chore: finalize backend feature scope

app/schemas/learning_assistant.py

@@ -1,10 +1,30 @@
 from pydantic import BaseModel, Field
 
 
-class LearningAssistantChatRequest(BaseModel):
-    """学习助手请求：普通用户面向机构知识库提出医学学习问题。"""
+class LearningAssistantSessionCreateRequest(BaseModel):
+    """学习助手会话创建请求：进入 AI 学习助手页面时初始化短期问答会话。"""
 
-    question: str = Field(..., min_length=2, max_length=1000, description="用户问题")
+    title: str | None = Field(default=None, max_length=100, description="会话标题，前端可不传")
+
+
+class LearningAssistantSessionResponse(BaseModel):
+    """学习助手会话响应：返回前端后续流式问答需要使用的会话 ID。"""
+
+    assistant_session_id: str
+    user_id: str
+    institution_id: int | None = None
+    institution_name: str | None = None
+    title: str
+    status: str
+    created_at: str
+    updated_at: str
+    expires_in_seconds: int
+
+
+class LearningAssistantChatRequest(BaseModel):
+    """学习助手问答请求：普通用户面向机构知识库提出医学学习问题。"""
+
+    question: str = Field(..., min_length=1, max_length=1000, description="用户问题")
     top_k: int | None = Field(default=None, ge=1, le=10, description="最终返回给 LLM 的来源片段数")
     score_threshold: float | None = Field(default=None, ge=0, le=1, description="向量相似度过滤阈值")
 
@@ -20,17 +40,3 @@ class LearningAssistantSource(BaseModel):
     chunk_uid: str
     score: float
     quote: str
-
-
-class LearningAssistantChatResponse(BaseModel):
-    """学习助手回答：返回答案、知识库命中状态、循证来源和耗时。"""
-
-    answer: str
-    retrieval_hit: bool
-    sources: list[LearningAssistantSource] = Field(default_factory=list)
-    retrieval_error: str | None = None
-    model: str | None = None
-    embedding_latency_ms: int | None = None
-    search_latency_ms: int | None = None
-    llm_latency_ms: int | None = None
-    total_latency_ms: int | None = None