修改rag，实现混合检索

backend/app/agent/agent_service.py

@@ -70,7 +70,7 @@ class AIAgentService:
             raise RuntimeError("没有可用的模型")
         return self
 
-    async def process_message(self, message: str, thread_id: str, model: str = "zhipu", user_id: str = "default_user") -> dict:
+    async def process_message(self, message: str, thread_id: str, model: str = "local", user_id: str = "default_user") -> dict:
         """处理用户消息，返回包含回复、token统计和耗时的字典"""
         if model not in self.graphs:
             # 回退到第一个可用模型
@@ -175,6 +175,8 @@ class AIAgentService:
         try:
             info(f"📡 开始调用 graph.astream()...")
             chunk_count = 0
+            full_message_content = ""  # 收集完整消息内容
+            
             async for chunk in graph.astream(
                 input_state,
                 config=config,
@@ -184,21 +186,11 @@ class AIAgentService:
             ):
                 chunk_count += 1
                 chunk_type = chunk["type"]
-                info(f"📦 收到第 {chunk_count} 个chunk, type: {chunk_type}")
                 processed_event = {}
 
                 if chunk_type == "messages":
                     message_chunk, metadata = chunk["data"]
                     node_name = metadata.get("langgraph_node", "unknown")
-                    info(f"📨 处理消息chunk, node: {node_name}")
-                    # 详细记录消息内容，看看这些 chunk 到底是什么
-                    if hasattr(message_chunk, "content"):
-                        content_preview = str(message_chunk.content)[:200]
-                        info(f"📄 消息内容预览 ({len(content_preview)} chars): {repr(content_preview)}")
-                    if hasattr(message_chunk, "type"):
-                        info(f"📋 消息类型: {message_chunk.type}")
-                    if hasattr(message_chunk, "tool_calls"):
-                        info(f"🔧 包含工具调用: {message_chunk.tool_calls}")
 
                     # 检测节点变化，发送节点开始事件
                     if node_name != current_node:
@@ -218,8 +210,6 @@ class AIAgentService:
                     reasoning_token = ""
                     if hasattr(message_chunk, 'additional_kwargs'):
                         reasoning_token = message_chunk.additional_kwargs.get("reasoning_content", "")
-                    
-                    info(f"💬 消息token: token_content='{repr(token_content[:50])}', reasoning_token='{repr(reasoning_token[:50])}', node_name='{node_name}'")
 
                     # 处理思考过程
                     if reasoning_token:
@@ -228,7 +218,6 @@ class AIAgentService:
                             "node": node_name,
                             "reasoning_token": reasoning_token
                         }
-                        info(f"✅ 生成 reasoning_token 事件: {processed_event}")
                     # 处理工具调用
                     elif hasattr(message_chunk, 'tool_calls') and message_chunk.tool_calls:
                         for tool_call in message_chunk.tool_calls:
@@ -248,7 +237,7 @@ class AIAgentService:
                                     "args": tool_args,
                                     "id": tool_call_id
                                 }
-                    # 处理普通 token
+                    # 处理普通 token - 只收集，不打印单个 token
                     elif token_content:
                         processed_event = {
                             "type": "llm_token",
@@ -256,18 +245,13 @@ class AIAgentService:
                             "token": token_content,
                             "reasoning_token": reasoning_token
                         }
-                        info(f"✅ 生成 llm_token 事件: {processed_event}")
-                    else:
-                        info(f"⚠️ 没有生成任何事件，token_content='{repr(token_content)}', reasoning_token='{repr(reasoning_token)}'")
+                        if node_name == "llm_call":
+                            full_message_content += token_content
 
                 elif chunk_type == "updates":
-                    info(f"🔄 处理updates chunk")
                     updates_data = chunk["data"]
                     serialized_data = self._serialize_value(updates_data)
                     
-                    # 关键修复：不再从 updates 中读取 latest_reasoning，避免重复
-                    # 因为我们现在直接通过 custom 事件发送推理结果了
-                    
                     # 检查是否有人工审核请求
                     if "review_pending" in serialized_data and serialized_data["review_pending"]:
                         review_id = serialized_data.get("review_id", "")
@@ -302,18 +286,12 @@ class AIAgentService:
                     }
 
                 elif chunk_type == "custom":
-                    info(f"🎯 处理custom chunk, 完整数据: {repr(chunk)}")
                     custom_data = chunk["data"]
-                    info(f"🎯 custom_data 内容: {repr(custom_data)}")
-                    info(f"🎯 custom_data 类型: {type(custom_data)}")
                     
-                    # 关键修复：处理我们从 react_reason_node 发送的自定义推理事件
-                    # LangGraph 的 adispatch_custom_event 发送的事件格式：
-                    # chunk["data"] 是我们传的第二个参数（dict）
+                    # 处理我们从 react_reason_node 发送的自定义推理事件
                     if isinstance(custom_data, dict):
                         # 检查是否是我们的推理事件
                         if "action" in custom_data and "reasoning" in custom_data:
-                            info(f"[Agent Service] 收到自定义推理事件: {custom_data}")
                             yield {
                                 "type": "react_reasoning",
                                 "step": custom_data.get("step", 1),
@@ -339,7 +317,10 @@ class AIAgentService:
                 if processed_event:
                     yield processed_event
 
+            # 完整消息集合完成后，一次性打印
             info(f"✅ graph.astream() 完成，共 {chunk_count} 个chunks")
+            if full_message_content:
+                info(f"📄 完整消息内容: {repr(full_message_content)}")
 
         except Exception as e:
             error(f"❌ 执行 React 图时出错: {e}")

backend/app/agent/history.py

@@ -12,18 +12,21 @@ class ThreadHistoryService:
     def __init__(self, checkpointer):
         self.checkpointer = checkpointer
     
-    async def get_user_threads(self, user_id: str, limit: int = 50) -> List[Dict[str, Any]]:
+    async def get_user_threads(self, user_id: str, limit: int = 4) -> List[Dict[str, Any]]:
         """
         获取指定用户的所有线程摘要信息
         
         Args:
             user_id: 用户 ID
-            limit: 返回数量限制
+            limit: 返回数量限制（强制最多4条）
             
         Returns:
             线程列表，每个包含 thread_id, last_updated, summary, message_count
         """
         try:
+            # 强制限制最多4条
+            actual_limit = min(limit, 4)
+            
             # 查询 checkpoints 表获取用户的线程列表
             async with self.checkpointer.conn.cursor() as cur:
                 # 在较新的 LangGraph 版本中，AsyncPostgresSaver 创建的 checkpoints 表
@@ -40,7 +43,7 @@ class ThreadHistoryService:
                     ORDER BY last_updated DESC
                     LIMIT %s
                 """
-                await cur.execute(query, (user_id, limit))
+                await cur.execute(query, (user_id, actual_limit))
                 rows = await cur.fetchall()
                 
                 threads = []

backend/app/backend.py

@@ -98,7 +98,7 @@ async def health_check():
 class ChatRequest(BaseModel):
     message: str
     thread_id: str | None = None
-    model: str = "zhipu"
+    model: str = "local"
     user_id: str = "default_user"
 
 class ChatResponse(BaseModel):
@@ -212,7 +212,7 @@ async def chat_endpoint(
 @app.get("/threads")
 async def list_threads(
     user_id: str = Query("default_user", description="用户 ID"),
-    limit: int = Query(50, ge=1, le=200, description="返回数量限制"),
+    limit: int = Query(4, ge=1, le=200, description="返回数量限制"),
     history_service: ThreadHistoryService = Depends(get_history_service)
 ):
     """获取当前用户的对话历史列表"""
@@ -312,7 +312,7 @@ async def websocket_endpoint(
             data = await websocket.receive_json()
             message = data.get("message")
             thread_id = data.get("thread_id", str(uuid.uuid4()))
-            model = data.get("model", "zhipu")
+            model = data.get("model", "local")
             user_id = data.get("user_id", "default_user")
             if not message:
                 await websocket.send_json({"error": "missing message"})
@@ -435,4 +435,10 @@ if __name__ == "__main__":
     import uvicorn
     # 使用环境变量或默认端口 8079（避免与 llama.cpp 的 8081 端口冲突）
     port = int(BACKEND_PORT)
-    uvicorn.run(app, host="0.0.0.0", port=port)
+    uvicorn.run(
+        app, 
+        host="0.0.0.0", 
+        port=port,
+        log_level="debug",
+        access_log=True
+    )

backend/app/core/intent.py

@@ -130,12 +130,16 @@ class ReactIntentReasoner:
         retrieved_docs = context.get("retrieved_docs", [])
         messages = context.get("messages", [])
         
-        # 关键修复 2：如果已经有 rag_context 或 web_search_results（通过 messages 推断），直接回答
-        # 检查是否已经执行过 rag_retrieve 或 web_search
-        if "rag_retrieve" in previous_actions or "web_search" in previous_actions:
+        # 关键修改：不要在第一次 rag_retrieve 后就直接回答，允许再推理一次
+        # 让推理逻辑有机会判断 RAG 结果好不好，要不要再检索或转 web search
+        rag_count = previous_actions.count("rag_retrieve")
+        web_search_count = previous_actions.count("web_search")
+        
+        # 只有当 rag 或 web search 已经超过 1 次，或者已经有推理在 rag 之后，才直接回答
+        if rag_count >= 2 or web_search_count >= 1:
             result.action = ReasoningAction.DIRECT_RESPONSE
             result.confidence = 0.95
-            result.reasoning = "已获取信息，直接回答"
+            result.reasoning = "已获取足够信息，直接回答"
             return result
 
         # 策略1：尝试使用 LLM 推理

backend/app/main_graph/nodes/rag_nodes.py

@@ -95,10 +95,10 @@ def inject_rag_tool_to_state(state: MainGraphState, rag_tool: Any) -> MainGraphS
     return state
 
 
-# ========== RAG 检索核心逻辑（真正利用已有代码）==========
-def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
+# ========== RAG 检索核心逻辑（真正利用已有代码） ==========
+async def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
     """
-    RAG 检索核心逻辑（真正利用 rag/tools.py）
+    RAG 检索核心逻辑（真正利用 rag/tools.py） - 异步版本
     
     Args:
         state: 主图状态
@@ -119,10 +119,10 @@ def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
     rag_tool = get_rag_tool_from_state(state)
     
     if rag_tool:
-        # 使用真正的 RAG 工具（来自 rag/tools.py）
+        # 使用真正的 RAG 工具（来自 rag/tools.py）- 异步版本
         try:
-            # 调用 LangChain Tool 的 invoke 方法
-            rag_context = rag_tool.invoke(retrieval_query)
+            # 直接 await 异步工具的 ainvoke 方法
+            rag_context = await rag_tool.ainvoke(retrieval_query)
             state.rag_context = rag_context
             state.rag_docs = [
                 {"source": "rag_retrieval", "content": rag_context}
@@ -134,9 +134,9 @@ def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
         except Exception as e:
             raise RuntimeError(f"RAG 工具调用失败: {str(e)}") from e
     elif _GLOBAL_RAG_PIPELINE:
-        # 使用 RAG Pipeline 直接检索
+        # 使用 RAG Pipeline 直接检索 - 直接用异步方法
         try:
-            documents = _GLOBAL_RAG_PIPELINE.retrieve(retrieval_query)
+            documents = await _GLOBAL_RAG_PIPELINE.aretrieve(retrieval_query)
             if documents:
                 rag_context = _GLOBAL_RAG_PIPELINE.format_context(documents)
                 state.rag_context = rag_context
@@ -158,7 +158,7 @@ def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
         raise RuntimeError("RAG 工具未初始化，请先调用 set_global_rag_tool() 或 set_global_rag_pipeline()")
 
 
-# ========== RAG 检索节点（带超时和重试）==========
+# ========== RAG 检索节点（带超时和重试） ==========
 async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
     """
     RAG 检索节点：带超时和重试，真正利用已有 RAG 代码
@@ -196,8 +196,13 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
     
     for attempt in range(RAG_RETRY_CONFIG.max_retries + 1):
         try:
-            # 执行核心逻辑
-            result = _rag_retrieve_core(state)
+            # 执行核心逻辑 - 异步 await
+            result = await _rag_retrieve_core(state)
+            
+            info(f"[rag_retrieve_node] RAG 检索成功，获取到上下文长度: {len(result.rag_context)} 字符")
+            if result.rag_docs:
+                for i, doc in enumerate(result.rag_docs[:3]):  # 只显示前3条
+                    info(f"[rag_retrieve_node] 文档 {i+1}: {doc.get('content', '')[:100]}...")
             
             # 成功
             state.debug_info["rag_retrieval"] = {
@@ -226,6 +231,15 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
                 except Exception as e:
                     info(f"[rag_retrieve_node] 无法发送完成事件: {e}")
             
+            # 关键修复：把 rag_retrieve 加到 reasoning_history 里，让下次推理知道
+            state.reasoning_history.append({
+                "step": state.reasoning_step,
+                "action": "rag_retrieve",
+                "confidence": 1.0,
+                "reasoning": "RAG 检索完成",
+                "timestamp": datetime.now().isoformat()
+            })
+            
             return result
             
         except Exception as e:
@@ -255,7 +269,7 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
             
             # 指数退避等待
             delay = RAG_RETRY_CONFIG.base_delay * (2 ** attempt)
-            time.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
+            await asyncio.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
     
     # 所有重试都失败，记录结构化错误
     error_record = ErrorRecord(

backend/app/main_graph/nodes/react_nodes.py

@@ -364,20 +364,27 @@ def route_by_reasoning(state: MainGraphState) -> str:
     if "subgraph_completed" in previous_actions or state.final_result:
         return "llm_call"
     
-    # 检查是否刚刚执行完 rag 或 web search，应该继续推理一次然后去 llm_call
-    # 但为了避免死循环，我们设置一个简单的规则
-    if len(previous_actions) > 3:
+    # 关键修复：如果已经执行过 rag_retrieve 并且又执行过推理，直接去 LLM_CALL
+    # 这样的流程：推理1 → RAG → 推理2（判断 RAG 结果） → LLM_CALL
+    rag_count = previous_actions.count("rag_retrieve")
+    if rag_count >= 1 and len(previous_actions) >= rag_count + 1:
+        info(f"[route_by_reasoning] 已完成 RAG 检索和结果判断，直接去 llm_call")
         return "llm_call"
-
+    
+    # 关键修复：限制最多 3 次推理，避免无限循环
+    if len(previous_actions) >= 3:
+        info(f"[route_by_reasoning] 已达到最大推理次数 ({len(previous_actions)})，直接去 llm_call")
+        return "llm_call"
+    
     # 获取推理结果
     reasoning_result: Optional[ReasoningResult] = state.debug_info.get("reasoning_result")
-
+    
     if not reasoning_result:
         return "llm_call"
-
+    
     # 使用 intent.py 提供的路由函数
     route = get_route_by_reasoning(reasoning_result)
-
+    
     # 映射到我们的节点名称
     # 注意：这些名称必须与 main_graph_builder.py 中定义的节点名称一致
     route_mapping = {
@@ -391,7 +398,8 @@ def route_by_reasoning(state: MainGraphState) -> str:
         "dictionary": "dictionary_subgraph",
         "news_analysis": "news_analysis_subgraph",
     }
-
+    
+    info(f"[route_by_reasoning] 推理结果={reasoning_result.action.name}, 路由={route_mapping.get(route, 'llm_call')}, 历史动作={previous_actions}")
     return route_mapping.get(route, "llm_call")
 
 

backend/app/main_graph/utils/rag_initializer.py

@@ -1,5 +1,5 @@
 # app/rag_initializer.py
-from app.rag.tools import create_rag_tool_sync
+from app.rag.tools import create_rag_tool_sync, create_rag_tool_async
 from rag_core import create_parent_retriever
 from app.model_services import get_embedding_service
 from app.logger import info, warning
@@ -16,11 +16,11 @@ async def init_rag_tool(local_llm_creator):
             embeddings=embeddings
         )
         rewrite_llm = local_llm_creator()
-        rag_tool = create_rag_tool_sync(
+        rag_tool = create_rag_tool_async(
             retriever, rewrite_llm,
             num_queries=3, rerank_top_n=5
         )
-        info("✅ RAG 检索工具初始化成功")
+        info("✅ RAG 检索工具初始化成功（异步版本）")
         return rag_tool
     except Exception as e:
         warning(f"⚠️ RAG 检索工具初始化失败: {e}")

backend/app/rag/tools.py

@@ -70,6 +70,63 @@ def create_rag_tool_sync(
     return search_knowledge_base_sync
 
 
+def create_rag_tool_async(
+    retriever: Optional[BaseRetriever] = None,
+    llm: Optional[BaseLanguageModel] = None,
+    num_queries: int = 3,
+    rerank_top_n: int = 5,
+    collection_name: str = "rag_documents",
+) -> Callable:
+    """
+    创建一个配置好的 RAG 检索工具（异步版本）。
+    
+    默认使用混合检索（稠密+BM25稀疏）+ 父子文档模式。
+    
+    Args:
+        retriever: 基础检索器对象（可选，不提供则自动创建）
+        llm: 用于生成多路查询的语言模型（可选）
+        num_queries: 生成的查询变体数量
+        rerank_top_n: 最终返回的文档数量
+        collection_name: Qdrant 集合名称
+    
+    Returns:
+        Async LangChain Tool 函数
+    """
+    pipeline = RAGPipeline(
+        retriever=retriever,
+        llm=llm,
+        num_queries=num_queries,
+        rerank_top_n=rerank_top_n,
+        collection_name=collection_name,
+    )
+
+    @tool
+    async def search_knowledge_base_async(query: str) -> str:
+        """
+        在知识库中搜索与查询相关的文档片段（异步版本）。
+        
+        使用混合检索（稠密向量语义 + BM25 关键词）+ 父子文档模式，
+        检索效果最优。
+        
+        Args:
+            query: 用户提出的问题或查询字符串
+        
+        Returns:
+            格式化后的相关文档内容
+        """
+        try:
+            documents = await pipeline.aretrieve(query)
+            if not documents:
+                return f"在知识库 '{collection_name}' 中未找到与 '{query}' 相关的信息。"
+            
+            context = pipeline.format_context(documents)
+            return context
+        except Exception as e:
+            return f"检索过程中发生错误: {str(e)}"
+    
+    return search_knowledge_base_async
+
+
 def create_rag_tool(
     collection_name: str = "rag_documents",
     llm: Optional[BaseLanguageModel] = None,

frontend/src/config.py

@@ -51,7 +51,7 @@ class FrontendConfig:
     layout: str = "wide"
     
     # ==================== 模型配置（固定值，无需环境变量） ====================
-    default_model: str = "zhipu"
+    default_model: str = "local"
     model_options: Optional[dict] = None
     
     # ==================== 用户配置（固定值，无需环境变量） ====================
@@ -73,7 +73,7 @@ class FrontendConfig:
         if self.model_options is None:
             self.model_options = {
                 "zhipu": "智谱 GLM-5.1（在线）",
-                "local": "本地 llama.cpp（Gemma-4）",
+                "local": "本地 llama.cpp（Qwen3.5-9B）",
                 "deepseek": "DeepSeek V4-Pro（在线）"
             }
         

tools/test/check_qdrant.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""
+检查 Qdrant 集合里的数据结构
+"""
+
+import asyncio
+import os
+import sys
+
+# 添加项目根目录到 Python 路径
+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_core import QdrantVectorStore
+from app.model_services import get_embedding_service
+
+
+def check_qdrant_data():
+    """检查 Qdrant 中的数据结构"""
+    print("="*70)
+    print("检查 Qdrant 中的数据结构...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    
+    # 先获取几个点看看 payload 结构
+    print("\n获取 5 个随机文档:")
+    results = client.scroll(
+        collection_name="rag_documents",
+        limit=5,
+        with_payload=True,
+        with_vectors=True
+    )
+    
+    for i, point in enumerate(results[0], 1):
+        print(f"\n{i}. ID: {point.id}")
+        print(f"   Payload: {point.payload}")
+        print(f"   Payload 键: {list(point.payload.keys())}")
+        if "text" in point.payload:
+            text = point.payload["text"]
+            print(f"   Text 长度: {len(text)}")
+            print(f"   Text 预览: {text[:150]}...")
+        if "page_content" in point.payload:
+            print(f"   page_content: {point.payload['page_content'][:150]}...")
+        
+        # 看看向量
+        if point.vector:
+            print(f"   向量存在: {type(point.vector)}")
+            if isinstance(point.vector, dict):
+                print(f"   向量键: {list(point.vector.keys())}")
+
+
+def check_sparse_embedder():
+    """检查稀疏嵌入器"""
+    from rag_core import get_sparse_embedder
+    
+    print("\n" + "="*70)
+    print("检查稀疏嵌入器...")
+    print("="*70)
+    
+    sparse_embedder = get_sparse_embedder()
+    
+    print(f"\n稀疏嵌入器: {sparse_embedder}")
+    print(f"Vocabulary 大小: {len(sparse_embedder.model.vocab)}")
+    print(f"示例查询: '冬天 食物'")
+    
+    # 用中文试试
+    sparse_vec = sparse_embedder.embed_query("冬天 食物")
+    print(f"\n生成的稀疏向量:")
+    print(f"  索引数量: {len(sparse_vec['indices'])}")
+    print(f"  索引: {sparse_vec['indices'][:10]}")
+    print(f"  值: {sparse_vec['values'][:10]}")
+
+
+if __name__ == "__main__":
+    check_qdrant_data()
+    check_sparse_embedder()

tools/test/quick_test.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+"""
+简单测试脚本：测试文档里真正有的内容
+"""
+
+import asyncio
+import os
+import sys
+
+project_root = os.path.join(os.path.dirname(__file__), "..", "..")
+sys.path.insert(0, os.path.join(project_root, "backend"))
+
+from qdrant_client import models
+from rag_core import QdrantVectorStore, get_sparse_embedder
+from app.model_services import get_embedding_service
+
+
+def test_dense_retrieval():
+    """测试稠密检索"""
+    print("="*70)
+    print("测试稠密检索...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    
+    query = "黄双银"  # 用文档里真正有的名字查询
+    print(f"\n查询: {query}")
+    
+    results = vs.similarity_search(query, k=3)
+    
+    print(f"\n找到 {len(results)} 个结果\n")
+    for i, doc in enumerate(results):
+        print(f"--- 结果 {i+1} ---")
+        print(doc.page_content[:200])
+        print()
+
+
+if __name__ == "__main__":
+    test_dense_retrieval()

tools/test/reset_qdrant.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+"""
+删除 Qdrant 集合并重新索引
+"""
+
+import asyncio
+import os
+import sys
+
+# 添加项目根目录到 Python 路径
+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_core import QdrantVectorStore
+from app.model_services import get_embedding_service
+
+
+async def delete_and_recreate():
+    """删除并重新创建集合"""
+    print("="*70)
+    print("删除旧集合并重新创建...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    
+    # 删除旧集合
+    try:
+        vs.delete_collection()
+        print("✅ 旧集合已删除")
+    except Exception as e:
+        print(f"⚠️ 删除集合时出错（可能不存在）: {e}")
+    
+    # 重新创建
+    vs.create_collection()
+    print("✅ 新集合已创建")
+
+
+if __name__ == "__main__":
+    asyncio.run(delete_and_recreate())

tools/test/simple_delete.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+"""
+简单删除 Qdrant 集合
+"""
+
+import sys
+import os
+
+project_root = os.path.join(os.path.dirname(__file__), "..", "..")
+sys.path.insert(0, os.path.join(project_root, "backend"))
+
+from rag_core.client import create_qdrant_client
+
+
+def delete_collection():
+    print("="*70)
+    print("删除 rag_documents 集合...")
+    print("="*70)
+    
+    client = create_qdrant_client()
+    
+    try:
+        client.delete_collection("rag_documents")
+        print("✅ 删除成功")
+    except Exception as e:
+        print(f"⚠️ 删除失败: {e}")
+
+
+if __name__ == "__main__":
+    delete_collection()

tools/test/simple_test.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+"""
+简单测试脚本：检查 Qdrant 内容，测试各种检索方式
+"""
+
+import asyncio
+import os
+import sys
+
+project_root = os.path.join(os.path.dirname(__file__), "..", "..")
+sys.path.insert(0, os.path.join(project_root, "backend"))
+
+from qdrant_client import models
+from rag_core import QdrantVectorStore, get_sparse_embedder
+from app.model_services import get_embedding_service
+
+
+def check_qdrant_content():
+    """检查 Qdrant 里的内容"""
+    print("="*70)
+    print("检查 Qdrant 内容...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    
+    # 滚动获取前 5 个点
+    points, _ = client.scroll(
+        collection_name="rag_documents",
+        limit=5,
+        with_payload=True,
+        with_vectors=False
+    )
+    
+    print(f"\n找到 {len(points)} 个文档\n")
+    for i, point in enumerate(points):
+        print(f"--- 文档 {i+1} ---")
+        print(f"ID: {point.id}")
+        print(f"Payload 键: {list(point.payload.keys())}")
+        
+        # 打印完整 payload
+        for k, v in point.payload.items():
+            if isinstance(v, str) and len(v) > 150:
+                v = v[:150] + "..."
+            print(f"  {k}: {v}")
+        print()
+
+
+def test_dense_retrieval():
+    """测试稠密检索"""
+    print("="*70)
+    print("测试稠密检索...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    
+    query = "蚂蚁"  # 用中文查询
+    print(f"\n查询: {query}")
+    
+    results = vs.similarity_search(query, k=3)
+    
+    print(f"\n找到 {len(results)} 个结果\n")
+    for i, doc in enumerate(results):
+        print(f"--- 结果 {i+1} ---")
+        print(doc.page_content[:200])
+        print()
+
+
+def test_sparse_retrieval():
+    """测试稀疏检索"""
+    print("="*70)
+    print("测试稀疏检索（BM25）...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    sparse_embedder = get_sparse_embedder()
+    
+    query = "冬天"
+    print(f"\n查询: {query}")
+    
+    sparse_query = sparse_embedder.embed_query(query)
+    sparse_vec = models.SparseVector(
+        indices=sparse_query["indices"],
+        values=sparse_query["values"]
+    )
+    
+    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\n")
+    for i, point in enumerate(response.points):
+        print(f"--- 结果 {i+1} ---")
+        print(f"分数: {point.score:.4f}")
+        text = point.payload.get("page_content", point.payload.get("text", ""))
+        print(text[:200])
+        print()
+
+
+def test_hybrid_retrieval():
+    """测试混合检索"""
+    print("="*70)
+    print("测试混合检索（稠密+稀疏 RRF 融合）...")
+    print("="*70)
+    
+    embeddings = get_embedding_service()
+    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
+    client = vs.get_qdrant_client()
+    sparse_embedder = get_sparse_embedder()
+    
+    query = "蚂蚁和蚱蜢"
+    print(f"\n查询: {query}")
+    
+    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"]
+    )
+    
+    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\n")
+    for i, point in enumerate(response.points):
+        print(f"--- 结果 {i+1} ---")
+        print(f"分数: {point.score:.4f}")
+        text = point.payload.get("page_content", point.payload.get("text", ""))
+        print(text[:200])
+        print()
+
+
+if __name__ == "__main__":
+    check_qdrant_content()
+    test_dense_retrieval()
+    test_sparse_retrieval()
+    test_hybrid_retrieval()

tools/test/test_retrievers.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+"""
+测试 app/rag/retriever.py 里的混合检索函数
+"""
+
+import asyncio
+import os
+import sys
+
+project_root = os.path.join(os.path.dirname(__file__), "..", "..")
+sys.path.insert(0, os.path.join(project_root, "backend"))
+
+from app.rag.retriever import create_hybrid_retriever, create_parent_hybrid_retriever
+
+
+def test_hybrid_retriever():
+    """测试混合检索器"""
+    print("="*70)
+    print("测试 HybridRetriever...")
+    print("="*70)
+    
+    retriever = create_hybrid_retriever(collection_name="rag_documents", search_k=3)
+    results = retriever.invoke("黄双银")
+    
+    print(f"\n找到 {len(results)} 个结果\n")
+    for i, doc in enumerate(results):
+        print(f"--- 结果 {i+1} ---")
+        print(doc.page_content[:200])
+        print()
+
+
+def test_parent_hybrid_retriever():
+    """测试父子混合检索器"""
+    print("\n" + "="*70)
+    print("测试 ParentHybridRetriever...")
+    print("="*70)
+    
+    retriever = create_parent_hybrid_retriever(
+        collection_name="rag_documents", 
+        search_k=3,
+        use_docstore=False
+    )
+    results = retriever.invoke("黄双银")
+    
+    print(f"\n找到 {len(results)} 个结果\n")
+    for i, doc in enumerate(results):
+        print(f"--- 结果 {i+1} ---")
+        print(doc.page_content[:300])
+        print()
+
+
+if __name__ == "__main__":
+    test_hybrid_retriever()
+    test_parent_hybrid_retriever()