dify

dify/api/core/app/apps/common/graph_runtime_state_support.py

@@ -0,0 +1,55 @@
+"""Shared helpers for managing GraphRuntimeState across task pipelines."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.workflow.runtime import GraphRuntimeState
+
+if TYPE_CHECKING:
+    from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
+
+
+class GraphRuntimeStateSupport:
+    """
+    Mixin that centralises common GraphRuntimeState access patterns used by task pipelines.
+
+    Subclasses are expected to provide:
+      * `_base_task_pipeline` – exposing the queue manager with an optional cached runtime state.
+      * `_graph_runtime_state` attribute used as the local cache for the runtime state.
+    """
+
+    _base_task_pipeline: BasedGenerateTaskPipeline
+    _graph_runtime_state: GraphRuntimeState | None = None
+
+    def _ensure_graph_runtime_initialized(
+        self,
+        graph_runtime_state: GraphRuntimeState | None = None,
+    ) -> GraphRuntimeState:
+        """Validate and return the active graph runtime state."""
+        return self._resolve_graph_runtime_state(graph_runtime_state)
+
+    def _extract_workflow_run_id(self, graph_runtime_state: GraphRuntimeState) -> str:
+        system_variables = graph_runtime_state.variable_pool.system_variables
+        if not system_variables or not system_variables.workflow_execution_id:
+            raise ValueError("workflow_execution_id missing from runtime state")
+        return str(system_variables.workflow_execution_id)
+
+    def _resolve_graph_runtime_state(
+        self,
+        graph_runtime_state: GraphRuntimeState | None = None,
+    ) -> GraphRuntimeState:
+        """Return the cached runtime state or bootstrap it from the queue manager."""
+        if graph_runtime_state is not None:
+            self._graph_runtime_state = graph_runtime_state
+            return graph_runtime_state
+
+        if self._graph_runtime_state is None:
+            candidate = self._base_task_pipeline.queue_manager.graph_runtime_state
+            if candidate is not None:
+                self._graph_runtime_state = candidate
+
+        if self._graph_runtime_state is None:
+            raise ValueError("graph runtime state not initialized.")
+
+        return self._graph_runtime_state

dify/api/core/app/apps/common/workflow_response_converter.py

@@ -0,0 +1,677 @@
+import time
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, NewType, Union
+
+from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom, WorkflowAppGenerateEntity
+from core.app.entities.queue_entities import (
+    QueueAgentLogEvent,
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
+    QueueLoopCompletedEvent,
+    QueueLoopNextEvent,
+    QueueLoopStartEvent,
+    QueueNodeExceptionEvent,
+    QueueNodeFailedEvent,
+    QueueNodeRetryEvent,
+    QueueNodeStartedEvent,
+    QueueNodeSucceededEvent,
+)
+from core.app.entities.task_entities import (
+    AgentLogStreamResponse,
+    IterationNodeCompletedStreamResponse,
+    IterationNodeNextStreamResponse,
+    IterationNodeStartStreamResponse,
+    LoopNodeCompletedStreamResponse,
+    LoopNodeNextStreamResponse,
+    LoopNodeStartStreamResponse,
+    NodeFinishStreamResponse,
+    NodeRetryStreamResponse,
+    NodeStartStreamResponse,
+    WorkflowFinishStreamResponse,
+    WorkflowStartStreamResponse,
+)
+from core.file import FILE_MODEL_IDENTITY, File
+from core.plugin.impl.datasource import PluginDatasourceManager
+from core.tools.entities.tool_entities import ToolProviderType
+from core.tools.tool_manager import ToolManager
+from core.trigger.trigger_manager import TriggerManager
+from core.variables.segments import ArrayFileSegment, FileSegment, Segment
+from core.workflow.enums import (
+    NodeType,
+    SystemVariableKey,
+    WorkflowExecutionStatus,
+    WorkflowNodeExecutionMetadataKey,
+    WorkflowNodeExecutionStatus,
+)
+from core.workflow.runtime import GraphRuntimeState
+from core.workflow.system_variable import SystemVariable
+from core.workflow.workflow_entry import WorkflowEntry
+from core.workflow.workflow_type_encoder import WorkflowRuntimeTypeConverter
+from libs.datetime_utils import naive_utc_now
+from models import Account, EndUser
+from services.variable_truncator import BaseTruncator, DummyVariableTruncator, VariableTruncator
+
+NodeExecutionId = NewType("NodeExecutionId", str)
+
+
+@dataclass(slots=True)
+class _NodeSnapshot:
+    """In-memory cache for node metadata between start and completion events."""
+
+    title: str
+    index: int
+    start_at: datetime
+    iteration_id: str = ""
+    """Empty string means the node is not executing inside an iteration."""
+    loop_id: str = ""
+    """Empty string means the node is not executing inside a loop."""
+
+
+class WorkflowResponseConverter:
+    _truncator: BaseTruncator
+
+    def __init__(
+        self,
+        *,
+        application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity],
+        user: Union[Account, EndUser],
+        system_variables: SystemVariable,
+    ):
+        self._application_generate_entity = application_generate_entity
+        self._user = user
+        self._system_variables = system_variables
+        self._workflow_inputs = self._prepare_workflow_inputs()
+
+        # Disable truncation for SERVICE_API calls to keep backward compatibility.
+        if application_generate_entity.invoke_from == InvokeFrom.SERVICE_API:
+            self._truncator = DummyVariableTruncator()
+        else:
+            self._truncator = VariableTruncator.default()
+
+        self._node_snapshots: dict[NodeExecutionId, _NodeSnapshot] = {}
+        self._workflow_execution_id: str | None = None
+        self._workflow_started_at: datetime | None = None
+
+    # ------------------------------------------------------------------
+    # Workflow lifecycle helpers
+    # ------------------------------------------------------------------
+    def _prepare_workflow_inputs(self) -> Mapping[str, Any]:
+        inputs = dict(self._application_generate_entity.inputs)
+        for field_name, value in self._system_variables.to_dict().items():
+            # TODO(@future-refactor): store system variables separately from user inputs so we don't
+            # need to flatten `sys.*` entries into the input payload just for rerun/export tooling.
+            if field_name == SystemVariableKey.CONVERSATION_ID:
+                # Conversation IDs are session-scoped; omitting them keeps workflow inputs
+                # reusable without pinning new runs to a prior conversation.
+                continue
+            inputs[f"sys.{field_name}"] = value
+        handled = WorkflowEntry.handle_special_values(inputs)
+        return dict(handled or {})
+
+    def _ensure_workflow_run_id(self, workflow_run_id: str | None = None) -> str:
+        """Return the memoized workflow run id, optionally seeding it during start events."""
+        if workflow_run_id is not None:
+            self._workflow_execution_id = workflow_run_id
+        if not self._workflow_execution_id:
+            raise ValueError("workflow_run_id missing before streaming workflow events")
+        return self._workflow_execution_id
+
+    # ------------------------------------------------------------------
+    # Node snapshot helpers
+    # ------------------------------------------------------------------
+    def _store_snapshot(self, event: QueueNodeStartedEvent) -> _NodeSnapshot:
+        snapshot = _NodeSnapshot(
+            title=event.node_title,
+            index=event.node_run_index,
+            start_at=event.start_at,
+            iteration_id=event.in_iteration_id or "",
+            loop_id=event.in_loop_id or "",
+        )
+        node_execution_id = NodeExecutionId(event.node_execution_id)
+        self._node_snapshots[node_execution_id] = snapshot
+        return snapshot
+
+    def _get_snapshot(self, node_execution_id: str) -> _NodeSnapshot | None:
+        return self._node_snapshots.get(NodeExecutionId(node_execution_id))
+
+    def _pop_snapshot(self, node_execution_id: str) -> _NodeSnapshot | None:
+        return self._node_snapshots.pop(NodeExecutionId(node_execution_id), None)
+
+    @staticmethod
+    def _merge_metadata(
+        base_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] | None,
+        snapshot: _NodeSnapshot | None,
+    ) -> Mapping[WorkflowNodeExecutionMetadataKey, Any] | None:
+        if not base_metadata and not snapshot:
+            return base_metadata
+
+        merged: dict[WorkflowNodeExecutionMetadataKey, Any] = {}
+        if base_metadata:
+            merged.update(base_metadata)
+
+        if snapshot:
+            if snapshot.iteration_id:
+                merged[WorkflowNodeExecutionMetadataKey.ITERATION_ID] = snapshot.iteration_id
+            if snapshot.loop_id:
+                merged[WorkflowNodeExecutionMetadataKey.LOOP_ID] = snapshot.loop_id
+
+        return merged or None
+
+    def _truncate_mapping(
+        self,
+        mapping: Mapping[str, Any] | None,
+    ) -> tuple[Mapping[str, Any] | None, bool]:
+        if mapping is None:
+            return None, False
+        if not mapping:
+            return {}, False
+
+        normalized = WorkflowEntry.handle_special_values(dict(mapping))
+        if normalized is None:
+            return None, False
+
+        truncated, is_truncated = self._truncator.truncate_variable_mapping(dict(normalized))
+        return truncated, is_truncated
+
+    @staticmethod
+    def _encode_outputs(outputs: Mapping[str, Any] | None) -> Mapping[str, Any] | None:
+        if outputs is None:
+            return None
+        converter = WorkflowRuntimeTypeConverter()
+        return converter.to_json_encodable(outputs)
+
+    def workflow_start_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_run_id: str,
+        workflow_id: str,
+    ) -> WorkflowStartStreamResponse:
+        run_id = self._ensure_workflow_run_id(workflow_run_id)
+        started_at = naive_utc_now()
+        self._workflow_started_at = started_at
+
+        return WorkflowStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=run_id,
+            data=WorkflowStartStreamResponse.Data(
+                id=run_id,
+                workflow_id=workflow_id,
+                inputs=self._workflow_inputs,
+                created_at=int(started_at.timestamp()),
+            ),
+        )
+
+    def workflow_finish_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_id: str,
+        status: WorkflowExecutionStatus,
+        graph_runtime_state: GraphRuntimeState,
+        error: str | None = None,
+        exceptions_count: int = 0,
+    ) -> WorkflowFinishStreamResponse:
+        run_id = self._ensure_workflow_run_id()
+        started_at = self._workflow_started_at
+        if started_at is None:
+            raise ValueError(
+                "workflow_finish_to_stream_response called before workflow_start_to_stream_response",
+            )
+
+        finished_at = naive_utc_now()
+        elapsed_time = (finished_at - started_at).total_seconds()
+
+        outputs_mapping = graph_runtime_state.outputs or {}
+        encoded_outputs = WorkflowRuntimeTypeConverter().to_json_encodable(outputs_mapping)
+
+        created_by: Mapping[str, object] | None
+        user = self._user
+        if isinstance(user, Account):
+            created_by = {
+                "id": user.id,
+                "name": user.name,
+                "email": user.email,
+            }
+        else:
+            created_by = {
+                "id": user.id,
+                "user": user.session_id,
+            }
+
+        return WorkflowFinishStreamResponse(
+            task_id=task_id,
+            workflow_run_id=run_id,
+            data=WorkflowFinishStreamResponse.Data(
+                id=run_id,
+                workflow_id=workflow_id,
+                status=status.value,
+                outputs=encoded_outputs,
+                error=error,
+                elapsed_time=elapsed_time,
+                total_tokens=graph_runtime_state.total_tokens,
+                total_steps=graph_runtime_state.node_run_steps,
+                created_by=created_by,
+                created_at=int(started_at.timestamp()),
+                finished_at=int(finished_at.timestamp()),
+                files=self.fetch_files_from_node_outputs(outputs_mapping),
+                exceptions_count=exceptions_count,
+            ),
+        )
+
+    def workflow_node_start_to_stream_response(
+        self,
+        *,
+        event: QueueNodeStartedEvent,
+        task_id: str,
+    ) -> NodeStartStreamResponse | None:
+        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+            return None
+        run_id = self._ensure_workflow_run_id()
+        snapshot = self._store_snapshot(event)
+
+        response = NodeStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=run_id,
+            data=NodeStartStreamResponse.Data(
+                id=event.node_execution_id,
+                node_id=event.node_id,
+                node_type=event.node_type,
+                title=snapshot.title,
+                index=snapshot.index,
+                created_at=int(snapshot.start_at.timestamp()),
+                iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
+                agent_strategy=event.agent_strategy,
+            ),
+        )
+
+        if event.node_type == NodeType.TOOL:
+            response.data.extras["icon"] = ToolManager.get_tool_icon(
+                tenant_id=self._application_generate_entity.app_config.tenant_id,
+                provider_type=ToolProviderType(event.provider_type),
+                provider_id=event.provider_id,
+            )
+        elif event.node_type == NodeType.DATASOURCE:
+            manager = PluginDatasourceManager()
+            provider_entity = manager.fetch_datasource_provider(
+                self._application_generate_entity.app_config.tenant_id,
+                event.provider_id,
+            )
+            response.data.extras["icon"] = provider_entity.declaration.identity.generate_datasource_icon_url(
+                self._application_generate_entity.app_config.tenant_id
+            )
+        elif event.node_type == NodeType.TRIGGER_PLUGIN:
+            response.data.extras["icon"] = TriggerManager.get_trigger_plugin_icon(
+                self._application_generate_entity.app_config.tenant_id,
+                event.provider_id,
+            )
+
+        return response
+
+    def workflow_node_finish_to_stream_response(
+        self,
+        *,
+        event: QueueNodeSucceededEvent | QueueNodeFailedEvent | QueueNodeExceptionEvent,
+        task_id: str,
+    ) -> NodeFinishStreamResponse | None:
+        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+            return None
+        run_id = self._ensure_workflow_run_id()
+        snapshot = self._pop_snapshot(event.node_execution_id)
+
+        start_at = snapshot.start_at if snapshot else event.start_at
+        finished_at = naive_utc_now()
+        elapsed_time = (finished_at - start_at).total_seconds()
+
+        inputs, inputs_truncated = self._truncate_mapping(event.inputs)
+        process_data, process_data_truncated = self._truncate_mapping(event.process_data)
+        encoded_outputs = self._encode_outputs(event.outputs)
+        outputs, outputs_truncated = self._truncate_mapping(encoded_outputs)
+        metadata = self._merge_metadata(event.execution_metadata, snapshot)
+
+        if isinstance(event, QueueNodeSucceededEvent):
+            status = WorkflowNodeExecutionStatus.SUCCEEDED.value
+            error_message = event.error
+        elif isinstance(event, QueueNodeFailedEvent):
+            status = WorkflowNodeExecutionStatus.FAILED.value
+            error_message = event.error
+        else:
+            status = WorkflowNodeExecutionStatus.EXCEPTION.value
+            error_message = event.error
+
+        return NodeFinishStreamResponse(
+            task_id=task_id,
+            workflow_run_id=run_id,
+            data=NodeFinishStreamResponse.Data(
+                id=event.node_execution_id,
+                node_id=event.node_id,
+                node_type=event.node_type,
+                index=snapshot.index if snapshot else 0,
+                title=snapshot.title if snapshot else "",
+                inputs=inputs,
+                inputs_truncated=inputs_truncated,
+                process_data=process_data,
+                process_data_truncated=process_data_truncated,
+                outputs=outputs,
+                outputs_truncated=outputs_truncated,
+                status=status,
+                error=error_message,
+                elapsed_time=elapsed_time,
+                execution_metadata=metadata,
+                created_at=int(start_at.timestamp()),
+                finished_at=int(finished_at.timestamp()),
+                files=self.fetch_files_from_node_outputs(event.outputs or {}),
+                iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
+            ),
+        )
+
+    def workflow_node_retry_to_stream_response(
+        self,
+        *,
+        event: QueueNodeRetryEvent,
+        task_id: str,
+    ) -> NodeRetryStreamResponse | None:
+        if event.node_type in {NodeType.ITERATION, NodeType.LOOP}:
+            return None
+        run_id = self._ensure_workflow_run_id()
+
+        snapshot = self._get_snapshot(event.node_execution_id)
+        if snapshot is None:
+            raise AssertionError("node retry event arrived without a stored snapshot")
+        finished_at = naive_utc_now()
+        elapsed_time = (finished_at - event.start_at).total_seconds()
+
+        inputs, inputs_truncated = self._truncate_mapping(event.inputs)
+        process_data, process_data_truncated = self._truncate_mapping(event.process_data)
+        encoded_outputs = self._encode_outputs(event.outputs)
+        outputs, outputs_truncated = self._truncate_mapping(encoded_outputs)
+        metadata = self._merge_metadata(event.execution_metadata, snapshot)
+
+        return NodeRetryStreamResponse(
+            task_id=task_id,
+            workflow_run_id=run_id,
+            data=NodeRetryStreamResponse.Data(
+                id=event.node_execution_id,
+                node_id=event.node_id,
+                node_type=event.node_type,
+                index=snapshot.index,
+                title=snapshot.title,
+                inputs=inputs,
+                inputs_truncated=inputs_truncated,
+                process_data=process_data,
+                process_data_truncated=process_data_truncated,
+                outputs=outputs,
+                outputs_truncated=outputs_truncated,
+                status=WorkflowNodeExecutionStatus.RETRY.value,
+                error=event.error,
+                elapsed_time=elapsed_time,
+                execution_metadata=metadata,
+                created_at=int(snapshot.start_at.timestamp()),
+                finished_at=int(finished_at.timestamp()),
+                files=self.fetch_files_from_node_outputs(event.outputs or {}),
+                iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
+                retry_index=event.retry_index,
+            ),
+        )
+
+    def workflow_iteration_start_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_execution_id: str,
+        event: QueueIterationStartEvent,
+    ) -> IterationNodeStartStreamResponse:
+        new_inputs, truncated = self._truncator.truncate_variable_mapping(event.inputs or {})
+        return IterationNodeStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_execution_id,
+            data=IterationNodeStartStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_title,
+                created_at=int(time.time()),
+                extras={},
+                inputs=new_inputs,
+                inputs_truncated=truncated,
+                metadata=event.metadata or {},
+            ),
+        )
+
+    def workflow_iteration_next_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_execution_id: str,
+        event: QueueIterationNextEvent,
+    ) -> IterationNodeNextStreamResponse:
+        return IterationNodeNextStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_execution_id,
+            data=IterationNodeNextStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_title,
+                index=event.index,
+                created_at=int(time.time()),
+                extras={},
+            ),
+        )
+
+    def workflow_iteration_completed_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_execution_id: str,
+        event: QueueIterationCompletedEvent,
+    ) -> IterationNodeCompletedStreamResponse:
+        json_converter = WorkflowRuntimeTypeConverter()
+
+        new_inputs, inputs_truncated = self._truncator.truncate_variable_mapping(event.inputs or {})
+        new_outputs, outputs_truncated = self._truncator.truncate_variable_mapping(
+            json_converter.to_json_encodable(event.outputs) or {}
+        )
+        return IterationNodeCompletedStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_execution_id,
+            data=IterationNodeCompletedStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_title,
+                outputs=new_outputs,
+                outputs_truncated=outputs_truncated,
+                created_at=int(time.time()),
+                extras={},
+                inputs=new_inputs,
+                inputs_truncated=inputs_truncated,
+                status=WorkflowNodeExecutionStatus.SUCCEEDED
+                if event.error is None
+                else WorkflowNodeExecutionStatus.FAILED,
+                error=None,
+                elapsed_time=(naive_utc_now() - event.start_at).total_seconds(),
+                total_tokens=(lambda x: x if isinstance(x, int) else 0)(event.metadata.get("total_tokens", 0)),
+                execution_metadata=event.metadata,
+                finished_at=int(time.time()),
+                steps=event.steps,
+            ),
+        )
+
+    def workflow_loop_start_to_stream_response(
+        self, *, task_id: str, workflow_execution_id: str, event: QueueLoopStartEvent
+    ) -> LoopNodeStartStreamResponse:
+        new_inputs, truncated = self._truncator.truncate_variable_mapping(event.inputs or {})
+        return LoopNodeStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_execution_id,
+            data=LoopNodeStartStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_title,
+                created_at=int(time.time()),
+                extras={},
+                inputs=new_inputs,
+                inputs_truncated=truncated,
+                metadata=event.metadata or {},
+            ),
+        )
+
+    def workflow_loop_next_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_execution_id: str,
+        event: QueueLoopNextEvent,
+    ) -> LoopNodeNextStreamResponse:
+        return LoopNodeNextStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_execution_id,
+            data=LoopNodeNextStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_title,
+                index=event.index,
+                # The `pre_loop_output` field is not utilized by the frontend.
+                # Previously, it was assigned the value of `event.output`.
+                pre_loop_output={},
+                created_at=int(time.time()),
+                extras={},
+            ),
+        )
+
+    def workflow_loop_completed_to_stream_response(
+        self,
+        *,
+        task_id: str,
+        workflow_execution_id: str,
+        event: QueueLoopCompletedEvent,
+    ) -> LoopNodeCompletedStreamResponse:
+        json_converter = WorkflowRuntimeTypeConverter()
+        new_inputs, inputs_truncated = self._truncator.truncate_variable_mapping(event.inputs or {})
+        new_outputs, outputs_truncated = self._truncator.truncate_variable_mapping(
+            json_converter.to_json_encodable(event.outputs) or {}
+        )
+        return LoopNodeCompletedStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_execution_id,
+            data=LoopNodeCompletedStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_title,
+                outputs=new_outputs,
+                outputs_truncated=outputs_truncated,
+                created_at=int(time.time()),
+                extras={},
+                inputs=new_inputs,
+                inputs_truncated=inputs_truncated,
+                status=WorkflowNodeExecutionStatus.SUCCEEDED
+                if event.error is None
+                else WorkflowNodeExecutionStatus.FAILED,
+                error=None,
+                elapsed_time=(naive_utc_now() - event.start_at).total_seconds(),
+                total_tokens=(lambda x: x if isinstance(x, int) else 0)(event.metadata.get("total_tokens", 0)),
+                execution_metadata=event.metadata,
+                finished_at=int(time.time()),
+                steps=event.steps,
+            ),
+        )
+
+    def fetch_files_from_node_outputs(self, outputs_dict: Mapping[str, Any] | None) -> Sequence[Mapping[str, Any]]:
+        """
+        Fetch files from node outputs
+        :param outputs_dict: node outputs dict
+        :return:
+        """
+        if not outputs_dict:
+            return []
+
+        files = [self._fetch_files_from_variable_value(output_value) for output_value in outputs_dict.values()]
+        # Remove None
+        files = [file for file in files if file]
+        # Flatten list
+        # Flatten the list of sequences into a single list of mappings
+        flattened_files = [file for sublist in files if sublist for file in sublist]
+
+        # Convert to tuple to match Sequence type
+        return tuple(flattened_files)
+
+    @classmethod
+    def _fetch_files_from_variable_value(cls, value: Union[dict, list, Segment]) -> Sequence[Mapping[str, Any]]:
+        """
+        Fetch files from variable value
+        :param value: variable value
+        :return:
+        """
+        if not value:
+            return []
+
+        files: list[Mapping[str, Any]] = []
+        if isinstance(value, FileSegment):
+            files.append(value.value.to_dict())
+        elif isinstance(value, ArrayFileSegment):
+            files.extend([i.to_dict() for i in value.value])
+        elif isinstance(value, File):
+            files.append(value.to_dict())
+        elif isinstance(value, list):
+            for item in value:
+                file = cls._get_file_var_from_value(item)
+                if file:
+                    files.append(file)
+        elif isinstance(
+            value,
+            dict,
+        ):
+            file = cls._get_file_var_from_value(value)
+            if file:
+                files.append(file)
+
+        return files
+
+    @classmethod
+    def _get_file_var_from_value(cls, value: Union[dict, list]) -> Mapping[str, Any] | None:
+        """
+        Get file var from value
+        :param value: variable value
+        :return:
+        """
+        if not value:
+            return None
+
+        if isinstance(value, dict) and value.get("dify_model_identity") == FILE_MODEL_IDENTITY:
+            return value
+        elif isinstance(value, File):
+            return value.to_dict()
+
+        return None
+
+    def handle_agent_log(self, task_id: str, event: QueueAgentLogEvent) -> AgentLogStreamResponse:
+        """
+        Handle agent log
+        :param task_id: task id
+        :param event: agent log event
+        :return:
+        """
+        return AgentLogStreamResponse(
+            task_id=task_id,
+            data=AgentLogStreamResponse.Data(
+                node_execution_id=event.node_execution_id,
+                id=event.id,
+                parent_id=event.parent_id,
+                label=event.label,
+                error=event.error,
+                status=event.status,
+                data=event.data,
+                metadata=event.metadata,
+                node_id=event.node_id,
+            ),
+        )