Merge pull request #873 from SuanmoSuanyangTechnology/feature/agent-tool_xjn

feat(workflow and app)

api/app/controllers/app_controller.py

@@ -1250,9 +1250,11 @@ async def export_app(
 async def import_app(
         file: UploadFile = File(...),
         db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
+        current_user: User = Depends(get_current_user),
+        app_id: Optional[str] = Form(None),
 ):
     """从 YAML 文件导入 agent / multi_agent / workflow 应用。
+    传入 app_id 时覆盖该应用的配置（类型必须一致），否则创建新应用。
     跨空间/跨租户导入时，模型/工具/知识库会按名称匹配，匹配不到则置空并返回 warnings。
     """
     if not file.filename.lower().endswith((".yaml", ".yml")):
@@ -1263,13 +1265,15 @@ async def import_app(
     if not dsl or "app" not in dsl:
         return fail(msg="YAML 格式无效，缺少 app 字段", code=BizCode.BAD_REQUEST)
 
-    new_app, warnings = AppDslService(db).import_dsl(
+    target_app_id = uuid.UUID(app_id) if app_id else None
+    result_app, warnings = AppDslService(db).import_dsl(
         dsl=dsl,
         workspace_id=current_user.current_workspace_id,
         tenant_id=current_user.tenant_id,
         user_id=current_user.id,
+        app_id=target_app_id,
     )
     return success(
-        data={"app": app_schema.App.model_validate(new_app), "warnings": warnings},
+        data={"app": app_schema.App.model_validate(result_app), "warnings": warnings},
         msg="应用导入成功" + ("，但部分资源需手动配置" if warnings else "")
     )

api/app/core/workflow/nodes/cycle_graph/iteration.py

@@ -28,86 +28,135 @@ class IterationRuntime:
 
     def __init__(
             self,
-            start_id: str,
             stream: bool,
-            graph: CompiledStateGraph,
             node_id: str,
             config: dict[str, Any],
             state: WorkflowState,
             variable_pool: VariablePool,
-            child_variable_pool: VariablePool,
+            cycle_nodes: list,
+            cycle_edges: list,
     ):
         """
         Initialize the iteration runtime.
 
         Args:
-            graph: Compiled workflow graph capable of async invocation.
+            stream:       Whether to run in streaming mode. When True, each iteration
-            node_id: Unique identifier of the loop node.
+                          uses graph.astream and emits cycle_item events in real time.
-            config: Dictionary containing iteration node configuration.
+                          When False, graph.ainvoke is used instead.
-            state: Current workflow state at the point of iteration.
+            node_id:      The unique identifier of the iteration node in the workflow.
+                          Also used as the variable namespace for item/index inside
+                          the subgraph (e.g. {{ node_id.item }}).
+            config:       Raw configuration dict for the iteration node, parsed into
+                          IterationNodeConfig. Controls input/output variable selectors,
+                          parallel execution settings, and output flattening.
+            state:        The parent workflow state at the point the iteration node is
+                          entered. Each task receives a copy of this state as its
+                          starting point.
+            variable_pool: The parent VariablePool containing all variables available
+                           at the time the iteration node executes, including sys.*,
+                           conv.*, and outputs from upstream nodes. Used as the source
+                           for deep-copying into each task's independent child pool.
+            cycle_nodes:  List of node config dicts belonging to this iteration's
+                          subgraph (i.e. nodes whose cycle field equals node_id).
+                          Passed to GraphBuilder when constructing each task's subgraph.
+            cycle_edges:  List of edge config dicts connecting nodes within the subgraph.
+                          Passed to GraphBuilder alongside cycle_nodes.
         """
-        self.start_id = start_id
         self.stream = stream
-        self.graph = graph
         self.state = state
         self.node_id = node_id
         self.typed_config = IterationNodeConfig(**config)
         self.looping = True
         self.variable_pool = variable_pool
-        self.child_variable_pool = child_variable_pool
+        self.cycle_nodes = cycle_nodes
+        self.cycle_edges = cycle_edges
         self.event_write = get_stream_writer()
-        self.checkpoint = RunnableConfig(
-            configurable={
-                "thread_id": uuid.uuid4()
-            }
-        )
 
         self.output_value = None
         self.result: list = []
 
-    async def _init_iteration_state(self, item, idx):
+    def _build_child_graph(self) -> tuple[CompiledStateGraph, VariablePool, str]:
         """
-        Initialize a per-iteration copy of the workflow state.
+        Build an independent compiled subgraph for a single iteration task.
 
-        Args:
+        Each call creates a brand-new VariablePool by deep-copying the parent pool,
-            item: Current element from the input array for this iteration.
+        then passes it to GraphBuilder. GraphBuilder binds this pool to every node's
-            idx: Index of the element in the input array.
+        execution closure at build time, so the pool and the subgraph always reference
+        the same object. This is the key design invariant: item/index written into the
+        pool after build will be visible to all nodes inside the subgraph.
 
         Returns:
-            A copy of the workflow state with iteration-specific variables set.
+            graph:      The compiled LangGraph subgraph ready for invocation.
+            child_pool: The VariablePool bound to this subgraph's node closures.
+                        Callers must write item/index into this pool before invoking
+                        the graph, and read output from it after invocation.
+            start_node_id: The ID of the CYCLE_START node inside the subgraph,
+                           used to set the initial activation signal in workflow state.
         """
-        loopstate = WorkflowState(
+        from app.core.workflow.engine.graph_builder import GraphBuilder
-            **self.state
+        child_pool = VariablePool()
+        child_pool.copy(self.variable_pool)
+        builder = GraphBuilder(
+            {"nodes": self.cycle_nodes, "edges": self.cycle_edges},
+            stream=self.stream,
+            variable_pool=child_pool,
+            cycle=self.node_id,
         )
-        self.child_variable_pool.copy(self.variable_pool)
+        graph = builder.build()
-        await self.child_variable_pool.new(self.node_id, "item", item, VariableType.type_map(item), mut=True)
+        return graph, builder.variable_pool, builder.start_node_id
-        await self.child_variable_pool.new(self.node_id, "index", item, VariableType.type_map(item), mut=True)
+
-        loopstate["node_outputs"][self.node_id] = {
+    async def _init_iteration_state(self, item, idx, child_pool: VariablePool, start_id: str):
-            "item": item,
+        """
-            "index": idx,
+        Initialize the workflow state for a single iteration.
-        }
+
+        Writes the current item and its index into child_pool under the iteration
+        node's namespace (e.g. iteration_xxx.item, iteration_xxx.index), making them
+        accessible to downstream nodes inside the subgraph via variable selectors.
+
+        Also prepares a copy of the parent workflow state with:
+        - node_outputs[node_id] set to {item, index} so the state snapshot is consistent
+          with the pool values.
+        - looping flag set to 1 (active) to signal the subgraph is inside a cycle.
+        - activate[start_id] set to True to trigger the CYCLE_START node.
+
+        Args:
+            item:       The current element from the input array.
+            idx:        The zero-based index of this element in the input array.
+            child_pool: The VariablePool bound to this iteration's subgraph.
+                        Must be the same object returned by _build_child_graph.
+            start_id:   The ID of the CYCLE_START node inside the subgraph.
+
+        Returns:
+            A WorkflowState instance ready to be passed to graph.ainvoke or graph.astream.
+        """
+        loopstate = WorkflowState(**self.state)
+        await child_pool.new(self.node_id, "item", item, VariableType.type_map(item), mut=True)
+        await child_pool.new(self.node_id, "index", idx, VariableType.type_map(idx), mut=True)
+        loopstate["node_outputs"][self.node_id] = {"item": item, "index": idx}
         loopstate["looping"] = 1
-        loopstate["activate"][self.start_id] = True
+        loopstate["activate"][start_id] = True
         return loopstate
 
-    def merge_conv_vars(self):
+    def _merge_conv_vars(self, child_pool: VariablePool):
-        self.variable_pool.variables["conv"].update(
+        self.variable_pool.variables["conv"].update(child_pool.variables["conv"])
-            self.child_variable_pool.variables["conv"]
-        )
 
     async def run_task(self, item, idx):
         """
         Execute a single iteration asynchronously.
+        Each task builds its own subgraph so the variable pool closure is independent.
 
-        Args:
+        Returns:
-            item: The input element for this iteration.
+            Tuple of (idx, output, result, child_pool, stopped)
-            idx: The index of this iteration.
         """
+        graph, child_pool, start_id = self._build_child_graph()
+        checkpoint = RunnableConfig(configurable={"thread_id": uuid.uuid4()})
+        init_state = await self._init_iteration_state(item, idx, child_pool, start_id)
+
         if self.stream:
-            async for event in self.graph.astream(
+            async for event in graph.astream(
-                    await self._init_iteration_state(item, idx),
+                    init_state,
                     stream_mode=["debug"],
-                    config=self.checkpoint
+                    config=checkpoint
             ):
                 if isinstance(event, tuple) and len(event) == 2:
                     mode, data = event
@@ -117,7 +166,6 @@ class IterationRuntime:
                     event_type = data.get("type")
                     payload = data.get("payload", {})
                     node_name = payload.get("name")
-
                     if node_name and node_name.startswith("nop"):
                         continue
                     if event_type == "task_result":
@@ -140,17 +188,13 @@ class IterationRuntime:
                                 "token_usage": result.get("node_outputs", {}).get(node_name, {}).get("token_usage")
                             }
                         })
-            result = self.graph.get_state(config=self.checkpoint).values
+            result = graph.get_state(config=checkpoint).values
         else:
-            result = await self.graph.ainvoke(await self._init_iteration_state(item, idx))
+            result = await graph.ainvoke(init_state)
-        output = self.child_variable_pool.get_value(self.output_value)
+
-        if isinstance(output, list) and self.typed_config.flatten:
+        output = child_pool.get_value(self.output_value)
-            self.result.extend(output)
+        stopped = result["looping"] == 2
-        else:
+        return idx, output, result, child_pool, stopped
-            self.result.append(output)
-        if result["looping"] == 2:
-            self.looping = False
-        return result
 
     def _create_iteration_tasks(self, array_obj, idx):
         """
@@ -196,16 +240,32 @@ class IterationRuntime:
                 tasks = self._create_iteration_tasks(array_obj, idx)
                 logger.info(f"Iteration node {self.node_id}: running, concurrency {len(tasks)}")
                 idx += self.typed_config.parallel_count
-                child_state.extend(await asyncio.gather(*tasks))
+                batch = await asyncio.gather(*tasks)
-                self.merge_conv_vars()
+                # Sort by idx to preserve order, then collect results
+                batch_sorted = sorted(batch, key=lambda x: x[0])
+                for _, output, result, child_pool, stopped in batch_sorted:
+                    if isinstance(output, list) and self.typed_config.flatten:
+                        self.result.extend(output)
+                    else:
+                        self.result.append(output)
+                    child_state.append(result)
+                    self._merge_conv_vars(child_pool)
+                    if stopped:
+                        self.looping = False
         else:
             # Execute iterations sequentially
             while idx < len(array_obj) and self.looping:
                 logger.info(f"Iteration node {self.node_id}: running")
                 item = array_obj[idx]
-                result = await self.run_task(item, idx)
+                _, output, result, child_pool, stopped = await self.run_task(item, idx)
-                self.merge_conv_vars()
+                if isinstance(output, list) and self.typed_config.flatten:
+                    self.result.extend(output)
+                else:
+                    self.result.append(output)
+                self._merge_conv_vars(child_pool)
                 child_state.append(result)
+                if stopped:
+                    self.looping = False
                 idx += 1
         logger.info(f"Iteration node {self.node_id}: execution completed")
         return {

api/app/core/workflow/nodes/cycle_graph/node.py

@@ -123,7 +123,7 @@ class CycleGraphNode(BaseNode):
 
         return cycle_nodes, cycle_edges
 
-    def build_graph(self):
+    def build_graph(self, variable_pool: VariablePool):
         """
         Build and compile the internal subgraph for this cycle node.
 
@@ -135,6 +135,7 @@ class CycleGraphNode(BaseNode):
         from app.core.workflow.engine.graph_builder import GraphBuilder
 
         self.child_variable_pool = VariablePool()
+        self.child_variable_pool.copy(variable_pool)
         builder = GraphBuilder(
             {
                 "nodes": self.cycle_nodes,
@@ -165,8 +166,8 @@ class CycleGraphNode(BaseNode):
         Raises:
             RuntimeError: If the node type is unsupported.
         """
-        self.build_graph()
         if self.node_type == NodeType.LOOP:
+            self.build_graph(variable_pool)
             return await LoopRuntime(
                 start_id=self.start_node_id,
                 stream=False,
@@ -179,20 +180,19 @@ class CycleGraphNode(BaseNode):
             ).run()
         if self.node_type == NodeType.ITERATION:
             return await IterationRuntime(
-                start_id=self.start_node_id,
                 stream=False,
-                graph=self.graph,
                 node_id=self.node_id,
                 config=self.config,
                 state=state,
                 variable_pool=variable_pool,
-                child_variable_pool=self.child_variable_pool
+                cycle_nodes=self.cycle_nodes,
+                cycle_edges=self.cycle_edges,
             ).run()
         raise RuntimeError("Unknown cycle node type")
 
     async def execute_stream(self, state: WorkflowState, variable_pool: VariablePool):
-        self.build_graph()
         if self.node_type == NodeType.LOOP:
+            self.build_graph(variable_pool)
             yield {
                 "__final__": True,
                 "result": await LoopRuntime(
@@ -211,14 +211,13 @@ class CycleGraphNode(BaseNode):
             yield {
                 "__final__": True,
                 "result": await IterationRuntime(
-                    start_id=self.start_node_id,
                     stream=True,
-                    graph=self.graph,
                     node_id=self.node_id,
                     config=self.config,
                     state=state,
                     variable_pool=variable_pool,
-                    child_variable_pool=self.child_variable_pool
+                    cycle_nodes=self.cycle_nodes,
+                    cycle_edges=self.cycle_edges,
                 ).run()
             }
             return

api/app/schemas/app_schema.py

@@ -44,6 +44,8 @@ class FileInput(BaseModel):
     upload_file_id: Optional[uuid.UUID] = Field(None, description="已上传文件ID（local_file时必填）")
     url: Optional[str] = Field(None, description="远程URL（remote_url时必填）")
     file_type: Optional[str] = Field(None, description="具体文件格式（如image/jpg、audio/wav、document/docx、video/mp4）")
+    name: Optional[str] = Field(None, description="文件名")
+    size: Optional[int] = Field(None, description="文件大小（字节）")
 
     _content = None
 

api/app/services/app_chat_service.py

@@ -26,6 +26,7 @@ from app.services.model_service import ModelApiKeyService
 from app.services.multi_agent_orchestrator import MultiAgentOrchestrator
 from app.services.multimodal_service import MultimodalService
 from app.services.workflow_service import WorkflowService
+from app.models.file_metadata_model import FileMetadata
 
 logger = get_business_logger()
 
@@ -218,11 +219,29 @@ class AppChatService:
             "reasoning_content": result.get("reasoning_content")
         }
         if files:
+            local_ids = [f.upload_file_id for f in files
+                         if f.transfer_method.value == "local_file" and f.upload_file_id
+                         and (not f.name or not f.size)]
+            meta_map = {}
+            if local_ids:
+                rows = self.db.query(FileMetadata).filter(
+                    FileMetadata.id.in_(local_ids),
+                    FileMetadata.status == "completed"
+                ).all()
+                meta_map = {str(r.id): r for r in rows}
             for f in files:
-                # url = await MultimodalService(self.db).get_file_url(f)
+                name, size = f.name, f.size
+                if f.transfer_method.value == "local_file" and f.upload_file_id and (not name or not size):
+                    meta = meta_map.get(str(f.upload_file_id))
+                    if meta:
+                        name = name or meta.file_name
+                        size = size or meta.file_size
                 human_meta["files"].append({
                     "type": f.type,
-                    "url": f.url
+                    "url": f.url,
+                    "name": name,
+                    "size": size,
+                    "file_type": f.file_type,
                 })
 
         if processed_files:
@@ -509,10 +528,29 @@ class AppChatService:
             }
 
             if files:
+                local_ids = [f.upload_file_id for f in files
+                             if f.transfer_method.value == "local_file" and f.upload_file_id
+                             and (not f.name or not f.size)]
+                meta_map = {}
+                if local_ids:
+                    rows = self.db.query(FileMetadata).filter(
+                        FileMetadata.id.in_(local_ids),
+                        FileMetadata.status == "completed"
+                    ).all()
+                    meta_map = {str(r.id): r for r in rows}
                 for f in files:
+                    name, size = f.name, f.size
+                    if f.transfer_method.value == "local_file" and f.upload_file_id and (not name or not size):
+                        meta = meta_map.get(str(f.upload_file_id))
+                        if meta:
+                            name = name or meta.file_name
+                            size = size or meta.file_size
                     human_meta["files"].append({
                         "type": f.type,
-                        "url": f.url
+                        "url": f.url,
+                        "name": name,
+                        "size": size,
+                        "file_type": f.file_type,
                     })
             if processed_files:
                 human_meta["history_files"] = {

api/app/services/app_dsl_service.py

@@ -229,8 +229,11 @@ class AppDslService:
         workspace_id: uuid.UUID,
         tenant_id: uuid.UUID,
         user_id: uuid.UUID,
+        app_id: Optional[uuid.UUID] = None,
     ) -> tuple[App, list[str]]:
-        """解析 DSL，创建应用及配置，返回 (new_app, warnings)"""
+        """解析 DSL，创建或覆盖应用配置，返回 (app, warnings)。
+        app_id 不为空时：校验类型一致后覆盖配置；为空时创建新应用。
+        """
         app_meta = dsl.get("app", {})
         app_type = app_meta.get("type")
         if app_type not in (AppType.AGENT, AppType.MULTI_AGENT, AppType.WORKFLOW):
@@ -239,6 +242,9 @@ class AppDslService:
         warnings: list[str] = []
         now = datetime.datetime.now()
 
+        if app_id is not None:
+            return self._overwrite_dsl(dsl, app_id, app_type, workspace_id, tenant_id, warnings, now)
+
         new_app = App(
             id=uuid.uuid4(),
             workspace_id=workspace_id,
@@ -258,11 +264,57 @@ class AppDslService:
         self.db.add(new_app)
         self.db.flush()
 
+        self._write_config(new_app.id, app_type, dsl, workspace_id, tenant_id, warnings, now, create=True)
+
+        self.db.commit()
+        self.db.refresh(new_app)
+        return new_app, warnings
+
+    def _overwrite_dsl(
+        self,
+        dsl: dict,
+        app_id: uuid.UUID,
+        app_type: str,
+        workspace_id: uuid.UUID,
+        tenant_id: uuid.UUID,
+        warnings: list,
+        now: datetime.datetime,
+    ) -> tuple[App, list[str]]:
+        """覆盖已有应用的配置，类型不一致时抛出异常"""
+        app = self.db.query(App).filter(
+            App.id == app_id,
+            App.workspace_id == workspace_id,
+            App.is_active.is_(True)
+        ).first()
+        if not app:
+            raise ResourceNotFoundException("应用", str(app_id))
+        if app.type != app_type:
+            raise BusinessException(
+                f"YAML 类型 '{app_type}' 与应用类型 '{app.type}' 不一致，无法导入",
+                BizCode.BAD_REQUEST
+            )
+
+        self._write_config(app_id, app_type, dsl, workspace_id, tenant_id, warnings, now, create=False)
+
+        self.db.commit()
+        self.db.refresh(app)
+        return app, warnings
+
+    def _write_config(
+        self,
+        app_id: uuid.UUID,
+        app_type: str,
+        dsl: dict,
+        workspace_id: uuid.UUID,
+        tenant_id: uuid.UUID,
+        warnings: list,
+        now: datetime.datetime,
+        create: bool,
+    ) -> None:
+        """写入（新建或覆盖）应用配置"""
         if app_type == AppType.AGENT:
             cfg = dsl.get("agent_config") or {}
-            self.db.add(AgentConfig(
+            fields = dict(
-                id=uuid.uuid4(),
-                app_id=new_app.id,
                 system_prompt=cfg.get("system_prompt"),
                 model_parameters=cfg.get("model_parameters"),
                 default_model_config_id=self._resolve_model(cfg.get("default_model_config_ref"), tenant_id, warnings),
@@ -272,16 +324,21 @@ class AppDslService:
                 tools=self._resolve_tools(cfg.get("tools", []), tenant_id, warnings),
                 skills=self._resolve_skills(cfg.get("skills", {}), tenant_id, warnings),
                 features=cfg.get("features", {}),
-                is_active=True,
-                created_at=now,
                 updated_at=now,
-            ))
+            )
+            if create:
+                self.db.add(AgentConfig(id=uuid.uuid4(), app_id=app_id, is_active=True, created_at=now, **fields))
+            else:
+                existing = self.db.query(AgentConfig).filter(AgentConfig.app_id == app_id).first()
+                if existing:
+                    for k, v in fields.items():
+                        setattr(existing, k, v)
+                else:
+                    self.db.add(AgentConfig(id=uuid.uuid4(), app_id=app_id, is_active=True, created_at=now, **fields))
 
         elif app_type == AppType.MULTI_AGENT:
             cfg = dsl.get("multi_agent_config") or {}
-            self.db.add(MultiAgentConfig(
+            fields = dict(
-                id=uuid.uuid4(),
-                app_id=new_app.id,
                 orchestration_mode=cfg.get("orchestration_mode", "collaboration"),
                 master_agent_name=cfg.get("master_agent_name"),
                 model_parameters=cfg.get("model_parameters"),
@@ -291,10 +348,17 @@ class AppDslService:
                 routing_rules=self._resolve_routing_rules(cfg.get("routing_rules"), warnings),
                 execution_config=cfg.get("execution_config", {}),
                 aggregation_strategy=cfg.get("aggregation_strategy", "merge"),
-                is_active=True,
-                created_at=now,
                 updated_at=now,
-            ))
+            )
+            if create:
+                self.db.add(MultiAgentConfig(id=uuid.uuid4(), app_id=app_id, is_active=True, created_at=now, **fields))
+            else:
+                existing = self.db.query(MultiAgentConfig).filter(MultiAgentConfig.app_id == app_id).first()
+                if existing:
+                    for k, v in fields.items():
+                        setattr(existing, k, v)
+                else:
+                    self.db.add(MultiAgentConfig(id=uuid.uuid4(), app_id=app_id, is_active=True, created_at=now, **fields))
 
         elif app_type == AppType.WORKFLOW:
             adapter = MemoryBearAdapter(dsl)
@@ -306,20 +370,39 @@ class AppDslService:
             for w in result.warnings:
                 warnings.append(f"[节点警告] {w.node_name or w.node_id}: {w.detail}")
             wf = dsl.get("workflow") or {}
-            WorkflowService(self.db).create_workflow_config(
+            wf_service = WorkflowService(self.db)
-                app_id=new_app.id,
+            if create:
-                nodes=[n.model_dump() for n in result.nodes],
+                wf_service.create_workflow_config(
-                edges=[e.model_dump() for e in result.edges],
+                    app_id=app_id,
-                variables=[v.model_dump() for v in result.variables],
+                    nodes=[n.model_dump() for n in result.nodes],
-                execution_config=wf.get("execution_config", {}),
+                    edges=[e.model_dump() for e in result.edges],
-                features=wf.get("features", {}),
+                    variables=[v.model_dump() for v in result.variables],
-                triggers=wf.get("triggers", []),
+                    execution_config=wf.get("execution_config", {}),
-                validate=False,
+                    features=wf.get("features", {}),
-            )
+                    triggers=wf.get("triggers", []),
-
+                    validate=False,
-        self.db.commit()
+                )
-        self.db.refresh(new_app)
+            else:
-        return new_app, warnings
+                existing = self.db.query(WorkflowConfig).filter(WorkflowConfig.app_id == app_id).first()
+                if existing:
+                    existing.nodes = [n.model_dump() for n in result.nodes]
+                    existing.edges = [e.model_dump() for e in result.edges]
+                    existing.variables = [v.model_dump() for v in result.variables]
+                    existing.execution_config = wf.get("execution_config", {})
+                    existing.features = wf.get("features", {})
+                    existing.triggers = wf.get("triggers", [])
+                    existing.updated_at = now
+                else:
+                    wf_service.create_workflow_config(
+                        app_id=app_id,
+                        nodes=[n.model_dump() for n in result.nodes],
+                        edges=[e.model_dump() for e in result.edges],
+                        variables=[v.model_dump() for v in result.variables],
+                        execution_config=wf.get("execution_config", {}),
+                        features=wf.get("features", {}),
+                        triggers=wf.get("triggers", []),
+                        validate=False,
+                    )
 
     def _unique_app_name(self, name: str, workspace_id: uuid.UUID, app_type: AppType) -> str:
         """生成唯一应用名称，同时检查本空间自有应用和共享到本空间的应用"""

api/app/services/draft_run_service.py

@@ -1299,10 +1299,30 @@ class AgentRunService:
                 "history_files": {}
             }
             if files:
+                from app.models.file_metadata_model import FileMetadata
+                local_ids = [f.upload_file_id for f in files
+                             if f.transfer_method.value == "local_file" and f.upload_file_id
+                             and (not f.name or not f.size)]
+                meta_map = {}
+                if local_ids:
+                    rows = self.db.query(FileMetadata).filter(
+                        FileMetadata.id.in_(local_ids),
+                        FileMetadata.status == "completed"
+                    ).all()
+                    meta_map = {str(r.id): r for r in rows}
                 for f in files:
+                    name, size = f.name, f.size
+                    if f.transfer_method.value == "local_file" and f.upload_file_id and (not name or not size):
+                        meta = meta_map.get(str(f.upload_file_id))
+                        if meta:
+                            name = name or meta.file_name
+                            size = size or meta.file_size
                     human_meta["files"].append({
                         "type": f.type,
-                        "url": f.url
+                        "url": f.url,
+                        "file_type": f.file_type,
+                        "name": name,
+                        "size": size
                     })
 
             # 保存 history_files，包含 provider 和 is_omni 信息

api/app/services/workflow_service.py

@@ -957,7 +957,10 @@ class WorkflowService:
                                     for file in message["content"]:
                                         human_meta["files"].append({
                                             "type": file.get("type"),
-                                            "url": file.get("url")
+                                            "url": file.get("url"),
+                                            "file_type": file.get("origin_file_type"),
+                                            "name": file.get("name"),
+                                            "size": file.get("size")
                                         })
                             if message["role"] == "assistant":
                                 assistant_message = message["content"]