导入方式修改

backend/app/rag/retriever.py

@@ -19,10 +19,10 @@ from langchain_core.embeddings import Embeddings
 from langchain_core.retrievers import BaseRetriever
 from pydantic import Field, PrivateAttr
 
-from rag_core import QdrantHybridStore, get_sparse_embedder, create_docstore
-from rag_core.client import create_async_qdrant_client
-from app.model_services import get_embedding_service
-from app.logger import info, warning, debug
+from backend.rag_core import QdrantHybridStore, get_sparse_embedder, create_docstore
+from backend.rag_core.client import create_async_qdrant_client
+from ..model_services import get_embedding_service
+from ..logger import info, warning, debug
 
 
 # 模块级常量
@@ -131,20 +131,20 @@ class HybridRetriever(BaseRetriever):
 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,
@@ -188,7 +188,7 @@ class ParentHybridRetriever(BaseRetriever):
         self, query: str, *, run_manager: Any = None
     ) -> List[Document]:
         """
-        异步检索相关父文档
+        异步检索相关子文档
         """
         # 1. 生成查询向量
         dense_query = await self._vector_store.aembed_query(query)
@@ -197,10 +197,10 @@ class ParentHybridRetriever(BaseRetriever):
             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,
@@ -220,87 +220,27 @@ 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
-        
+
+        # 4. 构建子文档列表
+        child_docs = []
         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(
-                collection_name=self.collection_name,
-                ids=list(parent_ids),
-                with_payload=True
+            doc = Document(
+                page_content=payload_copy.pop("page_content", payload_copy.pop("text", "")),
+                metadata={
+                    **payload_copy,
+                    "child_id": point.id,
+                    "score": point.score
+                }
             )
-            
-            for point in parent_points:
-                payload_copy = point.payload.copy()
-                doc = Document(
-                    page_content=payload_copy.pop("page_content", payload_copy.pop("text", "")),
-                    metadata=payload_copy
-                )
-                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: {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
-            try:
-                docstore_docs = await self._docstore.amget(missing_parent_ids)
-                for doc_id, doc in zip(missing_parent_ids, docstore_docs):
-                    if doc is not None:
-                        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: {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: {missing_parent_ids}")
-            for parent_id in missing_parent_ids:
-                child_point = child_point_map.get(parent_id)
-                if child_point:
-                    payload_copy = child_point.payload.copy()
-                    doc = Document(
-                        page_content=payload_copy.pop("page_content", payload_copy.pop("text", "")),
-                        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 {len(final_docs)} 个父文档")
-        
-        return final_docs
+            child_docs.append(doc)
+
+        debug(f"父子文档混合检索返回 {len(child_docs)} 个子文档")
+        return child_docs
 
 
 def create_hybrid_retriever(