Initial commit: QMDSearch 分层信息检索服务

- FastAPI + Qdrant + Redis + Ollama 技术栈
- L1→L2→L3→chunk 四层分层检索（dense + sparse RRF 融合）
- 文档三级总结与 2.5 级回退
- query 解析路由与分类
- /admin 管理页面

app/models/search.py

@@ -0,0 +1,30 @@
+"""检索请求与响应的数据模型"""
+
+from pydantic import BaseModel, Field
+
+
+class SearchRequest(BaseModel):
+    """检索请求"""
+
+    query: str = Field(description="查询文本")
+    top_k: int | None = Field(default=None, description="返回结果数，为空时使用 settings.retrieval_final_k")
+
+
+class SearchHit(BaseModel):
+    """单条检索命中结果"""
+
+    text: str = Field(description="原文 chunk 内容")
+    doc_id: str = Field(description="所属文档 ID")
+    title: str = Field(default="", description="文档标题")
+    section_path: str = Field(default="", description="chunk 所在的章节路径")
+    score: float = Field(description="相关性得分")
+    doc_summary: str = Field(default="", description="L1 文档总结，仅用于上下文标注")
+
+
+class SearchResponse(BaseModel):
+    """检索响应"""
+
+    query: str = Field(description="原始查询文本")
+    hits: list[SearchHit] = Field(default_factory=list, description="命中结果列表")
+    routed_categories: list[str] = Field(default_factory=list, description="query 路由命中的类目")
+    fallback: bool = Field(default=False, description="是否走了全库兜底路径")