feat:增强记忆节点的嵌入生成和日志记录- 在 MemoryBuilder 中为 SUBJECT 和 VALUE 节点类型添加了嵌入生成，确保仅为内容足够的节点创建嵌入。- 改进了 MemoryTools 的日志记录，在初始向量搜索期间提供详细见解，包括低召回情况的警告。- 调整了不同记忆类型的评分权重，以强调相似性和重要性，提高记忆检索的质量。- 将向量搜索限制从 2 倍提高到 5 倍，以改善初始召回率。- 引入了一个新脚本，用于为现有节点生成缺失的嵌入，支持批量处理并改进索引。

scripts/generate_missing_embeddings.py

@@ -0,0 +1,268 @@
+"""
+为现有节点生成嵌入向量
+
+批量为图存储中缺少嵌入向量的节点生成并索引嵌入向量
+
+使用场景:
+1. 历史记忆节点没有嵌入向量
+2. 嵌入生成器之前未配置，现在需要补充生成
+3. 向量索引损坏需要重建
+
+使用方法:
+    python scripts/generate_missing_embeddings.py [--node-types TOPIC,OBJECT] [--batch-size 50]
+    
+参数说明:
+    --node-types: 需要生成嵌入的节点类型，默认为 TOPIC,OBJECT
+    --batch-size: 批量处理大小，默认为 50
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+from typing import List
+
+# 添加项目根目录到路径
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+async def generate_missing_embeddings(
+    target_node_types: List[str] = None,
+    batch_size: int = 50,
+):
+    """
+    为缺失嵌入向量的节点生成嵌入
+
+    Args:
+        target_node_types: 需要处理的节点类型列表（如 ["主题", "客体"]）
+        batch_size: 批处理大小
+    """
+    from src.common.logger import get_logger
+    from src.memory_graph.manager_singleton import get_memory_manager, initialize_memory_manager
+    from src.memory_graph.models import NodeType
+
+    logger = get_logger("generate_missing_embeddings")
+
+    if target_node_types is None:
+        target_node_types = [NodeType.TOPIC.value, NodeType.OBJECT.value]
+
+    print(f"\n{'='*80}")
+    print(f"🔧 为节点生成嵌入向量")
+    print(f"{'='*80}\n")
+    print(f"目标节点类型: {', '.join(target_node_types)}")
+    print(f"批处理大小: {batch_size}\n")
+
+    # 1. 初始化记忆管理器
+    print(f"🔧 正在初始化记忆管理器...")
+    await initialize_memory_manager()
+    manager = get_memory_manager()
+
+    if manager is None:
+        print("❌ 记忆管理器初始化失败")
+        return
+
+    print(f"✅ 记忆管理器已初始化\n")
+
+    # 2. 获取已索引的节点ID
+    print(f"🔍 检查现有向量索引...")
+    existing_node_ids = set()
+    try:
+        vector_count = manager.vector_store.collection.count()
+        if vector_count > 0:
+            # 分批获取所有已索引的ID
+            batch_size_check = 1000
+            for offset in range(0, vector_count, batch_size_check):
+                limit = min(batch_size_check, vector_count - offset)
+                result = manager.vector_store.collection.get(
+                    limit=limit,
+                    offset=offset,
+                )
+                if result and "ids" in result:
+                    existing_node_ids.update(result["ids"])
+        
+        print(f"✅ 发现 {len(existing_node_ids)} 个已索引节点\n")
+    except Exception as e:
+        logger.warning(f"获取已索引节点ID失败: {e}")
+        print(f"⚠️  无法获取已索引节点，将尝试跳过重复项\n")
+
+    # 3. 收集需要生成嵌入的节点
+    print(f"🔍 扫描需要生成嵌入的节点...")
+    all_memories = manager.graph_store.get_all_memories()
+
+    nodes_to_process = []
+    total_target_nodes = 0
+    type_stats = {nt: {"total": 0, "need_emb": 0, "already_indexed": 0} for nt in target_node_types}
+
+    for memory in all_memories:
+        for node in memory.nodes:
+            if node.node_type.value in target_node_types:
+                total_target_nodes += 1
+                type_stats[node.node_type.value]["total"] += 1
+
+                # 检查是否已在向量索引中
+                if node.id in existing_node_ids:
+                    type_stats[node.node_type.value]["already_indexed"] += 1
+                    continue
+
+                if not node.has_embedding():
+                    nodes_to_process.append({
+                        "node": node,
+                        "memory_id": memory.id,
+                    })
+                    type_stats[node.node_type.value]["need_emb"] += 1
+
+    print(f"\n📊 扫描结果:")
+    for node_type in target_node_types:
+        stats = type_stats[node_type]
+        already_ok = stats["already_indexed"]
+        coverage = (stats["total"] - stats["need_emb"]) / stats["total"] * 100 if stats["total"] > 0 else 0
+        print(f"  - {node_type}: {stats['total']} 个节点, {stats['need_emb']} 个缺失嵌入, "
+              f"{already_ok} 个已索引 (覆盖率: {coverage:.1f}%)")
+
+    print(f"\n  总计: {total_target_nodes} 个目标节点, {len(nodes_to_process)} 个需要生成嵌入\n")
+
+    if len(nodes_to_process) == 0:
+        print(f"✅ 所有节点已有嵌入向量，无需生成")
+        return
+
+    # 3. 批量生成嵌入
+    print(f"🚀 开始生成嵌入向量...\n")
+
+    total_batches = (len(nodes_to_process) + batch_size - 1) // batch_size
+    success_count = 0
+    failed_count = 0
+    indexed_count = 0
+
+    for i in range(0, len(nodes_to_process), batch_size):
+        batch = nodes_to_process[i : i + batch_size]
+        batch_num = i // batch_size + 1
+
+        print(f"📦 批次 {batch_num}/{total_batches} ({len(batch)} 个节点)...")
+
+        try:
+            # 提取文本内容
+            texts = [item["node"].content for item in batch]
+
+            # 批量生成嵌入
+            embeddings = await manager.embedding_generator.generate_batch(texts)
+
+            # 为节点设置嵌入并索引
+            batch_nodes_for_index = []
+
+            for j, (item, embedding) in enumerate(zip(batch, embeddings)):
+                node = item["node"]
+
+                if embedding is not None:
+                    # 设置嵌入向量
+                    node.embedding = embedding
+                    batch_nodes_for_index.append(node)
+                    success_count += 1
+                else:
+                    failed_count += 1
+                    logger.warning(f"  ⚠️  节点 {node.id[:8]}... '{node.content[:30]}' 嵌入生成失败")
+
+            # 批量索引到向量数据库
+            if batch_nodes_for_index:
+                try:
+                    await manager.vector_store.add_nodes_batch(batch_nodes_for_index)
+                    indexed_count += len(batch_nodes_for_index)
+                    print(f"  ✅ 成功: {len(batch_nodes_for_index)}/{len(batch)} 个节点已生成并索引")
+                except Exception as e:
+                    # 如果批量失败，尝试逐个添加（跳过重复）
+                    logger.warning(f"  批量索引失败，尝试逐个添加: {e}")
+                    individual_success = 0
+                    for node in batch_nodes_for_index:
+                        try:
+                            await manager.vector_store.add_node(node)
+                            individual_success += 1
+                            indexed_count += 1
+                        except Exception as e2:
+                            if "Expected IDs to be unique" in str(e2):
+                                logger.debug(f"    跳过已存在节点: {node.id}")
+                            else:
+                                logger.error(f"    节点 {node.id} 索引失败: {e2}")
+                    print(f"  ⚠️  逐个索引: {individual_success}/{len(batch_nodes_for_index)} 个成功")
+
+        except Exception as e:
+            failed_count += len(batch)
+            logger.error(f"批次 {batch_num} 处理失败", exc_info=True)
+            print(f"  ❌ 批次处理失败: {e}")
+
+        # 显示进度
+        total_processed = min(i + batch_size, len(nodes_to_process))
+        progress = total_processed / len(nodes_to_process) * 100
+        print(f"  📊 总进度: {total_processed}/{len(nodes_to_process)} ({progress:.1f}%)\n")
+
+    # 4. 保存图数据（更新节点的 embedding 字段）
+    print(f"💾 保存图数据...")
+    try:
+        await manager.persistence.save_graph_store(manager.graph_store)
+        print(f"✅ 图数据已保存\n")
+    except Exception as e:
+        logger.error(f"保存图数据失败", exc_info=True)
+        print(f"❌ 保存失败: {e}\n")
+
+    # 5. 验证结果
+    print(f"🔍 验证向量索引...")
+    final_vector_count = manager.vector_store.collection.count()
+    stats = manager.graph_store.get_statistics()
+    total_nodes = stats["total_nodes"]
+
+    print(f"\n{'='*80}")
+    print(f"📊 生成完成")
+    print(f"{'='*80}")
+    print(f"处理节点数: {len(nodes_to_process)}")
+    print(f"成功生成: {success_count}")
+    print(f"失败数量: {failed_count}")
+    print(f"成功索引: {indexed_count}")
+    print(f"向量索引节点数: {final_vector_count}")
+    print(f"图存储节点数: {total_nodes}")
+    print(f"索引覆盖率: {final_vector_count / total_nodes * 100:.1f}%\n")
+
+    # 6. 测试搜索
+    print(f"🧪 测试搜索功能...")
+    test_queries = ["小红帽蕾克", "拾风", "杰瑞喵"]
+
+    for query in test_queries:
+        results = await manager.search_memories(query=query, top_k=3)
+        if results:
+            print(f"\n✅ 查询 '{query}' 找到 {len(results)} 条记忆:")
+            for i, memory in enumerate(results[:2], 1):
+                subject_node = memory.get_subject_node()
+                # 获取主题节点（遍历所有节点找TOPIC类型）
+                from src.memory_graph.models import NodeType
+                topic_nodes = [n for n in memory.nodes if n.node_type == NodeType.TOPIC]
+                subject = subject_node.content if subject_node else "?"
+                topic = topic_nodes[0].content if topic_nodes else "?"
+                print(f"  {i}. {subject} - {topic} (重要性: {memory.importance:.2f})")
+        else:
+            print(f"\n⚠️  查询 '{query}' 返回 0 条结果")
+
+
+async def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="为节点生成嵌入向量")
+    parser.add_argument(
+        "--node-types",
+        type=str,
+        default="主题,客体",
+        help="需要生成嵌入的节点类型，逗号分隔（默认：主题,客体）",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=50,
+        help="批处理大小（默认：50）",
+    )
+
+    args = parser.parse_args()
+
+    target_types = [t.strip() for t in args.node_types.split(",")]
+    await generate_missing_embeddings(
+        target_node_types=target_types,
+        batch_size=args.batch_size,
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/api/memory_visualizer_router.py

@@ -7,9 +7,10 @@
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, List, Optional
+from collections import defaultdict
 
 import orjson
-from fastapi import APIRouter, HTTPException, Request
+from fastapi import APIRouter, HTTPException, Request, Query
 from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.templating import Jinja2Templates
 
@@ -227,6 +228,242 @@ async def get_full_graph():
         return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
 
 
+@router.get("/api/graph/summary")
+async def get_graph_summary():
+    """获取图的摘要信息（仅统计数据，不包含节点和边）"""
+    try:
+        from src.memory_graph.manager_singleton import get_memory_manager
+
+        memory_manager = get_memory_manager()
+
+        if memory_manager and memory_manager._initialized:
+            stats = memory_manager.get_statistics()
+            return JSONResponse(content={"success": True, "data": {
+                "stats": {
+                    "total_nodes": stats.get("total_nodes", 0),
+                    "total_edges": stats.get("total_edges", 0),
+                    "total_memories": stats.get("total_memories", 0),
+                },
+                "current_file": "memory_manager (实时数据)",
+            }})
+        else:
+            data = load_graph_data_from_file()
+            return JSONResponse(content={"success": True, "data": {
+                "stats": data.get("stats", {}),
+                "current_file": data.get("current_file", ""),
+            }})
+    except Exception as e:
+        return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
+
+
+@router.get("/api/graph/paginated")
+async def get_paginated_graph(
+    page: int = Query(1, ge=1, description="页码"),
+    page_size: int = Query(500, ge=100, le=2000, description="每页节点数"),
+    min_importance: float = Query(0.0, ge=0.0, le=1.0, description="最小重要性阈值"),
+    node_types: Optional[str] = Query(None, description="节点类型过滤，逗号分隔"),
+):
+    """分页获取图数据，支持重要性过滤"""
+    try:
+        from src.memory_graph.manager_singleton import get_memory_manager
+
+        memory_manager = get_memory_manager()
+
+        # 获取完整数据
+        if memory_manager and memory_manager._initialized:
+            full_data = _format_graph_data_from_manager(memory_manager)
+        else:
+            full_data = load_graph_data_from_file()
+
+        nodes = full_data.get("nodes", [])
+        edges = full_data.get("edges", [])
+
+        # 过滤节点类型
+        if node_types:
+            allowed_types = set(node_types.split(","))
+            nodes = [n for n in nodes if n.get("group") in allowed_types]
+
+        # 按重要性排序（如果有importance字段）
+        nodes_with_importance = []
+        for node in nodes:
+            # 计算节点重要性（连接的边数）
+            edge_count = sum(1 for e in edges if e.get("from") == node["id"] or e.get("to") == node["id"])
+            importance = edge_count / max(len(edges), 1)
+            if importance >= min_importance:
+                node["importance"] = importance
+                nodes_with_importance.append(node)
+
+        # 按重要性降序排序
+        nodes_with_importance.sort(key=lambda x: x.get("importance", 0), reverse=True)
+
+        # 分页
+        total_nodes = len(nodes_with_importance)
+        total_pages = (total_nodes + page_size - 1) // page_size
+        start_idx = (page - 1) * page_size
+        end_idx = min(start_idx + page_size, total_nodes)
+        
+        paginated_nodes = nodes_with_importance[start_idx:end_idx]
+        node_ids = set(n["id"] for n in paginated_nodes)
+
+        # 只保留连接分页节点的边
+        paginated_edges = [
+            e for e in edges 
+            if e.get("from") in node_ids and e.get("to") in node_ids
+        ]
+
+        return JSONResponse(content={"success": True, "data": {
+            "nodes": paginated_nodes,
+            "edges": paginated_edges,
+            "pagination": {
+                "page": page,
+                "page_size": page_size,
+                "total_nodes": total_nodes,
+                "total_pages": total_pages,
+                "has_next": page < total_pages,
+                "has_prev": page > 1,
+            },
+            "stats": {
+                "total_nodes": total_nodes,
+                "total_edges": len(paginated_edges),
+                "total_memories": full_data.get("stats", {}).get("total_memories", 0),
+            },
+        }})
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
+
+
+@router.get("/api/graph/clustered")
+async def get_clustered_graph(
+    max_nodes: int = Query(300, ge=50, le=1000, description="最大节点数"),
+    cluster_threshold: int = Query(10, ge=2, le=50, description="聚类阈值")
+):
+    """获取聚类简化后的图数据"""
+    try:
+        from src.memory_graph.manager_singleton import get_memory_manager
+
+        memory_manager = get_memory_manager()
+
+        # 获取完整数据
+        if memory_manager and memory_manager._initialized:
+            full_data = _format_graph_data_from_manager(memory_manager)
+        else:
+            full_data = load_graph_data_from_file()
+
+        nodes = full_data.get("nodes", [])
+        edges = full_data.get("edges", [])
+
+        # 如果节点数小于阈值，直接返回
+        if len(nodes) <= max_nodes:
+            return JSONResponse(content={"success": True, "data": {
+                "nodes": nodes,
+                "edges": edges,
+                "stats": full_data.get("stats", {}),
+                "clustered": False,
+            }})
+
+        # 执行聚类
+        clustered_data = _cluster_graph_data(nodes, edges, max_nodes, cluster_threshold)
+
+        return JSONResponse(content={"success": True, "data": {
+            **clustered_data,
+            "stats": {
+                "original_nodes": len(nodes),
+                "original_edges": len(edges),
+                "clustered_nodes": len(clustered_data["nodes"]),
+                "clustered_edges": len(clustered_data["edges"]),
+                "total_memories": full_data.get("stats", {}).get("total_memories", 0),
+            },
+            "clustered": True,
+        }})
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
+
+
+def _cluster_graph_data(nodes: List[Dict], edges: List[Dict], max_nodes: int, cluster_threshold: int) -> Dict:
+    """简单的图聚类算法：按类型和连接度聚类"""
+    # 构建邻接表
+    adjacency = defaultdict(set)
+    for edge in edges:
+        adjacency[edge["from"]].add(edge["to"])
+        adjacency[edge["to"]].add(edge["from"])
+
+    # 按类型分组
+    type_groups = defaultdict(list)
+    for node in nodes:
+        type_groups[node.get("group", "UNKNOWN")].append(node)
+
+    clustered_nodes = []
+    clustered_edges = []
+    node_mapping = {}  # 原始节点ID -> 聚类节点ID
+
+    for node_type, type_nodes in type_groups.items():
+        # 如果该类型节点少于阈值，直接保留
+        if len(type_nodes) <= cluster_threshold:
+            for node in type_nodes:
+                clustered_nodes.append(node)
+                node_mapping[node["id"]] = node["id"]
+        else:
+            # 按连接度排序，保留最重要的节点
+            node_importance = []
+            for node in type_nodes:
+                importance = len(adjacency[node["id"]])
+                node_importance.append((node, importance))
+            
+            node_importance.sort(key=lambda x: x[1], reverse=True)
+            
+            # 保留前N个重要节点
+            keep_count = min(len(type_nodes), max_nodes // len(type_groups))
+            for node, importance in node_importance[:keep_count]:
+                clustered_nodes.append(node)
+                node_mapping[node["id"]] = node["id"]
+            
+            # 其余节点聚合为一个超级节点
+            if len(node_importance) > keep_count:
+                clustered_node_ids = [n["id"] for n, _ in node_importance[keep_count:]]
+                cluster_id = f"cluster_{node_type}_{len(clustered_nodes)}"
+                cluster_label = f"{node_type} 集群 ({len(clustered_node_ids)}个节点)"
+                
+                clustered_nodes.append({
+                    "id": cluster_id,
+                    "label": cluster_label,
+                    "group": node_type,
+                    "title": f"包含 {len(clustered_node_ids)} 个{node_type}节点",
+                    "is_cluster": True,
+                    "cluster_size": len(clustered_node_ids),
+                    "clustered_nodes": clustered_node_ids[:10],  # 只保留前10个用于展示
+                })
+                
+                for node_id in clustered_node_ids:
+                    node_mapping[node_id] = cluster_id
+
+    # 重建边（去重）
+    edge_set = set()
+    for edge in edges:
+        from_id = node_mapping.get(edge["from"])
+        to_id = node_mapping.get(edge["to"])
+        
+        if from_id and to_id and from_id != to_id:
+            edge_key = tuple(sorted([from_id, to_id]))
+            if edge_key not in edge_set:
+                edge_set.add(edge_key)
+                clustered_edges.append({
+                    "id": f"{from_id}_{to_id}",
+                    "from": from_id,
+                    "to": to_id,
+                    "label": edge.get("label", ""),
+                    "arrows": "to",
+                })
+
+    return {
+        "nodes": clustered_nodes,
+        "edges": clustered_edges,
+    }
+
+
 @router.get("/api/files")
 async def list_files_api():
     """列出所有可用的数据文件"""

src/memory_graph/core/builder.py

@@ -185,12 +185,19 @@ class MemoryBuilder:
                 logger.debug(f"复用已存在的主体节点: {existing.id}")
                 return existing
 
+        # 为主体和值节点生成嵌入向量（用于人名/实体和重要描述检索）
+        embedding = None
+        if node_type in (NodeType.SUBJECT, NodeType.VALUE):
+            # 只为有足够内容的节点生成嵌入（避免浪费）
+            if len(content.strip()) >= 2:
+                embedding = await self._generate_embedding(content)
+
         # 创建新节点
         node = MemoryNode(
             id=self._generate_node_id(),
             content=content,
             node_type=node_type,
-            embedding=None,  # 主体和属性不需要嵌入
+            embedding=embedding,  # 主体、值需要嵌入，属性不需要
             metadata={"memory_ids": [memory_id]},
         )
 

src/memory_graph/tools/memory_tools.py

@@ -516,6 +516,22 @@ class MemoryTools:
                             # 记录最高分数
                             if mem_id not in memory_scores or similarity > memory_scores[mem_id]:
                                 memory_scores[mem_id] = similarity
+            
+            # 🔥 详细日志：检查初始召回情况
+            logger.info(
+                f"初始向量搜索: 返回{len(similar_nodes)}个节点 → "
+                f"提取{len(initial_memory_ids)}条记忆"
+            )
+            if len(initial_memory_ids) == 0:
+                logger.warning(
+                    f"⚠️ 向量搜索未找到任何记忆！"
+                    f"可能原因：1) 嵌入模型理解问题 2) 记忆节点未建立索引 3) 查询表达与存储内容差异过大"
+                )
+                # 输出相似节点的详细信息用于调试
+                if similar_nodes:
+                    logger.debug(f"向量搜索返回的节点元数据样例: {similar_nodes[0][2] if len(similar_nodes) > 0 else 'None'}")
+            elif len(initial_memory_ids) < 3:
+                logger.warning(f"⚠️ 初始召回记忆数量较少({len(initial_memory_ids)}条)，可能影响结果质量")
 
             # 3. 图扩展（如果启用且有expand_depth）
             expanded_memory_scores = {}
@@ -609,42 +625,37 @@ class MemoryTools:
                     if dominant_node_type in ["ATTRIBUTE", "REFERENCE"] or memory_type == "FACT":
                         # 事实性记忆（如文档地址、配置信息）：语义相似度最重要
                         weights = {
-                            "similarity": 0.65,   # 语义相似度 65% ⬆️
-                            "importance": 0.20,   # 重要性 20%
-                            "recency": 0.05,      # 时效性 5% ⬇️（事实不随时间失效）
-                            "activation": 0.10    # 激活度 10% ⬇️（避免冷门信息被压制）
+                            "similarity": 0.70,   # 语义相似度 70% ⬆️
+                            "importance": 0.25,   # 重要性 25% ⬆️
+                            "recency": 0.05,      # 时效性 5%（事实不随时间失效）
                         }
                     elif memory_type in ["CONVERSATION", "EPISODIC"] or dominant_node_type == "EVENT":
-                        # 对话/事件记忆：时效性和激活度更重要
+                        # 对话/事件记忆：时效性更重要
                         weights = {
-                            "similarity": 0.45,   # 语义相似度 45%
-                            "importance": 0.15,   # 重要性 15%
-                            "recency": 0.20,      # 时效性 20% ⬆️
-                            "activation": 0.20    # 激活度 20%
+                            "similarity": 0.55,   # 语义相似度 55% ⬆️
+                            "importance": 0.20,   # 重要性 20% ⬆️
+                            "recency": 0.25,      # 时效性 25% ⬆️
                         }
                     elif dominant_node_type == "ENTITY" or memory_type == "SEMANTIC":
                         # 实体/语义记忆：平衡各项
                         weights = {
-                            "similarity": 0.50,   # 语义相似度 50%
-                            "importance": 0.25,   # 重要性 25%
+                            "similarity": 0.60,   # 语义相似度 60% ⬆️
+                            "importance": 0.30,   # 重要性 30% ⬆️
                             "recency": 0.10,      # 时效性 10%
-                            "activation": 0.15    # 激活度 15%
                         }
                     else:
                         # 默认权重（保守策略，偏向语义）
                         weights = {
-                            "similarity": 0.55,   # 语义相似度 55%
-                            "importance": 0.20,   # 重要性 20%
+                            "similarity": 0.65,   # 语义相似度 65% ⬆️
+                            "importance": 0.25,   # 重要性 25% ⬆️
                             "recency": 0.10,      # 时效性 10%
-                            "activation": 0.15    # 激活度 15%
                         }
                     
-                    # 综合分数计算
+                    # 综合分数计算（🔥 移除激活度影响）
                     final_score = (
                         similarity_score * weights["similarity"] +
                         importance_score * weights["importance"] +
-                        recency_score * weights["recency"] +
-                        activation_score * weights["activation"]
+                        recency_score * weights["recency"]
                     )
                     
                     # 🆕 节点类型加权：对REFERENCE/ATTRIBUTE节点额外加分（促进事实性信息召回）
@@ -943,11 +954,16 @@ class MemoryTools:
             logger.warning("嵌入生成失败，跳过节点搜索")
             return []
 
-        # 向量搜索
+        # 向量搜索（增加返回数量以提高召回率）
         similar_nodes = await self.vector_store.search_similar_nodes(
             query_embedding=query_embedding,
-            limit=top_k * 2,  # 多取一些，后续过滤
+            limit=top_k * 5,  # 🔥 从2倍提升到5倍，提高初始召回率
+            min_similarity=0.0,  # 不在这里过滤，交给后续评分
         )
+        
+        logger.debug(f"单查询向量搜索: 查询='{query}', 返回节点数={len(similar_nodes)}")
+        if similar_nodes:
+            logger.debug(f"Top 3相似度: {[f'{sim:.3f}' for _, sim, _ in similar_nodes[:3]]}")
 
         return similar_nodes
 
@@ -1003,11 +1019,13 @@ class MemoryTools:
             similar_nodes = await self.vector_store.search_with_multiple_queries(
                 query_embeddings=query_embeddings,
                 query_weights=query_weights,
-                limit=top_k * 2,  # 多取一些，后续过滤
+                limit=top_k * 5,  # 🔥 从2倍提升到5倍，提高初始召回率
                 fusion_strategy="weighted_max",
             )
 
             logger.info(f"多查询检索完成: {len(similar_nodes)} 个节点 (偏好类型: {prefer_node_types})")
+            if similar_nodes:
+                logger.debug(f"Top 5融合相似度: {[f'{sim:.3f}' for _, sim, _ in similar_nodes[:5]]}")
 
             return similar_nodes, prefer_node_types