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] [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