refactor(memory): remove expired_at field and add dialog_at timestamp

Remove the deprecated expired_at field from all graph models, Neo4j
Cypher queries, repositories, and pipeline code. Replace with dialog_at
on StatementNode to track the original dialog timestamp.

- Strip expired_at from DialogueNode, ChunkNode, StatementNode,
  ExtractedEntityNode, edges, and all Cypher queries
- Add dialog_at to MessageItem schema and propagate through extraction
  and graph build steps
- Extract emotion/metadata async submission from WritePipeline into
  a generic _submit_celery_task helper
- Add post_store_dedup_and_alias_merge Celery task for async alias
  merging and second-layer dedup after Neo4j write
- Switch pytest async backend from anyio to asyncio_mode=auto

api/app/core/memory/agent/langgraph_graph/tools/tool.py

@@ -252,7 +252,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
         # TODO: fact_summary functionality temporarily disabled, will be enabled after future development
         fields_to_remove = {
             'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids',
-            'expired_at', 'created_at', 'chunk_id', 'apply_id',
+            'created_at', 'chunk_id', 'apply_id',
             'user_id', 'statement_ids', 'updated_at', "chunk_ids", "fact_summary"
         }
         # 注意：'id' 字段保留，community 展开时需要用 community id 查询成员 statements

api/app/core/memory/agent/services/search_service.py

@@ -16,7 +16,7 @@ logger = get_agent_logger(__name__)
 # 需要从展开结果中过滤的字段（含 Neo4j DateTime，不可 JSON 序列化）
 _EXPAND_FIELDS_TO_REMOVE = {
     'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids',
-    'expired_at', 'created_at', 'chunk_id', 'apply_id',
+    'created_at', 'chunk_id', 'apply_id',
     'user_id', 'statement_ids', 'updated_at', 'chunk_ids', 'fact_summary'
 }
 

api/app/core/memory/agent/utils/get_dialogs.py

@@ -18,7 +18,7 @@ async def get_chunked_dialogs(
     Args:
         chunker_strategy: The chunking strategy to use (default: RecursiveChunker)
         end_user_id: Group identifier
-        messages: Structured message list [{"role": "user", "content": "..."}, ...]
+        messages: Structured message list [{"role": "user", "content": "...", "dialog_at": "..."}]
         ref_id: Reference identifier
         config_id: Configuration ID for processing (used to load pruning config)
         snapshot: Optional PipelineSnapshot instance for saving pruning output
@@ -47,7 +47,12 @@ async def get_chunked_dialogs(
             raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}")
 
         if content.strip():
-            conversation_messages.append(ConversationMessage(role=role, msg=content.strip(), files=files))
+            conversation_messages.append(ConversationMessage(
+                role=role,
+                msg=content.strip(),
+                dialog_at=msg.get("dialog_at"),
+                files=files,
+            ))
 
     if not conversation_messages:
         raise ValueError("Message list cannot be empty after filtering")
@@ -57,7 +62,7 @@ async def get_chunked_dialogs(
         context=conversation_context,
         ref_id=ref_id,
         end_user_id=end_user_id,
-        config_id=config_id
+        config_id=config_id,
     )
     
 # step2: 语义剪枝步骤（在分块之前）

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

@@ -242,6 +242,7 @@ class ChunkerClient:
                     chunk = Chunk(
                         content=f"{msg.role}: {sub_chunk_text}",
                         speaker=msg.role,  # 直接继承角色
+                        dialog_at=getattr(msg, "dialog_at", None),
                         metadata={
                             "message_index": msg_idx,
                             "message_role": msg.role,
@@ -257,6 +258,7 @@ class ChunkerClient:
                 chunk = Chunk(
                     content=f"{msg.role}: {msg_content}",
                     speaker=msg.role,  # 直接继承角色
+                    dialog_at=getattr(msg, "dialog_at", None),
                     metadata={
                         "message_index": msg_idx,
                         "message_role": msg.role,

api/app/core/memory/memory_service.py

@@ -62,7 +62,7 @@ class MemoryService:
         """写入记忆：对话 → 萃取 → 存储 → 聚类 → 摘要
 
         Args:
-            messages: 结构化消息 [{"role": "user"/"assistant", "content": "..."}]
+            messages: 结构化消息 [{"role": "user"/"assistant", "content": "...", "dialog_at": "..."}]
             language: 语言 ("zh" | "en")
             ref_id: 引用 ID，为空则自动生成
             is_pilot_run: 试运行模式（只萃取不写入）

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

@@ -106,7 +106,6 @@ class Edge(BaseModel):
         end_user_id: End user ID for multi-tenancy
         run_id: Unique identifier for the pipeline run that created this edge
         created_at: Timestamp when the edge was created (system perspective)
-        expired_at: Optional timestamp when the edge expires (system perspective)
     """
     id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the edge.")
     source: str = Field(..., description="The ID of the source node.")
@@ -114,7 +113,6 @@ class Edge(BaseModel):
     end_user_id: str = Field(..., description="The end user ID of the edge.")
     run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.")
     created_at: datetime = Field(..., description="The valid time of the edge from system perspective.")
-    expired_at: Optional[datetime] = Field(default=None, description="The expired time of the edge from system perspective.")
 
 
 class ChunkEdge(Edge):
@@ -191,14 +189,12 @@ class Node(BaseModel):
         end_user_id: End user ID for multi-tenancy
         run_id: Unique identifier for the pipeline run that created this node
         created_at: Timestamp when the node was created (system perspective)
-        expired_at: Optional timestamp when the node expires (system perspective)
     """
     id: str = Field(..., description="The unique identifier for the node.")
     name: str = Field(..., description="The name of the node.")
     end_user_id: str = Field(..., description="The end user ID of the node.")
     run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.")
     created_at: datetime = Field(..., description="The valid time of the node from system perspective.")
-    expired_at: Optional[datetime] = Field(None, description="The expired time of the node from system perspective.")
 
 
 class DialogueNode(Node):
@@ -284,6 +280,7 @@ class StatementNode(Node):
     temporal_info: TemporalInfo = Field(..., description="Temporal information")
     valid_at: Optional[datetime] = Field(None, description="Temporal validity start")
     invalid_at: Optional[datetime] = Field(None, description="Temporal validity end")
+    dialog_at: Optional[datetime] = Field(None, description="Absolute timestamp of the conversation this statement belongs to")
 
     # Embedding and other fields
     statement_embedding: Optional[List[float]] = Field(None, description="Statement embedding vector")
@@ -319,7 +316,7 @@ class StatementNode(Node):
         description="Total number of times this node has been accessed"
     )
 
-    @field_validator('valid_at', 'invalid_at', mode='before')
+    @field_validator('valid_at', 'invalid_at', 'dialog_at', mode='before')
     @classmethod
     def validate_datetime(cls, v):
         """使用通用的历史日期解析函数"""

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

@@ -30,6 +30,7 @@ class ConversationMessage(BaseModel):
     """
     role: str = Field(..., description="The role of the speaker (e.g., 'user', 'assistant').")
     msg: str = Field(..., description="The text content of the message.")
+    dialog_at: Optional[str] = Field(None, description="Absolute timestamp of this message (ISO 8601).")
     files: list[tuple] = Field(default_factory=list, description="The file content of the message", exclude=True)
 
 
@@ -100,6 +101,7 @@ class Statement(BaseModel):
         False,
         description="Whether the statement reflects user's emotional state",
     )
+    dialog_at: Optional[str] = Field(None, description="Absolute timestamp of the source message (ISO 8601).")
 
 
 class ConversationContext(BaseModel):
@@ -139,6 +141,7 @@ class Chunk(BaseModel):
     statements: List[Statement] = Field(default_factory=list, description="A list of statements in the chunk.")
     files: list[tuple] = Field(default_factory=list, description="List of files in the chunk.")
     chunk_embedding: Optional[List[float]] = Field(default=None, description="The embedding vector of the chunk.")
+    dialog_at: Optional[str] = Field(None, description="Absolute timestamp of the source message (ISO 8601).")
     metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata for the chunk.")
 
     @classmethod
@@ -155,6 +158,7 @@ class Chunk(BaseModel):
         return cls(
             content=f"{message.role}: {message.msg}",
             speaker=message.role,
+            dialog_at=message.dialog_at,
             metadata=metadata or {}
         )
     
@@ -169,7 +173,6 @@ class DialogData(BaseModel):
         ref_id: Reference ID linking to external dialog system
         end_user_id: End user ID for multi-tenancy
         created_at: Timestamp when the dialog was created
-        expired_at: Timestamp when the dialog expires (default: far future)
         metadata: Additional metadata as key-value pairs
         chunks: List of chunks from the conversation
         config_id: Configuration ID used to process this dialog
@@ -184,7 +187,6 @@ class DialogData(BaseModel):
     end_user_id: str = Field(default=..., description="End user ID of dialogue data")
     run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.")
     created_at: datetime = Field(default_factory=datetime.now, description="The timestamp when the dialog was created.")
-    expired_at: datetime = Field(default_factory=lambda: datetime(9999, 12, 31), description="The timestamp when the dialog expires.")
     metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata for the dialog.")
     chunks: List[Chunk] = Field(default_factory=list, description="A list of chunks from the conversation context.")
     config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this dialog (integer or string)")

api/app/core/memory/pipelines/write_pipeline.py

@@ -198,11 +198,13 @@ class WritePipeline:
                     chunked_dialogs = await self._preprocess(messages, ref_id)
                     s.metadata(chunks=sum(len(d.chunks) for d in chunked_dialogs))
 
-                # Step 2: 萃取 - 知识提取
+                # Step 2: 萃取 - 知识提取 + 第一层去重 + 别名归并（内存侧）
                 async with bear.step(2, 5, "萃取", "知识提取") as s:
                     extraction_result = await self._extract(
                         chunked_dialogs, is_pilot_run
                     )
+                    # 别名归并（内存侧）：在写入前完成，确保写入的数据已归并
+                    self._merge_alias_in_memory(extraction_result)
                     stats = extraction_result.stats
                     s.metadata(
                         entities=stats["entity_count"],
@@ -222,15 +224,8 @@ class WritePipeline:
                 async with bear.step(3, 5, "存储", "写入 Neo4j"):
                     await self._store(extraction_result)
 
-                # Step 3.2: 别名归并
-                async with bear.step(3, 5, "别名归并", "处理别名属于关系"):
-                    await self._merge_alias_belongs_to(extraction_result)
-
-                # Step 3.5: 异步情绪提取（fire-and-forget，需在 _store 之后确保 Statement 节点已存在）
-                await self._extract_emotion(getattr(self, "_emotion_statements", []))
-
-                # Step 3.6: 异步元数据提取（fire-and-forget，需在 _store 之后确保 Entity 节点已存在）
-                await self._extract_metadata(extraction_result)
+                # Step 3.5: 异步后处理（别名归并 Neo4j 侧 + 第二层去重 + 情绪 + 元数据）
+                await self._post_store_async_tasks(extraction_result)
 
                 # Step 4: 聚类 - 增量更新社区（异步，不阻塞）
                 async with bear.step(4, 5, "聚类", "增量更新社区") as s:
@@ -359,16 +354,17 @@ class WritePipeline:
         # Snapshot: 图节点和边（去重前）
         recorder.record_graph_before_dedup(graph)
 
-        # step3: 两阶段去重消歧
+        # step3: 第一层去重消歧（同一轮对话内的实体碎片合并）
+        # 第二层（Neo4j 联合去重）后移到 _store 之后异步执行
         dedup_result = await run_dedup(
             entity_nodes=graph.entity_nodes,
             statement_entity_edges=graph.stmt_entity_edges,
             entity_entity_edges=graph.entity_entity_edges,
             dialog_data_list=dialog_data_list,
             pipeline_config=pipeline_config,
-            connector=self._neo4j_connector,
+            connector=None,
             llm_client=self._llm_client,
-            is_pilot_run=is_pilot_run,
+            is_pilot_run=True,
             progress_callback=self.progress_callback,
         )
 
@@ -455,29 +451,21 @@ class WritePipeline:
                     raise
 
     # ──────────────────────────────────────────────
-    # Step 3.2: 别名归并
+    # Step 3.2: 别名归并（内存侧）
     # ──────────────────────────────────────────────
 
-    async def _merge_alias_belongs_to(self, result: ExtractionResult) -> None:
+    def _merge_alias_in_memory(self, result: ExtractionResult) -> None:
+        """别名归并（内存侧）：处理 predicate="别名属于" 的边。
+
+        在写入 Neo4j 之前执行，确保写入的数据已经完成别名归并：
+        - 将别名实体的 name 追加到目标实体的 aliases
+        - 将别名实体的 description 拼接到目标实体的 description
+        - 重定向指向别名节点的边到目标节点
+
+        纯内存操作，不涉及 Neo4j。
         """
-        所有去重合并都可以使用这个这种统一的处理方式（未实现）
-        别名归并：处理 predicate="别名属于" 的 EXTRACTED_RELATIONSHIP 边。
-
-        对每条 source -[EXTRACTED_RELATIONSHIP {predicate:"别名属于"}]-> target 边：
-        - 将 source.name 追加到 target.aliases（去重）
-        - 将 source.description append 进 target.description_list（list 形式）
-
-        同时在内存中同步更新 ExtractionResult.entity_nodes，保持内存与 Neo4j 一致。
-        失败不中断主流程。
-        """
-        from app.repositories.neo4j.cypher_queries import (
-            MERGE_ALIAS_BELONGS_TO,
-            REDIRECT_ALIAS_EDGES,
-        )
-
         ALIAS_PREDICATE = "别名属于"
 
-        # 筛选出所有 predicate="别名属于" 的边
         alias_edges = [
             e
             for e in result.entity_entity_edges
@@ -490,10 +478,7 @@ class WritePipeline:
             return
 
         try:
-            # ── 1. 在内存中同步更新 entity_nodes ──
             entity_map = {e.id: e for e in result.entity_nodes}
-
-            # 构建 alias_id → target_id 映射（别名节点 → 用户节点）
             alias_to_target: dict[str, str] = {}
 
             for edge in alias_edges:
@@ -513,7 +498,7 @@ class WritePipeline:
                             source_name
                         ]
 
-                # 将 source.description append 进 target.description（追加，分号分隔）
+                # 将 source.description 拼接到 target.description（分号分隔，去重）
                 src_desc = (source_node.description or "").strip()
                 if src_desc:
                     tgt_desc = (target_node.description or "").strip()
@@ -522,12 +507,11 @@ class WritePipeline:
                             f"{tgt_desc}；{src_desc}" if tgt_desc else src_desc
                         )
 
-            # ── 1.1 内存中重定向指向别名节点的边到用户节点 ──
+            # 重定向指向别名节点的边到目标节点
             alias_ids = set(alias_to_target.keys())
             redirected_ee_count = 0
             redirected_se_count = 0
 
-            # 重定向 entity_entity_edges（排除"别名属于"边本身）
             for edge in result.entity_entity_edges:
                 rel_type = getattr(edge, "relation_type", "")
                 if rel_type == ALIAS_PREDICATE:
@@ -539,39 +523,101 @@ class WritePipeline:
                     edge.target = alias_to_target[edge.target]
                     redirected_ee_count += 1
 
-            # 重定向 stmt_entity_edges（陈述句 → 实体边）
             for edge in result.stmt_entity_edges:
                 if edge.target in alias_ids:
                     edge.target = alias_to_target[edge.target]
                     redirected_se_count += 1
 
             logger.info(
-                f"[AliasMerge] 内存同步完成，处理 {len(alias_edges)} 条 '别名属于' 边，"
+                f"[AliasMerge] 内存归并完成，处理 {len(alias_edges)} 条 '别名属于' 边，"
                 f"重定向 entity_entity 边 {redirected_ee_count} 次，"
                 f"重定向 stmt_entity 边 {redirected_se_count} 次"
             )
 
-            # ── 2. 写入 Neo4j：别名属性归并 ──
-            records = await self._neo4j_connector.execute_query(
-                MERGE_ALIAS_BELONGS_TO,
-                end_user_id=self.end_user_id,
-            )
-            merged_count = len(records) if records else 0
-            logger.info(f"[AliasMerge] Neo4j 别名归并完成，影响 {merged_count} 条记录")
-
-            # ── 3. 写入 Neo4j：重定向指向别名节点的边到用户节点 ──
-            redirect_records = await self._neo4j_connector.execute_query(
-                REDIRECT_ALIAS_EDGES,
-                end_user_id=self.end_user_id,
-            )
-            redirect_count = len(redirect_records) if redirect_records else 0
-            logger.info(
-                f"[AliasMerge] Neo4j 边重定向完成，影响 {redirect_count} 条记录"
-            )
-
         except Exception as e:
             logger.warning(
-                f"[AliasMerge] 别名归并失败（不影响主流程）: {e}", exc_info=True
+                f"[AliasMerge] 内存归并失败（不影响主流程）: {e}", exc_info=True
+            )
+
+    # ──────────────────────────────────────────────
+    # Step 3.5: 异步后处理（Neo4j 别名归并 + 第二层去重）
+    # ──────────────────────────────────────────────
+
+    async def _post_store_async_tasks(self, result: ExtractionResult) -> None:
+        """提交写入后的异步 Celery 任务（全部 fire-and-forget，失败不影响主流程）：
+
+        1. Neo4j 别名归并 + 第二层去重
+        2. 异步情绪提取
+        3. 异步元数据提取
+        """
+        from app.core.memory.storage_services.extraction_engine.knowledge_extraction.metadata_extractor import (
+            collect_user_entities_for_metadata,
+        )
+
+        llm_model_id = (
+            str(self.memory_config.llm_model_id)
+            if self.memory_config.llm_model_id
+            else None
+        )
+        recorder = getattr(self, "_recorder", None)
+        snapshot_dir = (
+            recorder.snapshot_dir
+            if recorder is not None and recorder.enabled
+            else None
+        )
+
+        # ── 1. Neo4j 别名归并 + 第二层去重 ──
+        self._submit_celery_task(
+            "PostStore",
+            "app.tasks.post_store_dedup_and_alias_merge",
+            {
+                "end_user_id": self.end_user_id,
+                "entity_ids": [e.id for e in result.entity_nodes],
+                "llm_model_id": llm_model_id,
+            },
+        )
+
+        # ── 2. 异步情绪提取 ──
+        emotion_statements = getattr(self, "_emotion_statements", [])
+        if emotion_statements and llm_model_id:
+            self._submit_celery_task(
+                "Emotion",
+                "app.tasks.extract_emotion_batch",
+                {
+                    "statements": emotion_statements,
+                    "llm_model_id": llm_model_id,
+                    "language": self.language,
+                    "snapshot_dir": snapshot_dir,
+                },
+            )
+
+        # ── 3. 异步元数据提取 ──
+        user_entities = collect_user_entities_for_metadata(result.entity_nodes)
+        if user_entities and llm_model_id:
+            self._submit_celery_task(
+                "Metadata",
+                "app.tasks.extract_metadata_batch",
+                {
+                    "user_entities": user_entities,
+                    "llm_model_id": llm_model_id,
+                    "language": self.language,
+                    "snapshot_dir": snapshot_dir,
+                },
+            )
+
+    def _submit_celery_task(
+        self, label: str, task_name: str, kwargs: dict
+    ) -> None:
+        """提交 Celery 异步任务的通用方法。失败只记日志，不抛异常。"""
+        try:
+            from app.celery_app import celery_app
+
+            task_result = celery_app.send_task(task_name, kwargs=kwargs)
+            logger.info(f"[{label}] 异步任务已提交 - task_id={task_result.id}")
+        except Exception as e:
+            logger.error(
+                f"[{label}] 提交异步任务失败（不影响主流程）: {e}",
+                exc_info=True,
             )
 
     # ──────────────────────────────────────────────
@@ -625,127 +671,6 @@ class WritePipeline:
                 exc_info=True,
             )
 
-    # ──────────────────────────────────────────────
-    # Step 4.5: 异步情绪提取
-    #    fire-and-forget 提交 Celery 任务，不阻塞主流程
-    # ──────────────────────────────────────────────
-
-    async def _extract_emotion(self, emotion_statements: list) -> None:
-        """提交异步情绪提取 Celery 任务。
-
-        从编排器收集的 user statement 列表中提取情绪，
-        异步回写到 Neo4j Statement 节点。失败不影响主流程。
-
-        在 PIPELINE_SNAPSHOT_ENABLED=true 时，会把当前运行的快照目录路径
-        通过 snapshot_dir 透传给 Celery 任务；worker 端在完成 LLM 抽取后，
-        将结果落盘到 <snapshot_dir>/4_emotion_outputs.json，避免主进程重复调用 LLM。
-        """
-        if not emotion_statements:
-            return
-
-        llm_model_id = (
-            str(self.memory_config.llm_model_id)
-            if self.memory_config.llm_model_id
-            else None
-        )
-        if not llm_model_id:
-            logger.warning("[Emotion] 无法提交情绪提取任务：llm_model_id 为空")
-            return
-
-        # 快照目录：仅在 PIPELINE_SNAPSHOT_ENABLED=true 时非空，供 worker 端落盘
-        recorder = getattr(self, "_recorder", None)
-        snapshot_dir = (
-            recorder.snapshot_dir
-            if recorder is not None and recorder.enabled
-            else None
-        )
-
-        try:
-            from app.celery_app import celery_app
-
-            result = celery_app.send_task(
-                "app.tasks.extract_emotion_batch",
-                kwargs={
-                    "statements": emotion_statements,
-                    "llm_model_id": llm_model_id,
-                    "language": self.language,
-                    "snapshot_dir": snapshot_dir,
-                },
-            )
-            logger.info(
-                f"[Emotion] 异步情绪提取任务已提交 - "
-                f"task_id = {result.id}, "
-                f"statement_count = {len(emotion_statements)}, "
-                f"snapshot_dir = {snapshot_dir}, "
-                f"source=async"
-            )
-        except Exception as e:
-            logger.error(
-                f"[Emotion] 提交情绪提取任务失败（不影响主流程）: {e}",
-                exc_info=True,
-            )
-
-    # ──────────────────────────────────────────────
-    # Step 3.6: 异步元数据提取
-    #    fire-and-forget 提交 Celery 任务，不阻塞主流程
-    # ──────────────────────────────────────────────
-
-    async def _extract_metadata(self, result: ExtractionResult) -> None:
-        """提交异步元数据提取 Celery 任务。
-
-        从去重后的用户实体 description 中提取结构化元数据，
-        异步回写到 Neo4j ExtractedEntity 节点。失败不影响主流程。
-        """
-        from app.core.memory.storage_services.extraction_engine.knowledge_extraction.metadata_extractor import (
-            collect_user_entities_for_metadata,
-        )
-
-        user_entities = collect_user_entities_for_metadata(result.entity_nodes)
-
-        if not user_entities:
-            return
-
-        llm_model_id = (
-            str(self.memory_config.llm_model_id)
-            if self.memory_config.llm_model_id
-            else None
-        )
-        if not llm_model_id:
-            logger.warning("[Metadata] 无法提交元数据提取任务：llm_model_id 为空")
-            return
-
-        # 快照目录
-        recorder = getattr(self, "_recorder", None)
-        snapshot_dir = (
-            recorder.snapshot_dir
-            if recorder is not None and recorder.enabled
-            else None
-        )
-
-        try:
-            from app.celery_app import celery_app
-
-            task_result = celery_app.send_task(
-                "app.tasks.extract_metadata_batch",
-                kwargs={
-                    "user_entities": user_entities,
-                    "llm_model_id": llm_model_id,
-                    "language": self.language,
-                    "snapshot_dir": snapshot_dir,
-                },
-            )
-            logger.info(
-                f"[Metadata] 异步元数据提取任务已提交 - "
-                f"task_id = {task_result.id}, "
-                f"entity_count = {len(user_entities)}, "
-                f"snapshot_dir = {snapshot_dir}"
-            )
-        except Exception as e:
-            logger.error(
-                f"[Metadata] 提交元数据提取任务失败（不影响主流程）: {e}",
-                exc_info=True,
-            )
-
     # ──────────────────────────────────────────────
     # Step 5: 摘要
     # （+ entity_description）+ meta_data部分在此提取

api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py

@@ -183,14 +183,8 @@ def _merge_attribute(canonical: ExtractedEntityNode, ent: ExtractedEntityNode):
 
     # 时间范围合并
     try:
-        # 统一使用 created_at / expired_at
         if getattr(ent, "created_at", None) and getattr(canonical, "created_at", None) and ent.created_at < canonical.created_at:
             canonical.created_at = ent.created_at
-        if getattr(ent, "expired_at", None) and getattr(canonical, "expired_at", None):
-            if canonical.expired_at is None:
-                canonical.expired_at = ent.expired_at
-            elif ent.expired_at and ent.expired_at > canonical.expired_at:
-                canonical.expired_at = ent.expired_at
     except Exception:
         pass
 

api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py

@@ -65,7 +65,6 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode:
         user_id=row.get("user_id") or "",
         apply_id=row.get("apply_id") or "",
         created_at=_parse_dt(row.get("created_at")),
-        expired_at=_parse_dt(row.get("expired_at")) if row.get("expired_at") else None,
         entity_idx=int(row.get("entity_idx") or 0),
         statement_id=row.get("statement_id") or "",
         entity_type=row.get("entity_type") or "",

api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py

@@ -1089,7 +1089,6 @@ class ExtractionOrchestrator:
                 content=dialog_data.context.content if dialog_data.context else "",
                 dialog_embedding=dialog_data.dialog_embedding if hasattr(dialog_data, 'dialog_embedding') else None,
                 created_at=dialog_data.created_at,
-                expired_at=dialog_data.expired_at,
                 metadata=dialog_data.metadata,
                 config_id=dialog_data.config_id if hasattr(dialog_data, 'config_id') else None,
             )
@@ -1109,7 +1108,6 @@ class ExtractionOrchestrator:
                     chunk_embedding=chunk.chunk_embedding,
                     sequence_number=chunk_idx,  # 添加必需的 sequence_number 字段
                     created_at=dialog_data.created_at,
-                    expired_at=dialog_data.expired_at,
                     metadata=chunk.metadata,
                 )
                 chunk_nodes.append(chunk_node)
@@ -1175,7 +1173,6 @@ class ExtractionOrchestrator:
                         invalid_at=statement.temporal_validity.invalid_at if hasattr(statement,
                                                                                      'temporal_validity') and statement.temporal_validity else None,
                         created_at=dialog_data.created_at,
-                        expired_at=dialog_data.expired_at,
                         config_id=dialog_data.config_id if hasattr(dialog_data, 'config_id') else None,
                         # Emotion fields
                         emotion_type=getattr(statement, 'emotion_type', None),
@@ -1232,7 +1229,6 @@ class ExtractionOrchestrator:
                                     end_user_id=dialog_data.end_user_id,
                                     run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                                     created_at=dialog_data.created_at,
-                                    expired_at=dialog_data.expired_at,
                                     config_id=dialog_data.config_id if hasattr(dialog_data, 'config_id') else None,
                                 )
                                 entity_nodes.append(entity_node)
@@ -1269,7 +1265,6 @@ class ExtractionOrchestrator:
                                     end_user_id=dialog_data.end_user_id,
                                     run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                                     created_at=dialog_data.created_at,
-                                    expired_at=dialog_data.expired_at,
                                     valid_at=_tv.valid_at if _tv else None,
                                     invalid_at=_tv.invalid_at if _tv else None,
                                 )

api/app/core/memory/storage_services/extraction_engine/extraction_pipeline_orchestrator.py

@@ -912,6 +912,7 @@ class NewExtractionOrchestrator:
                         has_emotional_state=stmt_out.has_emotional_state,
                         triplet_extraction_info=triplet_info,
                         statement_embedding=stmt_embedding,
+                        dialog_at=getattr(chunk, "dialog_at", None),
                         **emotion_kwargs,
                     )
                     new_statements.append(stmt)

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

@@ -215,7 +215,6 @@ async def _process_chunk_summary(
             apply_id=dialog.end_user_id,
             run_id=dialog.run_id,  # 使用 dialog 的 run_id
             created_at=datetime.now(),
-            expired_at=datetime(9999, 12, 31),
             dialog_id=dialog.id,
             chunk_ids=[chunk.id],
             content=summary_text,

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

@@ -181,6 +181,7 @@ class StatementExtractor:
                     chunk_id=chunk.id,
                     end_user_id=end_user_id,
                     speaker=chunk_speaker,
+                    dialog_at=getattr(chunk, "dialog_at", None),
                     has_unsolved_reference=getattr(extracted_stmt, "has_unsolved_reference", False),
                 )
                 

api/app/core/memory/storage_services/extraction_engine/steps/graph_build_step.py

@@ -135,7 +135,6 @@ async def build_graph_nodes_and_edges(
             content=dialog_data.context.content if dialog_data.context else "",
             dialog_embedding=dialog_data.dialog_embedding if hasattr(dialog_data, "dialog_embedding") else None,
             created_at=dialog_data.created_at,
-            expired_at=dialog_data.expired_at,
             metadata=dialog_data.metadata,
             config_id=dialog_data.config_id if hasattr(dialog_data, "config_id") else None,
         )
@@ -154,7 +153,6 @@ async def build_graph_nodes_and_edges(
                 chunk_embedding=chunk.chunk_embedding,
                 sequence_number=chunk_idx,
                 created_at=dialog_data.created_at,
-                expired_at=dialog_data.expired_at,
                 metadata=chunk.metadata,
             )
             chunk_nodes.append(chunk_node)
@@ -227,7 +225,7 @@ async def build_graph_nodes_and_edges(
                         else None
                     ),
                     created_at=dialog_data.created_at,
-                    expired_at=dialog_data.expired_at,
+                    dialog_at=getattr(statement, "dialog_at", None),
                     config_id=dialog_data.config_id if hasattr(dialog_data, "config_id") else None,
                     emotion_type=getattr(statement, "emotion_type", None),
                     emotion_intensity=getattr(statement, "emotion_intensity", None),
@@ -280,7 +278,6 @@ async def build_graph_nodes_and_edges(
                             end_user_id=dialog_data.end_user_id,
                             run_id=dialog_data.run_id,
                             created_at=dialog_data.created_at,
-                            expired_at=dialog_data.expired_at,
                             config_id=dialog_data.config_id if hasattr(dialog_data, "config_id") else None,
                         )
                         entity_nodes.append(entity_node)
@@ -320,7 +317,6 @@ async def build_graph_nodes_and_edges(
                                 end_user_id=dialog_data.end_user_id,
                                 run_id=dialog_data.run_id,
                                 created_at=dialog_data.created_at,
-                                expired_at=dialog_data.expired_at,
                                 valid_at=_tv.valid_at if _tv else None,
                                 invalid_at=_tv.invalid_at if _tv else None,
                             )
@@ -382,7 +378,6 @@ async def build_graph_nodes_and_edges(
                 end_user_id=dialog_data.end_user_id,
                 run_id=dialog_data.run_id,
                 created_at=dialog_data.created_at,
-                expired_at=dialog_data.expired_at,
                 pair_id=pair_id,
                 dialog_id=dialog_data.id,
                 text=record["original_text"],
@@ -408,7 +403,6 @@ async def build_graph_nodes_and_edges(
                 end_user_id=dialog_data.end_user_id,
                 run_id=dialog_data.run_id,
                 created_at=dialog_data.created_at,
-                expired_at=dialog_data.expired_at,
                 pair_id=pair_id,
                 dialog_id=dialog_data.id,
                 text=record["pruned_text"],

api/app/core/memory/storage_services/reflection_engine/self_reflexion.py

@@ -483,7 +483,7 @@ class ReflectionEngine:
         result_data['memory_verifies'] = memory_verifies
         result_data['quality_assessments'] = quality_assessments
         conflicts_found = 0  # Initialize as integer 0 instead of empty string
-        REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"}
+        REMOVE_KEYS = {"created_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"}
         # Clean conflict_data, and memory_verify and quality_assessment
         cleaned_conflict_data = []
         for item in conflict_data:

api/app/core/memory/utils/config/get_data.py

@@ -26,7 +26,6 @@ async def _load_(data: List[Any]) -> List[Dict]:
         "end_user_id",
         "chunk_id",
         "created_at",
-        "expired_at",
         "valid_at",
         "invalid_at",
     ]
@@ -93,7 +92,6 @@ async def get_data(result):
                         rel_filtered['run_id'] = value.get('run_id')
                         rel_filtered['statement'] = value.get('statement')
                         rel_filtered['statement_id'] = value.get('statement_id')
-                        rel_filtered['expired_at'] = value.get('expired_at')
                         rel_filtered['created_at'] = value.get('created_at')
                     filtered_item[key] = value
                 elif key == 'entity2' and value is not None: