refactor: 重构RAG核心组件，简化代码结构和测试文件

backend/app/rag/retriever.py

@@ -33,16 +33,16 @@ DEFAULT_PARENT_SEARCH_K = 5
 class HybridRetriever(BaseRetriever):
     """
     混合检索器：稠密向量 + BM25 稀疏向量 RRF 分数融合（异步）
-
+    
     使用 Qdrant Universal Query API (query_points)
     """
     collection_name: str = Field(description="Qdrant 集合名称")
     search_k: int = Field(default=DEFAULT_SEARCH_K, description="检索返回结果数")
-
+    
     _vector_store: Any = PrivateAttr()
     _client: Any = PrivateAttr()
     _sparse_embedder: Any = PrivateAttr()
-
+    
     def __init__(
         self,
         collection_name: str,
@@ -62,21 +62,39 @@ class HybridRetriever(BaseRetriever):
         self._vector_store = vector_store
         self._client = vector_store.get_async_qdrant_client()
         self._sparse_embedder = get_sparse_embedder()
-
+    
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: Any = None
+    ) -> List[Document]:
+        """
+        同步检索（不推荐使用，仅供兼容性）
+        
+        注意：在异步环境中请使用 _aget_relevant_documents 或 ainvoke
+        """
+        import asyncio
+        try:
+            loop = asyncio.get_running_loop()
+            # 已有事件循环，使用 create_task
+            task = loop.create_task(self._aget_relevant_documents(query))
+            return loop.run_until_complete(task)
+        except RuntimeError:
+            # 没有事件循环，创建新的
+            return asyncio.run(self._aget_relevant_documents(query))
+    
     async def _aget_relevant_documents(
-        self, query: str, **kwargs
+        self, query: str, *, run_manager: Any = None
     ) -> List[Document]:
         """
         异步混合检索相关文档
         """
         # 1. 生成查询向量
-        dense_query = await self._vector_store._aembed_query(query)
+        dense_query = await self._vector_store.aembed_query(query)
         sparse_query = self._sparse_embedder.embed_query(query)
         sparse_vec = models.SparseVector(
             indices=sparse_query["indices"],
             values=sparse_query["values"]
         )
-
+        
         # 2. 使用 Qdrant 的 query_points API
         response = await self._client.query_points(
             collection_name=self.collection_name,
@@ -96,7 +114,7 @@ class HybridRetriever(BaseRetriever):
             limit=self.search_k,
             with_payload=True
         )
-
+        
         # 3. 转换结果
         results = []
         for point in response.points:
@@ -105,28 +123,28 @@ class HybridRetriever(BaseRetriever):
                 metadata=point.payload
             )
             results.append(doc)
-
-        debug(f"混合检索返回 %d 个文档", len(results))
+        
+        debug(f"混合检索返回 {len(results)} 个文档")
         return results
 
 
 class ParentHybridRetriever(BaseRetriever):
     """
     父子文档混合检索器（异步）：
-
+    
     1. 先用混合检索找到相关子文档
     2. 根据子文档的 parent_id 找到对应的父文档
     3. 去重并返回父文档
     """
-
+    
     collection_name: str = Field(description="Qdrant 集合名称")
     search_k: int = Field(default=DEFAULT_PARENT_SEARCH_K, description="检索返回结果数")
-
+    
     _vector_store: Any = PrivateAttr()
     _client: Any = PrivateAttr()
     _sparse_embedder: Any = PrivateAttr()
     _docstore: Any = PrivateAttr()
-
+    
     def __init__(
         self,
         collection_name: str,
@@ -149,24 +167,40 @@ class ParentHybridRetriever(BaseRetriever):
         self._client = vector_store.get_async_qdrant_client()
         self._sparse_embedder = get_sparse_embedder()
         self._docstore = docstore
-
+    
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: Any = None
+    ) -> List[Document]:
+        """
+        同步检索（不推荐使用，仅供兼容性）
+        
+        注意：在异步环境中请使用 _aget_relevant_documents 或 ainvoke
+        """
+        import asyncio
+        try:
+            loop = asyncio.get_running_loop()
+            task = loop.create_task(self._aget_relevant_documents(query))
+            return loop.run_until_complete(task)
+        except RuntimeError:
+            return asyncio.run(self._aget_relevant_documents(query))
+    
     async def _aget_relevant_documents(
-        self, query: str, **kwargs
+        self, query: str, *, run_manager: Any = None
     ) -> List[Document]:
         """
         异步检索相关父文档
         """
         # 1. 生成查询向量
-        dense_query = await self._vector_store._aembed_query(query)
+        dense_query = await self._vector_store.aembed_query(query)
         sparse_query = self._sparse_embedder.embed_query(query)
         sparse_vec = models.SparseVector(
             indices=sparse_query["indices"],
             values=sparse_query["values"]
         )
-
+        
         # 2. 多取一些子文档，避免去重后数量不足
         search_limit = self.search_k * 2
-
+        
         # 3. 使用 query_points API 进行混合检索
         response = await self._client.query_points(
             collection_name=self.collection_name,
@@ -186,30 +220,30 @@ class ParentHybridRetriever(BaseRetriever):
             limit=search_limit,
             with_payload=True
         )
-
+        
         if not response.points:
             debug("混合检索未找到任何文档")
             return []
-
+        
         # 4. 收集 parent_id 和对应最高得分
         parent_score_map = {}
         parent_ids = set()
         child_point_map = {}  # 保存子文档点用于降级
-
+        
         for point in response.points:
             payload_copy = point.payload.copy()
             parent_id = payload_copy.get("parent_id", point.id)
             score = point.score
-
+            
             if parent_id not in parent_score_map or score > parent_score_map[parent_id]:
                 parent_score_map[parent_id] = score
                 parent_ids.add(parent_id)
                 child_point_map[parent_id] = point
-
+        
         # 5. 批量查询父文档
         parent_docs = []
         found_parent_ids = set()
-
+        
         # 先尝试从 Qdrant 直接查询（如果父文档也在 Qdrant 中）
         try:
             parent_points = await self._client.retrieve(
@@ -217,7 +251,7 @@ class ParentHybridRetriever(BaseRetriever):
                 ids=list(parent_ids),
                 with_payload=True
             )
-
+            
             for point in parent_points:
                 payload_copy = point.payload.copy()
                 doc = Document(
@@ -226,10 +260,10 @@ class ParentHybridRetriever(BaseRetriever):
                 )
                 parent_docs.append(doc)
                 found_parent_ids.add(point.id)
-
+            
         except Exception as e:
-            warning(f"从 Qdrant 查询父文档失败: %s", e)
-
+            warning(f"从 Qdrant 查询父文档失败: {e}")
+        
         # 6. 如果有 docstore，尝试从 docstore 查询剩余的父文档
         if self._docstore and len(found_parent_ids) < len(parent_ids):
             missing_parent_ids = parent_ids - found_parent_ids
@@ -240,12 +274,12 @@ class ParentHybridRetriever(BaseRetriever):
                         parent_docs.append(doc)
                         found_parent_ids.add(doc_id)
             except Exception as e:
-                warning(f"从 docstore 查询父文档失败: %s", e)
-
+                warning(f"从 docstore 查询父文档失败: {e}")
+        
         # 7. 降级：对于仍未找到的父文档，用子文档本身代替
         missing_parent_ids = parent_ids - found_parent_ids
         if missing_parent_ids:
-            warning(f"以下 parent_id 未找到对应的父文档，将返回子文档本身: %s", missing_parent_ids)
+            warning(f"以下 parent_id 未找到对应的父文档，将返回子文档本身: {missing_parent_ids}")
             for parent_id in missing_parent_ids:
                 child_point = child_point_map.get(parent_id)
                 if child_point:
@@ -255,17 +289,17 @@ class ParentHybridRetriever(BaseRetriever):
                         metadata=payload_copy
                     )
                     parent_docs.append(doc)
-
+        
         # 8. 按照得分降序排序，返回前 k 个
         parent_docs_with_scores = [
             (doc, parent_score_map.get(doc.metadata.get("id", doc.id if hasattr(doc, "id") else ""), 0.0))
             for doc in parent_docs
         ]
         parent_docs_with_scores.sort(key=lambda x: x[1], reverse=True)
-
+        
         final_docs = [doc for doc, _ in parent_docs_with_scores[:self.search_k]]
-        debug(f"父子文档混合检索返回 %d 个父文档", len(final_docs))
-
+        debug(f"父子文档混合检索返回 {len(final_docs)} 个父文档")
+        
         return final_docs
 
 
@@ -291,7 +325,7 @@ def create_hybrid_retriever(
         embeddings = get_embedding_service()
         info("使用统一嵌入服务（本地 llama.cpp → 智谱 API 自动降级）")
 
-    vector_store = QdrantHybridStore(collection_name=collection_name, embeddings=embeddings)
+    vector_store = QdrantHybridStore(collection_name=collection_name)
 
     try:
         vector_store.get_client().get_collection(collection_name)
@@ -336,7 +370,7 @@ def create_parent_hybrid_retriever(
         embeddings = get_embedding_service()
         info("使用统一嵌入服务（本地 llama.cpp → 智谱 API 自动降级）")
 
-    vector_store = QdrantHybridStore(collection_name=collection_name, embeddings=embeddings)
+    vector_store = QdrantHybridStore(collection_name=collection_name)
 
     try:
         vector_store.get_client().get_collection(collection_name)