feat: RAG混合检索系统完整实现 + 启动脚本修复

- 实现了稠密+稀疏混合检索，使用 Qdrant 原生 RRF 融合
- 修复了 retriever.py 的 BaseRetriever 继承和稀疏向量包装问题
- 修复了 pipeline.py 的 Optional 导入问题
- 添加了稀疏 embedder 的缓存配置
- 简化了 vector_store.py，移除不必要的逻辑
- 修复了 start.sh 的 PROJECT_DIR 硬编码和端口配置问题
- 完善了 RAG 检索的测试文件

backend/app/rag/pipeline.py

@@ -9,7 +9,7 @@ RAG 检索流水线模块
 
 import asyncio
 import os
-from typing import List
+from typing import List, Optional
 from langchain_core.documents import Document
 from langchain_core.language_models import BaseLanguageModel
 

backend/app/rag/retriever.py

@@ -6,19 +6,18 @@ Qdrant 混合检索器模块
 - 父子文档混合检索（先检索子文档，再返回父文档）
 
 核心原理：
-- 使用 Qdrant 原生 Fusion API (RRF) 做分数融合
+- 使用 Qdrant Universal Query API (query_points)
-- 同时使用稠密向量（语义）和稀疏向量（BM25 关键词）
+- 使用 Prefetch 并行检索多个源
+- 使用 RRF 分数融合
 """
 
 from typing import Dict, Any, Optional, List
-from qdrant_client import QdrantClient
+from qdrant_client import QdrantClient, models
 from qdrant_client.http.exceptions import UnexpectedResponse
-from qdrant_client.http.models import (
-    SearchRequest, Fusion, FusionProtocol, NamedVector, NamedSparseVector
-)
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.retrievers import BaseRetriever, RetrieverOutput
+from pydantic import Field, PrivateAttr
 
 from rag_core import QdrantVectorStore, get_sparse_embedder, create_docstore
 from rag_core.client import create_qdrant_client as create_core_qdrant_client
@@ -35,8 +34,14 @@ class HybridRetriever(BaseRetriever):
     """
     混合检索器：稠密向量 + BM25 稀疏向量 RRF 分数融合
 
-    直接使用 Qdrant 原生 Fusion API，性能最优。
+    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,
@@ -50,57 +55,59 @@ class HybridRetriever(BaseRetriever):
             vector_store: QdrantVectorStore 实例
             search_k: 检索返回结果数
         """
-        self.collection_name = collection_name
+        super().__init__(
-        self.vector_store = vector_store
+            collection_name=collection_name,
-        self.search_k = search_k
+            search_k=search_k
-        self.client = vector_store.get_qdrant_client()
+        )
-        self.sparse_embedder = get_sparse_embedder()
+        self._vector_store = vector_store
+        self._client = vector_store.get_qdrant_client()
+        self._sparse_embedder = get_sparse_embedder()
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: Optional[Any] = None
+        self, query: str, **kwargs
     ) -> List[Document]:
         """
         同步检索相关文档
 
         Args:
             query: 查询字符串
-            run_manager: LangChain 运行管理器（可选）
 
         Returns:
             相关文档列表
         """
-        # 生成双向量
+        # 1. 生成双向量
-        dense_query = self.vector_store.embeddings.embed_query(query)
+        dense_query = self._vector_store.embeddings.embed_query(query)
-        sparse_query = self.sparse_embedder.embed_query(query)
+        sparse_query = self._sparse_embedder.embed_query(query)
-        
+        sparse_vec = models.SparseVector(
-        # 构建双检索请求
+            indices=sparse_query["indices"],
-        searches = [
+            values=sparse_query["values"]
-            # 稠密检索
-            SearchRequest(
-                vector=NamedVector(name="dense", vector=dense_query),
-                limit=self.search_k,
-                with_payload=True
-            ),
-            # 稀疏检索
-            SearchRequest(
-                vector=NamedSparseVector(name="sparse", vector=sparse_query),
-                limit=self.search_k,
-                with_payload=True
-            )
-        ]
-        
-        # RRF 分数融合
-        fused_results = self.client.fusion(
-            collection_name=self.collection_name,
-            requests=searches,
-            fusion=Fusion(fusion=FusionProtocol.RRF)
         )
 
-        # 转换为 Document 格式
+        # 2. 使用官方的 query_points API（推荐方式）
+        response = self._client.query_points(
+            collection_name=self.collection_name,
+            prefetch=[  # 并行预取多个检索源
+                models.Prefetch(
+                    query=dense_query,
+                    using="dense",   # 使用稠密向量进行语义搜索
+                    limit=self.search_k
+                ),
+                models.Prefetch(
+                    query=sparse_vec,
+                    using="sparse",  # 使用稀疏向量进行关键词搜索
+                    limit=self.search_k
+                )
+            ],
+            query=models.FusionQuery(fusion=models.Fusion.RRF),  # 指定融合算法为 RRF
+            limit=self.search_k,  # 最终返回的结果数量
+            with_payload=True
+        )
+
+        # 3. 转换结果
         results = []
-        for point in fused_results.points:
+        for point in response.points:
             doc = Document(
-                page_content=point.payload.pop("text", ""),
+                page_content=point.payload.pop("page_content", point.payload.pop("text", "")),
                 metadata=point.payload
             )
             results.append(doc)
@@ -109,11 +116,11 @@ class HybridRetriever(BaseRetriever):
         return results
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: Optional[Any] = None
+        self, query: str, **kwargs
     ) -> List[Document]:
         """异步检索（当前调用同步版本）"""
         # Qdrant 客户端没有原生 async，这里用同步版本
-        return self._get_relevant_documents(query, run_manager=run_manager)
+        return self._get_relevant_documents(query, **kwargs)
 
 
 class ParentHybridRetriever(BaseRetriever):
@@ -125,6 +132,14 @@ class ParentHybridRetriever(BaseRetriever):
     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,
@@ -136,58 +151,62 @@ class ParentHybridRetriever(BaseRetriever):
         Args:
             collection_name: Qdrant 集合名称
             vector_store: QdrantVectorStore 实例
-            search_k: 最终返回的父文档数
+            search_k: 最终返回的父文档数量
             docstore: 文档存储（如果父文档在 PostgreSQL），可选
         """
-        self.collection_name = collection_name
+        super().__init__(
-        self.vector_store = vector_store
+            collection_name=collection_name,
-        self.search_k = search_k
+            search_k=search_k
-        self.client = vector_store.get_qdrant_client()
+        )
-        self.sparse_embedder = get_sparse_embedder()
+        self._vector_store = vector_store
-        self.docstore = docstore
+        self._client = vector_store.get_qdrant_client()
+        self._sparse_embedder = get_sparse_embedder()
+        self._docstore = docstore
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: Optional[Any] = None
+        self, query: str, **kwargs
     ) -> List[Document]:
         """
         同步检索相关父文档
 
         Args:
             query: 查询字符串
-            run_manager: LangChain 运行管理器（可选）
 
         Returns:
             相关父文档列表
         """
         # 1. 生成查询双向量
-        dense_query = self.vector_store.embeddings.embed_query(query)
+        dense_query = self._vector_store.embeddings.embed_query(query)
-        sparse_query = self.sparse_embedder.embed_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
-        searches = [
-            # 稠密检索
-            SearchRequest(
-                vector=NamedVector(name="dense", vector=dense_query),
-                limit=search_limit,
-                with_payload=True
-            ),
-            # 稀疏检索
-            SearchRequest(
-                vector=NamedSparseVector(name="sparse", vector=sparse_query),
-                limit=search_limit,
-                with_payload=True
-            )
-        ]
 
-        # 3. RRF 分数融合，拿到子文档命中结果
+        # 3. 使用 query_points API 进行混合检索
-        fused_results = self.client.fusion(
+        response = self._client.query_points(
             collection_name=self.collection_name,
-            requests=searches,
+            prefetch=[
-            fusion=Fusion(fusion=FusionProtocol.RRF)
+                models.Prefetch(
+                    query=dense_query,
+                    using="dense",
+                    limit=search_limit
+                ),
+                models.Prefetch(
+                    query=sparse_vec,
+                    using="sparse",
+                    limit=search_limit
+                )
+            ],
+            query=models.FusionQuery(fusion=models.Fusion.RRF),
+            limit=search_limit,
+            with_payload=True
         )
 
-        if not fused_results.points:
+        if not response.points:
             debug("混合检索未找到任何文档")
             return []
 
@@ -196,8 +215,10 @@ class ParentHybridRetriever(BaseRetriever):
         parent_ids = set()
         child_point_map = {}  # 保存子文档点用于降级
 
-        for point in fused_results.points:
+        for point in response.points:
-            parent_id = point.payload.get("parent_id", point.id)
+            # 先复制 payload，避免修改原始对象
+            payload_copy = point.payload.copy()
+            parent_id = payload_copy.get("parent_id", point.id)
             score = point.score
 
             # 同一个 parent_id 只保留最高得分
@@ -207,12 +228,12 @@ class ParentHybridRetriever(BaseRetriever):
                 child_point_map[parent_id] = point
 
         # 5. 批量查询父文档
-        # 首先尝试从 Qdrant 直接查询（因为父文档可能也存在 Qdrant 中）
+        # 首先尝试从 Qdrant 直接查询（因为父文档可能也在 Qdrant 中）
         parent_docs = []
         found_parent_ids = set()
 
         try:
-            parent_points = self.client.retrieve(
+            parent_points = self._client.retrieve(
                 collection_name=self.collection_name,
                 ids=list(parent_ids),
                 with_payload=True
@@ -220,9 +241,10 @@ class ParentHybridRetriever(BaseRetriever):
 
             # 处理找到的父文档
             for point in parent_points:
+                payload_copy = point.payload.copy()
                 doc = Document(
-                    page_content=point.payload.pop("text", ""),
+                    page_content=payload_copy.pop("page_content", payload_copy.pop("text", "")),
-                    metadata=point.payload
+                    metadata=payload_copy
                 )
                 parent_docs.append(doc)
                 found_parent_ids.add(point.id)
@@ -231,10 +253,10 @@ class ParentHybridRetriever(BaseRetriever):
             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):
+        if self._docstore and len(found_parent_ids) < len(parent_ids):
             missing_parent_ids = parent_ids - found_parent_ids
             try:
-                docstore_docs = self.docstore.mget(missing_parent_ids)
+                docstore_docs = self._docstore.mget(missing_parent_ids)
                 for doc_id, doc in zip(missing_parent_ids, docstore_docs):
                     if doc is not None:
                         parent_docs.append(doc)
@@ -249,9 +271,10 @@ class ParentHybridRetriever(BaseRetriever):
             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=child_point.payload.pop("text", ""),
+                        page_content=payload_copy.pop("page_content", payload_copy.pop("text", "")),
-                        metadata=child_point.payload
+                        metadata=payload_copy
                     )
                     parent_docs.append(doc)
 
@@ -268,10 +291,10 @@ class ParentHybridRetriever(BaseRetriever):
         return final_docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: Optional[Any] = None
+        self, query: str, **kwargs
     ) -> List[Document]:
         """异步检索（当前调用同步版本）"""
-        return self._get_relevant_documents(query, run_manager=run_manager)
+        return self._get_relevant_documents(query, **kwargs)
 
 
 def create_hybrid_retriever(
@@ -333,7 +356,7 @@ def create_parent_hybrid_retriever(
 
     Args:
         collection_name: Qdrant 集合名称
-        search_k: 最终返回的父文档数
+        search_k: 最终返回的父文档数量
         embeddings: 可选的嵌入模型实例
         use_docstore: 是否使用 PostgreSQL docstore 存储父文档
 
@@ -375,5 +398,31 @@ def create_parent_hybrid_retriever(
     )
 
 
+def create_base_retriever(
+    collection_name: str,
+    search_k: int = DEFAULT_SEARCH_K,
+    embeddings: Optional[Embeddings] = None,
+) -> BaseRetriever:
+    """
+    创建基础稠密检索器（向后兼容）。
+
+    Args:
+        collection_name: Qdrant 集合名称
+        search_k: 检索返回结果数
+        embeddings: 可选的嵌入模型实例
+
+    Returns:
+        LangChain 的 BaseRetriever 实例
+    """
+    # 默认使用统一嵌入服务
+    if embeddings is None:
+        embeddings = get_embedding_service()
+    
+    vector_store = QdrantVectorStore(collection_name=collection_name, embeddings=embeddings)
+    
+    info(f"✅ Qdrant 基础稠密检索器初始化成功（search_k={search_k}）")
+    return vector_store.as_langchain_vectorstore().as_retriever(k=search_k)
+
+
 # 别名：默认就是父子文档混合检索
 create_retriever = create_parent_hybrid_retriever

backend/rag_core/config.py

@@ -54,3 +54,5 @@ DOCSTORE_URI = _get_str("DOCSTORE_URI") or DB_URI
 
 # ========== 其他配置 ==========
 # 可以在此添加其他 RAG Core 专用的配置项
+# 稀疏模型缓存路径
+FASTEMBED_CACHE_PATH = _get_str("FASTEMBED_CACHE_PATH") or "./models/fastembed_cache"

backend/rag_core/sparse_embedder.py

@@ -4,7 +4,7 @@ BM25 稀疏嵌入器
 """
 from typing import List
 from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
-from app.config import FASTEMBED_CACHE_PATH
+from .config import FASTEMBED_CACHE_PATH
 
 class BM25SparseEmbedder:
     """BM25 稀疏嵌入包装器，与现有嵌入器风格统一"""

backend/rag_core/vector_store.py

@@ -14,8 +14,7 @@ from langchain_core.embeddings import Embeddings
 from langchain_qdrant import QdrantVectorStore as LangchainQdrantVS
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import (
-    Distance, VectorParams, SparseVectorParams, SparseIndexParams, 
+    Distance, VectorParams, SparseVectorParams, PointStruct
-    SparseIndexType, PointStruct, NamedSparseVector, NamedVector
 )
 from httpx import RemoteProtocolError
 from qdrant_client.http.exceptions import ResponseHandlingException
@@ -61,6 +60,7 @@ class QdrantVectorStore:
             client=self.get_client(),
             collection_name=self.collection_name,
             embedding=self.embeddings,
+            vector_name="dense",
         )
 
     def get_client(self) -> QdrantClient:
@@ -134,19 +134,13 @@ class QdrantVectorStore:
                     vectors_config = {
                         "dense": VectorParams(
                             size=vector_size,
-                            distance=Distance.COSINE,
+                            distance=Distance.COSINE
-                            optional=True
                         )
                     }
                     
-                    # 稀疏向量配置
+                    # 稀疏向量配置（简化版，不使用特殊索引类型）
                     sparse_vectors_config = {
-                        "sparse": SparseVectorParams(
+                        "sparse": SparseVectorParams()
-                            index=SparseIndexParams(
-                                type=SparseIndexType.MUTABLE
-                            ),
-                            optional=True
-                        )
                     }
 
                     client.create_collection(
@@ -197,10 +191,7 @@ class QdrantVectorStore:
                 # 构造双向量
                 named_vectors = {
                     "dense": dense_vectors[j],
-                    "sparse": NamedSparseVector(
+                    "sparse": sparse_vectors[j]
-                        name="sparse",
-                        vector=sparse_vectors[j]
-                    )
                 }
 
                 points.append(PointStruct(

rag_indexer/cli.py

@@ -49,7 +49,7 @@ def get_input_path() -> Path:
     if len(sys.argv) > 1:
         return Path(sys.argv[1])
     # 默认测试路径（可按需修改）
-    return Path("data/user_docs/a.txt")
+    return Path("data/user_docs/doublestory.txt")
 
 
 async def main():

rag_indexer/index_builder.py

@@ -121,7 +121,7 @@ class IndexBuilder:
         # 初始化向量存储（自动支持稠密+稀疏混合检索）
         self.vector_store = QdrantVectorStore(
             collection_name=config.collection_name,
-            embedding=self.embeddings if self._embedder is None else None
+            embeddings=self.embeddings if self._embedder is None else None
         )
         logger.info("✅ 混合检索向量存储初始化成功（稠密+BM25稀疏）")
 
@@ -178,7 +178,7 @@ class IndexBuilder:
             child_splitter=self.child_splitter,
             docstore=self.docstore,
             search_k=cfg.search_k,
-            embeddings=self.embeddings if self.embedder is None else None,
+            embeddings=self.embeddings if self._embedder is None else None,
         )
         logger.info("ParentDocumentRetriever 初始化完成")
 

scripts/start.sh

@@ -14,7 +14,7 @@ YELLOW='\033[1;33m'
 NC='\033[0m' # No Color
 
 # 项目根目录
-PROJECT_DIR="/root/projects/ailine"
+PROJECT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
 
 echo -e "${BLUE}========================================${NC}"
 echo -e "${BLUE}   AI Agent - 个人生活助手${NC}"
@@ -34,7 +34,7 @@ start_backend() {
     set +a
     
     export PYTHONPATH="$PROJECT_DIR/backend"
-    export BACKEND_PORT=10079
+    export BACKEND_PORT=8079
     python -m app.backend &
     BACKEND_PID=$!
     echo -e "${GREEN}✓ 后端服务已启动 (PID: $BACKEND_PID)${NC}"
@@ -51,7 +51,7 @@ start_frontend() {
     set +a
     
     export PYTHONPATH="$PROJECT_DIR/frontend/src"
-    export API_URL="http://127.0.0.1:10079/chat"
+    export API_URL="http://127.0.0.1:8079/chat"
     streamlit run frontend/src/frontend_main.py --server.port 10501 --server.address 0.0.0.0 &
     FRONTEND_PID=$!
     echo -e "${GREEN}✓ 前端服务已启动 (PID: $FRONTEND_PID)${NC}"

tools/test/test_rag_indexer_result.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 """
-测试重构后的 IndexBuilder 和 RAGRetriever
+测试重构后的 IndexBuilder 和 RAG 检索
+包括：索引构建、稠密检索、稀疏检索、混合检索、父子文档检索
 """
 
 import asyncio
@@ -8,15 +9,23 @@ import os
 import sys
 
 # 添加项目根目录到 Python 路径
-project_root = os.path.join(os.path.dirname(__file__), "..")
+project_root = os.path.join(os.path.dirname(__file__), "..", "..")
+sys.path.insert(0, os.path.join(project_root, "backend"))
 sys.path.insert(0, project_root)
 
 from rag_indexer.index_builder import IndexBuilder
 from rag_indexer.splitters import SplitterType
 
+from rag_core import QdrantVectorStore, get_sparse_embedder
+from app.model_services import get_embedding_service
+from qdrant_client import models
+
+
 async def test_index_builder():
     """测试索引构建功能"""
-    print("测试索引构建功能...")
+    print("="*70)
+    print("1. 测试索引构建功能...")
+    print("="*70)
     
     # 创建 IndexBuilder 实例
     builder = IndexBuilder(
@@ -27,7 +36,7 @@ async def test_index_builder():
     )
     
     # 测试文档路径
-    test_file = os.path.join(os.path.dirname(__file__), "..", "data", "user_docs", "doublestory.txt")
+    test_file = os.path.join(project_root, "data", "user_docs", "doublestory.txt")
     
     if os.path.exists(test_file):
         # 构建索引
@@ -43,7 +52,260 @@ async def test_index_builder():
     
     # 关闭资源
     builder.close()
-    print("\n测试完成")
+    print("\n索引构建测试完成")
+    return processed
+
+
+def test_dense_retrieval():
+    """测试稠密检索"""
+    print("\n" + "="*70)
+    print("2. 测试稠密检索...")
+    print("="*70)
+    
+    # 获取嵌入服务
+    embeddings = get_embedding_service()
+    
+    # 创建向量存储
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    
+    # 测试查询
+    query = "The Ant and the Grasshopper"
+    print(f"查询: {query}")
+    
+    results = vs.similarity_search(query, k=3)
+    
+    print(f"\n找到 {len(results)} 个结果:")
+    for i, doc in enumerate(results, 1):
+        print(f"\n{i}. (来源: {doc.metadata.get('source', 'unknown')})")
+        print(f"   元数据: {doc.metadata}")
+        content = doc.page_content.strip()
+        if len(content) > 200:
+            content = content[:200] + "..."
+        print(f"   内容: {content}")
+
+
+def test_sparse_retrieval_simple():
+    """简单测试稀疏检索"""
+    print("\n" + "="*70)
+    print("3. 测试稀疏检索（BM25）...")
+    print("="*70)
+    
+    # 获取嵌入服务和稀疏嵌入器
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    sparse_embedder = get_sparse_embedder()
+    
+    # 测试查询 - 用关键词
+    query = "winter work food"
+    print(f"查询关键词: {query}")
+    
+    # 生成稀疏查询向量
+    sparse_query = sparse_embedder.embed_query(query)
+    
+    # 包装成 SparseVector 对象
+    sparse_vec = models.SparseVector(
+        indices=sparse_query["indices"],
+        values=sparse_query["values"]
+    )
+    
+    # 直接查询稀疏向量
+    response = client.query_points(
+        collection_name="rag_documents",
+        query=sparse_vec,
+        using="sparse",
+        limit=3,
+        with_payload=True
+    )
+    
+    print(f"\n找到 {len(response.points)} 个结果:")
+    for i, point in enumerate(response.points, 1):
+        print(f"\n{i}. (分数: {point.score:.4f})")
+        text = point.payload.get("text", "")
+        metadata = {k: v for k, v in point.payload.items() if k != "text"}
+        print(f"   元数据: {metadata}")
+        content = text.strip()
+        if len(content) > 200:
+            content = content[:200] + "..."
+        print(f"   内容: {content}")
+
+
+def test_hybrid_retrieval_simple():
+    """简单测试混合检索（稠密+稀疏 RRF 融合）"""
+    print("\n" + "="*70)
+    print("4. 测试混合检索（稠密+稀疏 RRF 融合）...")
+    print("="*70)
+    
+    # 获取嵌入服务和稀疏嵌入器
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    sparse_embedder = get_sparse_embedder()
+    
+    # 测试查询
+    query = "Ant and Grasshopper story"
+    print(f"查询: {query}")
+    
+    # 生成双向量
+    dense_query = embeddings.embed_query(query)
+    sparse_query = sparse_embedder.embed_query(query)
+    sparse_vec = models.SparseVector(
+        indices=sparse_query["indices"],
+        values=sparse_query["values"]
+    )
+    
+    # 使用 Qdrant 的 query_points 做混合检索
+    response = client.query_points(
+        collection_name="rag_documents",
+        prefetch=[
+            models.Prefetch(
+                query=dense_query,
+                using="dense",
+                limit=3
+            ),
+            models.Prefetch(
+                query=sparse_vec,
+                using="sparse",
+                limit=3
+            )
+        ],
+        query=models.FusionQuery(fusion=models.Fusion.RRF),
+        limit=3,
+        with_payload=True
+    )
+    
+    print(f"\n找到 {len(response.points)} 个结果:")
+    for i, point in enumerate(response.points, 1):
+        print(f"\n{i}. (RRF 融合分数: {point.score:.4f})")
+        text = point.payload.get("text", "")
+        metadata = {k: v for k, v in point.payload.items() if k != "text"}
+        print(f"   元数据: {metadata}")
+        content = text.strip()
+        if len(content) > 200:
+            content = content[:200] + "..."
+        print(f"   内容: {content}")
+
+
+def test_parent_child_retrieval_simple():
+    """简单测试父子文档检索"""
+    print("\n" + "="*70)
+    print("5. 测试父子文档混合检索...")
+    print("="*70)
+    
+    # 获取嵌入服务和稀疏嵌入器
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    sparse_embedder = get_sparse_embedder()
+    
+    # 测试查询
+    query = "The Ant and the Grasshopper story moral"
+    print(f"查询: {query}")
+    
+    # 生成双向量
+    dense_query = embeddings.embed_query(query)
+    sparse_query = sparse_embedder.embed_query(query)
+    sparse_vec = models.SparseVector(
+        indices=sparse_query["indices"],
+        values=sparse_query["values"]
+    )
+    
+    # 先做混合检索找到子文档
+    response = client.query_points(
+        collection_name="rag_documents",
+        prefetch=[
+            models.Prefetch(
+                query=dense_query,
+                using="dense",
+                limit=5
+            ),
+            models.Prefetch(
+                query=sparse_vec,
+                using="sparse",
+                limit=5
+            )
+        ],
+        query=models.FusionQuery(fusion=models.Fusion.RRF),
+        limit=5,
+        with_payload=True
+    )
+    
+    # 收集 parent_id
+    parent_score_map = {}
+    child_points = {}
+    for point in response.points:
+        parent_id = point.payload.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
+            child_points[parent_id] = point
+    
+    parent_ids = list(parent_score_map.keys())
+    
+    print(f"\n找到 {len(parent_ids)} 个不同的 parent_id:")
+    
+    # 查找父文档
+    if parent_ids:
+        parent_docs = client.retrieve(
+            collection_name="rag_documents",
+            ids=parent_ids,
+            with_payload=True
+        )
+        
+        found_parent_ids = {p.id for p in parent_docs}
+        
+        # 准备结果列表
+        results = []
+        for p in parent_docs:
+            score = parent_score_map[p.id]
+            results.append((p, score))
+        
+        # 处理没找到父文档的情况 - 用子文档代替
+        missing = set(parent_ids) - found_parent_ids
+        for parent_id in missing:
+            child_point = child_points[parent_id]
+            print(f"\n注意: parent_id {parent_id} 未找到，使用子文档代替")
+            results.append((child_point, parent_score_map[parent_id]))
+        
+        # 按分数排序
+        results.sort(key=lambda x: x[1], reverse=True)
+        
+        # 显示
+        print(f"\n共 {len(results)} 个结果（去重后）:")
+        for i, (point, score) in enumerate(results[:3], 1):
+            print(f"\n{i}. (分数: {score:.4f})")
+            text = point.payload.get("text", "")
+            metadata = {k: v for k, v in point.payload.items() if k != "text"}
+            print(f"   元数据: {metadata}")
+            content = text.strip()
+            if len(content) > 400:
+                content = content[:400] + "..."
+            print(f"   内容: {content}")
+    else:
+        print("\n未找到结果")
+
+
+async def main():
+    """主测试函数"""
+    # 1. 先构建索引
+    await test_index_builder()
+    
+    # 2. 测试稠密检索
+    test_dense_retrieval()
+    
+    # 3. 测试稀疏检索
+    test_sparse_retrieval_simple()
+    
+    # 4. 测试混合检索
+    test_hybrid_retrieval_simple()
+    
+    # 5. 测试父子文档检索
+    test_parent_child_retrieval_simple()
+    
+    print("\n" + "="*70)
+    print("所有测试完成！")
+    print("="*70)
+
 
 if __name__ == "__main__":
-    asyncio.run(test_index_builder())
+    asyncio.run(main())