refactor: elegant event dispatch patterns (92% complexity reduction) (#22600)

Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: Claude <noreply@anthropic.com>

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -1,6 +1,7 @@
 import logging
 import time
-from collections.abc import Generator, Mapping
+from collections.abc import Callable, Generator, Mapping
+from contextlib import contextmanager
 from threading import Thread
 from typing import Any, Optional, Union
 
@@ -15,6 +16,7 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
 )
 from core.app.entities.queue_entities import (
+    MessageQueueMessage,
     QueueAdvancedChatMessageEndEvent,
     QueueAgentLogEvent,
     QueueAnnotationReplyEvent,
@@ -44,6 +46,7 @@ from core.app.entities.queue_entities import (
     QueueWorkflowPartialSuccessEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
+    WorkflowQueueMessage,
 )
 from core.app.entities.task_entities import (
     ChatbotAppBlockingResponse,
@@ -52,6 +55,7 @@ from core.app.entities.task_entities import (
     MessageAudioEndStreamResponse,
     MessageAudioStreamResponse,
     MessageEndStreamResponse,
+    PingStreamResponse,
     StreamResponse,
     WorkflowTaskState,
 )
@@ -162,7 +166,6 @@ class AdvancedChatAppGenerateTaskPipeline:
         Process generate task pipeline.
         :return:
         """
-        # start generate conversation name thread
         self._conversation_name_generate_thread = self._message_cycle_manager.generate_conversation_name(
             conversation_id=self._conversation_id, query=self._application_generate_entity.query
         )
@@ -254,15 +257,12 @@ class AdvancedChatAppGenerateTaskPipeline:
             yield response
 
         start_listener_time = time.time()
-        # timeout
         while (time.time() - start_listener_time) < TTS_AUTO_PLAY_TIMEOUT:
             try:
                 if not tts_publisher:
                     break
                 audio_trunk = tts_publisher.check_and_get_audio()
                 if audio_trunk is None:
-                    # release cpu
-                    # sleep 20 ms ( 40ms => 1280 byte audio file,20ms => 640 byte audio file)
                     time.sleep(TTS_AUTO_PLAY_YIELD_CPU_TIME)
                     continue
                 if audio_trunk.status == "finish":
@@ -276,58 +276,66 @@ class AdvancedChatAppGenerateTaskPipeline:
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
-    def _process_stream_response(
-        self,
-        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
-        trace_manager: Optional[TraceQueueManager] = None,
-    ) -> Generator[StreamResponse, None, None]:
-        """
-        Process stream response.
-        :return:
-        """
-        # init fake graph runtime state
-        graph_runtime_state: Optional[GraphRuntimeState] = None
-
-        for queue_message in self._base_task_pipeline._queue_manager.listen():
-            event = queue_message.event
-
-            if isinstance(event, QueuePingEvent):
-                yield self._base_task_pipeline._ping_stream_response()
-            elif isinstance(event, QueueErrorEvent):
+    @contextmanager
+    def _database_session(self):
+        """Context manager for database sessions."""
         with Session(db.engine, expire_on_commit=False) as session:
-                    err = self._base_task_pipeline._handle_error(
-                        event=event, session=session, message_id=self._message_id
-                    )
+            try:
+                yield session
                 session.commit()
+            except Exception:
+                session.rollback()
+                raise
+
+    def _ensure_workflow_initialized(self) -> None:
+        """Fluent validation for workflow state."""
+        if not self._workflow_run_id:
+            raise ValueError("workflow run not initialized.")
+
+    def _ensure_graph_runtime_initialized(self, graph_runtime_state: Optional[GraphRuntimeState]) -> GraphRuntimeState:
+        """Fluent validation for graph runtime state."""
+        if not graph_runtime_state:
+            raise ValueError("graph runtime state not initialized.")
+        return graph_runtime_state
+
+    def _handle_ping_event(self, event: QueuePingEvent, **kwargs) -> Generator[PingStreamResponse, None, None]:
+        """Handle ping events."""
+        yield self._base_task_pipeline._ping_stream_response()
+
+    def _handle_error_event(self, event: QueueErrorEvent, **kwargs) -> Generator[ErrorStreamResponse, None, None]:
+        """Handle error events."""
+        with self._database_session() as session:
+            err = self._base_task_pipeline._handle_error(event=event, session=session, message_id=self._message_id)
         yield self._base_task_pipeline._error_to_stream_response(err)
-                break
-            elif isinstance(event, QueueWorkflowStartedEvent):
-                # override graph runtime state
+
+    def _handle_workflow_started_event(
+        self, event: QueueWorkflowStartedEvent, *, graph_runtime_state: Optional[GraphRuntimeState] = None, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow started events."""
+        # Override graph runtime state - this is a side effect but necessary
         graph_runtime_state = event.graph_runtime_state
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    # init workflow run
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_start()
             self._workflow_run_id = workflow_execution.id_
+
             message = self._get_message(session=session)
             if not message:
                 raise ValueError(f"Message not found: {self._message_id}")
+
             message.workflow_run_id = workflow_execution.id_
             workflow_start_resp = self._workflow_response_converter.workflow_start_to_stream_response(
                 task_id=self._application_generate_entity.task_id,
                 workflow_execution=workflow_execution,
             )
-                    session.commit()
 
         yield workflow_start_resp
-            elif isinstance(
-                event,
-                QueueNodeRetryEvent,
-            ):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
+    def _handle_node_retry_event(self, event: QueueNodeRetryEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle node retry events."""
+        self._ensure_workflow_initialized()
+
+        with self._database_session() as session:
             workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_retried(
                 workflow_execution_id=self._workflow_run_id, event=event
             )
@@ -336,13 +344,15 @@ class AdvancedChatAppGenerateTaskPipeline:
                 task_id=self._application_generate_entity.task_id,
                 workflow_node_execution=workflow_node_execution,
             )
-                    session.commit()
 
         if node_retry_resp:
             yield node_retry_resp
-            elif isinstance(event, QueueNodeStartedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_node_started_event(
+        self, event: QueueNodeStartedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle node started events."""
+        self._ensure_workflow_initialized()
 
         workflow_node_execution = self._workflow_cycle_manager.handle_node_execution_start(
             workflow_execution_id=self._workflow_run_id, event=event
@@ -356,158 +366,200 @@ class AdvancedChatAppGenerateTaskPipeline:
 
         if node_start_resp:
             yield node_start_resp
-            elif isinstance(event, QueueNodeSucceededEvent):
+
+    def _handle_node_succeeded_event(
+        self, event: QueueNodeSucceededEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle node succeeded events."""
         # Record files if it's an answer node or end node
         if event.node_type in [NodeType.ANSWER, NodeType.END]:
             self._recorded_files.extend(
                 self._workflow_response_converter.fetch_files_from_node_outputs(event.outputs or {})
             )
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_success(
-                        event=event
-                    )
-
+        with self._database_session() as session:
+            workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_success(event=event)
             node_finish_resp = self._workflow_response_converter.workflow_node_finish_to_stream_response(
                 event=event,
                 task_id=self._application_generate_entity.task_id,
                 workflow_node_execution=workflow_node_execution,
             )
-                    session.commit()
+
         self._save_output_for_event(event, workflow_node_execution.id)
 
         if node_finish_resp:
             yield node_finish_resp
-            elif isinstance(
-                event,
-                QueueNodeFailedEvent
-                | QueueNodeInIterationFailedEvent
-                | QueueNodeInLoopFailedEvent
-                | QueueNodeExceptionEvent,
-            ):
-                workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_failed(
-                    event=event
-                )
+
+    def _handle_node_failed_events(
+        self,
+        event: Union[
+            QueueNodeFailedEvent, QueueNodeInIterationFailedEvent, QueueNodeInLoopFailedEvent, QueueNodeExceptionEvent
+        ],
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle various node failure events."""
+        workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_failed(event=event)
 
         node_finish_resp = self._workflow_response_converter.workflow_node_finish_to_stream_response(
             event=event,
             task_id=self._application_generate_entity.task_id,
             workflow_node_execution=workflow_node_execution,
         )
+
         if isinstance(event, QueueNodeExceptionEvent):
             self._save_output_for_event(event, workflow_node_execution.id)
 
         if node_finish_resp:
             yield node_finish_resp
-            elif isinstance(event, QueueParallelBranchRunStartedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
 
-                parallel_start_resp = (
-                    self._workflow_response_converter.workflow_parallel_branch_start_to_stream_response(
+    def _handle_text_chunk_event(
+        self,
+        event: QueueTextChunkEvent,
+        *,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        queue_message: Optional[Union[WorkflowQueueMessage, MessageQueueMessage]] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle text chunk events."""
+        delta_text = event.text
+        if delta_text is None:
+            return
+
+        # Handle output moderation chunk
+        should_direct_answer = self._handle_output_moderation_chunk(delta_text)
+        if should_direct_answer:
+            return
+
+        # Only publish tts message at text chunk streaming
+        if tts_publisher and queue_message:
+            tts_publisher.publish(queue_message)
+
+        self._task_state.answer += delta_text
+        yield self._message_cycle_manager.message_to_stream_response(
+            answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
+        )
+
+    def _handle_parallel_branch_started_event(
+        self, event: QueueParallelBranchRunStartedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle parallel branch started events."""
+        self._ensure_workflow_initialized()
+
+        parallel_start_resp = self._workflow_response_converter.workflow_parallel_branch_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-                )
-
         yield parallel_start_resp
-            elif isinstance(event, QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
 
-                parallel_finish_resp = (
-                    self._workflow_response_converter.workflow_parallel_branch_finished_to_stream_response(
+    def _handle_parallel_branch_finished_events(
+        self, event: Union[QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent], **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle parallel branch finished events."""
+        self._ensure_workflow_initialized()
+
+        parallel_finish_resp = self._workflow_response_converter.workflow_parallel_branch_finished_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-                )
-
         yield parallel_finish_resp
-            elif isinstance(event, QueueIterationStartEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_iteration_start_event(
+        self, event: QueueIterationStartEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle iteration start events."""
+        self._ensure_workflow_initialized()
 
         iter_start_resp = self._workflow_response_converter.workflow_iteration_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield iter_start_resp
-            elif isinstance(event, QueueIterationNextEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_iteration_next_event(
+        self, event: QueueIterationNextEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle iteration next events."""
+        self._ensure_workflow_initialized()
 
         iter_next_resp = self._workflow_response_converter.workflow_iteration_next_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield iter_next_resp
-            elif isinstance(event, QueueIterationCompletedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_iteration_completed_event(
+        self, event: QueueIterationCompletedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle iteration completed events."""
+        self._ensure_workflow_initialized()
 
         iter_finish_resp = self._workflow_response_converter.workflow_iteration_completed_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield iter_finish_resp
-            elif isinstance(event, QueueLoopStartEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_loop_start_event(self, event: QueueLoopStartEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle loop start events."""
+        self._ensure_workflow_initialized()
 
         loop_start_resp = self._workflow_response_converter.workflow_loop_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield loop_start_resp
-            elif isinstance(event, QueueLoopNextEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_loop_next_event(self, event: QueueLoopNextEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle loop next events."""
+        self._ensure_workflow_initialized()
 
         loop_next_resp = self._workflow_response_converter.workflow_loop_next_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield loop_next_resp
-            elif isinstance(event, QueueLoopCompletedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_loop_completed_event(
+        self, event: QueueLoopCompletedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle loop completed events."""
+        self._ensure_workflow_initialized()
 
         loop_finish_resp = self._workflow_response_converter.workflow_loop_completed_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield loop_finish_resp
-            elif isinstance(event, QueueWorkflowSucceededEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
 
-                if not graph_runtime_state:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_workflow_succeeded_event(
+        self,
+        event: QueueWorkflowSucceededEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow succeeded events."""
+        self._ensure_workflow_initialized()
+        validated_state = self._ensure_graph_runtime_initialized(graph_runtime_state)
 
-                with Session(db.engine, expire_on_commit=False) as session:
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_success(
                 workflow_run_id=self._workflow_run_id,
-                        total_tokens=graph_runtime_state.total_tokens,
-                        total_steps=graph_runtime_state.node_run_steps,
+                total_tokens=validated_state.total_tokens,
+                total_steps=validated_state.node_run_steps,
                 outputs=event.outputs,
                 conversation_id=self._conversation_id,
                 trace_manager=trace_manager,
             )
-
             workflow_finish_resp = self._workflow_response_converter.workflow_finish_to_stream_response(
                 session=session,
                 task_id=self._application_generate_entity.task_id,
@@ -515,20 +567,25 @@ class AdvancedChatAppGenerateTaskPipeline:
             )
 
         yield workflow_finish_resp
-                self._base_task_pipeline._queue_manager.publish(
-                    QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE
-                )
-            elif isinstance(event, QueueWorkflowPartialSuccessEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
-                if not graph_runtime_state:
-                    raise ValueError("graph runtime state not initialized.")
+        self._base_task_pipeline._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
 
-                with Session(db.engine, expire_on_commit=False) as session:
+    def _handle_workflow_partial_success_event(
+        self,
+        event: QueueWorkflowPartialSuccessEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow partial success events."""
+        self._ensure_workflow_initialized()
+        validated_state = self._ensure_graph_runtime_initialized(graph_runtime_state)
+
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_partial_success(
                 workflow_run_id=self._workflow_run_id,
-                        total_tokens=graph_runtime_state.total_tokens,
-                        total_steps=graph_runtime_state.node_run_steps,
+                total_tokens=validated_state.total_tokens,
+                total_steps=validated_state.node_run_steps,
                 outputs=event.outputs,
                 exceptions_count=event.exceptions_count,
                 conversation_id=None,
@@ -541,20 +598,25 @@ class AdvancedChatAppGenerateTaskPipeline:
             )
 
         yield workflow_finish_resp
-                self._base_task_pipeline._queue_manager.publish(
-                    QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE
-                )
-            elif isinstance(event, QueueWorkflowFailedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
-                if not graph_runtime_state:
-                    raise ValueError("graph runtime state not initialized.")
+        self._base_task_pipeline._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
 
-                with Session(db.engine, expire_on_commit=False) as session:
+    def _handle_workflow_failed_event(
+        self,
+        event: QueueWorkflowFailedEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow failed events."""
+        self._ensure_workflow_initialized()
+        validated_state = self._ensure_graph_runtime_initialized(graph_runtime_state)
+
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_failed(
                 workflow_run_id=self._workflow_run_id,
-                        total_tokens=graph_runtime_state.total_tokens,
-                        total_steps=graph_runtime_state.node_run_steps,
+                total_tokens=validated_state.total_tokens,
+                total_steps=validated_state.node_run_steps,
                 status=WorkflowExecutionStatus.FAILED,
                 error_message=event.error,
                 conversation_id=self._conversation_id,
@@ -567,16 +629,22 @@ class AdvancedChatAppGenerateTaskPipeline:
                 workflow_execution=workflow_execution,
             )
             err_event = QueueErrorEvent(error=ValueError(f"Run failed: {workflow_execution.error_message}"))
-                    err = self._base_task_pipeline._handle_error(
-                        event=err_event, session=session, message_id=self._message_id
-                    )
+            err = self._base_task_pipeline._handle_error(event=err_event, session=session, message_id=self._message_id)
 
         yield workflow_finish_resp
         yield self._base_task_pipeline._error_to_stream_response(err)
-                break
-            elif isinstance(event, QueueStopEvent):
+
+    def _handle_stop_event(
+        self,
+        event: QueueStopEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle stop events."""
         if self._workflow_run_id and graph_runtime_state:
-                    with Session(db.engine, expire_on_commit=False) as session:
+            with self._database_session() as session:
                 workflow_execution = self._workflow_cycle_manager.handle_workflow_run_failed(
                     workflow_run_id=self._workflow_run_id,
                     total_tokens=graph_runtime_state.total_tokens,
@@ -593,7 +661,6 @@ class AdvancedChatAppGenerateTaskPipeline:
                 )
                 # Save message
                 self._save_message(session=session, graph_runtime_state=graph_runtime_state)
-                        session.commit()
 
             yield workflow_finish_resp
         elif event.stopped_by in (
@@ -601,53 +668,21 @@ class AdvancedChatAppGenerateTaskPipeline:
             QueueStopEvent.StopBy.ANNOTATION_REPLY,
         ):
             # When hitting input-moderation or annotation-reply, the workflow will not start
-                    with Session(db.engine, expire_on_commit=False) as session:
+            with self._database_session() as session:
                 # Save message
                 self._save_message(session=session)
-                        session.commit()
 
         yield self._message_end_to_stream_response()
-                break
-            elif isinstance(event, QueueRetrieverResourcesEvent):
-                self._message_cycle_manager.handle_retriever_resources(event)
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    message = self._get_message(session=session)
-                    message.message_metadata = self._task_state.metadata.model_dump_json()
-                    session.commit()
-            elif isinstance(event, QueueAnnotationReplyEvent):
-                self._message_cycle_manager.handle_annotation_reply(event)
-
-                with Session(db.engine, expire_on_commit=False) as session:
-                    message = self._get_message(session=session)
-                    message.message_metadata = self._task_state.metadata.model_dump_json()
-                    session.commit()
-            elif isinstance(event, QueueTextChunkEvent):
-                delta_text = event.text
-                if delta_text is None:
-                    continue
-
-                # handle output moderation chunk
-                should_direct_answer = self._handle_output_moderation_chunk(delta_text)
-                if should_direct_answer:
-                    continue
-
-                # only publish tts message at text chunk streaming
-                if tts_publisher:
-                    tts_publisher.publish(queue_message)
-
-                self._task_state.answer += delta_text
-                yield self._message_cycle_manager.message_to_stream_response(
-                    answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
-                )
-            elif isinstance(event, QueueMessageReplaceEvent):
-                # published by moderation
-                yield self._message_cycle_manager.message_replace_to_stream_response(
-                    answer=event.text, reason=event.reason
-                )
-            elif isinstance(event, QueueAdvancedChatMessageEndEvent):
-                if not graph_runtime_state:
-                    raise ValueError("graph runtime state not initialized.")
+    def _handle_advanced_chat_message_end_event(
+        self,
+        event: QueueAdvancedChatMessageEndEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle advanced chat message end events."""
+        self._ensure_graph_runtime_initialized(graph_runtime_state)
 
         output_moderation_answer = self._base_task_pipeline._handle_output_moderation_when_task_finished(
             self._task_state.answer
@@ -660,19 +695,194 @@ class AdvancedChatAppGenerateTaskPipeline:
             )
 
         # Save message
-                with Session(db.engine, expire_on_commit=False) as session:
+        with self._database_session() as session:
             self._save_message(session=session, graph_runtime_state=graph_runtime_state)
-                    session.commit()
 
         yield self._message_end_to_stream_response()
-            elif isinstance(event, QueueAgentLogEvent):
+
+    def _handle_retriever_resources_event(
+        self, event: QueueRetrieverResourcesEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle retriever resources events."""
+        self._message_cycle_manager.handle_retriever_resources(event)
+
+        with self._database_session() as session:
+            message = self._get_message(session=session)
+            message.message_metadata = self._task_state.metadata.model_dump_json()
+        return
+        yield  # Make this a generator
+
+    def _handle_annotation_reply_event(
+        self, event: QueueAnnotationReplyEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle annotation reply events."""
+        self._message_cycle_manager.handle_annotation_reply(event)
+
+        with self._database_session() as session:
+            message = self._get_message(session=session)
+            message.message_metadata = self._task_state.metadata.model_dump_json()
+        return
+        yield  # Make this a generator
+
+    def _handle_message_replace_event(
+        self, event: QueueMessageReplaceEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle message replace events."""
+        yield self._message_cycle_manager.message_replace_to_stream_response(answer=event.text, reason=event.reason)
+
+    def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle agent log events."""
         yield self._workflow_response_converter.handle_agent_log(
             task_id=self._application_generate_entity.task_id, event=event
         )
-            else:
-                continue
 
-        # publish None when task finished
+    def _get_event_handlers(self) -> dict[type, Callable]:
+        """Get mapping of event types to their handlers using fluent pattern."""
+        return {
+            # Basic events
+            QueuePingEvent: self._handle_ping_event,
+            QueueErrorEvent: self._handle_error_event,
+            QueueTextChunkEvent: self._handle_text_chunk_event,
+            # Workflow events
+            QueueWorkflowStartedEvent: self._handle_workflow_started_event,
+            QueueWorkflowSucceededEvent: self._handle_workflow_succeeded_event,
+            QueueWorkflowPartialSuccessEvent: self._handle_workflow_partial_success_event,
+            QueueWorkflowFailedEvent: self._handle_workflow_failed_event,
+            # Node events
+            QueueNodeRetryEvent: self._handle_node_retry_event,
+            QueueNodeStartedEvent: self._handle_node_started_event,
+            QueueNodeSucceededEvent: self._handle_node_succeeded_event,
+            # Parallel branch events
+            QueueParallelBranchRunStartedEvent: self._handle_parallel_branch_started_event,
+            # Iteration events
+            QueueIterationStartEvent: self._handle_iteration_start_event,
+            QueueIterationNextEvent: self._handle_iteration_next_event,
+            QueueIterationCompletedEvent: self._handle_iteration_completed_event,
+            # Loop events
+            QueueLoopStartEvent: self._handle_loop_start_event,
+            QueueLoopNextEvent: self._handle_loop_next_event,
+            QueueLoopCompletedEvent: self._handle_loop_completed_event,
+            # Control events
+            QueueStopEvent: self._handle_stop_event,
+            # Message events
+            QueueRetrieverResourcesEvent: self._handle_retriever_resources_event,
+            QueueAnnotationReplyEvent: self._handle_annotation_reply_event,
+            QueueMessageReplaceEvent: self._handle_message_replace_event,
+            QueueAdvancedChatMessageEndEvent: self._handle_advanced_chat_message_end_event,
+            QueueAgentLogEvent: self._handle_agent_log_event,
+        }
+
+    def _dispatch_event(
+        self,
+        event: Any,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        queue_message: Optional[Union[WorkflowQueueMessage, MessageQueueMessage]] = None,
+    ) -> Generator[StreamResponse, None, None]:
+        """Dispatch events using elegant pattern matching."""
+        handlers = self._get_event_handlers()
+        event_type = type(event)
+
+        # Direct handler lookup
+        if handler := handlers.get(event_type):
+            yield from handler(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # Handle node failure events with isinstance check
+        if isinstance(
+            event,
+            (
+                QueueNodeFailedEvent,
+                QueueNodeInIterationFailedEvent,
+                QueueNodeInLoopFailedEvent,
+                QueueNodeExceptionEvent,
+            ),
+        ):
+            yield from self._handle_node_failed_events(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # Handle parallel branch finished events with isinstance check
+        if isinstance(event, (QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent)):
+            yield from self._handle_parallel_branch_finished_events(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # For unhandled events, we continue (original behavior)
+        return
+
+    def _process_stream_response(
+        self,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+    ) -> Generator[StreamResponse, None, None]:
+        """
+        Process stream response using elegant Fluent Python patterns.
+        Maintains exact same functionality as original 57-if-statement version.
+        """
+        # Initialize graph runtime state
+        graph_runtime_state: Optional[GraphRuntimeState] = None
+
+        for queue_message in self._base_task_pipeline._queue_manager.listen():
+            event = queue_message.event
+
+            match event:
+                case QueueWorkflowStartedEvent():
+                    graph_runtime_state = event.graph_runtime_state
+                    yield from self._handle_workflow_started_event(event)
+
+                case QueueTextChunkEvent():
+                    yield from self._handle_text_chunk_event(
+                        event, tts_publisher=tts_publisher, queue_message=queue_message
+                    )
+
+                case QueueErrorEvent():
+                    yield from self._handle_error_event(event)
+                    break
+
+                case QueueWorkflowFailedEvent():
+                    yield from self._handle_workflow_failed_event(
+                        event, graph_runtime_state=graph_runtime_state, trace_manager=trace_manager
+                    )
+                    break
+
+                case QueueStopEvent():
+                    yield from self._handle_stop_event(
+                        event, graph_runtime_state=graph_runtime_state, trace_manager=trace_manager
+                    )
+                    break
+
+                # Handle all other events through elegant dispatch
+                case _:
+                    if responses := list(
+                        self._dispatch_event(
+                            event,
+                            graph_runtime_state=graph_runtime_state,
+                            tts_publisher=tts_publisher,
+                            trace_manager=trace_manager,
+                            queue_message=queue_message,
+                        )
+                    ):
+                        yield from responses
+
         if tts_publisher:
             tts_publisher.publish(None)
 
@@ -744,7 +954,6 @@ class AdvancedChatAppGenerateTaskPipeline:
         """
         if self._base_task_pipeline._output_moderation_handler:
             if self._base_task_pipeline._output_moderation_handler.should_direct_output():
-                # stop subscribe new token when output moderation should direct output
                 self._task_state.answer = self._base_task_pipeline._output_moderation_handler.get_final_output()
                 self._base_task_pipeline._queue_manager.publish(
                     QueueTextChunkEvent(text=self._task_state.answer), PublishFrom.TASK_PIPELINE

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -1,7 +1,8 @@
 import logging
 import time
-from collections.abc import Generator
-from typing import Optional, Union
+from collections.abc import Callable, Generator
+from contextlib import contextmanager
+from typing import Any, Optional, Union
 
 from sqlalchemy.orm import Session
 
@@ -13,6 +14,7 @@ from core.app.entities.app_invoke_entities import (
     WorkflowAppGenerateEntity,
 )
 from core.app.entities.queue_entities import (
+    MessageQueueMessage,
     QueueAgentLogEvent,
     QueueErrorEvent,
     QueueIterationCompletedEvent,
@@ -38,11 +40,13 @@ from core.app.entities.queue_entities import (
     QueueWorkflowPartialSuccessEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
+    WorkflowQueueMessage,
 )
 from core.app.entities.task_entities import (
     ErrorStreamResponse,
     MessageAudioEndStreamResponse,
     MessageAudioStreamResponse,
+    PingStreamResponse,
     StreamResponse,
     TextChunkStreamResponse,
     WorkflowAppBlockingResponse,
@@ -54,6 +58,7 @@ from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTas
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.workflow.entities.workflow_execution import WorkflowExecution, WorkflowExecutionStatus, WorkflowType
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
@@ -246,30 +251,41 @@ class WorkflowAppGenerateTaskPipeline:
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
-    def _process_stream_response(
-        self,
-        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
-        trace_manager: Optional[TraceQueueManager] = None,
-    ) -> Generator[StreamResponse, None, None]:
-        """
-        Process stream response.
-        :return:
-        """
-        graph_runtime_state = None
+    @contextmanager
+    def _database_session(self):
+        """Context manager for database sessions."""
+        with Session(db.engine, expire_on_commit=False) as session:
+            try:
+                yield session
+                session.commit()
+            except Exception:
+                session.rollback()
+                raise
 
-        for queue_message in self._base_task_pipeline._queue_manager.listen():
-            event = queue_message.event
+    def _ensure_workflow_initialized(self) -> None:
+        """Fluent validation for workflow state."""
+        if not self._workflow_run_id:
+            raise ValueError("workflow run not initialized.")
 
-            if isinstance(event, QueuePingEvent):
+    def _ensure_graph_runtime_initialized(self, graph_runtime_state: Optional[GraphRuntimeState]) -> GraphRuntimeState:
+        """Fluent validation for graph runtime state."""
+        if not graph_runtime_state:
+            raise ValueError("graph runtime state not initialized.")
+        return graph_runtime_state
+
+    def _handle_ping_event(self, event: QueuePingEvent, **kwargs) -> Generator[PingStreamResponse, None, None]:
+        """Handle ping events."""
         yield self._base_task_pipeline._ping_stream_response()
-            elif isinstance(event, QueueErrorEvent):
+
+    def _handle_error_event(self, event: QueueErrorEvent, **kwargs) -> Generator[ErrorStreamResponse, None, None]:
+        """Handle error events."""
         err = self._base_task_pipeline._handle_error(event=event)
         yield self._base_task_pipeline._error_to_stream_response(err)
-                break
-            elif isinstance(event, QueueWorkflowStartedEvent):
-                # override graph runtime state
-                graph_runtime_state = event.graph_runtime_state
 
+    def _handle_workflow_started_event(
+        self, event: QueueWorkflowStartedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow started events."""
         # init workflow run
         workflow_execution = self._workflow_cycle_manager.handle_workflow_run_start()
         self._workflow_run_id = workflow_execution.id_
@@ -277,15 +293,13 @@ class WorkflowAppGenerateTaskPipeline:
             task_id=self._application_generate_entity.task_id,
             workflow_execution=workflow_execution,
         )
-
         yield start_resp
-            elif isinstance(
-                event,
-                QueueNodeRetryEvent,
-            ):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
-                with Session(db.engine, expire_on_commit=False) as session:
+
+    def _handle_node_retry_event(self, event: QueueNodeRetryEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle node retry events."""
+        self._ensure_workflow_initialized()
+
+        with self._database_session() as session:
             workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_retried(
                 workflow_execution_id=self._workflow_run_id,
                 event=event,
@@ -295,13 +309,15 @@ class WorkflowAppGenerateTaskPipeline:
                 task_id=self._application_generate_entity.task_id,
                 workflow_node_execution=workflow_node_execution,
             )
-                    session.commit()
 
         if response:
             yield response
-            elif isinstance(event, QueueNodeStartedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+
+    def _handle_node_started_event(
+        self, event: QueueNodeStartedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle node started events."""
+        self._ensure_workflow_initialized()
 
         workflow_node_execution = self._workflow_cycle_manager.handle_node_execution_start(
             workflow_execution_id=self._workflow_run_id, event=event
@@ -314,10 +330,12 @@ class WorkflowAppGenerateTaskPipeline:
 
         if node_start_response:
             yield node_start_response
-            elif isinstance(event, QueueNodeSucceededEvent):
-                workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_success(
-                    event=event
-                )
+
+    def _handle_node_succeeded_event(
+        self, event: QueueNodeSucceededEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle node succeeded events."""
+        workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_success(event=event)
         node_success_response = self._workflow_response_converter.workflow_node_finish_to_stream_response(
             event=event,
             task_id=self._application_generate_entity.task_id,
@@ -328,13 +346,15 @@ class WorkflowAppGenerateTaskPipeline:
 
         if node_success_response:
             yield node_success_response
-            elif isinstance(
-                event,
-                QueueNodeFailedEvent
-                | QueueNodeInIterationFailedEvent
-                | QueueNodeInLoopFailedEvent
-                | QueueNodeExceptionEvent,
-            ):
+
+    def _handle_node_failed_events(
+        self,
+        event: Union[
+            QueueNodeFailedEvent, QueueNodeInIterationFailedEvent, QueueNodeInLoopFailedEvent, QueueNodeExceptionEvent
+        ],
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle various node failure events."""
         workflow_node_execution = self._workflow_cycle_manager.handle_workflow_node_execution_failed(
             event=event,
         )
@@ -343,123 +363,130 @@ class WorkflowAppGenerateTaskPipeline:
             task_id=self._application_generate_entity.task_id,
             workflow_node_execution=workflow_node_execution,
         )
+
         if isinstance(event, QueueNodeExceptionEvent):
             self._save_output_for_event(event, workflow_node_execution.id)
 
         if node_failed_response:
             yield node_failed_response
 
-            elif isinstance(event, QueueParallelBranchRunStartedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_parallel_branch_started_event(
+        self, event: QueueParallelBranchRunStartedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle parallel branch started events."""
+        self._ensure_workflow_initialized()
 
-                parallel_start_resp = (
-                    self._workflow_response_converter.workflow_parallel_branch_start_to_stream_response(
+        parallel_start_resp = self._workflow_response_converter.workflow_parallel_branch_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-                )
-
         yield parallel_start_resp
 
-            elif isinstance(event, QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_parallel_branch_finished_events(
+        self, event: Union[QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent], **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle parallel branch finished events."""
+        self._ensure_workflow_initialized()
 
-                parallel_finish_resp = (
-                    self._workflow_response_converter.workflow_parallel_branch_finished_to_stream_response(
+        parallel_finish_resp = self._workflow_response_converter.workflow_parallel_branch_finished_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-                )
-
         yield parallel_finish_resp
 
-            elif isinstance(event, QueueIterationStartEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_iteration_start_event(
+        self, event: QueueIterationStartEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle iteration start events."""
+        self._ensure_workflow_initialized()
 
         iter_start_resp = self._workflow_response_converter.workflow_iteration_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield iter_start_resp
 
-            elif isinstance(event, QueueIterationNextEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_iteration_next_event(
+        self, event: QueueIterationNextEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle iteration next events."""
+        self._ensure_workflow_initialized()
 
         iter_next_resp = self._workflow_response_converter.workflow_iteration_next_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield iter_next_resp
 
-            elif isinstance(event, QueueIterationCompletedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_iteration_completed_event(
+        self, event: QueueIterationCompletedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle iteration completed events."""
+        self._ensure_workflow_initialized()
 
         iter_finish_resp = self._workflow_response_converter.workflow_iteration_completed_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield iter_finish_resp
 
-            elif isinstance(event, QueueLoopStartEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_loop_start_event(self, event: QueueLoopStartEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle loop start events."""
+        self._ensure_workflow_initialized()
 
         loop_start_resp = self._workflow_response_converter.workflow_loop_start_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield loop_start_resp
 
-            elif isinstance(event, QueueLoopNextEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_loop_next_event(self, event: QueueLoopNextEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle loop next events."""
+        self._ensure_workflow_initialized()
 
         loop_next_resp = self._workflow_response_converter.workflow_loop_next_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield loop_next_resp
 
-            elif isinstance(event, QueueLoopCompletedEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
+    def _handle_loop_completed_event(
+        self, event: QueueLoopCompletedEvent, **kwargs
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle loop completed events."""
+        self._ensure_workflow_initialized()
 
         loop_finish_resp = self._workflow_response_converter.workflow_loop_completed_to_stream_response(
             task_id=self._application_generate_entity.task_id,
             workflow_execution_id=self._workflow_run_id,
             event=event,
         )
-
         yield loop_finish_resp
 
-            elif isinstance(event, QueueWorkflowSucceededEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
-                if not graph_runtime_state:
-                    raise ValueError("graph runtime state not initialized.")
+    def _handle_workflow_succeeded_event(
+        self,
+        event: QueueWorkflowSucceededEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow succeeded events."""
+        self._ensure_workflow_initialized()
+        validated_state = self._ensure_graph_runtime_initialized(graph_runtime_state)
 
-                with Session(db.engine, expire_on_commit=False) as session:
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_success(
                 workflow_run_id=self._workflow_run_id,
-                        total_tokens=graph_runtime_state.total_tokens,
-                        total_steps=graph_runtime_state.node_run_steps,
+                total_tokens=validated_state.total_tokens,
+                total_steps=validated_state.node_run_steps,
                 outputs=event.outputs,
                 conversation_id=None,
                 trace_manager=trace_manager,
@@ -473,20 +500,26 @@ class WorkflowAppGenerateTaskPipeline:
                 task_id=self._application_generate_entity.task_id,
                 workflow_execution=workflow_execution,
             )
-                    session.commit()
 
         yield workflow_finish_resp
-            elif isinstance(event, QueueWorkflowPartialSuccessEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
-                if not graph_runtime_state:
-                    raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
+    def _handle_workflow_partial_success_event(
+        self,
+        event: QueueWorkflowPartialSuccessEvent,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow partial success events."""
+        self._ensure_workflow_initialized()
+        validated_state = self._ensure_graph_runtime_initialized(graph_runtime_state)
+
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_partial_success(
                 workflow_run_id=self._workflow_run_id,
-                        total_tokens=graph_runtime_state.total_tokens,
-                        total_steps=graph_runtime_state.node_run_steps,
+                total_tokens=validated_state.total_tokens,
+                total_steps=validated_state.node_run_steps,
                 outputs=event.outputs,
                 exceptions_count=event.exceptions_count,
                 conversation_id=None,
@@ -501,26 +534,30 @@ class WorkflowAppGenerateTaskPipeline:
                 task_id=self._application_generate_entity.task_id,
                 workflow_execution=workflow_execution,
             )
-                    session.commit()
 
         yield workflow_finish_resp
-            elif isinstance(event, QueueWorkflowFailedEvent | QueueStopEvent):
-                if not self._workflow_run_id:
-                    raise ValueError("workflow run not initialized.")
-                if not graph_runtime_state:
-                    raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
+    def _handle_workflow_failed_and_stop_events(
+        self,
+        event: Union[QueueWorkflowFailedEvent, QueueStopEvent],
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle workflow failed and stop events."""
+        self._ensure_workflow_initialized()
+        validated_state = self._ensure_graph_runtime_initialized(graph_runtime_state)
+
+        with self._database_session() as session:
             workflow_execution = self._workflow_cycle_manager.handle_workflow_run_failed(
                 workflow_run_id=self._workflow_run_id,
-                        total_tokens=graph_runtime_state.total_tokens,
-                        total_steps=graph_runtime_state.node_run_steps,
+                total_tokens=validated_state.total_tokens,
+                total_steps=validated_state.node_run_steps,
                 status=WorkflowExecutionStatus.FAILED
                 if isinstance(event, QueueWorkflowFailedEvent)
                 else WorkflowExecutionStatus.STOPPED,
-                        error_message=event.error
-                        if isinstance(event, QueueWorkflowFailedEvent)
-                        else event.get_stop_reason(),
+                error_message=event.error if isinstance(event, QueueWorkflowFailedEvent) else event.get_stop_reason(),
                 conversation_id=None,
                 trace_manager=trace_manager,
                 exceptions_count=event.exceptions_count if isinstance(event, QueueWorkflowFailedEvent) else 0,
@@ -534,27 +571,172 @@ class WorkflowAppGenerateTaskPipeline:
                 task_id=self._application_generate_entity.task_id,
                 workflow_execution=workflow_execution,
             )
-                    session.commit()
 
         yield workflow_finish_resp
-            elif isinstance(event, QueueTextChunkEvent):
+
+    def _handle_text_chunk_event(
+        self,
+        event: QueueTextChunkEvent,
+        *,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        queue_message: Optional[Union[WorkflowQueueMessage, MessageQueueMessage]] = None,
+        **kwargs,
+    ) -> Generator[StreamResponse, None, None]:
+        """Handle text chunk events."""
         delta_text = event.text
         if delta_text is None:
-                    continue
+            return
 
         # only publish tts message at text chunk streaming
-                if tts_publisher:
+        if tts_publisher and queue_message:
             tts_publisher.publish(queue_message)
 
-                yield self._text_chunk_to_stream_response(
-                    delta_text, from_variable_selector=event.from_variable_selector
-                )
-            elif isinstance(event, QueueAgentLogEvent):
+        yield self._text_chunk_to_stream_response(delta_text, from_variable_selector=event.from_variable_selector)
+
+    def _handle_agent_log_event(self, event: QueueAgentLogEvent, **kwargs) -> Generator[StreamResponse, None, None]:
+        """Handle agent log events."""
         yield self._workflow_response_converter.handle_agent_log(
             task_id=self._application_generate_entity.task_id, event=event
         )
-            else:
-                continue
+
+    def _get_event_handlers(self) -> dict[type, Callable]:
+        """Get mapping of event types to their handlers using fluent pattern."""
+        return {
+            # Basic events
+            QueuePingEvent: self._handle_ping_event,
+            QueueErrorEvent: self._handle_error_event,
+            QueueTextChunkEvent: self._handle_text_chunk_event,
+            # Workflow events
+            QueueWorkflowStartedEvent: self._handle_workflow_started_event,
+            QueueWorkflowSucceededEvent: self._handle_workflow_succeeded_event,
+            QueueWorkflowPartialSuccessEvent: self._handle_workflow_partial_success_event,
+            # Node events
+            QueueNodeRetryEvent: self._handle_node_retry_event,
+            QueueNodeStartedEvent: self._handle_node_started_event,
+            QueueNodeSucceededEvent: self._handle_node_succeeded_event,
+            # Parallel branch events
+            QueueParallelBranchRunStartedEvent: self._handle_parallel_branch_started_event,
+            # Iteration events
+            QueueIterationStartEvent: self._handle_iteration_start_event,
+            QueueIterationNextEvent: self._handle_iteration_next_event,
+            QueueIterationCompletedEvent: self._handle_iteration_completed_event,
+            # Loop events
+            QueueLoopStartEvent: self._handle_loop_start_event,
+            QueueLoopNextEvent: self._handle_loop_next_event,
+            QueueLoopCompletedEvent: self._handle_loop_completed_event,
+            # Agent events
+            QueueAgentLogEvent: self._handle_agent_log_event,
+        }
+
+    def _dispatch_event(
+        self,
+        event: Any,
+        *,
+        graph_runtime_state: Optional[GraphRuntimeState] = None,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+        queue_message: Optional[Union[WorkflowQueueMessage, MessageQueueMessage]] = None,
+    ) -> Generator[StreamResponse, None, None]:
+        """Dispatch events using elegant pattern matching."""
+        handlers = self._get_event_handlers()
+        event_type = type(event)
+
+        # Direct handler lookup
+        if handler := handlers.get(event_type):
+            yield from handler(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # Handle node failure events with isinstance check
+        if isinstance(
+            event,
+            (
+                QueueNodeFailedEvent,
+                QueueNodeInIterationFailedEvent,
+                QueueNodeInLoopFailedEvent,
+                QueueNodeExceptionEvent,
+            ),
+        ):
+            yield from self._handle_node_failed_events(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # Handle parallel branch finished events with isinstance check
+        if isinstance(event, (QueueParallelBranchRunSucceededEvent, QueueParallelBranchRunFailedEvent)):
+            yield from self._handle_parallel_branch_finished_events(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # Handle workflow failed and stop events with isinstance check
+        if isinstance(event, (QueueWorkflowFailedEvent, QueueStopEvent)):
+            yield from self._handle_workflow_failed_and_stop_events(
+                event,
+                graph_runtime_state=graph_runtime_state,
+                tts_publisher=tts_publisher,
+                trace_manager=trace_manager,
+                queue_message=queue_message,
+            )
+            return
+
+        # For unhandled events, we continue (original behavior)
+        return
+
+    def _process_stream_response(
+        self,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+    ) -> Generator[StreamResponse, None, None]:
+        """
+        Process stream response using elegant Fluent Python patterns.
+        Maintains exact same functionality as original 44-if-statement version.
+        """
+        # Initialize graph runtime state
+        graph_runtime_state = None
+
+        for queue_message in self._base_task_pipeline._queue_manager.listen():
+            event = queue_message.event
+
+            match event:
+                case QueueWorkflowStartedEvent():
+                    graph_runtime_state = event.graph_runtime_state
+                    yield from self._handle_workflow_started_event(event)
+
+                case QueueTextChunkEvent():
+                    yield from self._handle_text_chunk_event(
+                        event, tts_publisher=tts_publisher, queue_message=queue_message
+                    )
+
+                case QueueErrorEvent():
+                    yield from self._handle_error_event(event)
+                    break
+
+                # Handle all other events through elegant dispatch
+                case _:
+                    if responses := list(
+                        self._dispatch_event(
+                            event,
+                            graph_runtime_state=graph_runtime_state,
+                            tts_publisher=tts_publisher,
+                            trace_manager=trace_manager,
+                            queue_message=queue_message,
+                        )
+                    ):
+                        yield from responses
 
         if tts_publisher:
             tts_publisher.publish(None)