[add] Create trigger events for the purpose of completing the existing data

api/app/core/memory/storage_services/clustering_engine/label_propagation.py

@@ -165,8 +165,15 @@ class LabelPropagationEngine:
             f"{len(labels)} 个实体"
         )
         # 为所有社区生成元数据
-        unique_communities = list(set(labels.values()))
-        for cid in unique_communities:
+        # 注意：_evaluate_merge 后部分社区已被合并消解，需重新从 Neo4j 查询实际存活的社区
+        # 不能复用 labels.values()，那里包含已被 dissolve 的旧社区 ID
+        surviving_communities = await self.repo.get_all_entities(end_user_id)
+        surviving_community_ids = list({
+            e.get("community_id") for e in surviving_communities
+            if e.get("community_id")
+        })
+        logger.info(f"[Clustering] 合并后实际存活社区数: {len(surviving_community_ids)}")
+        for cid in surviving_community_ids:
             await self._generate_community_metadata(cid, end_user_id)
 
     async def incremental_update(
@@ -249,7 +256,7 @@ class LabelPropagationEngine:
 
         全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。
         """
-        MERGE_THRESHOLD = 0.75
+        MERGE_THRESHOLD = 0.85
         BATCH_THRESHOLD = 20  # 超过此数量走批量查询
 
         community_embeddings: Dict[str, Optional[List[float]]] = {}
@@ -305,34 +312,65 @@ class LabelPropagationEngine:
 
         logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区")
 
-        # 执行合并：用 union-find 思路避免重复迁移已被合并的社区
-        # 维护一个 canonical 映射，确保链式合并正确收敛
-        canonical: Dict[str, str] = {cid: cid for cid in cids}
+        # 执行合并：逐对处理，每次合并后重新计算合并社区的平均向量
+        # 避免 union-find 链式传递导致语义不相关的社区被间接合并
+        # （A≈B、B≈C 不代表 A≈C，不能因传递性把 A/B/C 全部合并）
+        merged_into: Dict[str, str] = {}  # dissolve → keep 的最终映射
 
-        def find(x: str) -> str:
-            while canonical[x] != x:
-                canonical[x] = canonical[canonical[x]]
-                x = canonical[x]
+        def get_root(x: str) -> str:
+            """路径压缩，找到 x 当前所属的根社区。"""
+            while x in merged_into:
+                merged_into[x] = merged_into.get(merged_into[x], merged_into[x])
+                x = merged_into[x]
             return x
 
         for c1, c2 in to_merge:
-            root1, root2 = find(c1), find(c2)
+            root1, root2 = get_root(c1), get_root(c2)
             if root1 == root2:
-                continue  # 已经在同一社区，跳过
+                continue
+
+            # 用合并后的最新平均向量重新验证相似度
+            # 防止链式传递：A≈B 合并后 B 的向量已更新，C 必须和新 B 相似才能合并
+            current_sim = _cosine_similarity(
+                community_embeddings.get(root1),
+                community_embeddings.get(root2),
+            )
+            if current_sim <= MERGE_THRESHOLD:
+                # 合并后向量已漂移，不再满足阈值，跳过
+                logger.debug(
+                    f"[Clustering] 跳过合并 {root1} ↔ {root2}，"
+                    f"当前相似度 {current_sim:.3f} ≤ {MERGE_THRESHOLD}"
+                )
+                continue
+
             keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2
             dissolve = root2 if keep == root1 else root1
-            canonical[dissolve] = keep
+            merged_into[dissolve] = keep
 
             members = await self.repo.get_community_members(dissolve, end_user_id)
             for m in members:
                 await self.repo.assign_entity_to_community(m["id"], keep, end_user_id)
-            # 更新 sizes 以便后续合并决策准确
-            community_sizes[keep] = community_sizes.get(keep, 0) + len(members)
+
+            # 合并后重新计算 keep 的平均向量（加权平均）
+            keep_emb = community_embeddings.get(keep)
+            dissolve_emb = community_embeddings.get(dissolve)
+            keep_size = community_sizes.get(keep, 0)
+            dissolve_size = community_sizes.get(dissolve, 0)
+            total_size = keep_size + dissolve_size
+            if keep_emb and dissolve_emb and total_size > 0:
+                dim = len(keep_emb)
+                community_embeddings[keep] = [
+                    (keep_emb[i] * keep_size + dissolve_emb[i] * dissolve_size) / total_size
+                    for i in range(dim)
+                ]
+            community_embeddings[dissolve] = None
+
+            community_sizes[keep] = total_size
             community_sizes[dissolve] = 0
             await self.repo.refresh_member_count(keep, end_user_id)
             logger.info(
                 f"[Clustering] 社区合并: {dissolve} → {keep}，"
-                f"迁移 {len(members)} 个成员"
+                f"相似度={current_sim:.3f}，迁移 {len(members)} 个成员"
             )
 
     async def _flush_labels(