hint" / "novice_case_hint.md"
+        self.template_path = Path(__file__).resolve().parents[1] / "prompts" / "hint" / "practice_case_hint.md"

    async def generate(
        self,
@@ -85,7 +85,7 @@ class HintAgent:
        }

    def _load_template(self) -> str:
-        """提示词读取：加载新手模式病例提示模板。"""
+        """提示词读取：加载练习模式病例提示模板。"""
        if self.template_path.exists():
            return self.template_path.read_text(encoding="utf-8")
        return "你是医疗问诊训练提示 Agent，只输出合法 JSON。"
@@ -1,37 +1,34 @@
 from collections.abc import AsyncIterator

-from app.agents.llm_adapter import LLMResponse, LLMStreamChunk, OpenAICompatibleLLMClient
+from app.agents.llm_adapter import LLMStreamChunk, OpenAICompatibleLLMClient
 from app.core.config import settings
 from app.schemas.learning_assistant import LearningAssistantSource


 class LearningAssistantAgent:
-    """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。"""
+    """AI 学习助手 Agent：根据 RAG 来源和短期上下文生成带循证出处的医学学习回答。"""

    def __init__(self, llm_client: OpenAICompatibleLLMClient | None = None) -> None:
        self.llm_client = llm_client or OpenAICompatibleLLMClient()

-    async def answer(self, question: str, sources: list[LearningAssistantSource]) -> LLMResponse:
-        """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。"""
-        return await self.llm_client.chat(
-            self._messages(question, sources),
-            model=settings.llm_fast_model,
-            thinking_enabled=settings.llm_fast_thinking_enabled,
-            max_tokens=1200,
-        )
-
-    async def stream_answer(self, question: str, sources: list[LearningAssistantSource]) -> AsyncIterator[LLMStreamChunk]:
+    async def stream_answer(
+        self,
+        question: str,
+        sources: list[LearningAssistantSource],
+        history: list[dict] | None = None,
+    ) -> AsyncIterator[LLMStreamChunk]:
        """流式回答：输出 AI 学习助手增量文本，前端可直接渲染。"""
        async for chunk in self.llm_client.stream_chat(
-            self._messages(question, sources),
+            self._messages(question, sources, history or []),
            model=settings.llm_fast_model,
            thinking_enabled=settings.llm_fast_thinking_enabled,
            max_tokens=1200,
        ):
            yield chunk

-    def _messages(self, question: str, sources: list[LearningAssistantSource]) -> list[dict]:
-        """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。"""
+    def _messages(self, question: str, sources: list[LearningAssistantSource], history: list[dict]) -> list[dict]:
+        """提示词拼接：命中知识库时强制引用来源，未命中时必须声明未找到机构参考。"""
+        history_text = self._history_text(history)
        if sources:
            context = "\n\n".join(
                (
@@ -42,17 +39,41 @@ class LearningAssistantAgent:
                for index, source in enumerate(sources, start=1)
            )
            system = (
-                "你是医学学习助手，只用于医学教育学习，不替代临床诊疗。"
-                "请优先依据给定知识库片段回答，回答要清晰、准确、分点。"
+                "你是医学学习助手，用于医学教育、课程学习和临床思维训练，不替代临床诊疗。"
+                "优先依据给定知识库片段回答，回答要清晰、准确、分点。"
                "每个关键结论后标注对应来源编号，例如【来源1】。"
-                "不得编造不存在的PDF、页码或指南来源。"
+                "不得编造不存在的 PDF、页码或指南来源。"
+            )
+            user = (
+                f"{history_text}"
+                f"用户当前问题：{question}\n\n"
+                f"可用知识库片段：\n{context}\n\n"
+                "请给出带来源的学习回答。"
            )
-            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。"
        else:
            system = (
-                "你是医学学习助手，只用于医学教育学习，不替代临床诊疗。"
-                "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。"
-                "不得伪造PDF来源、页码或指南名称。"
+                "你是医学学习助手，用于医学教育、课程学习和临床思维训练，不替代临床诊疗。"
+                "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。"
+                "不得伪造 PDF 来源、页码或指南名称。"
+            )
+            user = (
+                f"{history_text}"
+                f"用户当前问题：{question}\n\n"
+                "请给出通用学习回答，并提醒用户以课程教材、指南和临床医生判断为准。"
            )
-            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。"
        return [{"role": "system", "content": system}, {"role": "user", "content": user}]
+
+    def _history_text(self, history: list[dict]) -> str:
+        """上下文摘要：把当前学习助手会话最近几轮问答压缩为提示词上下文。"""
+        if not history:
+            return ""
+        lines: list[str] = []
+        for item in history[-settings.learning_assistant_history_limit :]:
+            role = "用户" if item.get("role") == "user" else "助手"
+            content = str(item.get("content") or "").strip()
+            if content:
+                lines.append(f"{role}：{content[:500]}")
+        if not lines:
+            return ""
+        return "当前会话最近上下文：\n" + "\n".join(lines) + "\n\n"
@@ -1,6 +1,6 @@
 from collections.abc import AsyncIterator

-from app.agents.llm_adapter import LLMResponse, LLMStreamChunk
+from app.agents.llm_adapter import LLMStreamChunk
 from app.agents.hint_agent import HintAgent
 from app.agents.patient_agent import PatientAgent
 from app.agents.report_agent import ReportAgent
@@ -18,10 +18,6 @@ class MedicalConsultationOrchestrator:
        self.scoring_agent = ScoringAgent()
        self.report_agent = ReportAgent()

-    async def patient_reply(self, session: TrainingSession, case: CaseBase, memory_messages: list[dict], message: str) -> LLMResponse:
-        """问诊编排：调用 Patient Agent 生成 AI 病人回复。"""
-        return await self.patient_agent.reply(case, memory_messages, message, session.mode, self._patient_config(session))
-
    async def patient_stream_reply(
        self,
        session: TrainingSession,
@@ -84,7 +80,7 @@ class MedicalConsultationOrchestrator:
        orders: list[SessionOrder],
        last_user_message: str | None = None,
    ) -> dict:
-        """新手提示编排：基于当前会话上下文生成轻量训练提醒。"""
+        """练习提示编排：基于当前会话上下文生成轻量训练提醒。"""
        return await self.hint_agent.generate(session, case, memory_messages, orders, last_user_message)

    def _patient_config(self, session: TrainingSession) -> dict | None:
@@ -1,6 +1,6 @@
 from collections.abc import AsyncIterator

-from app.agents.llm_adapter import DeepSeekClient, LLMResponse, LLMStreamChunk
+from app.agents.llm_adapter import DeepSeekClient, LLMStreamChunk
 from app.core.config import settings
 from app.models.source_case import CaseBase

@@ -11,23 +11,6 @@ class PatientAgent:
    def __init__(self, llm: DeepSeekClient | None = None) -> None:
        self.llm = llm or DeepSeekClient()

-    async def reply(
-        self,
-        case: CaseBase,
-        memory_messages: list[dict],
-        user_message: str,
-        mode: str,
-        patient_config: dict | None = None,
-    ) -> LLMResponse:
-        """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。"""
-        messages = self._build_messages(case, memory_messages, user_message, mode, patient_config)
-        return await self.llm.chat(
-            messages,
-            settings.llm_fast_model,
-            thinking_enabled=settings.llm_fast_thinking_enabled,
-            max_tokens=settings.llm_fast_max_tokens,
-        )
-
    async def stream_reply(
        self,
        case: CaseBase,
@@ -58,11 +41,7 @@ class PatientAgent:
        profile = case.ai_patient_profile or {}
        hidden_info = case.hidden_patient_info or {}
        config_rule = self._build_patient_config_rule(patient_config)
-        mode_rule = {
-            "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。",
-            "practice": "练习模式：只回答被问到的信息，不主动给诊断建议。",
-            "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。",
-        }.get(mode, "只回答被问到的信息。")
+        mode_rule = "练习模式：只回答被问到的信息，不主动给诊断建议。"
        system = f"""
 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。
 病例主诉：{case.chief_complaint}