From 5b431400be9959442dca774ddd95dc95719fd343 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Tue, 10 Mar 2026 17:06:43 +0800 Subject: [PATCH 1/7] [add] Create community nodes --- .../clustering_engine/__init__.py | 3 + .../clustering_engine/label_propagation.py | 311 ++++++++++++++++++ .../neo4j/community_repository.py | 129 ++++++++ api/app/repositories/neo4j/cypher_queries.py | 93 +++++- api/app/repositories/neo4j/graph_saver.py | 34 ++ 5 files changed, 569 insertions(+), 1 deletion(-) create mode 100644 api/app/core/memory/storage_services/clustering_engine/__init__.py create mode 100644 api/app/core/memory/storage_services/clustering_engine/label_propagation.py create mode 100644 api/app/repositories/neo4j/community_repository.py diff --git a/api/app/core/memory/storage_services/clustering_engine/__init__.py b/api/app/core/memory/storage_services/clustering_engine/__init__.py new file mode 100644 index 00000000..992d8bff --- /dev/null +++ b/api/app/core/memory/storage_services/clustering_engine/__init__.py @@ -0,0 +1,3 @@ +from app.core.memory.storage_services.clustering_engine.label_propagation import LabelPropagationEngine + +__all__ = ["LabelPropagationEngine"] diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py new file mode 100644 index 00000000..80e238fd --- /dev/null +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -0,0 +1,311 @@ +"""标签传播聚类引擎 + +基于 ZEP 论文的动态标签传播算法，对 Neo4j 中的 ExtractedEntity 节点进行社区聚类。 + +支持两种模式： +- 全量初始化（full_clustering）：首次运行，对所有实体做完整 LPA 迭代 +- 增量更新（incremental_update）：新实体到达时，只处理新实体及其邻居 +""" + +import logging +import uuid +from math import sqrt +from typing import Dict, List, Optional + +from app.repositories.neo4j.community_repository import CommunityRepository +from app.repositories.neo4j.neo4j_connector import Neo4jConnector + +logger = logging.getLogger(__name__) + +# 全量迭代最大轮数，防止不收敛 +MAX_ITERATIONS = 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。""" + if not v1 or not v2 or len(v1) != len(v2): + return 0.0 + dot = sum(a * b for a, b in zip(v1, v2)) + norm1 = sqrt(sum(a * a for a in v1)) + norm2 = sqrt(sum(b * b for b in v2)) + if norm1 == 0 or norm2 == 0: + return 0.0 + return dot / (norm1 * norm2) + + +def _weighted_vote( + neighbors: List[Dict], + self_embedding: Optional[List[float]], +) -> Optional[str]: + """ + 加权多数投票，选出得票最高的社区。 + + 权重 = 语义相似度（name_embedding 余弦）* activation_value 加成 + 没有 community_id 的邻居不参与投票。 + """ + votes: Dict[str, float] = {} + for nb in neighbors: + cid = nb.get("community_id") + if not cid: + continue + sem = _cosine_similarity(self_embedding, nb.get("name_embedding")) + act = nb.get("activation_value") or 0.5 + # 语义相似度权重 0.6，激活值权重 0.4 + weight = 0.6 * sem + 0.4 * act + votes[cid] = votes.get(cid, 0.0) + weight + + if not votes: + return None + return max(votes, key=votes.__getitem__) + + +class LabelPropagationEngine: + """标签传播聚类引擎""" + + def __init__(self, connector: Neo4jConnector): + self.connector = connector + self.repo = CommunityRepository(connector) + + # ────────────────────────────────────────────────────────────────────────── + # 公开接口 + # ────────────────────────────────────────────────────────────────────────── + + async def run( + self, + end_user_id: str, + new_entity_ids: Optional[List[str]] = None, + ) -> None: + """ + 统一入口：自动判断全量还是增量。 + + - 若该用户尚无 Community 节点 → 全量初始化 + - 否则 → 增量更新（仅处理 new_entity_ids） + """ + has_communities = await self.repo.has_communities(end_user_id) + if not has_communities: + logger.info(f"[Clustering] 用户 {end_user_id} 首次聚类，执行全量初始化") + await self.full_clustering(end_user_id) + else: + if new_entity_ids: + logger.info( + f"[Clustering] 增量更新，新实体数: {len(new_entity_ids)}" + ) + await self.incremental_update(new_entity_ids, end_user_id) + + async def full_clustering(self, end_user_id: str) -> None: + """ + 全量标签传播初始化。 + + 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 + """ + entities = await self.repo.get_all_entities(end_user_id) + if not 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 + 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 + } + + 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"] + neighbors = await self.repo.get_entity_neighbors(eid, end_user_id) + + # 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}" + ) + if changed == 0: + logger.info("[Clustering] 标签已收敛，提前结束迭代") + break + + # 将最终标签写入 Neo4j + await self._flush_labels(labels, end_user_id) + logger.info( + f"[Clustering] 全量聚类完成，共 {len(set(labels.values()))} 个社区，" + f"{len(labels)} 个实体" + ) + + async def incremental_update( + self, new_entity_ids: List[str], end_user_id: str + ) -> None: + """ + 增量更新：只处理新实体及其邻居，不重跑全图。 + + 1. 对每个新实体查询邻居 + 2. 加权多数投票决定社区归属 + 3. 若邻居无社区 → 创建新社区 + 4. 若邻居分属多个社区 → 评估是否合并 + """ + for entity_id in new_entity_ids: + await self._process_single_entity(entity_id, end_user_id) + + # ────────────────────────────────────────────────────────────────────────── + # 内部方法 + # ────────────────────────────────────────────────────────────────────────── + + async def _process_single_entity( + self, entity_id: str, end_user_id: str + ) -> None: + """处理单个新实体的社区分配。""" + neighbors = await self.repo.get_entity_neighbors(entity_id, end_user_id) + + # 查询自身 embedding（从邻居查询结果中无法获取，需单独查） + self_embedding = await self._get_entity_embedding(entity_id, end_user_id) + + if not neighbors: + # 孤立实体：创建单成员社区 + new_cid = self._new_community_id() + await self.repo.upsert_community(new_cid, end_user_id, member_count=1) + await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id) + logger.debug(f"[Clustering] 孤立实体 {entity_id} → 新社区 {new_cid}") + return + + # 统计邻居社区分布 + community_ids_in_neighbors = set( + nb["community_id"] for nb in neighbors if nb.get("community_id") + ) + + target_cid = _weighted_vote(neighbors, self_embedding) + + if target_cid is None: + # 邻居都没有社区，连同新实体一起创建新社区 + new_cid = self._new_community_id() + await self.repo.upsert_community(new_cid, end_user_id) + await self.repo.assign_entity_to_community(entity_id, new_cid, end_user_id) + for nb in neighbors: + await self.repo.assign_entity_to_community( + nb["id"], new_cid, end_user_id + ) + await self.repo.refresh_member_count(new_cid, end_user_id) + logger.debug( + f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}" + ) + else: + # 加入得票最多的社区 + await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id) + await self.repo.refresh_member_count(target_cid, end_user_id) + logger.debug(f"[Clustering] 新实体 {entity_id} → 社区 {target_cid}") + + # 若邻居分属多个社区，评估合并 + if len(community_ids_in_neighbors) > 1: + await self._evaluate_merge( + list(community_ids_in_neighbors), end_user_id + ) + + async def _evaluate_merge( + self, community_ids: List[str], end_user_id: str + ) -> None: + """ + 评估多个社区是否应合并。 + + 策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。 + 合并时保留成员数最多的社区，其余成员迁移过来。 + """ + MERGE_THRESHOLD = 0.75 + + community_embeddings: Dict[str, Optional[List[float]]] = {} + community_sizes: Dict[str, int] = {} + + for cid in community_ids: + members = await self.repo.get_community_members(cid, end_user_id) + community_sizes[cid] = len(members) + # 计算社区成员 embedding 的平均向量 + valid_embeddings = [ + m["name_embedding"] + for m in members + if m.get("name_embedding") + ] + if valid_embeddings: + dim = len(valid_embeddings[0]) + avg = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + community_embeddings[cid] = avg + else: + community_embeddings[cid] = None + + # 找出应合并的社区对 + to_merge: List[tuple] = [] + cids = list(community_ids) + for i in range(len(cids)): + for j in range(i + 1, len(cids)): + sim = _cosine_similarity( + community_embeddings[cids[i]], + community_embeddings[cids[j]], + ) + if sim > MERGE_THRESHOLD: + to_merge.append((cids[i], cids[j])) + + for c1, c2 in to_merge: + keep = c1 if community_sizes.get(c1, 0) >= community_sizes.get(c2, 0) else c2 + dissolve = c2 if keep == c1 else c1 + members = await self.repo.get_community_members(dissolve, end_user_id) + for m in members: + await self.repo.assign_entity_to_community( + m["id"], keep, end_user_id + ) + await self.repo.refresh_member_count(keep, end_user_id) + logger.info( + f"[Clustering] 社区合并: {dissolve} → {keep}，" + f"迁移 {len(members)} 个成员" + ) + + async def _flush_labels( + self, labels: Dict[str, str], end_user_id: str + ) -> None: + """将内存中的标签批量写入 Neo4j。""" + # 先创建所有唯一社区节点 + unique_communities = set(labels.values()) + for cid in unique_communities: + await self.repo.upsert_community(cid, end_user_id) + + # 再批量分配实体 + for entity_id, community_id in labels.items(): + await self.repo.assign_entity_to_community( + entity_id, community_id, end_user_id + ) + + # 刷新成员数 + for cid in unique_communities: + await self.repo.refresh_member_count(cid, end_user_id) + + async def _get_entity_embedding( + self, entity_id: str, end_user_id: str + ) -> Optional[List[float]]: + """查询单个实体的 name_embedding。""" + try: + result = await self.connector.execute_query( + "MATCH (e:ExtractedEntity {id: $eid, end_user_id: $uid}) " + "RETURN e.name_embedding AS name_embedding", + eid=entity_id, + uid=end_user_id, + ) + return result[0]["name_embedding"] if result else None + except Exception: + return None + + @staticmethod + def _new_community_id() -> str: + return str(uuid.uuid4()) diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py new file mode 100644 index 00000000..16e30a10 --- /dev/null +++ b/api/app/repositories/neo4j/community_repository.py @@ -0,0 +1,129 @@ +"""Community 节点仓库 + +管理 Neo4j 中 Community 节点及 BELONGS_TO_COMMUNITY 边的 CRUD 操作。 +""" + +import logging +from typing import Dict, List, Optional + +from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.repositories.neo4j.cypher_queries import ( + COMMUNITY_NODE_UPSERT, + ENTITY_JOIN_COMMUNITY, + ENTITY_LEAVE_ALL_COMMUNITIES, + GET_ENTITY_NEIGHBORS, + GET_ALL_ENTITIES_FOR_USER, + GET_COMMUNITY_MEMBERS, + CHECK_USER_HAS_COMMUNITIES, + UPDATE_COMMUNITY_MEMBER_COUNT, +) + +logger = logging.getLogger(__name__) + + +class CommunityRepository: + def __init__(self, connector: Neo4jConnector): + self.connector = connector + + async def upsert_community( + self, community_id: str, end_user_id: str, member_count: int = 0 + ) -> Optional[str]: + """创建或更新 Community 节点，返回 community_id。""" + try: + result = await self.connector.execute_query( + COMMUNITY_NODE_UPSERT, + community_id=community_id, + end_user_id=end_user_id, + member_count=member_count, + ) + return result[0]["community_id"] if result else None + except Exception as e: + logger.error(f"upsert_community failed: {e}") + return None + + async def assign_entity_to_community( + self, entity_id: str, community_id: str, end_user_id: str + ) -> bool: + """将实体关联到社区（先解除旧关联，再建立新关联）。""" + try: + await self.connector.execute_query( + ENTITY_LEAVE_ALL_COMMUNITIES, + entity_id=entity_id, + end_user_id=end_user_id, + ) + result = await self.connector.execute_query( + ENTITY_JOIN_COMMUNITY, + entity_id=entity_id, + community_id=community_id, + end_user_id=end_user_id, + ) + return bool(result) + except Exception as e: + logger.error(f"assign_entity_to_community failed: {e}") + return False + + async def get_entity_neighbors( + self, entity_id: str, end_user_id: str + ) -> List[Dict]: + """查询实体的直接邻居及其社区归属。""" + try: + return await self.connector.execute_query( + GET_ENTITY_NEIGHBORS, + entity_id=entity_id, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_entity_neighbors failed: {e}") + return [] + + async def get_all_entities(self, end_user_id: str) -> List[Dict]: + """拉取某用户下所有实体及其当前社区归属。""" + try: + return await self.connector.execute_query( + GET_ALL_ENTITIES_FOR_USER, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_all_entities failed: {e}") + return [] + + async def get_community_members( + self, community_id: str, end_user_id: str + ) -> List[Dict]: + """查询社区成员列表。""" + try: + return await self.connector.execute_query( + GET_COMMUNITY_MEMBERS, + community_id=community_id, + end_user_id=end_user_id, + ) + except Exception as e: + logger.error(f"get_community_members failed: {e}") + return [] + + async def has_communities(self, end_user_id: str) -> bool: + """检查该用户是否已有 Community 节点（用于判断全量 vs 增量）。""" + try: + result = await self.connector.execute_query( + CHECK_USER_HAS_COMMUNITIES, + end_user_id=end_user_id, + ) + return result[0]["community_count"] > 0 if result else False + except Exception as e: + logger.error(f"has_communities failed: {e}") + return False + + async def refresh_member_count( + self, community_id: str, end_user_id: str + ) -> int: + """重新统计并更新社区成员数，返回最新数量。""" + try: + result = await self.connector.execute_query( + UPDATE_COMMUNITY_MEMBER_COUNT, + community_id=community_id, + end_user_id=end_user_id, + ) + return result[0]["member_count"] if result else 0 + except Exception as e: + logger.error(f"refresh_member_count failed: {e}") + return 0 diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 651c513f..947097a2 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1058,4 +1058,95 @@ Graph_Node_query = """ 3 AS priority LIMIT $limit - """ \ No newline at end of file + """ + + +# ============================================================ +# Community 节点 & BELONGS_TO_COMMUNITY 边 +# ============================================================ + +COMMUNITY_NODE_SAVE = """ +MERGE (c:Community {community_id: $community_id}) +SET c.end_user_id = $end_user_id, + c.formed_at = $formed_at, + c.updated_at = datetime(), + c.status = $status, + c.member_count = $member_count +RETURN c.community_id AS community_id +""" + +COMMUNITY_ADD_MEMBER = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) +SET c.updated_at = datetime(), + c.member_count = $member_count +""" + + + +# ─── Community 聚类相关 Cypher 模板 ─────────────────────────────────────────── + +COMMUNITY_NODE_UPSERT = """ +MERGE (c:Community {community_id: $community_id}) +SET c.end_user_id = $end_user_id, + c.member_count = $member_count, + c.updated_at = datetime() +RETURN c.community_id AS community_id +""" + +ENTITY_JOIN_COMMUNITY = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) +MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) +SET c.updated_at = datetime() +RETURN e.id AS entity_id, c.community_id AS community_id +""" + +ENTITY_LEAVE_ALL_COMMUNITIES = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +MATCH (e)-[r:BELONGS_TO_COMMUNITY]->(:Community) +DELETE r +""" + +GET_ENTITY_NEIGHBORS = """ +MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb:ExtractedEntity {end_user_id: $end_user_id}) +OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) +RETURN DISTINCT + 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_ENTITIES_FOR_USER = """ +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 +""" + +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 +ORDER BY 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 +""" + +UPDATE_COMMUNITY_MEMBER_COUNT = """ +MATCH (e:ExtractedEntity {end_user_id: $end_user_id})-[:BELONGS_TO_COMMUNITY]->(c:Community {community_id: $community_id}) +WITH c, count(e) AS cnt +SET c.member_count = cnt +RETURN c.community_id AS community_id, cnt AS member_count +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index 526d16ec..a94bc23b 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -1,3 +1,4 @@ +import asyncio from typing import List # 使用新的仓储层 @@ -288,6 +289,14 @@ 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: @@ -295,3 +304,28 @@ async def save_dialog_and_statements_to_neo4j( print(f"Neo4j integration error: {e}") print("Continuing without database storage...") return False + + +async def _trigger_clustering( + new_entity_ids: List[str], + end_user_id: str, +) -> None: + """ + 聚类触发函数，自动判断全量初始化还是增量更新。 + """ + connector = None + try: + 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) + 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: + logger.error(f"[Clustering] 聚类触发失败: {e}", exc_info=True) + finally: + if connector: + try: + await connector.close() + except Exception: + pass From fc58ac0408c6110fcbd852ce85e17c9e95353ff0 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Wed, 11 Mar 2026 18:04:04 +0800 Subject: [PATCH 2/7] [changes] Initial stage of community integration --- .../clustering_engine/label_propagation.py | 94 ++++++++++++++----- .../neo4j/community_repository.py | 20 ++++ api/app/repositories/neo4j/cypher_queries.py | 48 +++++----- 3 files changed, 115 insertions(+), 47 deletions(-) diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 80e238fd..cb6e5804 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -141,8 +141,18 @@ class LabelPropagationEngine: # 将最终标签写入 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" + ) + + # 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) + + 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" ) @@ -221,30 +231,50 @@ class LabelPropagationEngine: 策略：计算各社区成员 embedding 的平均向量，若两两余弦相似度 > 0.75 则合并。 合并时保留成员数最多的社区，其余成员迁移过来。 + + 全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。 """ MERGE_THRESHOLD = 0.75 + BATCH_THRESHOLD = 20 # 超过此数量走批量查询 community_embeddings: Dict[str, Optional[List[float]]] = {} community_sizes: Dict[str, int] = {} - for cid in community_ids: - members = await self.repo.get_community_members(cid, end_user_id) - community_sizes[cid] = len(members) - # 计算社区成员 embedding 的平均向量 - valid_embeddings = [ - m["name_embedding"] - for m in members - if m.get("name_embedding") - ] - if valid_embeddings: - dim = len(valid_embeddings[0]) - avg = [ - sum(e[i] for e in valid_embeddings) / len(valid_embeddings) - for i in range(dim) + if len(community_ids) > BATCH_THRESHOLD: + # 批量查询：一次拉取所有社区成员 + all_members = await self.repo.get_all_community_members_batch( + community_ids, end_user_id + ) + for cid in community_ids: + members = all_members.get(cid, []) + community_sizes[cid] = len(members) + valid_embeddings = [ + m["name_embedding"] for m in members if m.get("name_embedding") ] - community_embeddings[cid] = avg - else: - community_embeddings[cid] = None + if valid_embeddings: + dim = len(valid_embeddings[0]) + community_embeddings[cid] = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + else: + community_embeddings[cid] = None + else: + # 增量场景：逐个查询 + for cid in community_ids: + members = await self.repo.get_community_members(cid, end_user_id) + community_sizes[cid] = len(members) + valid_embeddings = [ + m["name_embedding"] for m in members if m.get("name_embedding") + ] + if valid_embeddings: + dim = len(valid_embeddings[0]) + community_embeddings[cid] = [ + sum(e[i] for e in valid_embeddings) / len(valid_embeddings) + for i in range(dim) + ] + else: + community_embeddings[cid] = None # 找出应合并的社区对 to_merge: List[tuple] = [] @@ -258,14 +288,32 @@ class LabelPropagationEngine: if sim > MERGE_THRESHOLD: to_merge.append((cids[i], cids[j])) + logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区") + + # 执行合并：用 union-find 思路避免重复迁移已被合并的社区 + # 维护一个 canonical 映射，确保链式合并正确收敛 + canonical: Dict[str, str] = {cid: cid for cid in cids} + + def find(x: str) -> str: + while canonical[x] != x: + canonical[x] = canonical[canonical[x]] + x = canonical[x] + return x + for c1, c2 in to_merge: - keep = c1 if community_sizes.get(c1, 0) >= community_sizes.get(c2, 0) else c2 - dissolve = c2 if keep == c1 else c1 + root1, root2 = find(c1), find(c2) + if root1 == root2: + continue # 已经在同一社区，跳过 + keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2 + dissolve = root2 if keep == root1 else root1 + canonical[dissolve] = keep + members = await self.repo.get_community_members(dissolve, end_user_id) for m in members: - await self.repo.assign_entity_to_community( - m["id"], keep, end_user_id - ) + await self.repo.assign_entity_to_community(m["id"], keep, end_user_id) + # 更新 sizes 以便后续合并决策准确 + community_sizes[keep] = community_sizes.get(keep, 0) + len(members) + community_sizes[dissolve] = 0 await self.repo.refresh_member_count(keep, end_user_id) logger.info( f"[Clustering] 社区合并: {dissolve} → {keep}，" diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 16e30a10..2a1f4f2b 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -14,6 +14,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ENTITY_NEIGHBORS, GET_ALL_ENTITIES_FOR_USER, GET_COMMUNITY_MEMBERS, + GET_ALL_COMMUNITY_MEMBERS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, ) @@ -101,6 +102,25 @@ class CommunityRepository: logger.error(f"get_community_members failed: {e}") return [] + async def get_all_community_members_batch( + self, community_ids: List[str], end_user_id: str + ) -> Dict[str, List[Dict]]: + """批量查询多个社区的成员，返回 {community_id: [members]} 字典。""" + try: + rows = await self.connector.execute_query( + GET_ALL_COMMUNITY_MEMBERS_BATCH, + community_ids=community_ids, + end_user_id=end_user_id, + ) + result: Dict[str, List[Dict]] = {} + for row in rows: + cid = row["community_id"] + result.setdefault(cid, []).append(row) + return result + except Exception as e: + logger.error(f"get_all_community_members_batch failed: {e}") + return {} + async def has_communities(self, end_user_id: str) -> bool: """检查该用户是否已有 Community 节点（用于判断全量 vs 增量）。""" try: diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 947097a2..84889d65 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1065,26 +1065,6 @@ Graph_Node_query = """ # Community 节点 & BELONGS_TO_COMMUNITY 边 # ============================================================ -COMMUNITY_NODE_SAVE = """ -MERGE (c:Community {community_id: $community_id}) -SET c.end_user_id = $end_user_id, - c.formed_at = $formed_at, - c.updated_at = datetime(), - c.status = $status, - c.member_count = $member_count -RETURN c.community_id AS community_id -""" - -COMMUNITY_ADD_MEMBER = """ -MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) -MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id}) -MERGE (e)-[:BELONGS_TO_COMMUNITY]->(c) -SET c.updated_at = datetime(), - c.member_count = $member_count -""" - - - # ─── Community 聚类相关 Cypher 模板 ─────────────────────────────────────────── COMMUNITY_NODE_UPSERT = """ @@ -1111,12 +1091,23 @@ DELETE r GET_ENTITY_NEIGHBORS = """ MATCH (e:ExtractedEntity {id: $entity_id, end_user_id: $end_user_id}) -OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源一：直接关系邻居（EXTRACTED_RELATIONSHIP 边） +OPTIONAL MATCH (e)-[:EXTRACTED_RELATIONSHIP]-(nb1:ExtractedEntity {end_user_id: $end_user_id}) + +// 来源二：同 Statement 共现邻居（REFERENCES_ENTITY 边） +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 collect(DISTINCT nb1) + collect(DISTINCT nb2) AS all_neighbors +UNWIND all_neighbors AS nb +WITH nb WHERE nb IS NOT NULL OPTIONAL MATCH (nb)-[:BELONGS_TO_COMMUNITY]->(c:Community) RETURN DISTINCT - nb.id AS id, - nb.name AS name, - nb.name_embedding AS name_embedding, + 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 """ @@ -1139,6 +1130,15 @@ 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) +WHERE c.community_id IN $community_ids +RETURN c.community_id AS community_id, + e.id AS id, + e.name_embedding AS name_embedding, + e.activation_value AS activation_value +""" + CHECK_USER_HAS_COMMUNITIES = """ MATCH (c:Community {end_user_id: $end_user_id}) RETURN count(c) AS community_count From 7b8f101824055122631dd590f8f9b873211ec136 Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Thu, 12 Mar 2026 20:27:50 +0800 Subject: [PATCH 3/7] [add] Create the attribute values of the community nodes --- .../core/memory/agent/utils/write_tools.py | 4 +- .../clustering_engine/label_propagation.py | 83 ++++++++++++++++++- .../neo4j/community_repository.py | 24 ++++++ api/app/repositories/neo4j/cypher_queries.py | 9 ++ api/app/repositories/neo4j/graph_saver.py | 20 +++-- 5 files changed, 132 insertions(+), 8 deletions(-) diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 22030278..b3707083 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -165,7 +165,9 @@ async def write( statement_chunk_edges=all_statement_chunk_edges, statement_entity_edges=all_statement_entity_edges, entity_edges=all_entity_entity_edges, - connector=neo4j_connector + connector=neo4j_connector, + config_id=config_id, + llm_model_id=str(memory_config.llm_model_id) if memory_config.llm_model_id else None, ) if success: logger.info("Successfully saved all data to Neo4j") diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index cb6e5804..251d4fea 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -19,6 +19,8 @@ logger = logging.getLogger(__name__) # 全量迭代最大轮数，防止不收敛 MAX_ITERATIONS = 10 +# 社区摘要核心实体数量 +CORE_ENTITY_LIMIT = 5 def _cosine_similarity(v1: Optional[List[float]], v2: Optional[List[float]]) -> float: @@ -62,9 +64,16 @@ def _weighted_vote( class LabelPropagationEngine: """标签传播聚类引擎""" - def __init__(self, connector: Neo4jConnector): + def __init__( + self, + connector: Neo4jConnector, + config_id: Optional[str] = None, + llm_model_id: Optional[str] = None, + ): self.connector = connector self.repo = CommunityRepository(connector) + self.config_id = config_id + self.llm_model_id = llm_model_id # ────────────────────────────────────────────────────────────────────────── # 公开接口 @@ -155,6 +164,10 @@ class LabelPropagationEngine: f"[Clustering] 全量聚类完成，合并前 {pre_merge_count} 个社区，" f"{len(labels)} 个实体" ) + # 为所有社区生成元数据 + unique_communities = list(set(labels.values())) + for cid in unique_communities: + await self._generate_community_metadata(cid, end_user_id) async def incremental_update( self, new_entity_ids: List[str], end_user_id: str @@ -211,6 +224,7 @@ class LabelPropagationEngine: logger.debug( f"[Clustering] 新实体 {entity_id} 与 {len(neighbors)} 个无社区邻居 → 新社区 {new_cid}" ) + await self._generate_community_metadata(new_cid, end_user_id) else: # 加入得票最多的社区 await self.repo.assign_entity_to_community(entity_id, target_cid, end_user_id) @@ -222,6 +236,7 @@ class LabelPropagationEngine: await self._evaluate_merge( list(community_ids_in_neighbors), end_user_id ) + await self._generate_community_metadata(target_cid, end_user_id) async def _evaluate_merge( self, community_ids: List[str], end_user_id: str @@ -354,6 +369,72 @@ class LabelPropagationEngine: except Exception: return None + async def _generate_community_metadata( + self, community_id: str, end_user_id: str + ) -> None: + """ + 为社区生成并写入元数据：名称、摘要、核心实体。 + + - core_entities：按 activation_value 排序取 top-N 实体名称列表（无需 LLM） + - name / summary：若有 llm_model_id 则调用 LLM 生成，否则用实体名称拼接兜底 + """ + try: + members = await self.repo.get_community_members(community_id, end_user_id) + if not members: + return + + # 核心实体：按 activation_value 降序取 top-N + sorted_members = sorted( + members, + key=lambda m: m.get("activation_value") or 0, + reverse=True, + ) + core_entities = [m["name"] for m in sorted_members[:CORE_ENTITY_LIMIT] if m.get("name")] + all_names = [m["name"] for m in members if m.get("name")] + + name = "、".join(core_entities[:3]) if core_entities else community_id[:8] + summary = f"包含实体：{', '.join(all_names)}" + + # 若有 LLM 配置，调用 LLM 生成更好的名称和摘要 + if self.llm_model_id: + try: + from app.db import get_db_context + from app.core.memory.utils.llm.llm_utils import MemoryClientFactory + + entity_list_str = "、".join(all_names) + prompt = ( + f"以下是一组语义相关的实体：{entity_list_str}\n\n" + f"请为这组实体所代表的主题：\n" + f"1. 起一个简洁的中文名称（不超过10个字）\n" + f"2. 写一句话摘要（不超过50个字）\n\n" + f"严格按以下格式输出，不要有其他内容：\n" + f"名称：<名称>\n摘要：<摘要>" + ) + with get_db_context() as db: + factory = MemoryClientFactory(db) + llm_client = factory.get_llm_client(self.llm_model_id) + response = await llm_client.chat([{"role": "user", "content": prompt}]) + text = response.content if hasattr(response, "content") else str(response) + + for line in text.strip().splitlines(): + if line.startswith("名称："): + name = line[3:].strip() + elif line.startswith("摘要："): + summary = line[3:].strip() + except Exception as e: + logger.warning(f"[Clustering] LLM 生成社区元数据失败，使用兜底值: {e}") + + await self.repo.update_community_metadata( + community_id=community_id, + end_user_id=end_user_id, + name=name, + summary=summary, + core_entities=core_entities, + ) + logger.debug(f"[Clustering] 社区 {community_id} 元数据已更新: name={name}") + except Exception as e: + logger.error(f"[Clustering] _generate_community_metadata failed for {community_id}: {e}") + @staticmethod def _new_community_id() -> str: return str(uuid.uuid4()) diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 2a1f4f2b..6c5c7618 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/api/app/repositories/neo4j/community_repository.py @@ -17,6 +17,7 @@ from app.repositories.neo4j.cypher_queries import ( GET_ALL_COMMUNITY_MEMBERS_BATCH, CHECK_USER_HAS_COMMUNITIES, UPDATE_COMMUNITY_MEMBER_COUNT, + UPDATE_COMMUNITY_METADATA, ) logger = logging.getLogger(__name__) @@ -147,3 +148,26 @@ 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], + ) -> bool: + """更新社区的名称、摘要和核心实体列表。""" + 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, + ) + return bool(result) + except Exception as e: + logger.error(f"update_community_metadata failed: {e}") + return False diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 84889d65..b270ed64 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -1150,3 +1150,12 @@ 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.updated_at = datetime() +RETURN c.community_id AS community_id +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index a94bc23b..2ef9bafc 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -1,5 +1,6 @@ import asyncio -from typing import List +import os +from typing import List, Optional # 使用新的仓储层 from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -156,7 +157,9 @@ 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, + 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. @@ -290,12 +293,15 @@ 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: + # 写入成功后，触发聚类（可通过环境变量 CLUSTERING_ENABLED=false 禁用，用于基准测试对比） + clustering_enabled = os.getenv("CLUSTERING_ENABLED", "true").lower() != "false" + if entity_nodes and clustering_enabled: 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) + asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, config_id=config_id, llm_model_id=llm_model_id)) + elif entity_nodes and not clustering_enabled: + logger.info("[Clustering] 聚类已禁用（CLUSTERING_ENABLED=false），跳过聚类触发") return True @@ -309,6 +315,8 @@ async def save_dialog_and_statements_to_neo4j( async def _trigger_clustering( new_entity_ids: List[str], end_user_id: str, + config_id: Optional[str] = None, + llm_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 +326,7 @@ 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) 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: From f6d929ab7a48e9a5417f9f45f376de02a796058a Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 12:59:36 +0800 Subject: [PATCH 4/7] [add] Community node interface development --- .../controllers/user_memory_controllers.py | 37 ++++ api/app/services/user_memory_service.py | 160 ++++++++++++++++++ 2 files changed, 197 insertions(+) diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index d3fe7d83..be796ff9 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -17,6 +17,7 @@ from app.services.user_memory_service import ( UserMemoryService, analytics_memory_types, analytics_graph_data, + analytics_community_graph_data, ) from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction from app.schemas.response_schema import ApiResponse @@ -295,6 +296,42 @@ async def get_graph_data_api( return fail(BizCode.INTERNAL_ERROR, "图数据查询失败", str(e)) +@router.get("/analytics/community_graph", response_model=ApiResponse) +async def get_community_graph_data_api( + end_user_id: str, + current_user: User = Depends(get_current_user), + db: Session = Depends(get_db), +) -> dict: + workspace_id = current_user.current_workspace_id + + if workspace_id is None: + api_logger.warning(f"用户 {current_user.username} 尝试查询社区图谱但未选择工作空间") + return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") + + api_logger.info( + f"社区图谱查询请求: end_user_id={end_user_id}, user={current_user.username}, " + f"workspace={workspace_id}" + ) + + try: + result = await analytics_community_graph_data(db=db, end_user_id=end_user_id) + + if "message" in result and result["statistics"]["total_nodes"] == 0: + api_logger.warning(f"社区图谱查询返回空结果: {result.get('message')}") + return success(data=result, msg=result.get("message", "查询成功")) + + api_logger.info( + f"成功获取社区图谱: end_user_id={end_user_id}, " + f"nodes={result['statistics']['total_nodes']}, " + f"edges={result['statistics']['total_edges']}" + ) + return success(data=result, msg="查询成功") + + except Exception as e: + api_logger.error(f"社区图谱查询失败: end_user_id={end_user_id}, error={str(e)}") + return fail(BizCode.INTERNAL_ERROR, "社区图谱查询失败", str(e)) + + @router.get("/read_end_user/profile", response_model=ApiResponse) async def get_end_user_profile( end_user_id: str, diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 8bacc112..d21df064 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1727,6 +1727,166 @@ async def analytics_graph_data( # 辅助函数 +async def analytics_community_graph_data( + db: Session, + end_user_id: str, +) -> Dict[str, Any]: + """ + 获取社区图谱数据，包含 Community 节点、ExtractedEntity 节点及其关系。 + + Returns: + 包含 nodes、edges、statistics 的字典，格式与 analytics_graph_data 一致 + """ + try: + user_uuid = uuid.UUID(end_user_id) + repo = EndUserRepository(db) + end_user = repo.get_by_id(user_uuid) + if not end_user: + return { + "nodes": [], "edges": [], + "statistics": {"total_nodes": 0, "total_edges": 0, "node_types": {}, "edge_types": {}}, + "message": "用户不存在" + } + + # 查询社区节点、实体节点、BELONGS_TO_COMMUNITY 边、实体间关系 + cypher = """ + 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 + """ + rows = await _neo4j_connector.execute_query(cypher, end_user_id=end_user_id) + + nodes_map: Dict[str, dict] = {} + edges_map: Dict[str, dict] = {} + # 记录每个 Community 对应的实体 id 列表 + community_members: Dict[str, list] = {} + + for row in rows: + # Community 节点 + c_id = row["c_id"] + if c_id and c_id not in nodes_map: + raw = row["c_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "community_id", "end_user_id", "member_count", "updated_at", + "name", "summary", "core_entities", + ) if k in raw} + nodes_map[c_id] = { + "id": c_id, + "label": "Community", + "properties": props, + } + + # ExtractedEntity 节点 (e) + e_id = row["e_id"] + if e_id and e_id not in nodes_map: + raw = row["e_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "name", "end_user_id", "description", "created_at", "entity_type", + ) if k in raw} + # 注入所属社区名称（c 是 e 直接归属的社区） + c_raw = row["c_props"] or {} + props["community_name"] = _clean_neo4j_value(c_raw.get("name")) or "" + nodes_map[e_id] = { + "id": e_id, + "label": "ExtractedEntity", + "properties": props, + } + + # ExtractedEntity 节点 (e2，可选) + e2_id = row.get("e2_id") + if e2_id and e2_id not in nodes_map: + raw = row["e2_props"] or {} + props = {k: _clean_neo4j_value(raw.get(k)) for k in ( + "name", "end_user_id", "description", "created_at", "entity_type", + ) if k in raw} + # e2 的社区归属在后处理阶段通过 community_members 补充 + props["community_name"] = "" + nodes_map[e2_id] = { + "id": e2_id, + "label": "ExtractedEntity", + "properties": props, + } + + # BELONGS_TO_COMMUNITY 边 + b_id = row["b_id"] + if b_id and b_id not in edges_map: + edges_map[b_id] = { + "id": b_id, + "source": e_id, + "target": c_id, + } + # 收集社区成员 id + if c_id and e_id: + community_members.setdefault(c_id, []) + if e_id not in community_members[c_id]: + community_members[c_id].append(e_id) + + # EXTRACTED_RELATIONSHIP 边（可选） + r_id = row.get("r_id") + if r_id and r_id not in edges_map and e2_id: + r_props = {k: _clean_neo4j_value(v) for k, v in (row["r_props"] or {}).items()} + source = e_id if row.get("r_from_e") else e2_id + target = e2_id if row.get("r_from_e") else e_id + edges_map[r_id] = { + "id": r_id, + "source": source, + "target": target, + } + + nodes = list(nodes_map.values()) + edges = list(edges_map.values()) + + # 为每个 Community 节点注入 member_entity_ids，同时补全 e2 节点的 community_name + for c_id, member_ids in community_members.items(): + c_node = nodes_map.get(c_id) + if c_node: + c_node["properties"]["member_entity_ids"] = member_ids + c_name = c_node["properties"].get("name") or "" + # 补全属于该社区但 community_name 为空的实体（即 e2 节点） + for eid in member_ids: + e_node = nodes_map.get(eid) + if e_node and e_node["label"] == "ExtractedEntity": + if not e_node["properties"].get("community_name"): + e_node["properties"]["community_name"] = c_name + + node_type_counts: Dict[str, int] = {} + for n in nodes: + node_type_counts[n["label"]] = node_type_counts.get(n["label"], 0) + 1 + + return { + "nodes": nodes, + "edges": edges, + "statistics": { + "total_nodes": len(nodes), + "total_edges": len(edges), + "node_types": node_type_counts, + } + } + + except ValueError: + logger.error(f"无效的 end_user_id 格式: {end_user_id}") + return { + "nodes": [], "edges": [], + "statistics": {"total_nodes": 0, "total_edges": 0, "node_types": {}, "edge_types": {}}, + "message": "无效的用户ID格式" + } + except Exception as e: + logger.error(f"获取社区图谱数据失败: {str(e)}", exc_info=True) + raise + + async def _extract_node_properties(label: str, properties: Dict[str, Any],node_id: str) -> Dict[str, Any]: """ 根据节点类型提取需要的属性字段 From 6a0ee22d8145d17654b010656e52f7ead22bc92c Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 14:43:29 +0800 Subject: [PATCH 5/7] [add] Create trigger events for the purpose of completing the existing data --- api/app/celery_app.py | 1 + .../memory_dashboard_controller.py | 14 +- .../clustering_engine/label_propagation.py | 70 +++++++-- api/app/tasks.py | 138 ++++++++++++++++++ 4 files changed, 206 insertions(+), 17 deletions(-) diff --git a/api/app/celery_app.py b/api/app/celery_app.py index 0319e079..dbfa9d51 100644 --- a/api/app/celery_app.py +++ b/api/app/celery_app.py @@ -113,6 +113,7 @@ celery_app.conf.update( 'app.tasks.run_forgetting_cycle_task': {'queue': 'periodic_tasks'}, 'app.tasks.write_all_workspaces_memory_task': {'queue': 'periodic_tasks'}, 'app.tasks.update_implicit_emotions_storage': {'queue': 'periodic_tasks'}, + 'app.tasks.init_community_clustering_for_users': {'queue': 'periodic_tasks'}, }, ) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 1b5b45fb..f01445d3 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -177,7 +177,19 @@ async def get_workspace_end_users( await aio_redis_set(cache_key, json.dumps(result), expire=30) except Exception as e: api_logger.warning(f"Redis 缓存写入失败: {str(e)}") - + + # 触发社区聚类补全任务（异步，不阻塞接口响应） + # 对有 ExtractedEntity 但无 Community 节点的存量用户自动补跑全量聚类 + try: + from app.tasks import init_community_clustering_for_users + init_community_clustering_for_users.apply_async( + kwargs={"end_user_ids": end_user_ids}, + queue="periodic_tasks", + ) + api_logger.info(f"已触发社区聚类补全任务，候选用户数: {len(end_user_ids)}") + except Exception as e: + api_logger.warning(f"触发社区聚类补全任务失败（不影响主流程）: {str(e)}") + api_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return success(data=result, msg="宿主列表获取成功") diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 251d4fea..4491b416 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py @@ -165,8 +165,15 @@ class LabelPropagationEngine: f"{len(labels)} 个实体" ) # 为所有社区生成元数据 - unique_communities = list(set(labels.values())) - for cid in unique_communities: + # 注意：_evaluate_merge 后部分社区已被合并消解，需重新从 Neo4j 查询实际存活的社区 + # 不能复用 labels.values()，那里包含已被 dissolve 的旧社区 ID + surviving_communities = await self.repo.get_all_entities(end_user_id) + surviving_community_ids = list({ + e.get("community_id") for e in surviving_communities + if e.get("community_id") + }) + logger.info(f"[Clustering] 合并后实际存活社区数: {len(surviving_community_ids)}") + for cid in surviving_community_ids: await self._generate_community_metadata(cid, end_user_id) async def incremental_update( @@ -249,7 +256,7 @@ class LabelPropagationEngine: 全量场景（社区数 > 20）使用批量查询，避免 N 次数据库往返。 """ - MERGE_THRESHOLD = 0.75 + MERGE_THRESHOLD = 0.85 BATCH_THRESHOLD = 20 # 超过此数量走批量查询 community_embeddings: Dict[str, Optional[List[float]]] = {} @@ -305,34 +312,65 @@ class LabelPropagationEngine: logger.info(f"[Clustering] 发现 {len(to_merge)} 对可合并社区") - # 执行合并：用 union-find 思路避免重复迁移已被合并的社区 - # 维护一个 canonical 映射，确保链式合并正确收敛 - canonical: Dict[str, str] = {cid: cid for cid in cids} + # 执行合并：逐对处理，每次合并后重新计算合并社区的平均向量 + # 避免 union-find 链式传递导致语义不相关的社区被间接合并 + # （A≈B、B≈C 不代表 A≈C，不能因传递性把 A/B/C 全部合并） + merged_into: Dict[str, str] = {} # dissolve → keep 的最终映射 - def find(x: str) -> str: - while canonical[x] != x: - canonical[x] = canonical[canonical[x]] - x = canonical[x] + def get_root(x: str) -> str: + """路径压缩，找到 x 当前所属的根社区。""" + while x in merged_into: + merged_into[x] = merged_into.get(merged_into[x], merged_into[x]) + x = merged_into[x] return x for c1, c2 in to_merge: - root1, root2 = find(c1), find(c2) + root1, root2 = get_root(c1), get_root(c2) if root1 == root2: - continue # 已经在同一社区，跳过 + continue + + # 用合并后的最新平均向量重新验证相似度 + # 防止链式传递：A≈B 合并后 B 的向量已更新，C 必须和新 B 相似才能合并 + current_sim = _cosine_similarity( + community_embeddings.get(root1), + community_embeddings.get(root2), + ) + if current_sim <= MERGE_THRESHOLD: + # 合并后向量已漂移，不再满足阈值，跳过 + logger.debug( + f"[Clustering] 跳过合并 {root1} ↔ {root2}，" + f"当前相似度 {current_sim:.3f} ≤ {MERGE_THRESHOLD}" + ) + continue + keep = root1 if community_sizes.get(root1, 0) >= community_sizes.get(root2, 0) else root2 dissolve = root2 if keep == root1 else root1 - canonical[dissolve] = keep + merged_into[dissolve] = keep members = await self.repo.get_community_members(dissolve, end_user_id) for m in members: await self.repo.assign_entity_to_community(m["id"], keep, end_user_id) - # 更新 sizes 以便后续合并决策准确 - community_sizes[keep] = community_sizes.get(keep, 0) + len(members) + + # 合并后重新计算 keep 的平均向量（加权平均） + keep_emb = community_embeddings.get(keep) + dissolve_emb = community_embeddings.get(dissolve) + keep_size = community_sizes.get(keep, 0) + dissolve_size = community_sizes.get(dissolve, 0) + total_size = keep_size + dissolve_size + if keep_emb and dissolve_emb and total_size > 0: + dim = len(keep_emb) + community_embeddings[keep] = [ + (keep_emb[i] * keep_size + dissolve_emb[i] * dissolve_size) / total_size + for i in range(dim) + ] + community_embeddings[dissolve] = None + + community_sizes[keep] = total_size community_sizes[dissolve] = 0 await self.repo.refresh_member_count(keep, end_user_id) logger.info( f"[Clustering] 社区合并: {dissolve} → {keep}，" - f"迁移 {len(members)} 个成员" + f"相似度={current_sim:.3f}，迁移 {len(members)} 个成员" ) async def _flush_labels( diff --git a/api/app/tasks.py b/api/app/tasks.py index a6ebbb8e..134d4744 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -2416,3 +2416,141 @@ def update_implicit_emotions_storage(self) -> Dict[str, Any]: "elapsed_time": elapsed_time, "task_id": self.request.id } + + +# ============================================================================= +# 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) + + # 获取 llm_model_id（从第一个用户的配置中读取，作为全局兜底） + llm_model_id = None + try: + with get_db_context() as db: + from app.services.memory_agent_service import get_end_user_connected_config + from app.services.memory_config_service import MemoryConfigService + for uid in end_user_ids: + try: + connected = get_end_user_connected_config(uid, db) + config_id = connected.get("memory_config_id") + workspace_id = connected.get("workspace_id") + if config_id or workspace_id: + cfg = MemoryConfigService(db).load_memory_config( + config_id=config_id, workspace_id=workspace_id + ) + llm_model_id = str(cfg.llm_model_id) + break + except Exception: + continue + except Exception as e: + logger.warning(f"[CommunityCluster] 获取 LLM 配置失败，将使用兜底值: {e}") + + engine = LabelPropagationEngine( + connector=connector, + llm_model_id=llm_model_id, + ) + + 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 + + logger.info(f"[CommunityCluster] 用户 {end_user_id} 有 {len(entities)} 个实体，开始全量聚类") + 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 + + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(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, + } From 01a1e8eab1ee97dbb04b6c79d1d3fd085b491b0f Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Fri, 13 Mar 2026 14:50:21 +0800 Subject: [PATCH 6/7] [changes] Update the pointers in the main repository to point to the submodules --- redbear-mem-benchmark | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index 8494e824..b4ddbe9b 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit 8494e82498cb99c70ac67a64a544ff872432363a +Subproject commit b4ddbe9b19014bb8d2d20f1b41eb656d03a5e5ed From c244e9834f20a121789d8b7bdccb3704b270ec1f Mon Sep 17 00:00:00 2001 From: lanceyq <1982376970@qq.com> Date: Mon, 16 Mar 2026 12:30:00 +0800 Subject: [PATCH 7/7] [changes] Community Clustering Retrieval Module --- .../memory/agent/services/search_service.py | 38 +- .../core/memory/agent/utils/write_tools.py | 11 +- api/app/core/memory/src/search.py | 12 +- .../clustering_engine/label_propagation.py | 224 ++++++++-- api/app/main.py | 12 +- .../neo4j/community_repository.py | 65 +++ api/app/repositories/neo4j/cypher_queries.py | 134 ++++++ api/app/repositories/neo4j/graph_saver.py | 45 +- api/app/repositories/neo4j/graph_search.py | 79 ++++ api/app/repositories/neo4j/index_manager.py | 254 ++++++++++++ api/app/tasks.py | 388 ++++++++++++++++++ redbear-mem-benchmark | 2 +- 12 files changed, 1203 insertions(+), 61 deletions(-) create mode 100644 api/app/repositories/neo4j/index_manager.py diff --git a/api/app/core/memory/agent/services/search_service.py b/api/app/core/memory/agent/services/search_service.py index 4fc4256e..2be18c97 100644 --- a/api/app/core/memory/agent/services/search_service.py +++ b/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: + 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 = [ diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 22030278..4e71f2c5 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/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") diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 0e1d8424..3570d707 100644 --- a/api/app/core/memory/src/search.py +++ b/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 diff --git a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py b/api/app/core/memory/storage_services/clustering_engine/label_propagation.py index 80e238fd..a116ba3b 100644 --- a/api/app/core/memory/storage_services/clustering_engine/label_propagation.py +++ b/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 - 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 = 2000 # 每批实体数，可按需调整 + + # 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 - 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 - } + total_count = len(total_entities) + 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") - 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"] - neighbors = await self.repo.get_entity_neighbors(eid, end_user_id) + # labels 跨批次共享：先用全量数据初始化（只存 id，内存极小） + labels: Dict[str, str] = {e["id"]: e["id"] for e in total_entities} + # 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） + # 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 + # 实际上 embeddings 只在 _weighted_vote 中用于计算 self_embedding， + # 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） - 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( - 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()) diff --git a/api/app/main.py b/api/app/main.py index af5ed796..21f56766 100644 --- a/api/app/main.py +++ b/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") diff --git a/api/app/repositories/neo4j/community_repository.py b/api/app/repositories/neo4j/community_repository.py index 16e30a10..78ecf6f6 100644 --- a/api/app/repositories/neo4j/community_repository.py +++ b/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 diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index 947097a2..b42351b0 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/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 +""" diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index a94bc23b..29e337f1 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/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: diff --git a/api/app/repositories/neo4j/graph_search.py b/api/app/repositories/neo4j/graph_search.py index e8f52535..19e40a82 100644 --- a/api/app/repositories/neo4j/graph_search.py +++ b/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, diff --git a/api/app/repositories/neo4j/index_manager.py b/api/app/repositories/neo4j/index_manager.py new file mode 100644 index 00000000..a1ab6689 --- /dev/null +++ b/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()) diff --git a/api/app/tasks.py b/api/app/tasks.py index a6ebbb8e..defa1aa0 100644 --- a/api/app/tasks.py +++ b/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, + } diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index 8494e824..89053e48 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit 8494e82498cb99c70ac67a64a544ff872432363a +Subproject commit 89053e48e932332d2a0f17760034ee2bce75ea43