【change】 1.Standardize log specifications；2.Cluster settings trigger explicitly

api/app/celery_app.py

@@ -77,6 +77,7 @@ celery_app.conf.update(
 
     # Worker 设置 (per-worker settings are in docker-compose command line)
     worker_prefetch_multiplier=1,  # Don't hoard tasks, fairer distribution
+    worker_redirect_stdouts_level='INFO',  # stdout/print → INFO instead of WARNING
 
     # 结果过期时间
     result_expires=3600,  # 结果保存1小时

api/app/core/logging_config.py

@@ -529,8 +529,9 @@ def log_time(step_name: str, duration: float, log_file: str = "logs/time.log") -
         # Fallback to console only if file write fails
         print(f"Warning: Could not write to timing log: {e}")
     
-    # Always print to console (backward compatible behavior)
+    # Always log at INFO level (avoids Celery treating stdout as WARNING)
-    print(f"✓ {step_name}: {duration:.2f}s")
+    _timing_logger = logging.getLogger(__name__)
+    _timing_logger.info(f"✓ {step_name}: {duration:.2f}s")
 
 
 def get_agent_logger(name: str = "agent_service", 

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, schedule_clustering_after_write
+from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j, _trigger_clustering_sync
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import MemoryConfig
 
@@ -169,8 +169,8 @@ async def write(
             )
             if success:
                 logger.info("Successfully saved all data to Neo4j")
-                # 写入成功后，异步触发聚类（不阻塞写入响应）
+                # 写入成功后，同步等待聚类完成（避免与 Memory Summary 并发冲突）
-                schedule_clustering_after_write(
+                await _trigger_clustering_sync(
                     all_entity_nodes,
                     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,

api/app/core/memory/llm_tools/openai_client.py

@@ -82,16 +82,26 @@ class OpenAIClient(LLMClient):
             LLMClientException: LLM 调用失败
         """
         try:
-            template = """{messages}"""
+            from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
-            prompt = ChatPromptTemplate.from_template(template)
+
-            chain = prompt | self.client
+            # 将 dict 消息列表转换为 LangChain 消息对象
+            lc_messages = []
+            for m in messages:
+                role = m.get("role", "user")
+                content = m.get("content", "")
+                if role == "system":
+                    lc_messages.append(SystemMessage(content=content))
+                elif role == "assistant":
+                    lc_messages.append(AIMessage(content=content))
+                else:
+                    lc_messages.append(HumanMessage(content=content))
 
             # 添加 Langfuse 回调（如果可用）
             config = {}
             if self.langfuse_handler:
                 config["callbacks"] = [self.langfuse_handler]
 
-            response = await chain.ainvoke({"messages": messages}, config=config)
+            response = await self.client.ainvoke(lc_messages, config=config)
             return response
 
         except Exception as e:

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

@@ -71,13 +71,11 @@ class LabelPropagationEngine:
         connector: Neo4jConnector,
         llm_model_id: Optional[str] = None,
         embedding_model_id: Optional[str] = None,
-        embedding_model_id: Optional[str] = None,
     ):
         self.connector = connector
         self.repo = CommunityRepository(connector)
         self.llm_model_id = llm_model_id
         self.embedding_model_id = embedding_model_id
-        self.embedding_model_id = embedding_model_id
 
     # ──────────────────────────────────────────────────────────────────────────
     # 公开接口
@@ -239,6 +237,7 @@ class LabelPropagationEngine:
             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}")
+            await self._generate_community_metadata([new_cid], end_user_id)
             return
 
         # 统计邻居社区分布
@@ -273,7 +272,8 @@ class LabelPropagationEngine:
                 await self._evaluate_merge(
                     list(community_ids_in_neighbors), end_user_id
                 )
-            await self._generate_community_metadata([target_cid], end_user_id)
+            # 新实体加入后成员变化，强制重新生成元数据
+            await self._generate_community_metadata([target_cid], end_user_id, force=True)
 
     async def _evaluate_merge(
         self, community_ids: List[str], end_user_id: str
@@ -453,7 +453,7 @@ class LabelPropagationEngine:
         return lines
 
     async def _generate_community_metadata(
-        self, community_ids: List[str], end_user_id: str
+        self, community_ids: List[str], end_user_id: str, force: bool = False
     ) -> None:
         """
         为一个或多个社区生成并写入元数据。
@@ -462,69 +462,82 @@ class LabelPropagationEngine:
         1. 逐个社区调 LLM 生成 name / summary（串行）
         2. 收集所有 summary，一次性批量 embed
         3. 单个社区用 update_community_metadata，多个用 batch_update_community_metadata
-        """
-        if not community_ids:
-            return
 
+        Args:
+            force: 为 True 时跳过完整性检查，强制重新生成（用于增量更新成员变化后）
+        """
         from app.db import get_db_context
         from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 
-        # --- 阶段1：并发调 LLM 生成每个社区的 name / summary ---
+        async def _build_one(cid: str) -> Optional[Dict]:
-        async def _build_one(cid: str):
+            try:
-            members = await self.repo.get_community_members(cid, end_user_id)
+                if not force:
-            if not members:
+                    check_embedding = bool(self.embedding_model_id)
+                    if await self.repo.is_community_complete(cid, end_user_id, check_embedding=check_embedding):
+                        return None
+
+                members = await self.repo.get_community_members(cid, end_user_id)
+                if not members:
+                    logger.warning(f"[Clustering] 社区 {cid} 无成员，跳过元数据生成")
+                    return None
+
+                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 cid[:8]
+                summary = f"包含实体：{', '.join(all_names)}"
+
+                if self.llm_model_id:
+                    try:
+                        entity_list_str = "\n".join(self._build_entity_lines(members))
+                        relationships = await self.repo.get_community_relationships(cid, end_user_id)
+                        rel_lines = [
+                            f"- {r['subject']} → {r['predicate']} → {r['object']}"
+                            for r in relationships
+                            if r.get("subject") and r.get("predicate") and r.get("object")
+                        ]
+                        rel_section = (
+                            f"\n实体间关系：\n" + "\n".join(rel_lines)
+                            if rel_lines else ""
+                        )
+                        prompt = (
+                            f"以下是一组语义相关的实体：\n{entity_list_str}{rel_section}\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80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:
+                            llm_client = MemoryClientFactory(db).get_llm_client(self.llm_model_id)
+                            response = await llm_client.chat([{"role": "user", "content": prompt}])
+                            text = response.content if hasattr(response, "content") else str(response)
+
+                        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] 社区 {cid} LLM 生成失败，使用兜底值: {e}")
+
+                return {
+                    "community_id": cid,
+                    "end_user_id": end_user_id,
+                    "name": name,
+                    "summary": summary,
+                    "core_entities": core_entities,
+                    "summary_embedding": None,
+                }
+            except Exception as e:
+                logger.error(f"[Clustering] 社区 {cid} 元数据准备失败: {e}", exc_info=True)
                 return None
 
-            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")]
-
-            entity_list_str = "\n".join(self._build_entity_lines(members))
-
-            # 方案四：注入社区内实体间关系三元组
-            relationships = await self.repo.get_community_relationships(cid, end_user_id)
-            rel_lines = [
-                f"- {r['subject']} → {r['predicate']} → {r['object']}"
-                for r in relationships
-                if r.get("subject") and r.get("predicate") and r.get("object")
-            ]
-            rel_section = (
-                f"\n实体间关系：\n" + "\n".join(rel_lines)
-                if rel_lines else ""
-            )
-
-            prompt = (
-                f"以下是一组语义相关的实体：\n{entity_list_str}{rel_section}\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80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:
-                llm_client = MemoryClientFactory(db).get_llm_client(self.llm_model_id)
-                response = await llm_client.chat([{"role": "user", "content": prompt}])
-                text = response.content if hasattr(response, "content") else str(response)
-
-            name, summary = "", ""
-            for line in text.strip().splitlines():
-                if line.startswith("名称："):
-                    name = line[3:].strip()
-                elif line.startswith("摘要："):
-                    summary = line[3:].strip()
-
-            return {
-                "community_id": cid,
-                "end_user_id": end_user_id,
-                "name": name,
-                "summary": summary,
-                "core_entities": core_entities,
-                "summary_embedding": None,
-            }
-
         results = await asyncio.gather(
             *[_build_one(cid) for cid in community_ids],
             return_exceptions=True,
@@ -537,15 +550,20 @@ class LabelPropagationEngine:
                 metadata_list.append(res)
 
         if not metadata_list:
+            logger.warning(f"[Clustering] 无有效元数据可写入，community_ids={community_ids}")
             return
 
         # --- 阶段2：批量生成 summary_embedding ---
-        summaries = [m["summary"] for m in metadata_list]
+        if self.embedding_model_id:
-        with get_db_context() as db:
+            try:
-            embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
+                summaries = [m["summary"] for m in metadata_list]
-        embeddings = await embedder.response(summaries)
+                with get_db_context() as db:
-        for i, meta in enumerate(metadata_list):
+                    embedder = MemoryClientFactory(db).get_embedder_client(self.embedding_model_id)
-            meta["summary_embedding"] = embeddings[i] if i < len(embeddings) else None
+                    embeddings = await embedder.response(summaries)
+                for i, meta in enumerate(metadata_list):
+                    meta["summary_embedding"] = embeddings[i] if i < len(embeddings) else None
+            except Exception as e:
+                logger.error(f"[Clustering] 批量生成 summary_embedding 失败: {e}", exc_info=True)
 
         # --- 阶段3：写入（单个 or 批量）---
         if len(metadata_list) == 1:
@@ -558,16 +576,12 @@ class LabelPropagationEngine:
                 core_entities=m["core_entities"],
                 summary_embedding=m["summary_embedding"],
             )
-            if result:
+            if not result:
-                logger.info(f"[Clustering] 社区 {m['community_id']} 元数据写入成功: name={m['name']}, summary={m['summary'][:30]}...")
+                logger.error(f"[Clustering] 社区 {m['community_id']} 元数据写入失败")
-            else:
-                logger.warning(f"[Clustering] 社区 {m['community_id']} 元数据写入返回 False")
         else:
             ok = await self.repo.batch_update_community_metadata(metadata_list)
-            if ok:
+            if not ok:
-                logger.info(f"[Clustering] 批量写入 {len(metadata_list)} 个社区元数据成功")
+                logger.error(f"[Clustering] 批量写入 {len(metadata_list)} 个社区元数据失败")
-            else:
-                logger.warning(f"[Clustering] 批量写入社区元数据失败")
 
     @staticmethod
     def _new_community_id() -> str:

api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_pruning.py

@@ -9,6 +9,7 @@
 """
 
 import asyncio
+import logging
 import os
 import hashlib
 import json
@@ -26,6 +27,8 @@ from app.core.memory.storage_services.extraction_engine.data_preprocessing.scene
     ScenePatterns
 )
 
+logger = logging.getLogger(__name__)
+
 
 class DialogExtractionResponse(BaseModel):
     """对话级一次性抽取的结构化返回，用于加速剪枝。
@@ -706,7 +709,7 @@ class SemanticPruner:
         # 阈值保护：最高0.9
         proportion = float(self.config.pruning_threshold)
         if proportion > 0.9:
-            print(f"[剪枝-数据集] 阈值{proportion}超过上限0.9，已自动调整为0.9")
+            logger.warning(f"[剪枝-数据集] 阈值{proportion}超过上限0.9，已自动调整为0.9")
             proportion = 0.9
         if proportion < 0.0:
             proportion = 0.0
@@ -905,7 +908,7 @@ class SemanticPruner:
 
         # Safety: avoid empty dataset
         if not result:
-            print("警告: 语义剪枝后数据集为空，已回退为未剪枝数据以避免流程中断")
+            logger.warning("语义剪枝后数据集为空，已回退为未剪枝数据以避免流程中断")
             return dialogs
 
         return result
@@ -915,8 +918,7 @@ class SemanticPruner:
         try:
             self.run_logs.append(msg)
         except Exception:
-            # 任何异常都不影响打印
             pass
-        print(msg)
+        logger.debug(msg)
 
 

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/embedding_generation.py

@@ -5,8 +5,11 @@
 """
 
 import asyncio
+import logging
 from typing import Any, Dict, List, Tuple
 
+logger = logging.getLogger(__name__)
+
 from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
 from app.core.memory.models.message_models import DialogData
 from app.core.models.base import RedBearModelConfig
@@ -48,9 +51,9 @@ class EmbeddingGenerator:
             return await self.embedder_client.response(texts)
         
         # 分批并行处理
-        print(f"文本数量 {len(texts)} 超过批次大小 {batch_size}，分批并行处理")
+        logger.info(f"文本数量 {len(texts)} 超过批次大小 {batch_size}，分批并行处理")
         batches = [texts[i:i+batch_size] for i in range(0, len(texts), batch_size)]
-        print(f"分成 {len(batches)} 批，每批最多 {batch_size} 个文本")
+        logger.info(f"分成 {len(batches)} 批，每批最多 {batch_size} 个文本")
         
         # 并行发送所有批次
         batch_results = await asyncio.gather(*[
@@ -62,7 +65,7 @@ class EmbeddingGenerator:
         for batch_result in batch_results:
             embeddings.extend(batch_result)
         
-        print(f"分批并行处理完成，共生成 {len(embeddings)} 个嵌入向量")
+        logger.info(f"分批并行处理完成，共生成 {len(embeddings)} 个嵌入向量")
         return embeddings
 
     async def generate_statement_embeddings(
@@ -77,7 +80,7 @@ class EmbeddingGenerator:
         Returns:
             每个对话的陈述句嵌入向量映射列表
         """
-        print("\n=== 生成陈述句嵌入向量 ===")
+        logger.debug("=== 生成陈述句嵌入向量 ===")
 
         # 收集所有陈述句
         all_statements = []
@@ -102,7 +105,7 @@ class EmbeddingGenerator:
             stmt_id = chunked_dialogs[d_idx].chunks[c_idx].statements[s_idx].id
             stmt_embedding_maps[d_idx][stmt_id] = embedding
 
-        print(f"为 {len(all_statements)} 个陈述句生成了嵌入向量")
+        logger.info(f"为 {len(all_statements)} 个陈述句生成了嵌入向量")
         return stmt_embedding_maps
 
     async def generate_chunk_embeddings(
@@ -117,7 +120,7 @@ class EmbeddingGenerator:
         Returns:
             每个对话的分块嵌入向量映射列表
         """
-        print("\n=== 生成分块嵌入向量 ===")
+        logger.debug("=== 生成分块嵌入向量 ===")
 
         # 收集所有分块
         all_chunks = []
@@ -138,7 +141,7 @@ class EmbeddingGenerator:
             chunk_id = chunked_dialogs[d_idx].chunks[c_idx].id
             chunk_embedding_maps[d_idx][chunk_id] = embedding
 
-        print(f"为 {len(all_chunks)} 个分块生成了嵌入向量")
+        logger.info(f"为 {len(all_chunks)} 个分块生成了嵌入向量")
         return chunk_embedding_maps
 
     async def generate_dialog_embeddings(
@@ -172,7 +175,7 @@ class EmbeddingGenerator:
         Returns:
             (陈述句嵌入映射列表, 分块嵌入映射列表, 对话嵌入列表)
         """
-        print("\n=== 生成所有嵌入向量 ===")
+        logger.debug("=== 生成所有嵌入向量 ===")
 
         # 并发生成陈述句和分块嵌入向量
         stmt_embedding_maps, chunk_embedding_maps = await asyncio.gather(
@@ -183,9 +186,7 @@ class EmbeddingGenerator:
         # 对话嵌入向量（当前跳过）
         dialog_embeddings = await self.generate_dialog_embeddings(chunked_dialogs)
 
-        print(
+        logger.info(f"生成完成：{len(chunked_dialogs)} 个对话的嵌入向量")
-            f"生成完成：{len(chunked_dialogs)} 个对话的嵌入向量"
-        )
 
         return stmt_embedding_maps, chunk_embedding_maps, dialog_embeddings
 
@@ -201,7 +202,7 @@ class EmbeddingGenerator:
         Returns:
             更新后的三元组映射列表（实体包含嵌入向量）
         """
-        print("\n=== 生成实体嵌入向量 ===")
+        logger.debug("=== 生成实体嵌入向量 ===")
 
         entity_texts: List[str] = []
         entity_refs: List[Any] = []
@@ -219,7 +220,7 @@ class EmbeddingGenerator:
                         entity_refs.append(ent)
 
         if not entity_texts:
-            print("没有找到需要生成嵌入向量的实体")
+            logger.debug("没有找到需要生成嵌入向量的实体")
             return triplet_maps
 
         # 批量生成嵌入向量
@@ -227,13 +228,13 @@ class EmbeddingGenerator:
 
         # 打印前几个嵌入向量的维度
         for i in range(min(5, len(embeddings))):
-            print(f"实体 '{entity_texts[i]}' 嵌入向量维度: {len(embeddings[i])}")
+            logger.debug(f"实体 '{entity_texts[i]}' 嵌入向量维度: {len(embeddings[i])}")
 
         # 将嵌入向量赋值给实体
         for ent, emb in zip(entity_refs, embeddings):
             setattr(ent, "name_embedding", emb)
 
-        print(f"为 {len(entity_refs)} 个实体生成了嵌入向量")
+        logger.info(f"为 {len(entity_refs)} 个实体生成了嵌入向量")
         return triplet_maps
 
 
@@ -296,7 +297,7 @@ async def embedding_generation_all(
     Returns:
         (陈述句嵌入映射列表, 分块嵌入映射列表, 对话嵌入列表, 更新后的三元组映射列表)
     """
-    print("\n=== 综合嵌入向量生成（陈述句/分块/对话 + 实体）===")
+    logger.debug("=== 综合嵌入向量生成（陈述句/分块/对话 + 实体）===")
 
     generator = EmbeddingGenerator(embedding_id)
 

api/app/repositories/neo4j/add_nodes.py

@@ -1,10 +1,13 @@
 from typing import List, Optional
+import logging
 
 from app.repositories.neo4j.cypher_queries import DIALOGUE_NODE_SAVE, STATEMENT_NODE_SAVE, CHUNK_NODE_SAVE,MEMORY_SUMMARY_NODE_SAVE
 from app.core.memory.models.graph_models import DialogueNode, StatementNode, ChunkNode, MemorySummaryNode
 # 使用新的仓储层
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 
+logger = logging.getLogger(__name__)
+
 
 async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector):
     """Delete all nodes in the database."""
@@ -217,10 +220,10 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector
             summaries=flattened
         )
         created_ids = [record.get("uuid") for record in result]
-        print(f"Successfully saved {len(created_ids)} MemorySummary nodes to Neo4j")
+        logger.info(f"Successfully saved {len(created_ids)} MemorySummary nodes to Neo4j")
         return created_ids
     except Exception as e:
-        print(f"Failed to save MemorySummary nodes to Neo4j: {e}")
+        logger.error(f"Failed to save MemorySummary nodes to Neo4j: {e}")
         return None
 
 

api/app/repositories/neo4j/community_repository.py

@@ -300,7 +300,7 @@ class CommunityRepository:
             )
             return bool(result)
         except Exception as e:
-            logger.error(f"update_community_metadata failed: {e}")
+            logger.error(f"update_community_metadata failed: {e}", exc_info=True)
             return False
 
     async def batch_update_community_metadata(

api/app/repositories/neo4j/cypher_queries.py

@@ -1069,6 +1069,7 @@ Graph_Node_query = """
 
 COMMUNITY_NODE_UPSERT = """
 MERGE (c:Community {community_id: $community_id})
+ON CREATE SET c.id = $community_id
 SET c.end_user_id = $end_user_id,
     c.member_count = $member_count,
     c.updated_at = datetime()
@@ -1175,7 +1176,8 @@ 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,
+SET c.id               = coalesce(c.id, $community_id),
+    c.name             = $name,
     c.summary          = $summary,
     c.core_entities    = $core_entities,
     c.summary_embedding = $summary_embedding,
@@ -1186,7 +1188,8 @@ RETURN c.community_id AS community_id
 BATCH_UPDATE_COMMUNITY_METADATA = """
 UNWIND $communities AS row
 MATCH (c:Community {community_id: row.community_id, end_user_id: row.end_user_id})
-SET c.name             = row.name,
+SET c.id               = coalesce(c.id, row.community_id),
+    c.name             = row.name,
     c.summary          = row.summary,
     c.core_entities    = row.core_entities,
     c.summary_embedding = row.summary_embedding,
@@ -1270,6 +1273,40 @@ RETURN
     startNode(r) = e      AS r_from_e
 """
 
+CHECK_COMMUNITY_IS_COMPLETE = """
+MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
+RETURN (
+    c.name IS NOT NULL AND c.name <> '' AND
+    c.summary IS NOT NULL AND c.summary <> '' AND
+    c.core_entities IS NOT NULL
+) AS is_complete
+"""
+
+CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING = """
+MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
+RETURN (
+    c.name IS NOT NULL AND c.name <> '' AND
+    c.summary IS NOT NULL AND c.summary <> '' AND
+    c.core_entities IS NOT NULL AND
+    c.summary_embedding IS NOT NULL
+) AS is_complete
+"""
+
+GET_INCOMPLETE_COMMUNITIES = """
+MATCH (c:Community {end_user_id: $end_user_id})
+WHERE c.name IS NULL OR c.summary IS NULL OR c.core_entities IS NULL
+   OR c.name = '' OR c.summary = ''
+RETURN c.community_id AS community_id
+"""
+
+GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING = """
+MATCH (c:Community {end_user_id: $end_user_id})
+WHERE c.name IS NULL OR c.name = ''
+   OR c.summary IS NULL OR c.summary = ''
+   OR c.core_entities IS NULL
+   OR (c.summary_embedding IS NULL AND c.summary IS NOT NULL AND c.summary <> '(empty)')
+RETURN c.community_id AS community_id
+"""
 
 # Community keyword search: matches name or summary via fulltext index
 SEARCH_COMMUNITIES_BY_KEYWORD = """
@@ -1326,38 +1363,3 @@ RETURN s.statement AS statement,
 ORDER BY COALESCE(s.activation_value, 0) DESC
 LIMIT $limit
 """
-
-CHECK_COMMUNITY_IS_COMPLETE = """
-MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
-RETURN (
-    c.name IS NOT NULL AND c.name <> '' AND
-    c.summary IS NOT NULL AND c.summary <> '' AND
-    c.core_entities IS NOT NULL
-) AS is_complete
-"""
-
-CHECK_COMMUNITY_IS_COMPLETE_WITH_EMBEDDING = """
-MATCH (c:Community {community_id: $community_id, end_user_id: $end_user_id})
-RETURN (
-    c.name IS NOT NULL AND c.name <> '' AND
-    c.summary IS NOT NULL AND c.summary <> '' AND
-    c.core_entities IS NOT NULL AND
-    c.summary_embedding IS NOT NULL
-) AS is_complete
-"""
-
-GET_INCOMPLETE_COMMUNITIES = """
-MATCH (c:Community {end_user_id: $end_user_id})
-WHERE c.name IS NULL OR c.summary IS NULL OR c.core_entities IS NULL
-   OR c.name = '' OR c.summary = ''
-RETURN c.community_id AS community_id
-"""
-
-GET_INCOMPLETE_COMMUNITIES_WITH_EMBEDDING = """
-MATCH (c:Community {end_user_id: $end_user_id})
-WHERE c.name IS NULL OR c.name = ''
-   OR c.summary IS NULL OR c.summary = ''
-   OR c.core_entities IS NULL
-   OR (c.summary_embedding IS NULL AND c.summary IS NOT NULL AND c.summary <> '(empty)')
-RETURN c.community_id AS community_id
-"""

api/app/repositories/neo4j/graph_saver.py

@@ -162,7 +162,7 @@ async def save_dialog_and_statements_to_neo4j(
     """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。
+    _trigger_clustering_sync() 显式触发。
 
     Args:
         dialogue_nodes: List of DialogueNode objects to save
@@ -303,16 +303,13 @@ async def save_dialog_and_statements_to_neo4j(
         return False
 
 
-def schedule_clustering_after_write(
+async def _trigger_clustering_sync(
     entity_nodes: List,
     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。
+    同步等待聚类完成，避免与其他 LLM 任务并发冲突。
-
-    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
@@ -324,8 +321,8 @@ def schedule_clustering_after_write(
 
     end_user_id = entity_nodes[0].end_user_id
     new_entity_ids = [e.id for e in entity_nodes]
-    logger.info(f"[Clustering] 准备触发聚类，实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
+    logger.info(f"[Clustering] 准备触发聚类（同步），实体数: {len(new_entity_ids)}, end_user_id: {end_user_id}")
-    asyncio.create_task(_trigger_clustering(new_entity_ids, end_user_id, llm_model_id=llm_model_id, embedding_model_id=embedding_model_id))
+    await _trigger_clustering(new_entity_ids, end_user_id, llm_model_id=llm_model_id, embedding_model_id=embedding_model_id)
 
 
 async def _trigger_clustering(

api/app/services/memory_agent_service.py

@@ -350,9 +350,6 @@ class MemoryAgentService:
                             langchain_messages.append(HumanMessage(content=msg['content']))
                         elif msg['role'] == 'assistant':
                             langchain_messages.append(AIMessage(content=msg['content']))
-                    print(100 * '-')
-                    print(langchain_messages)
-                    print(100 * '-')
                     # 初始状态 - 包含所有必要字段
                     initial_state = {
                         "messages": langchain_messages,

api/app/tasks.py

@@ -2760,7 +2760,7 @@ def init_community_clustering_for_users(self, end_user_ids: List[str], workspace
                         patch_fail = 0
                         for cid in incomplete_ids:
                             try:
-                                await engine._generate_community_metadata(cid, end_user_id)
+                                await engine._generate_community_metadata([cid], end_user_id)
                                 patch_ok += 1
                             except Exception as patch_err:
                                 patch_fail += 1