prepare fastapi root layout for server deployment

2026-06-04 10:55:23 +08:00
parent eb43573a44
commit b46e43aadc
103 changed files with 347 additions and 197 deletions
@@ -0,0 +1,69 @@
+from collections.abc import AsyncIterator
+
+from app.agents.llm_adapter import LLMResponse, LLMStreamChunk
+from app.agents.hint_agent import HintAgent
+from app.agents.patient_agent import PatientAgent
+from app.agents.report_agent import ReportAgent
+from app.agents.scoring_agent import ScoringAgent
+from app.models.source_case import CaseBase
+from app.models.training import SessionOrder, SessionSubmission, TrainingSession
+
+
+class MedicalConsultationOrchestrator:
+    """主编排器：统一调度 Patient、Scoring、Report 等子 Agent。"""
+
+    def __init__(self) -> None:
+        self.patient_agent = PatientAgent()
+        self.hint_agent = HintAgent()
+        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)
+
+    async def patient_stream_reply(
+        self,
+        session: TrainingSession,
+        case: CaseBase,
+        memory_messages: list[dict],
+        message: str,
+    ) -> AsyncIterator[LLMStreamChunk]:
+        """流式问诊编排：调用 Patient Agent 并返回流式片段。"""
+        async for chunk in self.patient_agent.stream_reply(case, memory_messages, message, session.mode):
+            yield chunk
+
+    async def evaluate(
+        self,
+        session: TrainingSession,
+        case: CaseBase,
+        memory_messages: list[dict],
+        orders: list[SessionOrder],
+        submission: SessionSubmission,
+        rubric: object | None,
+        guideline_refs: list[dict],
+        scoring_rules: list | None = None,
+    ) -> dict:
+        """评价编排：调用 Scoring Agent 后交给 Report Agent 整理报告。"""
+        scoring_result = await self.scoring_agent.score(
+            session=session,
+            case=case,
+            memory_messages=memory_messages,
+            orders=orders,
+            submission=submission,
+            rubric=rubric,
+            guideline_refs=guideline_refs,
+            scoring_rules=scoring_rules or [],
+        )
+        return self.report_agent.build_report(scoring_result)
+
+    async def generate_hints(
+        self,
+        session: TrainingSession,
+        case: CaseBase,
+        memory_messages: list[dict],
+        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。"""
+        return await self.hint_agent.generate(session, case, memory_messages, orders, last_user_message)