[add] Create the attribute values of the community nodes

api/app/core/memory/agent/utils/write_tools.py

@@ -165,7 +165,9 @@ async def write(
                 statement_chunk_edges=all_statement_chunk_edges,
                 statement_entity_edges=all_statement_entity_edges,
                 entity_edges=all_entity_entity_edges,
-                connector=neo4j_connector
+                connector=neo4j_connector,
+                config_id=config_id,
+                llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
             )
             if success:
                 logger.info("Successfully saved all data to Neo4j")

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

@@ -19,6 +19,8 @@ logger = logging.getLogger(__name__)
 
 # 全量迭代最大轮数，防止不收敛
 MAX_ITERATIONS = 10
+# 社区摘要核心实体数量
+CORE_ENTITY_LIMIT = 5
 
 
 def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float:
@@ -62,9 +64,16 @@ def _weighted_vote(
 class LabelPropagationEngine:
     """标签传播聚类引擎"""
 
-    def __init__(self, connector: Neo4jConnector):
+    def __init__(
+        self,
+        connector: Neo4jConnector,
+        config_id: Optional[str] = None,
+        llm_model_id: Optional[str] = None,
+    ):
         self.connector = connector
         self.repo = CommunityRepository(connector)
+        self.config_id = config_id
+        self.llm_model_id = llm_model_id
 
     # ──────────────────────────────────────────────────────────────────────────
     # 公开接口
@@ -155,6 +164,10 @@ class LabelPropagationEngine:
             f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，"
             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:
+            await self._generate_community_metadata(cid, end_user_id)
 
     async def incremental_update(
         self, new_entity_ids: List[str], end_user_id: str
@@ -211,6 +224,7 @@ class LabelPropagationEngine:
             logger.debug(
                 f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}"
             )
+            await self._generate_community_metadata(new_cid, end_user_id)
         else:
             # 加入得票最多的社区
             await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id)
@@ -222,6 +236,7 @@ class LabelPropagationEngine:
                 await self._evaluate_merge(
                     list(community_ids_in_neighbors), end_user_id
                 )
+            await self._generate_community_metadata(target_cid, end_user_id)
 
     async def _evaluate_merge(
         self, community_ids: List[str], end_user_id: str
@@ -354,6 +369,72 @@ class LabelPropagationEngine:
         except Exception:
             return None
 
+    async def _generate_community_metadata(
+        self, community_id: str, end_user_id: str
+    ) -> None:
+        """
+        为社区生成并写入元数据：名称、摘要、核心实体。
+
+        - core_entities：按 activation_value 排序取 top-N 实体名称列表（无需 LLM）
+        - name / summary：若有 llm_model_id 则调用 LLM 生成，否则用实体名称拼接兜底
+        """
+        try:
+            members = await self.repo.get_community_members(community_id, end_user_id)
+            if not members:
+                return
+
+            # 核心实体：按 activation_value 降序取 top-N
+            sorted_members = sorted(
+                members,
+                key=lambda m: m.get("activation_value") or 0,
+                reverse=True,
+            )
+            core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")]
+            all_names = [m["name"] for m in members if m.get("name")]
+
+            name = "、".join(core_entities[:3]) if core_entities else community_id[:8]
+            summary = f"包含实体：{', '.join(all_names)}"
+
+            # 若有 LLM 配置，调用 LLM 生成更好的名称和摘要
+            if self.llm_model_id:
+                try:
+                    from app.db import get_db_context
+                    from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+
+                    entity_list_str = "、".join(all_names)
+                    prompt = (
+                        f"以下是一组语义相关的实体：{entity_list_str}\n\n"
+                        f"请为这组实体所代表的主题：\n"
+                        f"1. 起一个简洁的中文名称（不超过10个字）\n"
+                        f"2. 写一句话摘要（不超过50个字）\n\n"
+                        f"严格按以下格式输出，不要有其他内容：\n"
+                        f"名称：<名称>\n摘要：<摘要>"
+                    )
+                    with get_db_context() as db:
+                        factory = MemoryClientFactory(db)
+                        llm_client = factory.get_llm_client(self.llm_model_id)
+                        response = await llm_client.chat([{"role": "user", "content": prompt}])
+                        text = response.content if hasattr(response, "content") else str(response)
+
+                    for line in text.strip().splitlines():
+                        if line.startswith("名称："):
+                            name = line[3:].strip()
+                        elif line.startswith("摘要："):
+                            summary = line[3:].strip()
+                except Exception as e:
+                    logger.warning(f"[Clustering] LLM 生成社区元数据失败，使用兜底值: {e}")
+
+            await self.repo.update_community_metadata(
+                community_id=community_id,
+                end_user_id=end_user_id,
+                name=name,
+                summary=summary,
+                core_entities=core_entities,
+            )
+            logger.debug(f"[Clustering] 社区 {community_id} 元数据已更新: name={name}")
+        except Exception as e:
+            logger.error(f"[Clustering] _generate_community_metadata failed for {community_id}: {e}")
+
     @staticmethod
     def _new_community_id() -> str:
         return str(uuid.uuid4())