[changes] Community Clustering Retrieval Module

api/app/core/memory/agent/services/search_service.py

@@ -120,7 +120,7 @@ class SearchService:
             raw_results is None if return_raw_results=False
         """
         if include is None:
-            include = ["statements", "chunks", "entities", "summaries"]
+            include = ["statements", "chunks", "entities", "summaries", "communities"]
         
         # Clean query
         cleaned_query = self.clean_query(question)
@@ -146,8 +146,8 @@ class SearchService:
             if search_type == "hybrid":
                 reranked_results = answer.get('reranked_results', {})
                 
-                # Priority order: summaries first (most contextual), then statements, chunks, entities
+                # Priority order: summaries first (most contextual), then communities, statements, chunks, entities
-                priority_order = ['summaries', 'statements', 'chunks', 'entities']
+                priority_order = ['summaries', 'communities', 'statements', 'chunks', 'entities']
                 
                 for category in priority_order:
                     if category in include and category in reranked_results:
@@ -157,13 +157,43 @@ class SearchService:
             else:
                 # For keyword or embedding search, results are directly in answer dict
                 # Apply same priority order
-                priority_order = ['summaries', 'statements', 'chunks', 'entities']
+                priority_order = ['summaries', 'communities', 'statements', 'chunks', 'entities']
                 
                 for category in priority_order:
                     if category in include and category in answer:
                         category_results = answer[category]
                         if isinstance(category_results, list):
                             answer_list.extend(category_results)
+
+            # 对命中的 community 节点展开其成员 statements
+            if "communities" in include:
+                community_results = (
+                    answer.get('reranked_results', {}).get('communities', [])
+                    if search_type == "hybrid"
+                    else answer.get('communities', [])
+                )
+                community_ids = [
+                    r.get("id") for r in community_results if r.get("id")
+                ]
+                if community_ids and end_user_id:
+                    try:
+                        from app.repositories.neo4j.graph_search import search_graph_community_expand
+                        from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+                        connector = Neo4jConnector()
+                        expand_result = await search_graph_community_expand(
+                            connector=connector,
+                            community_ids=community_ids,
+                            end_user_id=end_user_id,
+                            limit=10,
+                        )
+                        await connector.close()
+                        expanded_stmts = expand_result.get("expanded_statements", [])
+                        if expanded_stmts:
+                            # 展开的 statements 插入 communities 之后、statements 之前
+                            answer_list.extend(expanded_stmts)
+                            logger.info(f"社区展开检索追加 {len(expanded_stmts)} 条 statements")
+                    except Exception as e:
+                        logger.warning(f"社区展开检索失败，跳过: {e}")
             
             # Extract clean content from all results
             content_list = [

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

@@ -19,7 +19,7 @@ from app.core.memory.utils.log.logging_utils import log_time
 from app.db import get_db_context
 from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges
 from app.repositories.neo4j.add_nodes import add_memory_summary_nodes
-from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j
+from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j, schedule_clustering_after_write
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import MemoryConfig
 
@@ -165,10 +165,17 @@ 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,
             )
             if success:
                 logger.info("Successfully saved all data to Neo4j")
+                # 写入成功后，异步触发聚类（不阻塞写入响应）
+                schedule_clustering_after_write(
+                    all_entity_nodes,
+                    config_id=config_id,
+                    llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None,
+                    embedding_model_id=str(memory_config.embedding_model_id) if memory_config.embedding_model_id else None,
+                )
                 break
             else:
                 logger.warning("Failed to save some data to Neo4j")

api/app/core/memory/src/search.py

@@ -238,7 +238,7 @@ def rerank_with_activation(
     
     reranked: Dict[str, List[Dict[str, Any]]] = {}
     
-    for category in ["statements", "chunks", "entities", "summaries"]:
+    for category in ["statements", "chunks", "entities", "summaries", "communities"]:
         keyword_items = keyword_results.get(category, [])
         embedding_items = embedding_results.get(category, [])
         
@@ -281,21 +281,23 @@ def rerank_with_activation(
         for item in items_list:
             item_id = item.get("id") or item.get("uuid") or item.get("chunk_id")
             if item_id and item_id in combined_items:
-                combined_items[item_id]["normalized_activation_value"] = item.get("normalized_activation_value", 0)
+                combined_items[item_id]["normalized_activation_value"] = item.get("normalized_activation_value")
         
         # 步骤 4: 计算基础分数和最终分数
         for item_id, item in combined_items.items():
             bm25_norm = float(item.get("bm25_score", 0) or 0)
             emb_norm = float(item.get("embedding_score", 0) or 0)
-            act_norm = float(item.get("normalized_activation_value", 0) or 0)
+            # normalized_activation_value 为 None 表示该节点无激活值，保留 None 语义
+            raw_act_norm = item.get("normalized_activation_value")
+            act_norm = float(raw_act_norm) if raw_act_norm is not None else None
             
             # 第一阶段：只考虑内容相关性（BM25 + Embedding）
             # alpha 控制 BM25 权重，(1-alpha) 控制 Embedding 权重
             content_score = alpha * bm25_norm + (1 - alpha) * emb_norm
             base_score = content_score  # 第一阶段用内容分数
             
-            # 存储激活度分数供第二阶段使用
+            # 存储激活度分数供第二阶段使用（None 表示无激活值，不参与激活值排序）
-            item["activation_score"] = act_norm
+            item["activation_score"] = act_norm  # 可能为 None
             item["content_score"] = content_score
             item["base_score"] = base_score
             

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

@@ -20,6 +20,9 @@ logger = logging.getLogger(__name__)
 # 全量迭代最大轮数，防止不收敛
 MAX_ITERATIONS = 10
 
+# 社区核心实体取 top-N 数量
+CORE_ENTITY_LIMIT = 10
+
 
 def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float:
     """计算两个向量的余弦相似度，任一为空则返回 0。"""
@@ -62,9 +65,18 @@ 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,
+        embedding_model_id: Optional[str] = None,
+    ):
         self.connector = connector
         self.repo = CommunityRepository(connector)
+        self.config_id = config_id
+        self.llm_model_id = llm_model_id
+        self.embedding_model_id = embedding_model_id
 
     # ──────────────────────────────────────────────────────────────────────────
     # 公开接口
@@ -94,58 +106,110 @@ class LabelPropagationEngine:
 
     async def full_clustering(self, end_user_id: str) -> None:
         """
-        全量标签传播初始化。
+        全量标签传播初始化（分批处理，控制内存峰值）。
 
-        1. 拉取所有实体，初始化每个实体为独立社区
+        策略：
-        2. 迭代：每轮对所有实体做邻居投票，更新社区标签
+        - 每次只加载 BATCH_SIZE 个实体及其邻居进内存
-        3. 直到标签不再变化或达到 MAX_ITERATIONS
+        - labels 字典跨批次共享（只存 id→community_id，内存极小）
-        4. 将最终标签写入 Neo4j
+        - 每批独立跑 MAX_ITERATIONS 轮 LPA，批次间通过 labels 传递社区信息
+        - 所有批次完成后统一 flush 和 merge
         """
-        entities = await self.repo.get_all_entities(end_user_id)
+        BATCH_SIZE = 2000  # 每批实体数，可按需调整
-        if not entities:
+
+        # 先查总数，决定批次数
+        total_entities = await self.repo.get_all_entities(end_user_id)
+        if not total_entities:
             logger.info(f"[Clustering] 用户 {end_user_id} 无实体，跳过全量聚类")
             return
 
-        # 初始化：每个实体持有自己 id 作为社区标签
+        total_count = len(total_entities)
-        labels: Dict[str, str] = {e["id"]: e["id"] for e in entities}
+        logger.info(f"[Clustering] 用户 {end_user_id} 共 {total_count} 个实体，"
-        embeddings: Dict[str, Optional[List[float]]] = {
+                    f"分批大小 {BATCH_SIZE}，共 {(total_count + BATCH_SIZE - 1) // BATCH_SIZE} 批")
-            e["id"]: e.get("name_embedding") for e in entities
-        }
 
-        for iteration in range(MAX_ITERATIONS):
+        # labels 跨批次共享：先用全量数据初始化（只存 id，内存极小）
-            changed = 0
+        labels: Dict[str, str] = {e["id"]: e["id"] for e in total_entities}
-            # 随机顺序（Python dict 在 3.7+ 保持插入顺序，这里直接遍历）
+        # embeddings 也跨批次共享（每个向量 ~6KB，10万实体约 600MB，这是不可避免的）
-            for entity in entities:
+        # 但只在当前批次的实体需要时才保留，其余批次的 embedding 不常驻
-                eid = entity["id"]
+        # 实际上 embeddings 只在 _weighted_vote 中用于计算 self_embedding，
-                neighbors = await self.repo.get_entity_neighbors(eid, end_user_id)
+        # 所以只需要当前批次实体的 embedding，不需要全量
+        del total_entities  # 释放全量列表，后续按批次加载
 
-                # 将邻居的当前内存标签注入（覆盖 Neo4j 中的旧值）
+        for batch_start in range(0, total_count, BATCH_SIZE):
-                enriched = []
+            batch_entities = await self.repo.get_entities_page(
-                for nb in neighbors:
+                end_user_id, skip=batch_start, limit=BATCH_SIZE
-                    nb_copy = dict(nb)
-                    nb_copy["community_id"] = labels.get(nb["id"], nb.get("community_id"))
-                    enriched.append(nb_copy)
-
-                new_label = _weighted_vote(enriched, embeddings.get(eid))
-                if new_label and new_label != labels[eid]:
-                    labels[eid] = new_label
-                    changed += 1
-
-            logger.info(
-                f"[Clustering] 全量迭代 {iteration + 1}/{MAX_ITERATIONS}，"
-                f"标签变化数: {changed}"
             )
-            if changed == 0:
+            if not batch_entities:
-                logger.info("[Clustering] 标签已收敛，提前结束迭代")
                 break
 
-        # 将最终标签写入 Neo4j
+            batch_ids = [e["id"] for e in batch_entities]
+            batch_embeddings: Dict[str, Optional[List[float]]] = {
+                e["id"]: e.get("name_embedding") for e in batch_entities
+            }
+
+            logger.info(
+                f"[Clustering] 批次 {batch_start // BATCH_SIZE + 1}："
+                f"加载 {len(batch_entities)} 个实体的邻居图..."
+            )
+            neighbors_cache = await self.repo.get_entity_neighbors_for_ids(
+                batch_ids, end_user_id
+            )
+            logger.info(f"[Clustering] 邻居预加载完成，覆盖实体数: {len(neighbors_cache)}")
+
+            for iteration in range(MAX_ITERATIONS):
+                changed = 0
+                for entity in batch_entities:
+                    eid = entity["id"]
+                    neighbors = neighbors_cache.get(eid, [])
+
+                    # 注入跨批次的最新标签（邻居可能在其他批次，labels 里有其最新值）
+                    enriched = []
+                    for nb in neighbors:
+                        nb_copy = dict(nb)
+                        nb_copy["community_id"] = labels.get(nb["id"], nb.get("community_id"))
+                        enriched.append(nb_copy)
+
+                    new_label = _weighted_vote(enriched, batch_embeddings.get(eid))
+                    if new_label and new_label != labels[eid]:
+                        labels[eid] = new_label
+                        changed += 1
+
+                logger.info(
+                    f"[Clustering] 批次 {batch_start // BATCH_SIZE + 1} "
+                    f"迭代 {iteration + 1}/{MAX_ITERATIONS}，标签变化数: {changed}"
+                )
+                if changed == 0:
+                    logger.info("[Clustering] 标签已收敛，提前结束本批迭代")
+                    break
+
+            # 释放本批次的大对象
+            del neighbors_cache, batch_embeddings, batch_entities
+
+        # 所有批次完成，统一写入 Neo4j
         await self._flush_labels(labels, end_user_id)
+        pre_merge_count = len(set(labels.values()))
         logger.info(
-            f"[Clustering] 全量聚类完成，共 {len(set(labels.values()))} 个社区，"
+            f"[Clustering] 全量迭代完成，共 {pre_merge_count} 个社区，"
+            f"{len(labels)} 个实体，开始后处理合并"
+        )
+
+        all_community_ids = list(set(labels.values()))
+        await self._evaluate_merge(all_community_ids, end_user_id)
+
+        logger.info(
+            f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，"
             f"{len(labels)} 个实体"
         )
 
+        # 查询存活社区并生成元数据
+        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(
         self, new_entity_ids: List[str], end_user_id: str
     ) -> None:
@@ -306,6 +370,90 @@ 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
+        NOTE: core_entities按照激活值高低排序，会造成对边缘信息检索返回消息质量不高。
+        """
+        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}")
+
+            # 生成 summary_embedding
+            summary_embedding = None
+            if self.embedding_model_id and summary:
+                try:
+                    from app.db import get_db_context
+                    from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+                    with get_db_context() as db:
+                        embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
+                    results = await embedder.response([summary])
+                    summary_embedding = results[0] if results else None
+                except Exception as e:
+                    logger.warning(f"[Clustering] 社区 {community_id} 生成 summary_embedding 失败: {e}")
+
+            result = await self.repo.update_community_metadata(
+                community_id=community_id,
+                end_user_id=end_user_id,
+                name=name,
+                summary=summary,
+                core_entities=core_entities,
+                summary_embedding=summary_embedding,
+            )
+            if result:
+                logger.info(f"[Clustering] 社区 {community_id} 元数据写入成功: name={name}, summary={summary[:30]}...")
+            else:
+                logger.warning(f"[Clustering] 社区 {community_id} 元数据写入返回 False")
+        except Exception as e:
+            logger.error(f"[Clustering] _generate_community_metadata failed for {community_id}: {e}", exc_info=True)
+
     @staticmethod
     def _new_community_id() -> str:
         return str(uuid.uuid4())

api/app/main.py

@@ -18,6 +18,7 @@ from app.core.logging_config import LoggingConfig, get_logger
 from app.core.response_utils import fail
 from app.core.models.scripts.loader import load_models
 from app.db import get_db_context
+from app.repositories.neo4j.index_manager import ensure_indexes
 
 # Initialize logging system
 LoggingConfig.setup_logging()
@@ -61,9 +62,18 @@ async def lifespan(app: FastAPI):
     else:
         logger.info("预定义模型加载已禁用 (LOAD_MODEL=false)")
 
+    # 确保 Neo4j 索引存在（幂等，多环境安全）
+    try:
+        report = await ensure_indexes()
+        if report["errors"]:
+            logger.warning(f"Neo4j 索引部分创建失败: {report['errors']}")
+        else:
+            logger.info(f"Neo4j 索引检查完成 [{report['uri']}]")
+    except Exception as e:
+        logger.warning(f"Neo4j 索引检查跳过（连接失败）: {e}")
+
     logger.info("应用程序启动完成")
     yield
-    # 应用关闭事件
     logger.info("应用程序正在关闭")
 
 

api/app/repositories/neo4j/community_repository.py

@@ -13,9 +13,14 @@ from app.repositories.neo4j.cypher_queries import (
     ENTITY_LEAVE_ALL_COMMUNITIES,
     GET_ENTITY_NEIGHBORS,
     GET_ALL_ENTITIES_FOR_USER,
+    GET_ENTITIES_PAGE,
     GET_COMMUNITY_MEMBERS,
+    GET_ALL_COMMUNITY_MEMBERS_BATCH,
+    GET_ALL_ENTITY_NEIGHBORS_BATCH,
+    GET_ENTITY_NEIGHBORS_BATCH_FOR_IDS,
     CHECK_USER_HAS_COMMUNITIES,
     UPDATE_COMMUNITY_MEMBER_COUNT,
+    UPDATE_COMMUNITY_METADATA,
 )
 
 logger = logging.getLogger(__name__)
@@ -87,6 +92,41 @@ class CommunityRepository:
             logger.error(f"get_all_entities failed: {e}")
             return []
 
+    async def get_entities_page(
+        self, end_user_id: str, skip: int, limit: int
+    ) -> List[Dict]:
+        """分页拉取实体，用于全量聚类分批处理。"""
+        try:
+            return await self.connector.execute_query(
+                GET_ENTITIES_PAGE,
+                end_user_id=end_user_id,
+                skip=skip,
+                limit=limit,
+            )
+        except Exception as e:
+            logger.error(f"get_entities_page failed: {e}")
+            return []
+
+    async def get_entity_neighbors_for_ids(
+        self, entity_ids: List[str], end_user_id: str
+    ) -> Dict[str, List[Dict]]:
+        """批量拉取指定实体列表的邻居，返回 {entity_id: [neighbors]}。"""
+        try:
+            rows = await self.connector.execute_query(
+                GET_ENTITY_NEIGHBORS_BATCH_FOR_IDS,
+                entity_ids=entity_ids,
+                end_user_id=end_user_id,
+            )
+            result: Dict[str, List[Dict]] = {}
+            for row in rows:
+                eid = row["entity_id"]
+                neighbor = {k: v for k, v in row.items() if k != "entity_id"}
+                result.setdefault(eid, []).append(neighbor)
+            return result
+        except Exception as e:
+            logger.error(f"get_entity_neighbors_for_ids failed: {e}")
+            return {}
+
     async def get_community_members(
         self, community_id: str, end_user_id: str
     ) -> List[Dict]:
@@ -127,3 +167,28 @@ class CommunityRepository:
         except Exception as e:
             logger.error(f"refresh_member_count failed: {e}")
             return 0
+
+    async def update_community_metadata(
+        self,
+        community_id: str,
+        end_user_id: str,
+        name: str,
+        summary: str,
+        core_entities: List[str],
+        summary_embedding: Optional[List[float]] = None,
+    ) -> bool:
+        """更新社区的名称、摘要、核心实体列表和摘要向量。"""
+        try:
+            result = await self.connector.execute_query(
+                UPDATE_COMMUNITY_METADATA,
+                community_id=community_id,
+                end_user_id=end_user_id,
+                name=name,
+                summary=summary,
+                core_entities=core_entities,
+                summary_embedding=summary_embedding,
+            )
+            return bool(result)
+        except Exception as e:
+            logger.error(f"update_community_metadata failed: {e}")
+            return False

api/app/repositories/neo4j/cypher_queries.py

@@ -1139,6 +1139,15 @@ RETURN e.id AS id, e.name AS name, e.entity_type AS entity_type,
 ORDER BY coalesce(e.activation_value, 0) DESC
 """
 
+GET_ALL_COMMUNITY_MEMBERS_BATCH = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community)
+RETURN c.community_id AS community_id,
+       e.id AS id, e.name AS name, e.entity_type AS entity_type,
+       e.importance_score AS importance_score, e.activation_value AS activation_value,
+       e.name_embedding AS name_embedding
+ORDER BY c.community_id, coalesce(e.activation_value, 0) DESC
+"""
+
 CHECK_USER_HAS_COMMUNITIES = """
 MATCH (c:Community {end_user_id: $end_user_id})
 RETURN count(c) AS community_count
@@ -1150,3 +1159,128 @@ WITH c, count(e) AS cnt
 SET c.member_count = cnt
 RETURN c.community_id AS community_id, cnt AS member_count
 """
+
+UPDATE_COMMUNITY_METADATA = """
+MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
+SET c.name             = $name,
+    c.summary          = $summary,
+    c.core_entities    = $core_entities,
+    c.summary_embedding = $summary_embedding,
+    c.updated_at       = datetime()
+RETURN c.community_id AS community_id
+"""
+
+GET_ENTITIES_PAGE = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+OPTIONAL MATCH (e)-[:BELONGS_TO_COMMUNITY]->(c:Community)
+RETURN e.id AS id,
+       e.name AS name,
+       e.name_embedding AS name_embedding,
+       e.activation_value AS activation_value,
+       CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
+ORDER BY e.id
+SKIP $skip LIMIT $limit
+"""
+
+GET_ENTITY_NEIGHBORS_BATCH_FOR_IDS = """
+// 批量拉取指定实体列表的邻居（用于分批全量聚类）
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+WHERE e.id IN $entity_ids
+OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id})
+OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e)
+OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(nb2:ExtractedEntity {end_user_id: $end_user_id})
+WHERE nb2.id <> e.id
+WITH e, collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors
+UNWIND all_neighbors AS nb
+WITH e, nb WHERE nb IS NOT NULL
+OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community)
+RETURN DISTINCT
+    e.id                AS entity_id,
+    nb.id               AS id,
+    nb.name             AS name,
+    nb.name_embedding   AS name_embedding,
+    nb.activation_value AS activation_value,
+    CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
+"""
+
+GET_ALL_ENTITY_NEIGHBORS_BATCH = """
+// 批量拉取某用户下所有实体的邻居（用于全量聚类预加载）
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+
+// 来源一：直接关系邻居
+OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id})
+
+// 来源二：同 Statement 共现邻居
+OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e)
+OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(nb2:ExtractedEntity {end_user_id: $end_user_id})
+WHERE nb2.id <> e.id
+
+WITH e, collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors
+UNWIND all_neighbors AS nb
+WITH e, nb WHERE nb IS NOT NULL
+OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community)
+RETURN DISTINCT
+    e.id                AS entity_id,
+    nb.id               AS id,
+    nb.name             AS name,
+    nb.name_embedding   AS name_embedding,
+    nb.activation_value AS activation_value,
+    CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
+"""
+
+
+# Community keyword search: matches name or summary via fulltext index
+SEARCH_COMMUNITIES_BY_KEYWORD = """
+CALL db.index.fulltext.queryNodes("communitiesFulltext", $q) YIELD node AS c, score
+WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
+RETURN c.community_id AS id,
+       c.name AS name,
+       c.summary AS content,
+       c.core_entities AS core_entities,
+       c.member_count AS member_count,
+       c.end_user_id AS end_user_id,
+       c.updated_at AS updated_at,
+       score
+ORDER BY score DESC
+LIMIT $limit
+"""
+
+# Community 向量检索 ──────────────────────────────────────────────────
+# Community embedding-based search: cosine similarity on Community.summary_embedding
+COMMUNITY_EMBEDDING_SEARCH = """
+CALL db.index.vector.queryNodes('community_summary_embedding_index', $limit * 100, $embedding)
+YIELD node AS c, score
+WHERE c.summary_embedding IS NOT NULL
+  AND ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
+RETURN c.community_id AS id,
+       c.name AS name,
+       c.summary AS content,
+       c.core_entities AS core_entities,
+       c.member_count AS member_count,
+       c.end_user_id AS end_user_id,
+       c.updated_at AS updated_at,
+       score
+ORDER BY score DESC
+LIMIT $limit
+"""
+
+# Community 展开检索 ──────────────────────────────────────────────────
+# 命中社区后，拉取该社区所有成员实体关联的 Statement 节点（主题→细节两级检索）
+EXPAND_COMMUNITY_STATEMENTS = """
+MATCH (c:Community {community_id: $community_id})
+MATCH (e:ExtractedEntity)-[:BELONGS_TO_COMMUNITY]->(c)
+MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e)
+WHERE s.end_user_id = $end_user_id
+RETURN s.statement AS statement,
+       s.id AS id,
+       s.end_user_id AS end_user_id,
+       s.created_at AS created_at,
+       s.valid_at AS valid_at,
+       s.invalid_at AS invalid_at,
+       COALESCE(s.activation_value, s.importance_score, 0.5) AS activation_value,
+       COALESCE(s.importance_score, 0.5) AS importance_score,
+       e.name AS source_entity,
+       c.name AS community_name
+ORDER BY COALESCE(s.activation_value, 0) DESC
+LIMIT $limit
+"""

api/app/repositories/neo4j/graph_saver.py

@@ -1,5 +1,5 @@
 import asyncio
-from typing import List
+from typing import List, Optional
 
 # 使用新的仓储层
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
@@ -156,10 +156,13 @@ async def save_dialog_and_statements_to_neo4j(
         entity_edges: List[EntityEntityEdge],
         statement_chunk_edges: List[StatementChunkEdge],
         statement_entity_edges: List[StatementEntityEdge],
-        connector: Neo4jConnector
+        connector: Neo4jConnector,
 ) -> bool:
     """Save dialogue nodes, chunk nodes, statement nodes, entities, and all relationships to Neo4j using graph models.
 
+    只负责数据写入，不触发聚类。聚类由调用方在写入成功后通过
+    schedule_clustering_after_write() 显式触发。
+
     Args:
         dialogue_nodes: List of DialogueNode objects to save
         chunk_nodes: List of ChunkNode objects to save
@@ -290,13 +293,6 @@ async def save_dialog_and_statements_to_neo4j(
         logger.info("Transaction completed. Summary: %s", summary)
         logger.debug("Full transaction results: %r", results)
 
-        # 写入成功后，触发聚类
-        if entity_nodes:
-            end_user_id = entity_nodes[0].end_user_id
-            new_entity_ids = [e.id for e in entity_nodes]
-            logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
-            await _trigger_clustering(new_entity_ids, end_user_id)
-
         return True
 
     except Exception as e:
@@ -306,9 +302,38 @@ async def save_dialog_and_statements_to_neo4j(
         return False
 
 
+def schedule_clustering_after_write(
+    entity_nodes: List,
+    config_id: Optional[str] = None,
+    llm_model_id: Optional[str] = None,
+    embedding_model_id: Optional[str] = None,
+) -> None:
+    """
+    写入 Neo4j 成功后，调度后台聚类任务。
+
+    可通过环境变量 CLUSTERING_ENABLED=false 禁用（用于基准测试对比）。
+    使用 asyncio.create_task 异步触发，不阻塞写入响应。
+    """
+    if not entity_nodes:
+        return
+
+    clustering_enabled = os.getenv("CLUSTERING_ENABLED", "true").lower() != "false"
+    if not clustering_enabled:
+        logger.info("[Clustering] 聚类已禁用（CLUSTERING_ENABLED=false），跳过聚类触发")
+        return
+
+    end_user_id = entity_nodes[0].end_user_id
+    new_entity_ids = [e.id for e in entity_nodes]
+    logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
+    asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, config_id=config_id, llm_model_id=llm_model_id, embedding_model_id=embedding_model_id))
+
+
 async def _trigger_clustering(
     new_entity_ids: List[str],
     end_user_id: str,
+    config_id: Optional[str] = None,
+    llm_model_id: Optional[str] = None,
+    embedding_model_id: Optional[str] = None,
 ) -> None:
     """
     聚类触发函数，自动判断全量初始化还是增量更新。
@@ -318,7 +343,7 @@ async def _trigger_clustering(
         from app.core.memory.storage_services.clustering_engine import LabelPropagationEngine
         logger.info(f"[Clustering] 开始聚类，end_user_id={end_user_id}, 实体数={len(new_entity_ids)}")
         connector = Neo4jConnector()
-        engine = LabelPropagationEngine(connector)
+        engine = LabelPropagationEngine(connector, config_id=config_id, llm_model_id=llm_model_id, embedding_model_id=embedding_model_id)
         await engine.run(end_user_id=end_user_id, new_entity_ids=new_entity_ids)
         logger.info(f"[Clustering] 聚类完成，end_user_id={end_user_id}")
     except Exception as e:

api/app/repositories/neo4j/graph_search.py

@@ -4,10 +4,13 @@ from typing import Any, Dict, List, Optional
 
 from app.repositories.neo4j.cypher_queries import (
     CHUNK_EMBEDDING_SEARCH,
+    COMMUNITY_EMBEDDING_SEARCH,
     ENTITY_EMBEDDING_SEARCH,
+    EXPAND_COMMUNITY_STATEMENTS,
     MEMORY_SUMMARY_EMBEDDING_SEARCH,
     SEARCH_CHUNK_BY_CHUNK_ID,
     SEARCH_CHUNKS_BY_CONTENT,
+    SEARCH_COMMUNITIES_BY_KEYWORD,
     SEARCH_DIALOGUE_BY_DIALOG_ID,
     SEARCH_ENTITIES_BY_NAME,
     SEARCH_MEMORY_SUMMARIES_BY_KEYWORD,
@@ -285,6 +288,15 @@ async def search_graph(
             limit=limit,
         ))
         task_keys.append("summaries")
+
+    if "communities" in include:
+        tasks.append(connector.execute_query(
+            SEARCH_COMMUNITIES_BY_KEYWORD,
+            q=q,
+            end_user_id=end_user_id,
+            limit=limit,
+        ))
+        task_keys.append("communities")
     
     # Execute all queries in parallel
     task_results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -396,6 +408,16 @@ async def search_graph_by_embedding(
         ))
         task_keys.append("summaries")
 
+    # Communities (向量语义匹配)
+    if "communities" in include:
+        tasks.append(connector.execute_query(
+            COMMUNITY_EMBEDDING_SEARCH,
+            embedding=embedding,
+            end_user_id=end_user_id,
+            limit=limit,
+        ))
+        task_keys.append("communities")
+
     # Execute all queries in parallel
     query_start = time.time()
     task_results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -408,6 +430,7 @@ async def search_graph_by_embedding(
         "chunks": [],
         "entities": [],
         "summaries": [],
+        "communities": [],
     }
     
     for key, result in zip(task_keys, task_results):
@@ -661,6 +684,62 @@ async def search_graph_by_chunk_id(
     return {"chunks": chunks}
 
 
+async def search_graph_community_expand(
+    connector: Neo4jConnector,
+    community_ids: List[str],
+    end_user_id: str,
+    limit: int = 10,
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    三期：社区展开检索 —— 主题 → 细节两级检索。
+
+    命中 Community 节点后，沿 BELONGS_TO_COMMUNITY 关系拉取成员实体，
+    再沿 REFERENCES_ENTITY 关系拉取关联的 Statement 节点，
+    按 activation_value 降序返回，实现"主题摘要 → 具体记忆"的深度召回。
+
+    Args:
+        connector: Neo4j 连接器
+        community_ids: 已命中的社区 ID 列表
+        end_user_id: 用户 ID，用于数据隔离
+        limit: 每个社区最多返回的 Statement 数量
+
+    Returns:
+        {"expanded_statements": [Statement 列表，含 community_name / source_entity 字段]}
+    """
+    if not community_ids or not end_user_id:
+        return {"expanded_statements": []}
+
+    tasks = [
+        connector.execute_query(
+            EXPAND_COMMUNITY_STATEMENTS,
+            community_id=cid,
+            end_user_id=end_user_id,
+            limit=limit,
+        )
+        for cid in community_ids
+    ]
+
+    task_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+    expanded: List[Dict[str, Any]] = []
+    for cid, result in zip(community_ids, task_results):
+        if isinstance(result, Exception):
+            logger.warning(f"社区展开检索失败 community_id={cid}: {result}")
+        else:
+            expanded.extend(result)
+
+    # 按 activation_value 全局排序后去重
+    from app.core.memory.src.search import _deduplicate_results
+    expanded.sort(
+        key=lambda x: float(x.get("activation_value") or 0),
+        reverse=True,
+    )
+    expanded = _deduplicate_results(expanded)
+
+    logger.info(f"社区展开检索完成: community_ids={community_ids}, 展开 statements={len(expanded)}")
+    return {"expanded_statements": expanded}
+
+
 async def search_graph_by_created_at(
     connector: Neo4jConnector,
     end_user_id: Optional[str] = None,

api/app/repositories/neo4j/index_manager.py

@@ -0,0 +1,254 @@
+# -*- coding: utf-8 -*-
+"""Neo4j 索引管理模块
+
+负责检查和创建 Neo4j 全文索引与向量索引。
+支持多环境（通过 .env 中的 NEO4J_URI/USERNAME/PASSWORD 区分）。
+
+用法：
+    # 作为模块调用（应用启动时）
+    from app.repositories.neo4j.index_manager import ensure_indexes
+    await ensure_indexes()
+
+    # 作为独立脚本执行（手动建索引）
+    python -m app.repositories.neo4j.index_manager
+"""
+
+import asyncio
+import logging
+from dataclasses import dataclass
+from typing import List
+
+from app.core.config import settings
+from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+
+logger = logging.getLogger(__name__)
+
+
+# ─────────────────────────────────────────────────────────────
+# 索引定义表
+# ─────────────────────────────────────────────────────────────
+
+@dataclass
+class FulltextIndexDef:
+    name: str
+    label: str
+    properties: List[str]
+
+
+@dataclass
+class VectorIndexDef:
+    name: str
+    label: str
+    property: str
+    dimensions: int
+    similarity: str = "cosine"
+
+
+# 全文索引清单（现有 + 新增 communities）
+FULLTEXT_INDEXES: List[FulltextIndexDef] = [
+    FulltextIndexDef("statementsFulltext",  "Statement",      ["statement"]),
+    FulltextIndexDef("entitiesFulltext",    "ExtractedEntity", ["name"]),
+    FulltextIndexDef("chunksFulltext",      "Chunk",          ["content"]),
+    FulltextIndexDef("summariesFulltext",   "MemorySummary",  ["content"]),
+    FulltextIndexDef("communitiesFulltext", "Community",      ["name", "summary"]),  # 第五检索源
+]
+
+# 向量索引清单（预留 community 二期）
+VECTOR_INDEXES: List[VectorIndexDef] = [
+    VectorIndexDef("statement_embedding_index", "Statement",      "statement_embedding", 1536),
+    VectorIndexDef("chunk_embedding_index",     "Chunk",          "chunk_embedding",     1536),
+    VectorIndexDef("entity_embedding_index",    "ExtractedEntity","name_embedding",      1536),
+    VectorIndexDef("summary_embedding_index",   "MemorySummary",  "summary_embedding",   1536),
+    # 二期：社区向量索引
+    VectorIndexDef("community_summary_embedding_index", "Community", "summary_embedding", 1536),
+]
+
+
+# ─────────────────────────────────────────────────────────────
+# 核心检查 / 创建逻辑
+# ─────────────────────────────────────────────────────────────
+
+async def _get_existing_indexes(connector: Neo4jConnector) -> set:
+    """查询 Neo4j 中已存在的索引名称集合"""
+    rows = await connector.execute_query("SHOW INDEXES YIELD name RETURN name")
+    return {row["name"] for row in rows}
+
+
+async def _ensure_fulltext_index(
+    connector: Neo4jConnector,
+    idx: FulltextIndexDef,
+    existing: set,
+) -> str:
+    """检查并按需创建全文索引，返回操作状态描述"""
+    if idx.name in existing:
+        return f"[SKIP]   全文索引已存在: {idx.name}"
+
+    props = ", ".join(f"n.{p}" for p in idx.properties)
+    cypher = (
+        f'CREATE FULLTEXT INDEX {idx.name} IF NOT EXISTS '
+        f'FOR (n:{idx.label}) ON EACH [{props}]'
+    )
+    await connector.execute_query(cypher)
+    return f"[CREATE] 全文索引已创建: {idx.name}  ({idx.label} → {idx.properties})"
+
+
+async def _ensure_vector_index(
+    connector: Neo4jConnector,
+    idx: VectorIndexDef,
+    existing: set,
+) -> str:
+    """检查并按需创建向量索引，返回操作状态描述"""
+    if idx.name in existing:
+        return f"[SKIP]   向量索引已存在: {idx.name}"
+
+    cypher = (
+        f"CREATE VECTOR INDEX {idx.name} IF NOT EXISTS "
+        f"FOR (n:{idx.label}) ON n.{idx.property} "
+        f"OPTIONS {{indexConfig: {{"
+        f"`vector.dimensions`: {idx.dimensions}, "
+        f"`vector.similarity_function`: '{idx.similarity}'"
+        f"}}}}"
+    )
+    await connector.execute_query(cypher)
+    return (
+        f"[CREATE] 向量索引已创建: {idx.name}  "
+        f"({idx.label}.{idx.property}, dim={idx.dimensions})"
+    )
+
+
+async def ensure_indexes(connector: Neo4jConnector | None = None) -> dict:
+    """
+    检查并创建所有必要的 Neo4j 索引（幂等，可重复调用）。
+
+    Args:
+        connector: 可选，传入已有连接器；为 None 时自动创建。
+
+    Returns:
+        dict: {
+            "uri": 当前连接的 Neo4j URI,
+            "fulltext": [操作日志列表],
+            "vector":   [操作日志列表],
+            "errors":   [错误信息列表],
+        }
+    """
+    own_connector = connector is None
+    if own_connector:
+        connector = Neo4jConnector()
+
+    report = {
+        "uri": settings.NEO4J_URI,
+        "fulltext": [],
+        "vector": [],
+        "errors": [],
+    }
+
+    try:
+        # 一次性拉取所有已有索引名
+        existing = await _get_existing_indexes(connector)
+        logger.info(f"[IndexManager] 当前环境: {settings.NEO4J_URI}")
+        logger.info(f"[IndexManager] 已有索引数量: {len(existing)}")
+
+        # 处理全文索引
+        for idx in FULLTEXT_INDEXES:
+            try:
+                msg = await _ensure_fulltext_index(connector, idx, existing)
+                report["fulltext"].append(msg)
+                logger.info(f"[IndexManager] {msg}")
+            except Exception as e:
+                err = f"[ERROR]  全文索引 {idx.name} 创建失败: {e}"
+                report["errors"].append(err)
+                logger.error(f"[IndexManager] {err}")
+
+        # 处理向量索引
+        for idx in VECTOR_INDEXES:
+            try:
+                msg = await _ensure_vector_index(connector, idx, existing)
+                report["vector"].append(msg)
+                logger.info(f"[IndexManager] {msg}")
+            except Exception as e:
+                err = f"[ERROR]  向量索引 {idx.name} 创建失败: {e}"
+                report["errors"].append(err)
+                logger.error(f"[IndexManager] {err}")
+
+    finally:
+        if own_connector:
+            await connector.close()
+
+    return report
+
+
+async def check_indexes(connector: Neo4jConnector | None = None) -> dict:
+    """
+    仅检查索引状态，不创建任何索引。
+
+    Returns:
+        dict: {
+            "uri": ...,
+            "present":  [已存在的索引名],
+            "missing_fulltext": [缺失的全文索引名],
+            "missing_vector":   [缺失的向量索引名],
+        }
+    """
+    own_connector = connector is None
+    if own_connector:
+        connector = Neo4jConnector()
+
+    try:
+        existing = await _get_existing_indexes(connector)
+        missing_ft = [i.name for i in FULLTEXT_INDEXES if i.name not in existing]
+        missing_vec = [i.name for i in VECTOR_INDEXES if i.name not in existing]
+
+        return {
+            "uri": settings.NEO4J_URI,
+            "present": sorted(existing),
+            "missing_fulltext": missing_ft,
+            "missing_vector": missing_vec,
+        }
+    finally:
+        if own_connector:
+            await connector.close()
+
+
+# ─────────────────────────────────────────────────────────────
+# 独立脚本入口
+# ─────────────────────────────────────────────────────────────
+
+async def _main():
+    import sys
+
+    print(f"\n{'='*60}")
+    print(f"Neo4j 索引管理工具")
+    print(f"环境: {settings.NEO4J_URI}")
+    print(f"{'='*60}\n")
+
+    # 先检查
+    print(">>> 检查当前索引状态...\n")
+    status = await check_indexes()
+    print(f"  已存在索引数: {len(status['present'])}")
+    if status["missing_fulltext"]:
+        print(f"  缺失全文索引: {status['missing_fulltext']}")
+    if status["missing_vector"]:
+        print(f"  缺失向量索引: {status['missing_vector']}")
+
+    if not status["missing_fulltext"] and not status["missing_vector"]:
+        print("\n  所有索引均已存在，无需操作。")
+        return
+
+    # 再创建
+    print("\n>>> 开始创建缺失索引...\n")
+    report = await ensure_indexes()
+
+    for msg in report["fulltext"] + report["vector"]:
+        print(f"  {msg}")
+
+    if report["errors"]:
+        print("\n[!] 以下索引创建失败：")
+        for err in report["errors"]:
+            print(f"  {err}")
+        sys.exit(1)
+    else:
+        print("\n  全部索引处理完成。")
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())

api/app/tasks.py

@@ -2416,3 +2416,391 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]:
             "elapsed_time": elapsed_time,
             "task_id": self.request.id
         }
+
+
+# =============================================================================
+
+@celery_app.task(
+    name="app.tasks.init_implicit_emotions_for_users",
+    bind=True,
+    ignore_result=True,
+    max_retries=0,
+    acks_late=False,
+    time_limit=3600,
+    soft_time_limit=3300,
+    # 触发型任务标识，区别于 periodic_tasks 队列中的定时任务
+    triggered=True,
+)
+def init_implicit_emotions_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]:
+    """事件触发任务：对指定用户列表做存在性检查，无记录则执行首次初始化。
+
+    由 /dashboard/end_users 接口触发，已有数据的用户直接跳过。
+    存量用户的数据刷新由定时任务 update_implicit_emotions_storage 负责。
+
+    Args:
+        end_user_ids: 需要检查的用户ID列表
+
+    Returns:
+        包含任务执行结果的字典
+    """
+    start_time = time.time()
+
+    async def _run() -> Dict[str, Any]:
+        from app.repositories.implicit_emotions_storage_repository import (
+            ImplicitEmotionsStorageRepository,
+        )
+        from app.services.emotion_analytics_service import EmotionAnalyticsService
+        from app.services.implicit_memory_service import ImplicitMemoryService
+
+        logger.info(f"开始按需初始化隐性记忆/情绪数据，候选用户数: {len(end_user_ids)}")
+
+        initialized = 0
+        failed = 0
+        skipped = 0
+
+        with get_db_context() as db:
+            repo = ImplicitEmotionsStorageRepository(db)
+
+            for end_user_id in end_user_ids:
+                existing = repo.get_by_end_user_id(end_user_id)
+                if existing is not None:
+                    skipped += 1
+                    continue
+
+                logger.info(f"用户 {end_user_id} 无记录，开始初始化")
+                implicit_ok = False
+                emotion_ok = False
+                try:
+                    try:
+                        implicit_service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
+                        profile_data = await implicit_service.generate_complete_profile(user_id=end_user_id)
+                        await implicit_service.save_profile_cache(
+                            end_user_id=end_user_id, profile_data=profile_data, db=db
+                        )
+                        implicit_ok = True
+                    except Exception as e:
+                        logger.error(f"用户 {end_user_id} 隐性记忆初始化失败: {e}")
+
+                    try:
+                        emotion_service = EmotionAnalyticsService()
+                        suggestions_data = await emotion_service.generate_emotion_suggestions(
+                            end_user_id=end_user_id, db=db, language="zh"
+                        )
+                        await emotion_service.save_suggestions_cache(
+                            end_user_id=end_user_id, suggestions_data=suggestions_data, db=db
+                        )
+                        emotion_ok = True
+                    except Exception as e:
+                        logger.error(f"用户 {end_user_id} 情绪建议初始化失败: {e}")
+
+                    if implicit_ok or emotion_ok:
+                        initialized += 1
+                    else:
+                        failed += 1
+                except Exception as e:
+                    failed += 1
+                    logger.error(f"用户 {end_user_id} 初始化异常: {e}")
+
+        logger.info(f"按需初始化完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}")
+        return {
+            "status": "SUCCESS",
+            "initialized": initialized,
+            "skipped": skipped,
+            "failed": failed,
+        }
+
+    try:
+        loop = set_asyncio_event_loop()
+
+        result = loop.run_until_complete(_run())
+        result["elapsed_time"] = time.time() - start_time
+        result["task_id"] = self.request.id
+        return result
+    except Exception as e:
+        return {
+            "status": "FAILURE",
+            "error": str(e),
+            "elapsed_time": time.time() - start_time,
+            "task_id": self.request.id,
+        }
+
+
+# =============================================================================
+
+@celery_app.task(
+    name="app.tasks.init_interest_distribution_for_users",
+    bind=True,
+    ignore_result=True,
+    max_retries=0,
+    acks_late=False,
+    time_limit=3600,
+    soft_time_limit=3300,
+)
+def init_interest_distribution_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]:
+    """事件触发任务：检查指定用户列表的兴趣分布缓存，无缓存则生成并写入 Redis。
+
+    由 /dashboard/end_users 接口触发，已有缓存的用户直接跳过。
+    默认生成中文（zh）兴趣分布数据。
+
+    Args:
+        self: task object
+        end_user_ids: 需要检查的用户ID列表
+
+    Returns:
+        包含任务执行结果的字典
+    """
+    start_time = time.time()
+
+    async def _run() -> Dict[str, Any]:
+        from app.cache.memory.interest_memory import InterestMemoryCache, INTEREST_CACHE_EXPIRE
+        from app.services.memory_agent_service import MemoryAgentService
+
+        logger.info(f"开始按需初始化兴趣分布缓存，候选用户数: {len(end_user_ids)}")
+
+        initialized = 0
+        failed = 0
+        skipped = 0
+        language = "zh"
+
+        service = MemoryAgentService()
+
+        with get_db_context() as db:
+            for end_user_id in end_user_ids:
+                # 存在性检查：缓存有数据则跳过
+                cached = await InterestMemoryCache.get_interest_distribution(
+                    end_user_id=end_user_id,
+                    language=language,
+                )
+                if cached is not None:
+                    skipped += 1
+                    continue
+
+                logger.info(f"用户 {end_user_id} 无兴趣分布缓存，开始生成")
+                try:
+                    result = await service.get_interest_distribution_by_user(
+                        end_user_id=end_user_id,
+                        limit=5,
+                        language=language,
+                    )
+                    await InterestMemoryCache.set_interest_distribution(
+                        end_user_id=end_user_id,
+                        language=language,
+                        data=result,
+                        expire=INTEREST_CACHE_EXPIRE,
+                    )
+                    initialized += 1
+                    logger.info(f"用户 {end_user_id} 兴趣分布缓存生成成功")
+                except Exception as e:
+                    failed += 1
+                    logger.error(f"用户 {end_user_id} 兴趣分布缓存生成失败: {e}")
+
+        logger.info(f"兴趣分布按需初始化完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}")
+        return {
+            "status": "SUCCESS",
+            "initialized": initialized,
+            "skipped": skipped,
+            "failed": failed,
+        }
+
+    try:
+        loop = set_asyncio_event_loop()
+
+        result = loop.run_until_complete(_run())
+        result["elapsed_time"] = time.time() - start_time
+        result["task_id"] = self.request.id
+        return result
+    except Exception as e:
+        return {
+            "status": "FAILURE",
+            "error": str(e),
+            "elapsed_time": time.time() - start_time,
+            "task_id": self.request.id,
+        }
+
+
+@celery_app.task(
+    name="app.tasks.write_perceptual_memory",
+    bind=True,
+    ignore_result=True,
+    max_retries=0,
+    acks_late=False,
+    time_limit=3600,
+    soft_time_limit=3300,
+)
+def write_perceptual_memory(
+        self,
+        end_user_id: str,
+        model_api_config: dict,
+        file_type: str,
+        file_url: str,
+        file_message: dict
+):
+    """
+    Write perceptual memory for a user into PostgreSQL and Neo4j.
+
+    This task generates or updates the user's perceptual memory
+    in the backend databases. It is intended to be executed asynchronously
+    via Celery.
+
+    Args:
+        end_user_id (uuid.UUID): The unique identifier of the end user.
+        model_api_config (ModelInfo): API configuration for the model
+            used to generate perceptual memory.
+        file_type (str): The file type
+        file_url (url): The url of file
+        file_message (dict): The file message containing details about the file
+            to be processed.
+
+    Returns:
+        None
+    """
+    file_url_md5 = hashlib.md5(file_url.encode("utf-8")).hexdigest()
+    set_asyncio_event_loop()
+    with RedisLock(f"perceptual:{file_url_md5}", redis_client=get_sync_redis_client()):
+        model_info = ModelInfo(**model_api_config)
+        with get_db_context() as db:
+            memory_perceptual_service = MemoryPerceptualService(db)
+            return asyncio.run(memory_perceptual_service.generate_perceptual_memory(
+                end_user_id,
+                model_info,
+                file_type,
+                file_url,
+                file_message,
+            ))
+
+
+# =============================================================================
+# 社区聚类补全任务（触发型）
+# =============================================================================
+
+@celery_app.task(
+    name="app.tasks.init_community_clustering_for_users",
+    bind=True,
+    ignore_result=False,
+    max_retries=0,
+    acks_late=False,
+    time_limit=7200,   # 2小时硬超时
+    soft_time_limit=6900,
+)
+def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[str, Any]:
+    """触发型任务：检查指定用户列表，对有 ExtractedEntity 但无 Community 节点的用户执行全量聚类。
+
+    由 /dashboard/end_users 接口触发，已有社区节点的用户直接跳过。
+
+    Args:
+        end_user_ids: 需要检查的用户 ID 列表
+
+    Returns:
+        包含任务执行结果的字典
+    """
+    start_time = time.time()
+
+    async def _run() -> Dict[str, Any]:
+        from app.core.logging_config import get_logger
+        from app.repositories.neo4j.community_repository import CommunityRepository
+        from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+        from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine
+
+        logger = get_logger(__name__)
+        logger.info(f"[CommunityCluster] 开始社区聚类补全任务，候选用户数: {len(end_user_ids)}")
+
+        initialized = 0
+        skipped = 0
+        failed = 0
+
+        connector = Neo4jConnector()
+        try:
+            repo = CommunityRepository(connector)
+
+            # 批量预取所有用户的配置（内置兜底：用户配置不可用时自动回退到工作空间默认配置）
+            user_llm_map: Dict[str, Optional[str]] = {}
+            user_embedding_map: Dict[str, Optional[str]] = {}
+            try:
+                with get_db_context() as db:
+                    from app.services.memory_agent_service import get_end_users_connected_configs_batch
+                    from app.services.memory_config_service import MemoryConfigService
+                    batch_configs = get_end_users_connected_configs_batch(end_user_ids, db)
+                    for uid, cfg_info in batch_configs.items():
+                        config_id = cfg_info.get("memory_config_id")
+                        if config_id:
+                            try:
+                                cfg = MemoryConfigService(db).load_memory_config(config_id=config_id)
+                                user_llm_map[uid] = str(cfg.llm_model_id) if cfg.llm_model_id else None
+                                user_embedding_map[uid] = str(cfg.embedding_model_id) if cfg.embedding_model_id else None
+                            except Exception as e:
+                                logger.warning(f"[CommunityCluster] 用户 {uid} 加载 LLM 配置失败，将使用 None: {e}")
+                                user_llm_map[uid] = None
+                                user_embedding_map[uid] = None
+                        else:
+                            user_llm_map[uid] = None
+                            user_embedding_map[uid] = None
+            except Exception as e:
+                logger.warning(f"[CommunityCluster] 批量获取 LLM 配置失败，所有用户将使用 None: {e}")
+
+            for end_user_id in end_user_ids:
+                try:
+                    # 已有社区节点则跳过
+                    has_communities = await repo.has_communities(end_user_id)
+                    if has_communities:
+                        skipped += 1
+                        logger.debug(f"[CommunityCluster] 用户 {end_user_id} 已有社区节点，跳过")
+                        continue
+
+                    # 检查是否有 ExtractedEntity 节点
+                    entities = await repo.get_all_entities(end_user_id)
+                    if not entities:
+                        skipped += 1
+                        logger.debug(f"[CommunityCluster] 用户 {end_user_id} 无实体节点，跳过")
+                        continue
+
+                    # 每个用户使用自己的 llm_model_id
+                    llm_model_id = user_llm_map.get(end_user_id)
+                    embedding_model_id = user_embedding_map.get(end_user_id)
+                    engine = LabelPropagationEngine(
+                        connector=connector,
+                        llm_model_id=llm_model_id,
+                        embedding_model_id=embedding_model_id,
+                    )
+
+                    logger.info(f"[CommunityCluster] 用户 {end_user_id} 有 {len(entities)} 个实体，开始全量聚类，llm_model_id={llm_model_id}")
+                    await engine.full_clustering(end_user_id)
+                    initialized += 1
+                    logger.info(f"[CommunityCluster] 用户 {end_user_id} 聚类完成")
+
+                except Exception as e:
+                    failed += 1
+                    logger.error(f"[CommunityCluster] 用户 {end_user_id} 聚类失败: {e}")
+
+        finally:
+            await connector.close()
+
+        logger.info(
+            f"[CommunityCluster] 任务完成: 初始化={initialized}, 跳过={skipped}, 失败={failed}"
+        )
+        return {
+            "status": "SUCCESS",
+            "initialized": initialized,
+            "skipped": skipped,
+            "failed": failed,
+        }
+
+    try:
+        try:
+            import nest_asyncio
+            nest_asyncio.apply()
+        except ImportError:
+            pass
+
+        loop = set_asyncio_event_loop()
+        result = loop.run_until_complete(_run())
+        result["elapsed_time"] = time.time() - start_time
+        result["task_id"] = self.request.id
+        return result
+
+    except Exception as e:
+        return {
+            "status": "FAILURE",
+            "error": str(e),
+            "elapsed_time": time.time() - start_time,
+            "task_id": self.request.id,
+        }