refactor: 消除重复代码并优化记忆系统结构

- 提取共享工具函数到 utils 模块
  - 创建 utils/similarity.py: 统一余弦相似度计算
  - 创建 utils/graph_expansion.py: 统一图扩展算法
- 删除重复实现
  - manager.py: 删除 _cosine_similarity 和 _fast_cosine_similarity (60行)
  - tools/memory_tools.py: 删除 _expand_with_semantic_filter 和 _cosine_similarity (150行)
- 清理废弃代码
  - 删除 tools/memory_tools.py 中10行注释掉的旧代码
  - 删除空的 retrieval/ 模块
- 净减少 ~150行重复代码

Co-authored-by: Windpicker-owo <221029311+Windpicker-owo@users.noreply.github.com>

src/memory_graph/utils/__init__.py

@@ -3,6 +3,7 @@
 """
 
 from src.memory_graph.utils.embeddings import EmbeddingGenerator, get_embedding_generator
+from src.memory_graph.utils.similarity import cosine_similarity
 from src.memory_graph.utils.time_parser import TimeParser
 
-__all__ = ["EmbeddingGenerator", "TimeParser", "get_embedding_generator"]
+__all__ = ["EmbeddingGenerator", "TimeParser", "cosine_similarity", "get_embedding_generator"]

src/memory_graph/utils/graph_expansion.py

@@ -0,0 +1,156 @@
+"""
+图扩展工具
+
+提供记忆图的扩展算法，用于从初始记忆集合沿图结构扩展查找相关记忆
+"""
+
+from typing import TYPE_CHECKING
+
+from src.common.logger import get_logger
+from src.memory_graph.utils.similarity import cosine_similarity
+
+if TYPE_CHECKING:
+    import numpy as np
+
+    from src.memory_graph.storage.graph_store import GraphStore
+    from src.memory_graph.storage.vector_store import VectorStore
+
+logger = get_logger(__name__)
+
+
+async def expand_memories_with_semantic_filter(
+    graph_store: "GraphStore",
+    vector_store: "VectorStore",
+    initial_memory_ids: list[str],
+    query_embedding: "np.ndarray",
+    max_depth: int = 2,
+    semantic_threshold: float = 0.5,
+    max_expanded: int = 20,
+) -> list[tuple[str, float]]:
+    """
+    从初始记忆集合出发，沿图结构扩展，并用语义相似度过滤
+
+    这个方法解决了纯向量搜索可能遗漏的"语义相关且图结构相关"的记忆。
+
+    Args:
+        graph_store: 图存储
+        vector_store: 向量存储
+        initial_memory_ids: 初始记忆ID集合（由向量搜索得到）
+        query_embedding: 查询向量
+        max_depth: 最大扩展深度（1-3推荐）
+        semantic_threshold: 语义相似度阈值（0.5推荐）
+        max_expanded: 最多扩展多少个记忆
+
+    Returns:
+        List[(memory_id, relevance_score)] 按相关度排序
+    """
+    if not initial_memory_ids or query_embedding is None:
+        return []
+
+    try:
+        # 记录已访问的记忆，避免重复
+        visited_memories = set(initial_memory_ids)
+        # 记录扩展的记忆及其分数
+        expanded_memories: dict[str, float] = {}
+
+        # BFS扩展
+        current_level = initial_memory_ids
+
+        for depth in range(max_depth):
+            next_level = []
+
+            for memory_id in current_level:
+                memory = graph_store.get_memory_by_id(memory_id)
+                if not memory:
+                    continue
+
+                # 遍历该记忆的所有节点
+                for node in memory.nodes:
+                    if not node.has_embedding():
+                        continue
+
+                    # 获取邻居节点
+                    try:
+                        neighbors = list(graph_store.graph.neighbors(node.id))
+                    except Exception:
+                        continue
+
+                    for neighbor_id in neighbors:
+                        # 获取邻居节点信息
+                        neighbor_node_data = graph_store.graph.nodes.get(neighbor_id)
+                        if not neighbor_node_data:
+                            continue
+
+                        # 获取邻居节点的向量（从向量存储）
+                        neighbor_vector_data = await vector_store.get_node_by_id(neighbor_id)
+                        if not neighbor_vector_data or neighbor_vector_data.get("embedding") is None:
+                            continue
+
+                        neighbor_embedding = neighbor_vector_data["embedding"]
+
+                        # 计算与查询的语义相似度
+                        semantic_sim = cosine_similarity(query_embedding, neighbor_embedding)
+
+                        # 获取边的权重
+                        try:
+                            edge_data = graph_store.graph.get_edge_data(node.id, neighbor_id)
+                            edge_importance = edge_data.get("importance", 0.5) if edge_data else 0.5
+                        except Exception:
+                            edge_importance = 0.5
+
+                        # 综合评分：语义相似度(70%) + 图结构权重(20%) + 深度衰减(10%)
+                        depth_decay = 1.0 / (depth + 1)  # 深度越深，权重越低
+                        relevance_score = semantic_sim * 0.7 + edge_importance * 0.2 + depth_decay * 0.1
+
+                        # 只保留超过阈值的节点
+                        if relevance_score < semantic_threshold:
+                            continue
+
+                        # 提取邻居节点所属的记忆
+                        neighbor_memory_ids = neighbor_node_data.get("memory_ids", [])
+                        if isinstance(neighbor_memory_ids, str):
+                            import json
+
+                            try:
+                                neighbor_memory_ids = json.loads(neighbor_memory_ids)
+                            except Exception:
+                                neighbor_memory_ids = [neighbor_memory_ids]
+
+                        for neighbor_mem_id in neighbor_memory_ids:
+                            if neighbor_mem_id in visited_memories:
+                                continue
+
+                            # 记录这个扩展记忆
+                            if neighbor_mem_id not in expanded_memories:
+                                expanded_memories[neighbor_mem_id] = relevance_score
+                                visited_memories.add(neighbor_mem_id)
+                                next_level.append(neighbor_mem_id)
+                            else:
+                                # 如果已存在，取最高分
+                                expanded_memories[neighbor_mem_id] = max(
+                                    expanded_memories[neighbor_mem_id], relevance_score
+                                )
+
+            # 如果没有新节点或已达到数量限制，提前终止
+            if not next_level or len(expanded_memories) >= max_expanded:
+                break
+
+            current_level = next_level[:max_expanded]  # 限制每层的扩展数量
+
+        # 排序并返回
+        sorted_results = sorted(expanded_memories.items(), key=lambda x: x[1], reverse=True)[:max_expanded]
+
+        logger.info(
+            f"图扩展完成: 初始{len(initial_memory_ids)}个 → "
+            f"扩展{len(sorted_results)}个新记忆 "
+            f"(深度={max_depth}, 阈值={semantic_threshold:.2f})"
+        )
+
+        return sorted_results
+
+    except Exception as e:
+        logger.error(f"语义图扩展失败: {e}", exc_info=True)
+        return []
+
+
+__all__ = ["expand_memories_with_semantic_filter"]

src/memory_graph/utils/similarity.py

@@ -0,0 +1,50 @@
+"""
+相似度计算工具
+
+提供统一的向量相似度计算函数
+"""
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import numpy as np
+
+
+def cosine_similarity(vec1: "np.ndarray", vec2: "np.ndarray") -> float:
+    """
+    计算两个向量的余弦相似度
+
+    Args:
+        vec1: 第一个向量
+        vec2: 第二个向量
+
+    Returns:
+        余弦相似度 (0.0-1.0)
+    """
+    try:
+        import numpy as np
+
+        # 确保是numpy数组
+        if not isinstance(vec1, np.ndarray):
+            vec1 = np.array(vec1)
+        if not isinstance(vec2, np.ndarray):
+            vec2 = np.array(vec2)
+
+        # 归一化
+        vec1_norm = np.linalg.norm(vec1)
+        vec2_norm = np.linalg.norm(vec2)
+
+        if vec1_norm == 0 or vec2_norm == 0:
+            return 0.0
+
+        # 余弦相似度
+        similarity = np.dot(vec1, vec2) / (vec1_norm * vec2_norm)
+
+        # 确保在 [0, 1] 范围内（处理浮点误差）
+        return float(np.clip(similarity, 0.0, 1.0))
+
+    except Exception:
+        return 0.0
+
+
+__all__ = ["cosine_similarity"]