Merge pull request #573 from SuanmoSuanyangTechnology/feature/node-aggregation

Feature/node aggregation

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', 'statements', 'chunks', 'entities']
+                # Priority order: summaries first (most contextual), then communities, 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:
+                    from app.repositories.neo4j.graph_search import search_graph_community_expand
+                    from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+                    expand_connector = Neo4jConnector()
+                    try:
+                        expand_result = await search_graph_community_expand(
+                            connector=expand_connector,
+                            community_ids=community_ids,
+                            end_user_id=end_user_id,
+                            limit=10,
+                        )
+                        expanded_stmts = expand_result.get("expanded_statements", [])
+                        if expanded_stmts:
+                            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}")
+                    finally:
+                        await expand_connector.close()
             
             # 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
 
@@ -171,6 +171,13 @@ async def write(
             )
             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
-            item["activation_score"] = act_norm
+            # 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  # 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

@@ -7,6 +7,7 @@
 - 增量更新（incremental_update）：新实体到达时，只处理新实体及其邻居
 """
 
+import asyncio
 import logging
 import uuid
 from math import sqrt
@@ -19,8 +20,9 @@ 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
+
+# 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:
@@ -69,11 +71,13 @@ class LabelPropagationEngine:
         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
@@ -103,58 +107,81 @@ 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
-        2. 迭代：每轮对所有实体做邻居投票，更新社区标签
-        3. 直到标签不再变化或达到 MAX_ITERATIONS
-        4. 将最终标签写入 Neo4j
+        策略：
+        - 每次只加载 BATCH_SIZE 个实体及其邻居进内存
+        - labels 字典跨批次共享（只存 id→community_id，内存极小）
+        - 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)
-        if not entities:
+        BATCH_SIZE = 888  # 每批实体数，可按需调整
+
+        # 轻量查询：只获取总数和 ID 列表，不加载 embedding 等大字段
+        total_count = await self.repo.get_entity_count(end_user_id)
+        if not total_count:
             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
-        labels: Dict[str, str] = {e["id"]: e["id"] for e in entities}
-        embeddings: Dict[str, Optional[List[float]]] = {
-            e["id"]: e.get("name_embedding") for e in entities
-        }
+        all_entity_ids = await self.repo.get_all_entity_ids(end_user_id)
+        logger.info(f"[Clustering] 用户 {end_user_id} 共 {total_count} 个实体，"
+                    f"分批大小 {BATCH_SIZE}，共 {(total_count + BATCH_SIZE - 1) // BATCH_SIZE} 批")
 
-        # 预加载所有实体的邻居，避免迭代内 O(iterations * |E|) 次 Neo4j 往返
-        logger.info(f"[Clustering] 预加载 {len(entities)} 个实体的邻居图...")
-        neighbors_cache: Dict[str, List[Dict]] = await self.repo.get_all_entity_neighbors_batch(end_user_id)
-        logger.info(f"[Clustering] 邻居预加载完成，覆盖实体数: {len(neighbors_cache)}")
+        # labels 跨批次共享：只存 id→community_id，内存极小
+        labels: Dict[str, str] = {eid: eid for eid in all_entity_ids}
+        del all_entity_ids  # 释放 ID 列表，后续按批次加载完整数据
 
-        for iteration in range(MAX_ITERATIONS):
-            changed = 0
-            # 随机顺序（Python dict 在 3.7+ 保持插入顺序，这里直接遍历）
-            for entity in entities:
-                eid = entity["id"]
-                # 直接从缓存取邻居，不再发起 Neo4j 查询
-                neighbors = neighbors_cache.get(eid, [])
-
-                # 将邻居的当前内存标签注入（覆盖 Neo4j 中的旧值）
-                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, 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}"
+        for batch_start in range(0, total_count, BATCH_SIZE):
+            batch_entities = await self.repo.get_entities_page(
+                end_user_id, skip=batch_start, limit=BATCH_SIZE
             )
-            if changed == 0:
-                logger.info("[Clustering] 标签已收敛，提前结束迭代")
+            if not batch_entities:
                 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(
@@ -162,7 +189,6 @@ class LabelPropagationEngine:
             f"{len(labels)} 个实体，开始后处理合并"
         )
 
-        # 全量初始化后做一轮社区合并（基于 name_embedding 余弦相似度）
         all_community_ids = list(set(labels.values()))
         await self._evaluate_merge(all_community_ids, end_user_id)
 
@@ -170,17 +196,15 @@ 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
-        # 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
+
+        # 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)
+        await self._generate_community_metadata(surviving_community_ids, end_user_id)
 
     async def incremental_update(
         self, new_entity_ids: List[str], end_user_id: str
@@ -237,7 +261,7 @@ 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)
+            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)
@@ -249,7 +273,7 @@ class LabelPropagationEngine:
                 await self._evaluate_merge(
                     list(community_ids_in_neighbors), end_user_id
                 )
-            await self._generate_community_metadata(target_cid, end_user_id)
+            await self._generate_community_metadata([target_cid], end_user_id)
 
     async def _evaluate_merge(
         self, community_ids: List[str], end_user_id: str
@@ -413,71 +437,122 @@ class LabelPropagationEngine:
         except Exception:
             return None
 
+    @staticmethod
+    def _build_entity_lines(members: List[Dict]) -> List[str]:
+        """将实体列表格式化为 prompt 行，包含 name、aliases、description。"""
+        lines = []
+        for m in members:
+            m_name = m.get("name", "")
+            aliases = m.get("aliases") or []
+            description = m.get("description") or ""
+            aliases_str = f"（别名：{'、'.join(aliases)}）" if aliases else ""
+            desc_str = f"：{description}" if description else ""
+            lines.append(f"- {m_name}{aliases_str}{desc_str}")
+        return lines
+
     async def _generate_community_metadata(
-        self, community_id: str, end_user_id: str
+        self, community_ids: List[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。
+        为一个或多个社区生成并写入元数据。
 
-        - 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
+        流程：
+        1. 逐个社区调 LLM 生成 name / summary（串行）
+        2. 收集所有 summary，一次性批量 embed
+        3. 单个社区用 update_community_metadata，多个用 batch_update_community_metadata
         """
-        try:
-            members = await self.repo.get_community_members(community_id, end_user_id)
-            if not members:
-                return
+        if not community_ids:
+            return
+
+        from app.db import get_db_context
+        from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+
+        # --- 阶段1：并发调 LLM 生成每个社区的 name / summary ---
+        async def _build_one(cid: str):
+            members = await self.repo.get_community_members(cid, end_user_id)
+            if not members:
+                return None
 
-            # 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,
+            entity_list_str = "\n".join(self._build_entity_lines(members))
+            prompt = (
+                f"以下是一组语义相关的实体：\n{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>"
             )
-            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}")
+            with get_db_context() as db:
+                llm_client = MemoryClientFactory(db).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)
+
+            name, summary = "", ""
+            for line in text.strip().splitlines():
+                if line.startswith("名称："):
+                    name = line[3:].strip()
+                elif line.startswith("摘要："):
+                    summary = line[3:].strip()
+
+            return {
+                "community_id": cid,
+                "end_user_id": end_user_id,
+                "name": name,
+                "summary": summary,
+                "core_entities": core_entities,
+                "summary_embedding": None,
+            }
+
+        results = await asyncio.gather(
+            *[_build_one(cid) for cid in community_ids],
+            return_exceptions=True,
+        )
+        metadata_list = []
+        for cid, res in zip(community_ids, results):
+            if isinstance(res, Exception):
+                logger.error(f"[Clustering] 社区 {cid} 元数据准备失败: {res}", exc_info=res)
+            elif res is not None:
+                metadata_list.append(res)
+
+        if not metadata_list:
+            return
+
+        # --- 阶段2：批量生成 summary_embedding ---
+        summaries = [m["summary"] for m in metadata_list]
+        with get_db_context() as db:
+            embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
+        embeddings = await embedder.response(summaries)
+        for i, meta in enumerate(metadata_list):
+            meta["summary_embedding"] = embeddings[i] if i < len(embeddings) else None
+
+        # --- 阶段3：写入（单个 or 批量）---
+        if len(metadata_list) == 1:
+            m = metadata_list[0]
+            result = await self.repo.update_community_metadata(
+                community_id=m["community_id"],
+                end_user_id=m["end_user_id"],
+                name=m["name"],
+                summary=m["summary"],
+                core_entities=m["core_entities"],
+                summary_embedding=m["summary_embedding"],
+            )
+            if result:
+                logger.info(f"[Clustering] 社区 {m['community_id']} 元数据写入成功: name={m['name']}, summary={m['summary'][:30]}...")
+            else:
+                logger.warning(f"[Clustering] 社区 {m['community_id']} 元数据写入返回 False")
+        else:
+            ok = await self.repo.batch_update_community_metadata(metadata_list)
+            if ok:
+                logger.info(f"[Clustering] 批量写入 {len(metadata_list)} 个社区元数据成功")
+            else:
+                logger.warning(f"[Clustering] 批量写入社区元数据失败")
 
     @staticmethod
     def _new_community_id() -> str:

api/app/models/memory_perceptual_model.py

@@ -9,7 +9,6 @@ from sqlalchemy.dialects.postgresql import JSONB
 from app.db import Base
 from app.schemas import FileType
 
-
 class PerceptualType(IntEnum):
     VISION = 1
     AUDIO = 2

api/app/repositories/neo4j/community_repository.py

@@ -13,12 +13,17 @@ from app.repositories.neo4j.cypher_queries import (
     ENTITY_LEAVE_ALL_COMMUNITIES,
     GET_ENTITY_NEIGHBORS,
     GET_ALL_ENTITIES_FOR_USER,
+    GET_ENTITY_COUNT_FOR_USER,
+    GET_ALL_ENTITY_IDS_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,
+    BATCH_UPDATE_COMMUNITY_METADATA,
 )
 
 logger = logging.getLogger(__name__)
@@ -110,6 +115,65 @@ class CommunityRepository:
             logger.error(f"get_all_entities failed: {e}")
             return []
 
+    async def get_entity_count(self, end_user_id: str) -> int:
+        """仅返回用户实体总数，不加载实体数据。"""
+        try:
+            result = await self.connector.execute_query(
+                GET_ENTITY_COUNT_FOR_USER,
+                end_user_id=end_user_id,
+            )
+            return result[0]["entity_count"] if result else 0
+        except Exception as e:
+            logger.error(f"get_entity_count failed: {e}")
+            return 0
+
+    async def get_all_entity_ids(self, end_user_id: str) -> List[str]:
+        """仅返回用户所有实体 ID 列表，不加载 embedding 等大字段。"""
+        try:
+            result = await self.connector.execute_query(
+                GET_ALL_ENTITY_IDS_FOR_USER,
+                end_user_id=end_user_id,
+            )
+            return [r["id"] for r in result]
+        except Exception as e:
+            logger.error(f"get_all_entity_ids 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]:
@@ -177,8 +241,9 @@ class CommunityRepository:
         name: str,
         summary: str,
         core_entities: List[str],
+        summary_embedding: Optional[List[float]] = None,
     ) -> bool:
-        """更新社区的名称、摘要和核心实体列表。"""
+        """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,
@@ -187,8 +252,31 @@ class CommunityRepository:
                 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
+
+    async def batch_update_community_metadata(
+        self,
+        communities: List[Dict],
+    ) -> bool:
+        """批量更新多个社区的元数据。
+
+        Args:
+            communities: 每项包含 community_id, end_user_id, name, summary,
+                         core_entities, summary_embedding
+        """
+        if not communities:
+            return True
+        try:
+            await self.connector.execute_query(
+                BATCH_UPDATE_COMMUNITY_METADATA,
+                communities=communities,
+            )
+            return True
+        except Exception as e:
+            logger.error(f"batch_update_community_metadata failed: {e}")
+            return False

api/app/repositories/neo4j/create_indexes.py

@@ -42,6 +42,13 @@ async def create_fulltext_indexes():
             OPTIONS { indexConfig: { `fulltext.analyzer`: 'cjk' } }
         """)
         print("✓ Created: summariesFulltext")
+
+        # 创建 Community 索引
+        await connector.execute_query("""
+            CREATE FULLTEXT INDEX communitiesFulltext IF NOT EXISTS FOR (c:Community) ON EACH [c.name, c.summary]
+            OPTIONS { indexConfig: { `fulltext.analyzer`: 'cjk' } }
+        """)
+        print("✓ Created: communitiesFulltext")
         
         print("\nFull-text indexes created successfully with BM25 support.")
     except Exception as e:
@@ -124,6 +131,18 @@ async def create_vector_indexes():
             }}
         """)
         print("✓ Created: dialogue_embedding_index")
+
+        # Community summary embedding index
+        await connector.execute_query("""
+            CREATE VECTOR INDEX community_summary_embedding_index IF NOT EXISTS
+            FOR (c:Community)
+            ON c.summary_embedding
+            OPTIONS {indexConfig: {
+              `vector.dimensions`: 1024,
+              `vector.similarity_function`: 'cosine'
+            }}
+        """)
+        print("✓ Created: community_summary_embedding_index")
         
         print("\nVector indexes created successfully!")
         print("\nExpected performance improvement:")

api/app/repositories/neo4j/cypher_queries.py

@@ -1122,21 +1122,33 @@ RETURN e.id AS id,
        CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
 """
 
+GET_ENTITY_COUNT_FOR_USER = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+RETURN count(e) AS entity_count
+"""
+
+GET_ALL_ENTITY_IDS_FOR_USER = """
+MATCH (e:ExtractedEntity {end_user_id: $end_user_id})
+RETURN e.id AS id
+"""
+
 GET_COMMUNITY_MEMBERS = """
 MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id})
 RETURN 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
+       e.name_embedding AS name_embedding,
+       e.aliases AS aliases, e.description AS description
 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)
-WHERE c.community_id IN $community_ids
 RETURN c.community_id AS community_id,
-       e.id AS 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,
-       e.activation_value AS activation_value
+       e.aliases AS aliases, e.description AS description
+ORDER BY c.community_id, coalesce(e.activation_value, 0) DESC
 """
 
 CHECK_USER_HAS_COMMUNITIES = """
@@ -1153,13 +1165,58 @@ 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.updated_at   = datetime()
+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
 """
 
+BATCH_UPDATE_COMMUNITY_METADATA = """
+UNWIND $communities AS row
+MATCH (c:Community {community_id: row.community_id, end_user_id: row.end_user_id})
+SET c.name             = row.name,
+    c.summary          = row.summary,
+    c.core_entities    = row.core_entities,
+    c.summary_embedding = row.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})
@@ -1185,20 +1242,59 @@ RETURN DISTINCT
     CASE WHEN c IS NOT NULL THEN c.community_id ELSE null END AS community_id
 """
 
-GET_COMMUNITY_GRAPH_DATA = """
-MATCH (c:Community {end_user_id: $end_user_id})
-MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[b:BELONGS_TO_COMMUNITY]->(c)
-OPTIONAL MATCH (e)-[r:EXTRACTED_RELATIONSHIP]-(e2:ExtractedEntity {end_user_id: $end_user_id})
-RETURN
-    elementId(c)          AS c_id,
-    properties(c)         AS c_props,
-    elementId(e)          AS e_id,
-    properties(e)         AS e_props,
-    elementId(b)          AS b_id,
-    elementId(e2)         AS e2_id,
-    properties(e2)        AS e2_props,
-    elementId(r)          AS r_id,
-    type(r)               AS r_type,
-    properties(r)         AS r_props,
-    startNode(r) = e      AS r_from_e
+
+# 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 +1,4 @@
 import asyncio
-import os
 from typing import List, Optional
 
 # 使用新的仓储层
@@ -158,11 +157,12 @@ async def save_dialog_and_statements_to_neo4j(
         statement_chunk_edges: List[StatementChunkEdge],
         statement_entity_edges: List[StatementEntityEdge],
         connector: Neo4jConnector,
-        config_id: Optional[str] = None,
-        llm_model_id: Optional[str] = None,
 ) -> 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
@@ -293,9 +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，异步触发聚类（不阻塞写入响应）
-        schedule_clustering_after_write(entity_nodes, config_id=config_id, llm_model_id=llm_model_id)
-
         return True
 
     except Exception as e:
@@ -309,6 +306,7 @@ 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。
@@ -327,7 +325,7 @@ def schedule_clustering_after_write(
     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))
+    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(
@@ -335,6 +333,7 @@ async def _trigger_clustering(
     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。
@@ -344,7 +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, config_id=config_id, llm_model_id=llm_model_id)
+        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,