style(memory): Some code style optimizations

api/app/controllers/memory_storage_controller.py

@@ -75,11 +75,6 @@ async def get_storage_info(
         return fail(BizCode.INTERNAL_ERROR, "存储信息获取失败", str(e))
 
 
-
-
-
-
-
 @router.post("/create_config", response_model=ApiResponse)  # 创建配置文件，其他参数默认
 def create_config(
         payload: ConfigParamsCreate,
@@ -107,9 +102,11 @@ def create_config(
             api_logger.warning(f"重复的配置名称 '{config_name}' 在工作空间 {workspace_id}")
             lang = get_language_from_header(x_language_type)
             if lang == "en":
-                msg = fail(BizCode.BAD_REQUEST, "Config name already exists", f"A config named \"{config_name}\" already exists in the current workspace. Please use a different name.")
+                msg = fail(BizCode.BAD_REQUEST, "Config name already exists",
+                           f"A config named \"{config_name}\" already exists in the current workspace. Please use a different name.")
             else:
-                msg = fail(BizCode.BAD_REQUEST, "配置名称已存在", f"当前工作空间下已存在名为「{config_name}」的记忆配置，请使用其他名称")
+                msg = fail(BizCode.BAD_REQUEST, "配置名称已存在",
+                           f"当前工作空间下已存在名为「{config_name}」的记忆配置，请使用其他名称")
             return JSONResponse(status_code=400, content=msg)
         api_logger.error(f"Create config failed: {err_str}")
         return fail(BizCode.INTERNAL_ERROR, "创建配置失败", err_str)
@@ -119,9 +116,11 @@ def create_config(
             api_logger.warning(f"重复的配置名称 '{payload.config_name}' 在工作空间 {workspace_id}")
             lang = get_language_from_header(x_language_type)
             if lang == "en":
-                msg = fail(BizCode.BAD_REQUEST, "Config name already exists", f"A config named \"{payload.config_name}\" already exists in the current workspace. Please use a different name.")
+                msg = fail(BizCode.BAD_REQUEST, "Config name already exists",
+                           f"A config named \"{payload.config_name}\" already exists in the current workspace. Please use a different name.")
             else:
-                msg = fail(BizCode.BAD_REQUEST, "配置名称已存在", f"当前工作空间下已存在名为「{payload.config_name}」的记忆配置，请使用其他名称")
+                msg = fail(BizCode.BAD_REQUEST, "配置名称已存在",
+                           f"当前工作空间下已存在名为「{payload.config_name}」的记忆配置，请使用其他名称")
             return JSONResponse(status_code=400, content=msg)
         api_logger.error(f"Create config failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "创建配置失败", str(e))
@@ -217,7 +216,8 @@ def update_config(
     # 校验至少有一个字段需要更新
     if payload.config_name is None and payload.config_desc is None and payload.scene_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新配置但未提供任何更新字段")
-        return fail(BizCode.INVALID_PARAMETER, "请至少提供一个需要更新的字段", "config_name, config_desc, scene_id 均为空")
+        return fail(BizCode.INVALID_PARAMETER, "请至少提供一个需要更新的字段",
+                    "config_name, config_desc, scene_id 均为空")
 
     api_logger.info(f"用户 {current_user.username} 在工作空间 {workspace_id} 请求更新配置: {payload.config_id}")
     try:
@@ -278,6 +278,7 @@ def read_config_extracted(
         api_logger.error(f"Read config extracted failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "查询配置失败", str(e))
 
+
 @router.get("/read_all_config", response_model=ApiResponse)  # 读取所有配置文件列表
 def read_all_config(
         current_user: User = Depends(get_current_user),
@@ -443,8 +444,6 @@ async def search_entity_edges(
         return fail(BizCode.INTERNAL_ERROR, "边查询失败", str(e))
 
 
-
-
 @router.get("/analytics/hot_memory_tags", response_model=ApiResponse)
 async def get_hot_memory_tags_api(
         limit: int = 10,
@@ -553,4 +552,3 @@ async def get_recent_activity_stats_api(
     except Exception as e:
         api_logger.error(f"Recent activity stats failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "最近活动统计失败", str(e))
-

api/app/core/agent/langchain_agent.py

@@ -598,8 +598,10 @@ class LangChainAgent:
                 for msg in reversed(output_messages):
                     if isinstance(msg, AIMessage):
                         response_meta = msg.response_metadata if hasattr(msg, 'response_metadata') else None
-                        total_tokens = response_meta.get("token_usage", {}).get("total_tokens",
+                        total_tokens = response_meta.get("token_usage", {}).get(
-                                                                                0) if response_meta else 0
+                            "total_tokens",
+                            0
+                        ) if response_meta else 0
                         yield total_tokens
                         break
                 if memory_flag:

api/app/core/memory/models/graph_models.py

@@ -206,7 +206,8 @@ class DialogueNode(Node):
     ref_id: str = Field(..., description="Reference identifier of the dialog")
     content: str = Field(..., description="Dialogue content")
     dialog_embedding: Optional[List[float]] = Field(None, description="Dialog embedding vector")
-    config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this dialogue (integer or string)")
+    config_id: Optional[int | str] = Field(None,
+                                           description="Configuration ID used to process this dialogue (integer or string)")
 
 
 class StatementNode(Node):
@@ -281,7 +282,8 @@ class StatementNode(Node):
     statement_embedding: Optional[List[float]] = Field(None, description="Statement embedding vector")
     chunk_embedding: Optional[List[float]] = Field(None, description="Chunk embedding vector")
     connect_strength: str = Field(..., description="Strong VS Weak classification of this statement")
-    config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this statement (integer or string)")
+    config_id: Optional[int | str] = Field(None,
+                                           description="Configuration ID used to process this statement (integer or string)")
 
     # ACT-R Memory Activation Properties
     importance_score: float = Field(
@@ -416,7 +418,8 @@ class ExtractedEntityNode(Node):
     # TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
     # fact_summary: str = Field(default="", description="Summary of the fact about this entity")
     connect_strength: str = Field(..., description="Strong VS Weak about this entity")
-    config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this entity (integer or string)")
+    config_id: Optional[int | str] = Field(None,
+                                           description="Configuration ID used to process this entity (integer or string)")
 
     # ACT-R Memory Activation Properties
     importance_score: float = Field(
@@ -507,7 +510,8 @@ class MemorySummaryNode(Node):
     memory_type: Optional[str] = Field(None, description="Type/category of the episodic memory")
     summary_embedding: Optional[List[float]] = Field(None, description="Embedding vector for the summary")
     metadata: dict = Field(default_factory=dict, description="Additional metadata for the summary")
-    config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this summary (integer or string)")
+    config_id: Optional[int | str] = Field(None,
+                                           description="Configuration ID used to process this summary (integer or string)")
 
     # ACT-R Forgetting Engine Properties
     original_statement_id: Optional[str] = Field(

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

@@ -227,7 +227,8 @@ 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])}")
+            print(f"实体 '{entity_texts[i]}' "
+                  f"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):

api/app/repositories/neo4j/cypher_queries.py

@@ -709,7 +709,6 @@ SET r.end_user_id = e.end_user_id,
 RETURN elementId(r) AS uuid
 """
 
-
 # Entity Merge Query
 MERGE_ENTITIES = """
 MATCH (canonical:ExtractedEntity {id: $canonical_id})
@@ -829,7 +828,6 @@ neo4j_query_all = """
                 other as entity2
                           """
 
-
 '''针对当前节点下扩长的句子，实体和总结'''
 Memory_Timeline_ExtractedEntity = """
 MATCH (n)-[r1]-(e)-[r2]-(ms)
@@ -1060,7 +1058,6 @@ Graph_Node_query = """
 
             """
 
-
 # ============================================================
 # Community 节点 & BELONGS_TO_COMMUNITY 边
 # ============================================================

api/app/schemas/memory_storage_schema.py

@@ -8,9 +8,6 @@ import uuid
 from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator
 
 
-
-
-
 # ============================================================================
 # 从 json_schema.py 迁移的 Schema
 # ============================================================================
@@ -58,10 +55,13 @@ class MemoryVerifySchema(BaseModel):
 
 class ConflictResultSchema(BaseModel):
     """Schema for the conflict result data in the reflexion_data.json file."""
-    data: List[BaseDataSchema] = Field(..., description="The conflict memory data. Only contains conflicting records when conflict is True.")
+    data: List[BaseDataSchema] = Field(...,
+                                       description="The conflict memory data. Only contains conflicting records when conflict is True.")
     conflict: bool = Field(..., description="Whether the memory is in conflict.")
-    quality_assessment: Optional[QualityAssessmentSchema] = Field(None, description="The quality assessment object. Contains score and summary when quality_assessment is enabled, null otherwise.")
+    quality_assessment: Optional[QualityAssessmentSchema] = Field(None,
-    memory_verify: Optional[MemoryVerifySchema] = Field(None, description="The memory privacy verification object. Contains privacy detection results when memory_verify is enabled, null otherwise.")
+                                                                  description="The quality assessment object. Contains score and summary when quality_assessment is enabled, null otherwise.")
+    memory_verify: Optional[MemoryVerifySchema] = Field(None,
+                                                        description="The memory privacy verification object. Contains privacy detection results when memory_verify is enabled, null otherwise.")
 
     @model_validator(mode="before")
     def _normalize_data(cls, v):
@@ -105,12 +105,15 @@ class ChangeRecordSchema(BaseModel):
         description="List of field changes. First item: {id: value}, followed by changed fields as {field_name: [old_value, new_value]} or {field_name: new_value} or nested structures like {entity2: {field_name: [old, new]}}"
     )
 
+
 class ResolvedSchema(BaseModel):
     """Schema for the resolved memory data in the reflexion_data"""
     original_memory_id: Optional[str] = Field(None, description="The original memory identifier.")
     # resolved_memory: Optional[BaseDataSchema] = Field(None, description="The resolved memory data (only contains records that need modification).")
-    resolved_memory: Optional[Union[BaseDataSchema, List[BaseDataSchema]]] = Field(None, description="The resolved memory data (only contains records that need modification). Can be a single record or list of records.")
+    resolved_memory: Optional[Union[BaseDataSchema, List[BaseDataSchema]]] = Field(None,
-    change: Optional[List[ChangeRecordSchema]] = Field(None, description="List of detailed change records with IDs and field information.")
+                                                                                   description="The resolved memory data (only contains records that need modification). Can be a single record or list of records.")
+    change: Optional[List[ChangeRecordSchema]] = Field(None,
+                                                       description="List of detailed change records with IDs and field information.")
 
 
 class SingleReflexionResultSchema(BaseModel):
@@ -120,9 +123,11 @@ class SingleReflexionResultSchema(BaseModel):
     resolved: Optional[ResolvedSchema] = Field(None, description="The resolved memory data for this conflict.")
     type: str = Field("reflexion_result", description="The type identifier.")
 
+
 class ReflexionResultSchema(BaseModel):
     """Schema for the complete reflexion result data - a list of individual conflict resolutions."""
-    results: List[SingleReflexionResultSchema] = Field(..., description="List of individual conflict resolution results, grouped by conflict type.")
+    results: List[SingleReflexionResultSchema] = Field(...,
+                                                       description="List of individual conflict resolution results, grouped by conflict type.")
 
     @model_validator(mode="before")
     def _normalize_resolved(cls, v):
@@ -148,8 +153,8 @@ class ReflexionResultSchema(BaseModel):
 class ConfigKey(BaseModel):  # 配置参数键模型
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)")
-    user_id: str = Field("user_id", description="用户标识（字符串）")
+    user_id: str | None = Field(default=None, description="用户标识（字符串）")
-    apply_id: str = Field("apply_id", description="应用或场景标识（字符串）")
+    apply_id: str | None = Field(default=None, description="应用或场景标识（字符串）")
 
 
 # Allowed chunking strategies (extendable later)
@@ -241,6 +246,8 @@ class ConfigParamsCreate(BaseModel):  # 创建配置参数模型（仅 body，
     rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
     reflection_model_id: Optional[str] = Field(None, description="反思模型ID，默认与llm_id一致")
     emotion_model_id: Optional[str] = Field(None, description="情绪分析模型ID，默认与llm_id一致")
+
+
 class ConfigParamsDelete(BaseModel):  # 删除配置参数模型（请求体）
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     # config_name: str = Field("配置名称", description="配置名称（字符串）")
@@ -387,6 +394,7 @@ def fail(
         time=time or _now_ms(),
     )
 
+
 class GenerateCacheRequest(BaseModel):
     """缓存生成请求模型"""
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
@@ -472,7 +480,8 @@ class ForgettingStatsResponse(BaseModel):
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     activation_metrics: Dict[str, Any] = Field(..., description="激活值相关指标")
     node_distribution: Dict[str, int] = Field(..., description="节点类型分布")
-    recent_trends: List[ForgettingCycleHistoryPoint] = Field(..., description="最近7个日期的遗忘趋势数据（每天取最后一次执行）")
+    recent_trends: List[ForgettingCycleHistoryPoint] = Field(...,
+                                                             description="最近7个日期的遗忘趋势数据（每天取最后一次执行）")
     pending_nodes: List[PendingForgettingNode] = Field(..., description="待遗忘节点列表（前20个满足遗忘条件的节点）")
     timestamp: int = Field(..., description="统计时间（时间戳）")
 

api/app/services/memory_storage_service.py

@@ -11,9 +11,11 @@ import time
 from datetime import datetime
 from typing import Any, AsyncGenerator, Dict, List, Optional
 
+from dotenv import load_dotenv
+from sqlalchemy.orm import Session
+
 from app.core.logging_config import get_config_logger, get_logger
 from app.core.memory.analytics.hot_memory_tags import (
-    get_hot_memory_tags,
     get_raw_tags_from_db,
     filter_tags_with_llm,
 )
@@ -32,8 +34,6 @@ from app.schemas.memory_storage_schema import (
 )
 from app.services.memory_config_service import MemoryConfigService
 from app.utils.sse_utils import format_sse_message
-from dotenv import load_dotenv
-from sqlalchemy.orm import Session
 
 logger = get_logger(__name__)
 config_logger = get_config_logger()
@@ -241,7 +241,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
                 except (ValueError, TypeError):
                     config_id_old = None
 
-
             if config_id_old:
                 memory_config = config_id_old
             else:
@@ -289,7 +288,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
         # 将 created_at 和 updated_at 转换为 YYYYMMDDHHmmss 格式
         return self._convert_timestamps_to_format(data_list)
 
-
     async def pilot_run_stream(self, payload: ConfigPilotRun, language: str = "zh") -> AsyncGenerator[str, None]:
         """
         流式执行试运行，产生 SSE 格式的进度事件
@@ -344,11 +342,13 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
                 # 关联了本体场景，优先使用 custom_text
                 if hasattr(payload, 'custom_text') and payload.custom_text:
                     dialogue_text = payload.custom_text.strip()
-                    logger.info(f"[PILOT_RUN_STREAM] Using custom_text for scene_id={memory_config.scene_id}, length: {len(dialogue_text)}")
+                    logger.info(
+                        f"[PILOT_RUN_STREAM] Using custom_text for scene_id={memory_config.scene_id}, length: {len(dialogue_text)}")
                 else:
                     # 如果没有提供 custom_text，回退到 dialogue_text
                     dialogue_text = payload.dialogue_text.strip() if payload.dialogue_text else ""
-                    logger.info(f"[PILOT_RUN_STREAM] No custom_text provided, using dialogue_text for scene_id={memory_config.scene_id}")
+                    logger.info(
+                        f"[PILOT_RUN_STREAM] No custom_text provided, using dialogue_text for scene_id={memory_config.scene_id}")
             else:
                 # 没有关联本体场景，使用 dialogue_text
                 dialogue_text = payload.dialogue_text.strip() if payload.dialogue_text else ""
@@ -360,7 +360,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
 
             logger.info(f"[PILOT_RUN_STREAM] Final text preview: {dialogue_text[:100]}")
 
-
             # 步骤 2: 创建进度回调函数捕获管线进度
             # 使用队列在回调和生成器之间传递进度事件
             progress_queue: asyncio.Queue = asyncio.Queue()
@@ -382,7 +381,8 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
                 try:
                     from app.services.pilot_run_service import run_pilot_extraction
 
-                    logger.info(f"[PILOT_RUN_STREAM] Calling run_pilot_extraction with dialogue_text length: {len(dialogue_text)}")
+                    logger.info(
+                        f"[PILOT_RUN_STREAM] Calling run_pilot_extraction with dialogue_text length: {len(dialogue_text)}")
                     await run_pilot_extraction(
                         memory_config=memory_config,
                         dialogue_text=dialogue_text,
@@ -483,7 +483,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
                 "time": int(time.time() * 1000)
             })
 
-
     async def _compute_ontology_coverage(
             self,
             extracted_result: Dict[str, Any],
@@ -580,8 +579,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
 
 # -------------------- Neo4j Search & Analytics (fused from data_search_service.py) --------------------
 # Ensure env for connector (e.g., NEO4J_PASSWORD)
-load_dotenv()
-_neo4j_connector = Neo4jConnector()
 
 
 async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]:
@@ -701,6 +698,7 @@ async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]
     )
     return result
 
+
 async def analytics_hot_memory_tags(
         db: Session,
         current_user: User,
@@ -845,5 +843,3 @@ async def analytics_recent_activity_stats(workspace_id: Optional[str] = None) ->
 
     data = {"total": total, "stats": stats, "latest_relative": latest_relative, "source": source}
     return data
-
-

api/app/tasks.py

@@ -1073,9 +1073,15 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s
 
 
 @celery_app.task(name="app.core.memory.agent.write_message", bind=True)
-def write_message_task(self, end_user_id: str, message: list[dict], config_id: str | int, storage_type: str,
+def write_message_task(
+        self,
+        end_user_id: str,
+        message: list[dict],
+        config_id: str | int,
+        storage_type: str,
         user_rag_memory_id: str,
-                       language: str = "zh") -> Dict[str, Any]:
+        language: str = "zh"
+) -> Dict[str, Any]:
     """Celery task to process a write message via MemoryAgentService.
     Args:
         end_user_id: Group ID for the memory agent (also used as end_user_id)
@@ -1105,14 +1111,11 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s
         try:
             with get_db_context() as db:
                 actual_config_id = resolve_config_id(config_id, db)
-            print(100 * '-')
+            logger.info(f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} "
-            print(actual_config_id)
+                        f"(type: {type(actual_config_id).__name__})")
-            print(100 * '-')
-            logger.info(
-                f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} (type: {type(actual_config_id).__name__})")
         except (ValueError, AttributeError) as e:
-            logger.error(
+            logger.error(f"[CELERY WRITE] Invalid config_id format: {config_id} "
-                f"[CELERY WRITE] Invalid config_id format: {config_id} (type: {type(config_id).__name__}), error: {e}")
+                         f"(type: {type(config_id).__name__}), error: {e}")
             return {
                 "status": "FAILURE",
                 "error": f"Invalid config_id format: {config_id} - {str(e)}",
@@ -1151,8 +1154,8 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s
         result = loop.run_until_complete(_run())
         elapsed_time = time.time() - start_time
 
-        logger.info(
+        logger.info(f"[CELERY WRITE] Task completed successfully "
-            f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}")
+                    f"- elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}")
 
         # 记录该用户最后一次 write_message 成功的时间，供时间轴筛选使用
         try:
@@ -1167,7 +1170,6 @@ def write_message_task(self, end_user_id: str, message: list[dict], config_id: s
                 )
         except Exception as _e:
             logger.warning(f"[CELERY WRITE] 写入 last_done 时间戳失败（不影响主流程）: {_e}")
-
         return {
             "status": "SUCCESS",
             "result": result,
@@ -2749,7 +2751,8 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
                         llm_model_id=llm_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}")
+                    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")
@@ -2772,12 +2775,6 @@ def init_community_clustering_for_users(self, end_user_ids: List[str]) -> Dict[s
         }
 
     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