AI/dify
1
0
Fork 0
mirror of https://git.mirrors.martin98.com/https://github.com/langgenius/dify.git synced 2025-08-16 16:06:06 +08:00
Code Issues Packages Projects Releases Wiki Activity

feat(workflow): integrate workflow entry with workflow app

Browse Source
This commit is contained in:
takatost 2024-08-14 19:22:15 +08:00
parent 674af04c39
commit 6f6b32e1ee
29 changed files with 1058 additions and 580 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

72
api/core/app/apps/advanced_chat/app_generator.py
View File

@ -33,7 +33,8 @@ logger = logging.getLogger(__name__)
class AdvancedChatAppGenerator(MessageBasedAppGenerator):
def generate(
self, app_model: App,
self,
app_model: App,
workflow: Workflow,
user: Union[Account, EndUser],
args: dict,
@ -121,6 +122,65 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
stream=stream
)
def single_iteration_generate(self, app_model: App,
workflow: Workflow,
node_id: str,
user: Account,
args: dict,
stream: bool = True) \
-> dict[str, Any] | Generator[str, Any, None]:
"""
Generate App response.
:param app_model: App
:param workflow: Workflow
:param user: account or end user
:param args: request args
:param invoke_from: invoke from source
:param stream: is stream
"""
if not node_id:
raise ValueError('node_id is required')
if args.get('inputs') is None:
raise ValueError('inputs is required')
# convert to app config
app_config = AdvancedChatAppConfigManager.get_app_config(
app_model=app_model,
workflow=workflow
)
# init application generate entity
application_generate_entity = AdvancedChatAppGenerateEntity(
task_id=str(uuid.uuid4()),
app_config=app_config,
conversation_id=None,
inputs={},
query='',
files=[],
user_id=user.id,
stream=stream,
invoke_from=InvokeFrom.DEBUGGER,
extras={
"auto_generate_conversation_name": False
},
single_iteration_run=AdvancedChatAppGenerateEntity.SingleIterationRunEntity(
node_id=node_id,
inputs=args['inputs']
)
)
contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
return self._generate(
workflow=workflow,
user=user,
invoke_from=InvokeFrom.DEBUGGER,
application_generate_entity=application_generate_entity,
conversation=None,
stream=stream
)
def _generate(self, *,
workflow: Workflow,
user: Union[Account, EndUser],
@ -129,6 +189,16 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
conversation: Optional[Conversation] = None,
stream: bool = True) \
-> dict[str, Any] | Generator[str, Any, None]:
"""
Generate App response.
:param workflow: Workflow
:param user: account or end user
:param invoke_from: invoke from source
:param application_generate_entity: application generate entity
:param conversation: conversation
:param stream: is stream
"""
is_first_conversation = False
if not conversation:
is_first_conversation = True

272
api/core/app/apps/advanced_chat/app_runner.py
View File

@ -1,14 +1,14 @@
import logging
import os
from collections.abc import Mapping
from typing import Any, Optional, cast
from typing import Any, cast
from sqlalchemy import select
from sqlalchemy.orm import Session
from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
from core.app.apps.base_app_runner import AppRunner
from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
from core.app.apps.workflow_logging_callback import WorkflowLoggingCallback
from core.app.entities.app_invoke_entities import (
AdvancedChatAppGenerateEntity,
@ -17,52 +17,22 @@ from core.app.entities.app_invoke_entities import (
from core.app.entities.queue_entities import (
AppQueueEvent,
QueueAnnotationReplyEvent,
QueueIterationCompletedEvent,
QueueIterationNextEvent,
QueueIterationStartEvent,
QueueNodeFailedEvent,
QueueNodeStartedEvent,
QueueNodeSucceededEvent,
QueueParallelBranchRunFailedEvent,
QueueParallelBranchRunStartedEvent,
QueueRetrieverResourcesEvent,
QueueStopEvent,
QueueTextChunkEvent,
QueueWorkflowFailedEvent,
QueueWorkflowStartedEvent,
QueueWorkflowSucceededEvent,
)
from core.moderation.base import ModerationException
from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
from core.workflow.entities.node_entities import SystemVariable, UserFrom
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.graph_engine.entities.event import (
GraphEngineEvent,
GraphRunFailedEvent,
GraphRunStartedEvent,
GraphRunSucceededEvent,
IterationRunFailedEvent,
IterationRunNextEvent,
IterationRunStartedEvent,
IterationRunSucceededEvent,
NodeRunFailedEvent,
NodeRunRetrieverResourceEvent,
NodeRunStartedEvent,
NodeRunStreamChunkEvent,
NodeRunSucceededEvent,
ParallelBranchRunFailedEvent,
ParallelBranchRunStartedEvent,
ParallelBranchRunSucceededEvent,
)
from core.workflow.workflow_entry import WorkflowEntry
from extensions.ext_database import db
from models.model import App, Conversation, EndUser, Message
from models.workflow import ConversationVariable, Workflow
from models.workflow import ConversationVariable
logger = logging.getLogger(__name__)
class AdvancedChatAppRunner(AppRunner):
class AdvancedChatAppRunner(WorkflowBasedAppRunner):
"""
AdvancedChat Application Runner
"""
@ -80,8 +50,9 @@ class AdvancedChatAppRunner(AppRunner):
:param conversation: conversation
:param message: message
"""
super().__init__(queue_manager)
self.application_generate_entity = application_generate_entity
self.queue_manager = queue_manager
self.conversation = conversation
self.message = message
@ -101,10 +72,6 @@ class AdvancedChatAppRunner(AppRunner):
if not workflow:
raise ValueError('Workflow not initialized')
inputs = self.application_generate_entity.inputs
query = self.application_generate_entity.query
files = self.application_generate_entity.files
user_id = None
if self.application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
end_user = db.session.query(EndUser).filter(EndUser.id == self.application_generate_entity.user_id).first()
@ -113,6 +80,32 @@ class AdvancedChatAppRunner(AppRunner):
else:
user_id = self.application_generate_entity.user_id
workflow_callbacks: list[WorkflowCallback] = []
if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
workflow_callbacks.append(WorkflowLoggingCallback())
# if only single iteration run is requested
if self.application_generate_entity.single_iteration_run:
node_id = self.application_generate_entity.single_iteration_run.node_id
user_inputs = self.application_generate_entity.single_iteration_run.inputs
generator = WorkflowEntry.single_step_run_iteration(
workflow=workflow,
node_id=node_id,
user_id=self.application_generate_entity.user_id,
user_inputs=user_inputs,
callbacks=workflow_callbacks
)
for event in generator:
# TODO
self._handle_event(workflow_entry, event)
return
inputs = self.application_generate_entity.inputs
query = self.application_generate_entity.query
files = self.application_generate_entity.files
# moderation
if self.handle_input_moderation(
app_record=app_record,
@ -134,20 +127,16 @@ class AdvancedChatAppRunner(AppRunner):
db.session.close()
workflow_callbacks: list[WorkflowCallback] = []
if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
workflow_callbacks.append(WorkflowLoggingCallback())
# Init conversation variables
stmt = select(ConversationVariable).where(
ConversationVariable.app_id == conversation.app_id, ConversationVariable.conversation_id == conversation.id
ConversationVariable.app_id == self.conversation.app_id, ConversationVariable.conversation_id == self.conversation.id
)
with Session(db.engine) as session:
conversation_variables = session.scalars(stmt).all()
if not conversation_variables:
conversation_variables = [
ConversationVariable.from_variable(
app_id=conversation.app_id, conversation_id=conversation.id, variable=variable
app_id=self.conversation.app_id, conversation_id=self.conversation.id, variable=variable
)
for variable in workflow.conversation_variables
]
@ -160,9 +149,11 @@ class AdvancedChatAppRunner(AppRunner):
system_inputs = {
SystemVariable.QUERY: query,
SystemVariable.FILES: files,
SystemVariable.CONVERSATION_ID: conversation.id,
SystemVariable.CONVERSATION_ID: self.conversation.id,
SystemVariable.USER_ID: user_id,
}
# init variable pool
variable_pool = VariablePool(
system_variables=system_inputs,
user_inputs=inputs,
@ -174,9 +165,11 @@ class AdvancedChatAppRunner(AppRunner):
workflow_entry = WorkflowEntry(
workflow=workflow,
user_id=self.application_generate_entity.user_id,
user_from=UserFrom.ACCOUNT
if self.application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
else UserFrom.END_USER,
user_from=(
UserFrom.ACCOUNT
if self.application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
else UserFrom.END_USER
),
invoke_from=self.application_generate_entity.invoke_from,
call_depth=self.application_generate_entity.call_depth,
variable_pool=variable_pool,
@ -189,181 +182,6 @@ class AdvancedChatAppRunner(AppRunner):
for event in generator:
self._handle_event(workflow_entry, event)
def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent) -> None:
"""
Handle event
:param workflow_entry: workflow entry
:param event: event
"""
if isinstance(event, GraphRunStartedEvent):
self._publish_event(
QueueWorkflowStartedEvent(
graph_runtime_state=workflow_entry.graph_engine.graph_runtime_state
)
)
elif isinstance(event, GraphRunSucceededEvent):
self._publish_event(
QueueWorkflowSucceededEvent(outputs=event.outputs)
)
elif isinstance(event, GraphRunFailedEvent):
self._publish_event(
QueueWorkflowFailedEvent(error=event.error)
)
elif isinstance(event, NodeRunStartedEvent):
self._publish_event(
QueueNodeStartedEvent(
node_execution_id=event.id,
node_id=event.node_id,
node_type=event.node_type,
node_data=event.node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.route_node_state.start_at,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
predecessor_node_id=event.predecessor_node_id
)
)
elif isinstance(event, NodeRunSucceededEvent):
self._publish_event(
QueueNodeSucceededEvent(
node_execution_id=event.id,
node_id=event.node_id,
node_type=event.node_type,
node_data=event.node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.route_node_state.start_at,
inputs=event.route_node_state.node_run_result.inputs
if event.route_node_state.node_run_result else {},
process_data=event.route_node_state.node_run_result.process_data
if event.route_node_state.node_run_result else {},
outputs=event.route_node_state.node_run_result.outputs
if event.route_node_state.node_run_result else {},
execution_metadata=event.route_node_state.node_run_result.metadata
if event.route_node_state.node_run_result else {},
)
)
elif isinstance(event, NodeRunFailedEvent):
self._publish_event(
QueueNodeFailedEvent(
node_execution_id=event.id,
node_id=event.node_id,
node_type=event.node_type,
node_data=event.node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.route_node_state.start_at,
inputs=event.route_node_state.node_run_result.inputs
if event.route_node_state.node_run_result else {},
process_data=event.route_node_state.node_run_result.process_data
if event.route_node_state.node_run_result else {},
outputs=event.route_node_state.node_run_result.outputs
if event.route_node_state.node_run_result else {},
error=event.route_node_state.node_run_result.error
if event.route_node_state.node_run_result
and event.route_node_state.node_run_result.error
else "Unknown error"
)
)
elif isinstance(event, NodeRunStreamChunkEvent):
self._publish_event(
QueueTextChunkEvent(
text=event.chunk_content
)
)
elif isinstance(event, NodeRunRetrieverResourceEvent):
self._publish_event(
QueueRetrieverResourcesEvent(
retriever_resources=event.retriever_resources
)
)
elif isinstance(event, ParallelBranchRunStartedEvent):
self._publish_event(
QueueParallelBranchRunStartedEvent(
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id
)
)
elif isinstance(event, ParallelBranchRunSucceededEvent):
self._publish_event(
QueueParallelBranchRunStartedEvent(
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id
)
)
elif isinstance(event, ParallelBranchRunFailedEvent):
self._publish_event(
QueueParallelBranchRunFailedEvent(
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
error=event.error
)
)
elif isinstance(event, IterationRunStartedEvent):
self._publish_event(
QueueIterationStartEvent(
node_execution_id=event.iteration_id,
node_id=event.iteration_node_id,
node_type=event.iteration_node_type,
node_data=event.iteration_node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.start_at,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
inputs=event.inputs,
predecessor_node_id=event.predecessor_node_id,
metadata=event.metadata
)
)
elif isinstance(event, IterationRunNextEvent):
self._publish_event(
QueueIterationNextEvent(
node_execution_id=event.iteration_id,
node_id=event.iteration_node_id,
node_type=event.iteration_node_type,
node_data=event.iteration_node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
index=event.index,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
output=event.pre_iteration_output,
)
)
elif isinstance(event, (IterationRunSucceededEvent | IterationRunFailedEvent)):
self._publish_event(
QueueIterationCompletedEvent(
node_execution_id=event.iteration_id,
node_id=event.iteration_node_id,
node_type=event.iteration_node_type,
node_data=event.iteration_node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.start_at,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
inputs=event.inputs,
outputs=event.outputs,
metadata=event.metadata,
steps=event.steps,
error=event.error if isinstance(event, IterationRunFailedEvent) else None
)
)
def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
"""
Get workflow
"""
# fetch workflow by workflow_id
workflow = (
db.session.query(Workflow)
.filter(
Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
)
.first()
)
# return workflow
return workflow
def handle_input_moderation(
self,
app_record: App,
@ -450,9 +268,3 @@ class AdvancedChatAppRunner(AppRunner):
self._publish_event(
QueueStopEvent(stopped_by=stopped_by)
)
def _publish_event(self, event: AppQueueEvent) -> None:
self.queue_manager.publish(
event,
PublishFrom.APPLICATION_MANAGER
)

12
api/core/app/apps/advanced_chat/generate_task_pipeline.py
View File

@ -240,11 +240,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
graph_runtime_state = None
workflow_run = None
for message in self._queue_manager.listen():
if tts_publisher:
tts_publisher.publish(message=message)
event = message.event
for queue_message in self._queue_manager.listen():
event = queue_message.event
if isinstance(event, QueuePingEvent):
yield self._ping_stream_response()
@ -433,6 +430,10 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
if should_direct_answer:
continue
# only publish tts message at text chunk streaming
if tts_publisher:
tts_publisher.publish(message=queue_message)
self._task_state.answer += delta_text
yield self._message_to_stream_response(delta_text, self._message.id)
elif isinstance(event, QueueMessageReplaceEvent):
@ -454,6 +455,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
else:
continue
# publish None when task finished
if tts_publisher:
tts_publisher.publish(None)

48
api/core/app/apps/workflow/app_generator.py
View File

@ -4,7 +4,7 @@ import os
import threading
import uuid
from collections.abc import Generator
from typing import Union
from typing import Any, Union
from flask import Flask, current_app
from pydantic import ValidationError
@ -33,7 +33,8 @@ logger = logging.getLogger(__name__)
class WorkflowAppGenerator(BaseAppGenerator):
def generate(
self, app_model: App,
self,
app_model: App,
workflow: Workflow,
user: Union[Account, EndUser],
args: dict,
@ -101,13 +102,14 @@ class WorkflowAppGenerator(BaseAppGenerator):
)
def _generate(
self, app_model: App,
self, *,
app_model: App,
workflow: Workflow,
user: Union[Account, EndUser],
application_generate_entity: WorkflowAppGenerateEntity,
invoke_from: InvokeFrom,
stream: bool = True,
) -> Union[dict, Generator[dict, None, None]]:
) -> dict[str, Any] | Generator[str, Any, None]:
"""
Generate App response.
@ -128,7 +130,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
# new thread
worker_thread = threading.Thread(target=self._generate_worker, kwargs={
'flask_app': current_app._get_current_object(),
'flask_app': current_app._get_current_object(), # type: ignore
'application_generate_entity': application_generate_entity,
'queue_manager': queue_manager,
'context': contextvars.copy_context()
@ -155,7 +157,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
node_id: str,
user: Account,
args: dict,
stream: bool = True):
stream: bool = True) -> dict[str, Any] | Generator[str, Any, None]:
"""
Generate App response.
@ -172,10 +174,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
if args.get('inputs') is None:
raise ValueError('inputs is required')
extras = {
"auto_generate_conversation_name": False
}
# convert to app config
app_config = WorkflowAppConfigManager.get_app_config(
app_model=app_model,
@ -191,7 +189,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
user_id=user.id,
stream=stream,
invoke_from=InvokeFrom.DEBUGGER,
extras=extras,
extras={
"auto_generate_conversation_name": False
},
single_iteration_run=WorkflowAppGenerateEntity.SingleIterationRunEntity(
node_id=node_id,
inputs=args['inputs']
@ -224,22 +224,12 @@ class WorkflowAppGenerator(BaseAppGenerator):
with flask_app.app_context():
try:
# workflow app
runner = WorkflowAppRunner()
if application_generate_entity.single_iteration_run:
single_iteration_run = application_generate_entity.single_iteration_run
runner.single_iteration_run(
app_id=application_generate_entity.app_config.app_id,
workflow_id=application_generate_entity.app_config.workflow_id,
queue_manager=queue_manager,
inputs=single_iteration_run.inputs,
node_id=single_iteration_run.node_id,
user_id=application_generate_entity.user_id
)
else:
runner.run(
application_generate_entity=application_generate_entity,
queue_manager=queue_manager
)
runner = WorkflowAppRunner(
application_generate_entity=application_generate_entity,
queue_manager=queue_manager
)
runner.run()
except GenerateTaskStoppedException:
pass
except InvokeAuthorizationError:
@ -251,14 +241,14 @@ class WorkflowAppGenerator(BaseAppGenerator):
logger.exception("Validation Error when generating")
queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
except (ValueError, InvokeError) as e:
if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
if os.environ.get("DEBUG") and os.environ.get("DEBUG", "false").lower() == 'true':
logger.exception("Error when generating")
queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
except Exception as e:
logger.exception("Unknown Error when generating")
queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
finally:
db.session.remove()
db.session.close()
def _handle_response(self, application_generate_entity: WorkflowAppGenerateEntity,
workflow: Workflow,

115
api/core/app/apps/workflow/app_runner.py
View File

@ -1,9 +1,10 @@
import logging
import os
from typing import Optional, cast
from typing import cast
from core.app.apps.base_app_queue_manager import AppQueueManager
from core.app.apps.workflow.app_config_manager import WorkflowAppConfig
from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
from core.app.apps.workflow_logging_callback import WorkflowLoggingCallback
from core.app.entities.app_invoke_entities import (
InvokeFrom,
@ -15,33 +16,44 @@ from core.workflow.entities.variable_pool import VariablePool
from core.workflow.workflow_entry import WorkflowEntry
from extensions.ext_database import db
from models.model import App, EndUser
from models.workflow import Workflow
logger = logging.getLogger(__name__)
class WorkflowAppRunner:
class WorkflowAppRunner(WorkflowBasedAppRunner):
"""
Workflow Application Runner
"""
def run(self, application_generate_entity: WorkflowAppGenerateEntity, queue_manager: AppQueueManager) -> None:
def __init__(
self,
application_generate_entity: WorkflowAppGenerateEntity,
queue_manager: AppQueueManager
) -> None:
"""
:param application_generate_entity: application generate entity
:param queue_manager: application queue manager
"""
self.application_generate_entity = application_generate_entity
self.queue_manager = queue_manager
def run(self) -> None:
"""
Run application
:param application_generate_entity: application generate entity
:param queue_manager: application queue manager
:return:
"""
app_config = application_generate_entity.app_config
app_config = self.application_generate_entity.app_config
app_config = cast(WorkflowAppConfig, app_config)
user_id = None
if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
if self.application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
end_user = db.session.query(EndUser).filter(EndUser.id == self.application_generate_entity.user_id).first()
if end_user:
user_id = end_user.session_id
else:
user_id = application_generate_entity.user_id
user_id = self.application_generate_entity.user_id
app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
if not app_record:
@ -51,21 +63,39 @@ class WorkflowAppRunner:
if not workflow:
raise ValueError('Workflow not initialized')
inputs = application_generate_entity.inputs
files = application_generate_entity.files
db.session.close()
workflow_callbacks: list[WorkflowCallback] = []
if bool(os.environ.get('DEBUG', 'False').lower() == 'true'):
workflow_callbacks.append(WorkflowLoggingCallback())
# if only single iteration run is requested
if self.application_generate_entity.single_iteration_run:
node_id = self.application_generate_entity.single_iteration_run.node_id
user_inputs = self.application_generate_entity.single_iteration_run.inputs
generator = WorkflowEntry.single_step_run_iteration(
workflow=workflow,
node_id=node_id,
user_id=self.application_generate_entity.user_id,
user_inputs=user_inputs,
callbacks=workflow_callbacks
)
for event in generator:
# TODO
self._handle_event(workflow_entry, event)
return
inputs = self.application_generate_entity.inputs
files = self.application_generate_entity.files
# Create a variable pool.
system_inputs = {
SystemVariable.FILES: files,
SystemVariable.USER_ID: user_id,
}
variable_pool = VariablePool(
system_variables=system_inputs,
user_inputs=inputs,
@ -74,59 +104,22 @@ class WorkflowAppRunner:
)
# RUN WORKFLOW
workflow_entry = WorkflowEntry()
workflow_entry.run(
workflow_entry = WorkflowEntry(
workflow=workflow,
user_id=application_generate_entity.user_id,
user_from=UserFrom.ACCOUNT
if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
else UserFrom.END_USER,
invoke_from=application_generate_entity.invoke_from,
callbacks=workflow_callbacks,
call_depth=application_generate_entity.call_depth,
user_id=self.application_generate_entity.user_id,
user_from=(
UserFrom.ACCOUNT
if self.application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
else UserFrom.END_USER
),
invoke_from=self.application_generate_entity.invoke_from,
call_depth=self.application_generate_entity.call_depth,
variable_pool=variable_pool,
)
def single_iteration_run(
self, app_id: str, workflow_id: str, queue_manager: AppQueueManager, inputs: dict, node_id: str, user_id: str
) -> None:
"""
Single iteration run
"""
app_record = db.session.query(App).filter(App.id == app_id).first()
if not app_record:
raise ValueError('App not found')
if not app_record.workflow_id:
raise ValueError('Workflow not initialized')
workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
if not workflow:
raise ValueError("Workflow not initialized")
workflow_callbacks = []
workflow_entry = WorkflowEntry()
workflow_entry.single_step_run_iteration_workflow_node(
workflow=workflow,
node_id=node_id,
user_id=user_id,
user_inputs=inputs,
generator = workflow_entry.run(
callbacks=workflow_callbacks
)
def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
"""
Get workflow
"""
# fetch workflow by workflow_id
workflow = (
db.session.query(Workflow)
.filter(
Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
)
.first()
)
# return workflow
return workflow
for event in generator:
self._handle_event(workflow_entry, event)

179
api/core/app/apps/workflow/generate_task_pipeline.py
View File

@ -1,3 +1,4 @@
import json
import logging
import time
from collections.abc import Generator
@ -15,7 +16,6 @@ from core.app.entities.queue_entities import (
QueueIterationCompletedEvent,
QueueIterationNextEvent,
QueueIterationStartEvent,
QueueMessageReplaceEvent,
QueueNodeFailedEvent,
QueueNodeStartedEvent,
QueueNodeSucceededEvent,
@ -32,10 +32,10 @@ from core.app.entities.task_entities import (
MessageAudioStreamResponse,
StreamResponse,
TextChunkStreamResponse,
TextReplaceStreamResponse,
WorkflowAppBlockingResponse,
WorkflowAppStreamResponse,
WorkflowFinishStreamResponse,
WorkflowStartStreamResponse,
WorkflowTaskState,
)
from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
@ -120,24 +120,20 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
if isinstance(stream_response, ErrorStreamResponse):
raise stream_response.err
elif isinstance(stream_response, WorkflowFinishStreamResponse):
workflow_run = self._task_state.workflow_run
if not workflow_run:
raise Exception('Workflow run not found.')
response = WorkflowAppBlockingResponse(
task_id=self._application_generate_entity.task_id,
workflow_run_id=workflow_run.id,
workflow_run_id=stream_response.data.id,
data=WorkflowAppBlockingResponse.Data(
id=workflow_run.id,
workflow_id=workflow_run.workflow_id,
status=workflow_run.status,
outputs=workflow_run.outputs_dict,
error=workflow_run.error,
elapsed_time=workflow_run.elapsed_time,
total_tokens=workflow_run.total_tokens,
total_steps=workflow_run.total_steps,
created_at=int(workflow_run.created_at.timestamp()),
finished_at=int(workflow_run.finished_at.timestamp())
id=stream_response.data.id,
workflow_id=stream_response.data.workflow_id,
status=stream_response.data.status,
outputs=stream_response.data.outputs,
error=stream_response.data.error,
elapsed_time=stream_response.data.elapsed_time,
total_tokens=stream_response.data.total_tokens,
total_steps=stream_response.data.total_steps,
created_at=int(stream_response.data.created_at),
finished_at=int(stream_response.data.finished_at)
)
)
@ -153,12 +149,13 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
To stream response.
:return:
"""
workflow_run_id = None
for stream_response in generator:
if not self._task_state.workflow_run:
raise Exception('Workflow run not found.')
if isinstance(stream_response, WorkflowStartStreamResponse):
workflow_run_id = stream_response.workflow_run_id
yield WorkflowAppStreamResponse(
workflow_run_id=self._task_state.workflow_run.id,
workflow_run_id=workflow_run_id,
stream_response=stream_response
)
@ -173,17 +170,18 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
def _wrapper_process_stream_response(self, trace_manager: Optional[TraceQueueManager] = None) -> \
Generator[StreamResponse, None, None]:
publisher = None
tts_publisher = None
task_id = self._application_generate_entity.task_id
tenant_id = self._application_generate_entity.app_config.tenant_id
features_dict = self._workflow.features_dict
if features_dict.get('text_to_speech') and features_dict['text_to_speech'].get('enabled') and features_dict[
'text_to_speech'].get('autoPlay') == 'enabled':
publisher = AppGeneratorTTSPublisher(tenant_id, features_dict['text_to_speech'].get('voice'))
for response in self._process_stream_response(publisher=publisher, trace_manager=trace_manager):
tts_publisher = AppGeneratorTTSPublisher(tenant_id, features_dict['text_to_speech'].get('voice'))
for response in self._process_stream_response(tts_publisher=tts_publisher, trace_manager=trace_manager):
while True:
audio_response = self._listenAudioMsg(publisher, task_id=task_id)
audio_response = self._listenAudioMsg(tts_publisher, task_id=task_id)
if audio_response:
yield audio_response
else:
@ -193,9 +191,9 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
start_listener_time = time.time()
while (time.time() - start_listener_time) < TTS_AUTO_PLAY_TIMEOUT:
try:
if not publisher:
if not tts_publisher:
break
audio_trunk = publisher.checkAndGetAudio()
audio_trunk = tts_publisher.checkAndGetAudio()
if audio_trunk is None:
# release cpu
# sleep 20 ms ( 40ms => 1280 byte audio file,20ms => 640 byte audio file)
@ -213,55 +211,105 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
def _process_stream_response(
self,
publisher: AppGeneratorTTSPublisher,
tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
trace_manager: Optional[TraceQueueManager] = None
) -> Generator[StreamResponse, None, None]:
"""
Process stream response.
:return:
"""
for message in self._queue_manager.listen():
if publisher:
publisher.publish(message=message)
event = message.event
graph_runtime_state = None
workflow_run = None
if isinstance(event, QueueErrorEvent):
for queue_message in self._queue_manager.listen():
event = queue_message.event
if isinstance(event, QueuePingEvent):
yield self._ping_stream_response()
elif isinstance(event, QueueErrorEvent):
err = self._handle_error(event)
yield self._error_to_stream_response(err)
break
elif isinstance(event, QueueWorkflowStartedEvent):
# override graph runtime state
graph_runtime_state = event.graph_runtime_state
# init workflow run
workflow_run = self._handle_workflow_run_start()
yield self._workflow_start_to_stream_response(
task_id=self._application_generate_entity.task_id,
workflow_run=workflow_run
)
elif isinstance(event, QueueNodeStartedEvent):
workflow_node_execution = self._handle_execution_node_start(event)
if not workflow_run:
raise Exception('Workflow run not initialized.')
workflow_node_execution = self._handle_node_execution_start(
workflow_run=workflow_run,
event=event
)
yield self._workflow_node_start_to_stream_response(
event=event,
task_id=self._application_generate_entity.task_id,
workflow_node_execution=workflow_node_execution
)
elif isinstance(event, QueueNodeSucceededEvent | QueueNodeFailedEvent):
workflow_node_execution = self._handle_node_finished(event)
elif isinstance(event, QueueNodeSucceededEvent):
workflow_node_execution = self._handle_workflow_node_execution_success(event)
yield self._workflow_node_finish_to_stream_response(
task_id=self._application_generate_entity.task_id,
workflow_node_execution=workflow_node_execution
)
elif isinstance(event, QueueNodeFailedEvent):
workflow_node_execution = self._handle_workflow_node_execution_failed(event)
if isinstance(event, QueueNodeFailedEvent):
yield from self._handle_iteration_exception(
task_id=self._application_generate_entity.task_id,
error=f'Child node failed: {event.error}'
)
elif isinstance(event, QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent):
yield self._handle_iteration_to_stream_response(self._application_generate_entity.task_id, event)
self._handle_iteration_operation(event)
yield self._workflow_node_finish_to_stream_response(
task_id=self._application_generate_entity.task_id,
workflow_node_execution=workflow_node_execution
)
elif isinstance(event, QueueIterationStartEvent):
if not workflow_run:
raise Exception('Workflow run not initialized.')
yield self._workflow_iteration_start_to_stream_response(
task_id=self._application_generate_entity.task_id,
workflow_run=workflow_run,
event=event
)
elif isinstance(event, QueueIterationNextEvent):
if not workflow_run:
raise Exception('Workflow run not initialized.')
yield self._workflow_iteration_next_to_stream_response(
task_id=self._application_generate_entity.task_id,
workflow_run=workflow_run,
event=event
)
elif isinstance(event, QueueIterationCompletedEvent):
if not workflow_run:
raise Exception('Workflow run not initialized.')
yield self._workflow_iteration_completed_to_stream_response(
task_id=self._application_generate_entity.task_id,
workflow_run=workflow_run,
event=event
)
elif isinstance(event, QueueStopEvent | QueueWorkflowSucceededEvent | QueueWorkflowFailedEvent):
workflow_run = self._handle_workflow_finished(
event, trace_manager=trace_manager
if not workflow_run:
raise Exception('Workflow run not initialized.')
if not graph_runtime_state:
raise Exception('Graph runtime state not initialized.')
workflow_run = self._handle_workflow_run_success(
workflow_run=workflow_run,
start_at=graph_runtime_state.start_at,
total_tokens=graph_runtime_state.total_tokens,
total_steps=graph_runtime_state.node_run_steps,
outputs=json.dumps(event.outputs) if isinstance(event, QueueWorkflowSucceededEvent) and event.outputs else None,
conversation_id=None,
trace_manager=trace_manager,
)
# save workflow app log
@ -276,17 +324,17 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
if delta_text is None:
continue
# only publish tts message at text chunk streaming
if tts_publisher:
tts_publisher.publish(message=queue_message)
self._task_state.answer += delta_text
yield self._text_chunk_to_stream_response(delta_text)
elif isinstance(event, QueueMessageReplaceEvent):
yield self._text_replace_to_stream_response(event.text)
elif isinstance(event, QueuePingEvent):
yield self._ping_stream_response()
else:
continue
if publisher:
publisher.publish(None)
if tts_publisher:
tts_publisher.publish(None)
def _save_workflow_app_log(self, workflow_run: WorkflowRun) -> None:
@ -305,15 +353,15 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
# not save log for debugging
return
workflow_app_log = WorkflowAppLog(
tenant_id=workflow_run.tenant_id,
app_id=workflow_run.app_id,
workflow_id=workflow_run.workflow_id,
workflow_run_id=workflow_run.id,
created_from=created_from.value,
created_by_role=('account' if isinstance(self._user, Account) else 'end_user'),
created_by=self._user.id,
)
workflow_app_log = WorkflowAppLog()
workflow_app_log.tenant_id = workflow_run.tenant_id
workflow_app_log.app_id = workflow_run.app_id
workflow_app_log.workflow_id = workflow_run.workflow_id
workflow_app_log.workflow_run_id = workflow_run.id
workflow_app_log.created_from = created_from.value
workflow_app_log.created_by_role = 'account' if isinstance(self._user, Account) else 'end_user'
workflow_app_log.created_by = self._user.id
db.session.add(workflow_app_log)
db.session.commit()
db.session.close()
@ -330,14 +378,3 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
)
return response
def _text_replace_to_stream_response(self, text: str) -> TextReplaceStreamResponse:
"""
Text replace to stream response.
:param text: text
:return:
"""
return TextReplaceStreamResponse(
task_id=self._application_generate_entity.task_id,
text=TextReplaceStreamResponse.Data(text=text)
)

228
api/core/app/apps/workflow_app_runner.py Normal file
View File

@ -0,0 +1,228 @@
from typing import Optional
from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
from core.app.apps.base_app_runner import AppRunner
from core.app.entities.queue_entities import (
AppQueueEvent,
QueueIterationCompletedEvent,
QueueIterationNextEvent,
QueueIterationStartEvent,
QueueNodeFailedEvent,
QueueNodeStartedEvent,
QueueNodeSucceededEvent,
QueueParallelBranchRunFailedEvent,
QueueParallelBranchRunStartedEvent,
QueueRetrieverResourcesEvent,
QueueTextChunkEvent,
QueueWorkflowFailedEvent,
QueueWorkflowStartedEvent,
QueueWorkflowSucceededEvent,
)
from core.workflow.graph_engine.entities.event import (
GraphEngineEvent,
GraphRunFailedEvent,
GraphRunStartedEvent,
GraphRunSucceededEvent,
IterationRunFailedEvent,
IterationRunNextEvent,
IterationRunStartedEvent,
IterationRunSucceededEvent,
NodeRunFailedEvent,
NodeRunRetrieverResourceEvent,
NodeRunStartedEvent,
NodeRunStreamChunkEvent,
NodeRunSucceededEvent,
ParallelBranchRunFailedEvent,
ParallelBranchRunStartedEvent,
ParallelBranchRunSucceededEvent,
)
from core.workflow.workflow_entry import WorkflowEntry
from extensions.ext_database import db
from models.model import App
from models.workflow import Workflow
class WorkflowBasedAppRunner(AppRunner):
def __init__(self, queue_manager: AppQueueManager):
self.queue_manager = queue_manager
def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent) -> None:
"""
Handle event
:param workflow_entry: workflow entry
:param event: event
"""
if isinstance(event, GraphRunStartedEvent):
self._publish_event(
QueueWorkflowStartedEvent(
graph_runtime_state=workflow_entry.graph_engine.graph_runtime_state
)
)
elif isinstance(event, GraphRunSucceededEvent):
self._publish_event(
QueueWorkflowSucceededEvent(outputs=event.outputs)
)
elif isinstance(event, GraphRunFailedEvent):
self._publish_event(
QueueWorkflowFailedEvent(error=event.error)
)
elif isinstance(event, NodeRunStartedEvent):
self._publish_event(
QueueNodeStartedEvent(
node_execution_id=event.id,
node_id=event.node_id,
node_type=event.node_type,
node_data=event.node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.route_node_state.start_at,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
predecessor_node_id=event.predecessor_node_id
)
)
elif isinstance(event, NodeRunSucceededEvent):
self._publish_event(
QueueNodeSucceededEvent(
node_execution_id=event.id,
node_id=event.node_id,
node_type=event.node_type,
node_data=event.node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.route_node_state.start_at,
inputs=event.route_node_state.node_run_result.inputs
if event.route_node_state.node_run_result else {},
process_data=event.route_node_state.node_run_result.process_data
if event.route_node_state.node_run_result else {},
outputs=event.route_node_state.node_run_result.outputs
if event.route_node_state.node_run_result else {},
execution_metadata=event.route_node_state.node_run_result.metadata
if event.route_node_state.node_run_result else {},
)
)
elif isinstance(event, NodeRunFailedEvent):
self._publish_event(
QueueNodeFailedEvent(
node_execution_id=event.id,
node_id=event.node_id,
node_type=event.node_type,
node_data=event.node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.route_node_state.start_at,
inputs=event.route_node_state.node_run_result.inputs
if event.route_node_state.node_run_result else {},
process_data=event.route_node_state.node_run_result.process_data
if event.route_node_state.node_run_result else {},
outputs=event.route_node_state.node_run_result.outputs
if event.route_node_state.node_run_result else {},
error=event.route_node_state.node_run_result.error
if event.route_node_state.node_run_result
and event.route_node_state.node_run_result.error
else "Unknown error"
)
)
elif isinstance(event, NodeRunStreamChunkEvent):
self._publish_event(
QueueTextChunkEvent(
text=event.chunk_content
)
)
elif isinstance(event, NodeRunRetrieverResourceEvent):
self._publish_event(
QueueRetrieverResourcesEvent(
retriever_resources=event.retriever_resources
)
)
elif isinstance(event, ParallelBranchRunStartedEvent):
self._publish_event(
QueueParallelBranchRunStartedEvent(
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id
)
)
elif isinstance(event, ParallelBranchRunSucceededEvent):
self._publish_event(
QueueParallelBranchRunStartedEvent(
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id
)
)
elif isinstance(event, ParallelBranchRunFailedEvent):
self._publish_event(
QueueParallelBranchRunFailedEvent(
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
error=event.error
)
)
elif isinstance(event, IterationRunStartedEvent):
self._publish_event(
QueueIterationStartEvent(
node_execution_id=event.iteration_id,
node_id=event.iteration_node_id,
node_type=event.iteration_node_type,
node_data=event.iteration_node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.start_at,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
inputs=event.inputs,
predecessor_node_id=event.predecessor_node_id,
metadata=event.metadata
)
)
elif isinstance(event, IterationRunNextEvent):
self._publish_event(
QueueIterationNextEvent(
node_execution_id=event.iteration_id,
node_id=event.iteration_node_id,
node_type=event.iteration_node_type,
node_data=event.iteration_node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
index=event.index,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
output=event.pre_iteration_output,
)
)
elif isinstance(event, (IterationRunSucceededEvent | IterationRunFailedEvent)):
self._publish_event(
QueueIterationCompletedEvent(
node_execution_id=event.iteration_id,
node_id=event.iteration_node_id,
node_type=event.iteration_node_type,
node_data=event.iteration_node_data,
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
start_at=event.start_at,
node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
inputs=event.inputs,
outputs=event.outputs,
metadata=event.metadata,
steps=event.steps,
error=event.error if isinstance(event, IterationRunFailedEvent) else None
)
)
def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
"""
Get workflow
"""
# fetch workflow by workflow_id
workflow = (
db.session.query(Workflow)
.filter(
Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
)
.first()
)
# return workflow
return workflow
def _publish_event(self, event: AppQueueEvent) -> None:
self.queue_manager.publish(
event,
PublishFrom.APPLICATION_MANAGER
)

2
api/core/app/entities/task_entities.py
View File

@ -438,7 +438,7 @@ class WorkflowAppStreamResponse(AppStreamResponse):
"""
WorkflowAppStreamResponse entity
"""
workflow_run_id: str
workflow_run_id: Optional[str] = None
class AppBlockingResponse(BaseModel):

2
api/core/app/task_pipeline/based_generate_task_pipeline.py
View File

@ -50,7 +50,7 @@ class BasedGenerateTaskPipeline:
self._output_moderation_handler = self._init_output_moderation()
self._stream = stream
def _handle_error(self, event: QueueErrorEvent, message: Message) -> Exception:
def _handle_error(self, event: QueueErrorEvent, message: Optional[Message] = None) -> Exception:
"""
Handle error event.
:param event: event

9
api/core/workflow/errors.py
View File

@ -1,10 +1,9 @@
from core.workflow.entities.node_entities import NodeType
from core.workflow.nodes.base_node import BaseNode
class WorkflowNodeRunFailedError(Exception):
def __init__(self, node_id: str, node_type: NodeType, node_title: str, error: str):
self.node_id = node_id
self.node_type = node_type
self.node_title = node_title
def __init__(self, node_instance: BaseNode, error: str):
self.node_instance = node_instance
self.error = error
super().__init__(f"Node {node_title} run failed: {error}")
super().__init__(f"Node {node_instance.node_data.title} run failed: {error}")

14
api/core/workflow/nodes/answer/answer_node.py
View File

@ -1,7 +1,8 @@
from typing import cast
from typing import Any, Mapping, Sequence, cast
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType
from core.workflow.graph_engine.entities.graph import Graph
from core.workflow.nodes.answer.answer_stream_generate_router import AnswerStreamGeneratorRouter
from core.workflow.nodes.answer.entities import (
AnswerNodeData,
@ -52,9 +53,16 @@ class AnswerNode(BaseNode):
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: AnswerNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
@ -66,6 +74,6 @@ class AnswerNode(BaseNode):
variable_mapping = {}
for variable_selector in variable_selectors:
variable_mapping[variable_selector.variable] = variable_selector.value_selector
variable_mapping[node_id + '.' + variable_selector.variable] = variable_selector.value_selector
return variable_mapping

22
api/core/workflow/nodes/base_node.py
View File

@ -67,19 +67,35 @@ class BaseNode(ABC):
yield from result
@classmethod
def extract_variable_selector_to_variable_mapping(cls, config: dict):
def extract_variable_selector_to_variable_mapping(cls, graph_config: Mapping[str, Any], config: dict) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param config: node config
:return:
"""
node_id = config.get("id")
if not node_id:
raise ValueError("Node ID is required when extracting variable selector to variable mapping.")
node_data = cls._node_data_cls(**config.get("data", {}))
return cls._extract_variable_selector_to_variable_mapping(node_data)
return cls._extract_variable_selector_to_variable_mapping(
graph_config=graph_config,
node_id=node_id,
node_data=node_data
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> Mapping[str, Sequence[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: BaseNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""

14
api/core/workflow/nodes/code/code_node.py
View File

@ -1,4 +1,4 @@
from typing import Optional, Union, cast
from typing import Any, Mapping, Optional, Sequence, Union, cast
from configs import dify_config
from core.helper.code_executor.code_executor import CodeExecutionException, CodeExecutor, CodeLanguage
@ -314,13 +314,19 @@ class CodeNode(BaseNode):
return transformed_result
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: CodeNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: CodeNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
return {
variable_selector.variable: variable_selector.value_selector for variable_selector in node_data.variables
node_id + '.' + variable_selector.variable: variable_selector.value_selector for variable_selector in node_data.variables
}

11
api/core/workflow/nodes/end/end_node.py
View File

@ -1,4 +1,4 @@
from typing import cast
from typing import Any, Mapping, Sequence, cast
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType
@ -32,9 +32,16 @@ class EndNode(BaseNode):
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: EndNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""

14
api/core/workflow/nodes/http_request/http_request_node.py
View File

@ -1,7 +1,7 @@
import logging
from mimetypes import guess_extension
from os import path
from typing import cast
from typing import Any, Mapping, Sequence, cast
from core.app.segments import parser
from core.file.file_obj import FileTransferMethod, FileType, FileVar
@ -107,13 +107,19 @@ class HttpRequestNode(BaseNode):
return timeout
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: HttpRequestNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
node_data = cast(HttpRequestNodeData, node_data)
try:
http_executor = HttpExecutor(node_data=node_data, timeout=HTTP_REQUEST_DEFAULT_TIMEOUT)
@ -121,7 +127,7 @@ class HttpRequestNode(BaseNode):
variable_mapping = {}
for variable_selector in variable_selectors:
variable_mapping[variable_selector.variable] = variable_selector.value_selector
variable_mapping[node_id + '.' + variable_selector.variable] = variable_selector.value_selector
return variable_mapping
except Exception as e:

11
api/core/workflow/nodes/if_else/if_else_node.py
View File

@ -1,4 +1,4 @@
from typing import cast
from typing import Any, Mapping, Sequence, cast
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType
@ -99,9 +99,16 @@ class IfElseNode(BaseNode):
return data
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: IfElseNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""

64
api/core/workflow/nodes/iteration/iteration_node.py
View File

@ -1,10 +1,11 @@
import logging
from collections.abc import Generator
from collections.abc import Generator, Mapping, Sequence
from datetime import datetime, timezone
from typing import Any, cast
from configs import dify_config
from core.model_runtime.utils.encoders import jsonable_encoder
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
from core.workflow.graph_engine.entities.event import (
BaseGraphEvent,
@ -287,12 +288,67 @@ class IterationNode(BaseNode):
variable_pool.remove([self.node_id, 'item'])
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: IterationNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: IterationNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
return {
'input_selector': node_data.iterator_selector,
variable_mapping = {
f'{node_id}.input_selector': node_data.iterator_selector,
}
# init graph
iteration_graph = Graph.init(
graph_config=graph_config,
root_node_id=node_data.start_node_id
)
if not iteration_graph:
raise ValueError('iteration graph not found')
for sub_node_id, sub_node_config in iteration_graph.node_id_config_mapping.items():
if sub_node_config.get('data', {}).get('iteration_id') != node_id:
continue
# variable selector to variable mapping
try:
# Get node class
from core.workflow.nodes.node_mapping import node_classes
node_type = NodeType.value_of(sub_node_config.get('data', {}).get('type'))
node_cls = node_classes.get(node_type)
if not node_cls:
continue
node_cls = cast(BaseNode, node_cls)
sub_node_variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
graph_config=graph_config,
config=sub_node_config
)
sub_node_variable_mapping = cast(dict[str, list[str]], sub_node_variable_mapping)
except NotImplementedError:
sub_node_variable_mapping = {}
# remove iteration variables
sub_node_variable_mapping = {
sub_node_id + '.' + key: value for key, value in sub_node_variable_mapping.items()
if value[0] != node_id
}
variable_mapping.update(sub_node_variable_mapping)
# remove variable out from iteration
variable_mapping = {
key: value for key, value in variable_mapping.items()
if value[0] not in iteration_graph.node_ids
}
return variable_mapping

20
api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
View File

@ -1,5 +1,5 @@
import logging
from typing import Any, cast
from typing import Any, Mapping, Sequence, cast
from sqlalchemy import func
@ -232,11 +232,21 @@ class KnowledgeRetrievalNode(BaseNode):
return context_list
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
node_data = node_data
node_data = cast(cls._node_data_cls, node_data)
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: KnowledgeRetrievalNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
variable_mapping = {}
variable_mapping['query'] = node_data.query_variable_selector
variable_mapping[node_id + '.query'] = node_data.query_variable_selector
return variable_mapping
def _fetch_model_config(self, node_data: KnowledgeRetrievalNodeData) -> tuple[

16
api/core/workflow/nodes/llm/llm_node.py
View File

@ -1,7 +1,7 @@
import json
from collections.abc import Generator
from copy import deepcopy
from typing import Optional, cast
from typing import Any, Mapping, Optional, Sequence, cast
from pydantic import BaseModel
@ -678,13 +678,19 @@ class LLMNode(BaseNode):
db.session.commit()
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: LLMNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
node_data = cast(LLMNodeData, node_data)
prompt_template = node_data.prompt_template
variable_selectors = []
@ -734,6 +740,10 @@ class LLMNode(BaseNode):
for variable_selector in node_data.prompt_config.jinja2_variables or []:
variable_mapping[variable_selector.variable] = variable_selector.value_selector
variable_mapping = {
node_id + '.' + key: value for key, value in variable_mapping.items()
}
return variable_mapping
@classmethod

18
api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py
View File

@ -1,6 +1,6 @@
import json
import uuid
from typing import Optional, cast
from typing import Any, Mapping, Optional, Sequence, cast
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.memory.token_buffer_memory import TokenBufferMemory
@ -701,15 +701,19 @@ class ParameterExtractorNode(LLMNode):
return self._model_instance, self._model_config
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: ParameterExtractorNodeData) -> dict[
str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: ParameterExtractorNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
node_data = node_data
variable_mapping = {
'query': node_data.query
}
@ -719,4 +723,8 @@ class ParameterExtractorNode(LLMNode):
for selector in variable_template_parser.extract_variable_selectors():
variable_mapping[selector.variable] = selector.value_selector
variable_mapping = {
node_id + '.' + key: value for key, value in variable_mapping.items()
}
return variable_mapping

23
api/core/workflow/nodes/question_classifier/question_classifier_node.py
View File

@ -1,6 +1,6 @@
import json
import logging
from typing import Optional, Union, cast
from typing import Any, Mapping, Optional, Sequence, Union, cast
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.memory.token_buffer_memory import TokenBufferMemory
@ -137,9 +137,19 @@ class QuestionClassifierNode(LLMNode):
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
node_data = node_data
node_data = cast(cls._node_data_cls, node_data)
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: QuestionClassifierNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
variable_mapping = {'query': node_data.query_variable_selector}
variable_selectors = []
if node_data.instruction:
@ -147,6 +157,11 @@ class QuestionClassifierNode(LLMNode):
variable_selectors.extend(variable_template_parser.extract_variable_selectors())
for variable_selector in variable_selectors:
variable_mapping[variable_selector.variable] = variable_selector.value_selector
variable_mapping = {
node_id + '.' + key: value for key, value in variable_mapping.items()
}
return variable_mapping
@classmethod

10
api/core/workflow/nodes/start/start_node.py
View File

@ -1,4 +1,5 @@
from typing import Any, Mapping, Sequence
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType
from core.workflow.nodes.base_node import BaseNode
@ -28,9 +29,16 @@ class StartNode(BaseNode):
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: StartNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""

14
api/core/workflow/nodes/template_transform/template_transform_node.py
View File

@ -1,5 +1,5 @@
import os
from typing import Optional, cast
from typing import Any, Mapping, Optional, Sequence, cast
from core.helper.code_executor.code_executor import CodeExecutionException, CodeExecutor, CodeLanguage
from core.workflow.entities.node_entities import NodeRunResult, NodeType
@ -77,13 +77,19 @@ class TemplateTransformNode(BaseNode):
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: TemplateTransformNodeData) -> dict[
str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: TemplateTransformNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
return {
variable_selector.variable: variable_selector.value_selector for variable_selector in node_data.variables
node_id + '.' + variable_selector.variable: variable_selector.value_selector for variable_selector in node_data.variables
}

13
api/core/workflow/nodes/tool/tool_node.py
View File

@ -221,9 +221,16 @@ class ToolNode(BaseNode):
return [message.message for message in tool_response if message.type == ToolInvokeMessage.MessageType.JSON]
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: ToolNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: ToolNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
@ -239,4 +246,8 @@ class ToolNode(BaseNode):
elif input.type == 'constant':
pass
result = {
node_id + '.' + key: value for key, value in result.items()
}
return result

16
api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py
View File

@ -1,4 +1,4 @@
from typing import cast
from typing import Any, Mapping, Sequence, cast
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType
@ -48,5 +48,17 @@ class VariableAggregatorNode(BaseNode):
)
@classmethod
def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
def _extract_variable_selector_to_variable_mapping(
cls,
graph_config: Mapping[str, Any],
node_id: str,
node_data: VariableAssignerNodeData
) -> Mapping[str, Sequence[str]]:
"""
Extract variable selector to variable mapping
:param graph_config: graph config
:param node_id: node id
:param node_data: node data
:return:
"""
return {}

304
api/core/workflow/workflow_entry.py
View File

@ -1,6 +1,8 @@
import logging
import time
import uuid
from collections.abc import Generator, Mapping, Sequence
from typing import Any, Optional, cast
from typing import Any, Optional, Type, cast
from configs import dify_config
from core.app.app_config.entities import FileExtraConfig
@ -8,13 +10,18 @@ from core.app.apps.base_app_queue_manager import GenerateTaskStoppedException
from core.app.entities.app_invoke_entities import InvokeFrom
from core.file.file_obj import FileTransferMethod, FileType, FileVar
from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
from core.workflow.entities.node_entities import NodeRunResult, NodeType, SystemVariable, UserFrom
from core.workflow.entities.base_node_data_entities import BaseNodeData
from core.workflow.entities.node_entities import NodeRunResult, NodeType, UserFrom
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.errors import WorkflowNodeRunFailedError
from core.workflow.graph_engine.entities.event import GraphEngineEvent, GraphRunFailedEvent
from core.workflow.graph_engine.entities.event import GraphEngineEvent, GraphRunFailedEvent, InNodeEvent
from core.workflow.graph_engine.entities.graph import Graph
from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
from core.workflow.graph_engine.graph_engine import GraphEngine
from core.workflow.nodes.base_node import BaseNode
from core.workflow.nodes.event import RunCompletedEvent, RunEvent
from core.workflow.nodes.iteration.entities import IterationNodeData
from core.workflow.nodes.llm.entities import LLMNodeData
from core.workflow.nodes.node_mapping import node_classes
from models.workflow import (
@ -32,18 +39,17 @@ class WorkflowEntry:
user_id: str,
user_from: UserFrom,
invoke_from: InvokeFrom,
user_inputs: Mapping[str, Any],
system_inputs: Mapping[SystemVariable, Any],
call_depth: int = 0
call_depth: int,
variable_pool: VariablePool
) -> None:
"""
:param workflow: Workflow instance
:param user_id: user id
:param user_from: user from
:param invoke_from: invoke from service-api, web-app, debugger, explore
:param user_inputs: user variables inputs
:param system_inputs: system inputs, like: query, files
:param call_depth: call depth
:param variable_pool: variable pool
:param single_step_run_iteration_id: single step run iteration id
"""
# fetch workflow graph
graph_config = workflow.graph_dict
@ -71,13 +77,6 @@ class WorkflowEntry:
if not graph:
raise ValueError('graph not found in workflow')
# init variable pool
variable_pool = VariablePool(
system_variables=system_inputs,
user_inputs=user_inputs,
environment_variables=workflow.environment_variables,
)
# init workflow run state
self.graph_engine = GraphEngine(
tenant_id=workflow.tenant_id,
@ -134,10 +133,160 @@ class WorkflowEntry:
)
return
def single_step_run(self, workflow: Workflow,
node_id: str,
user_id: str,
user_inputs: dict) -> tuple[BaseNode, NodeRunResult]:
@classmethod
def single_step_run_iteration(
cls,
workflow: Workflow,
node_id: str,
user_id: str,
user_inputs: dict,
callbacks: Sequence[WorkflowCallback],
) -> Generator[GraphEngineEvent, None, None]:
"""
Single step run workflow node iteration
:param workflow: Workflow instance
:param node_id: node id
:param user_id: user id
:param user_inputs: user inputs
:return:
"""
# fetch workflow graph
graph_config = workflow.graph_dict
if not graph_config:
raise ValueError('workflow graph not found')
graph_config = cast(dict[str, Any], graph_config)
if 'nodes' not in graph_config or 'edges' not in graph_config:
raise ValueError('nodes or edges not found in workflow graph')
if not isinstance(graph_config.get('nodes'), list):
raise ValueError('nodes in workflow graph must be a list')
if not isinstance(graph_config.get('edges'), list):
raise ValueError('edges in workflow graph must be a list')
# filter nodes only in iteration
node_configs = [
node for node in graph_config.get('nodes', [])
if node.get('id') == node_id or node.get('data', {}).get('iteration_id', '') == node_id
]
graph_config['nodes'] = node_configs
node_ids = [node.get('id') for node in node_configs]
# filter edges only in iteration
edge_configs = [
edge for edge in graph_config.get('edges', [])
if (edge.get('source') is None or edge.get('source') in node_ids)
and (edge.get('target') is None or edge.get('target') in node_ids)
]
graph_config['edges'] = edge_configs
# init graph
graph = Graph.init(
graph_config=graph_config,
root_node_id=node_id
)
if not graph:
raise ValueError('graph not found in workflow')
# fetch node config from node id
iteration_node_config = None
for node in node_configs:
if node.get('id') == node_id:
iteration_node_config = node
break
if not iteration_node_config:
raise ValueError('iteration node id not found in workflow graph')
# Get node class
node_type = NodeType.value_of(iteration_node_config.get('data', {}).get('type'))
node_cls = node_classes.get(node_type)
node_cls = cast(type[BaseNode], node_cls)
# init variable pool
variable_pool = VariablePool(
system_variables={},
user_inputs={},
environment_variables=workflow.environment_variables,
)
try:
variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
graph_config=workflow.graph_dict,
config=iteration_node_config
)
except NotImplementedError:
variable_mapping = {}
cls._mapping_user_inputs_to_variable_pool(
variable_mapping=variable_mapping,
user_inputs=user_inputs,
variable_pool=variable_pool,
tenant_id=workflow.tenant_id,
node_type=node_type,
node_data=IterationNodeData(**iteration_node_config.get('data', {}))
)
# init workflow run state
graph_engine = GraphEngine(
tenant_id=workflow.tenant_id,
app_id=workflow.app_id,
workflow_type=WorkflowType.value_of(workflow.type),
workflow_id=workflow.id,
user_id=user_id,
user_from=UserFrom.ACCOUNT,
invoke_from=InvokeFrom.DEBUGGER,
call_depth=1,
graph=graph,
graph_config=graph_config,
variable_pool=variable_pool,
max_execution_steps=dify_config.WORKFLOW_MAX_EXECUTION_STEPS,
max_execution_time=dify_config.WORKFLOW_MAX_EXECUTION_TIME
)
try:
# run workflow
generator = graph_engine.run()
for event in generator:
if callbacks:
for callback in callbacks:
callback.on_event(
graph=graph_engine.graph,
graph_init_params=graph_engine.init_params,
graph_runtime_state=graph_engine.graph_runtime_state,
event=event
)
yield event
except GenerateTaskStoppedException:
pass
except Exception as e:
logger.exception("Unknown Error when workflow entry running")
if callbacks:
for callback in callbacks:
callback.on_event(
graph=graph_engine.graph,
graph_init_params=graph_engine.init_params,
graph_runtime_state=graph_engine.graph_runtime_state,
event=GraphRunFailedEvent(
error=str(e)
)
)
return
@classmethod
def single_step_run(
cls,
workflow: Workflow,
node_id: str,
user_id: str,
user_inputs: dict
) -> tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]:
"""
Single step run workflow node
:param workflow: Workflow instance
@ -168,61 +317,74 @@ class WorkflowEntry:
# Get node class
node_type = NodeType.value_of(node_config.get('data', {}).get('type'))
node_cls = node_classes.get(node_type)
node_cls = cast(type[BaseNode], node_cls)
if not node_cls:
raise ValueError(f'Node class not found for node type {node_type}')
# init variable pool
variable_pool = VariablePool(
system_variables={},
user_inputs={},
environment_variables=workflow.environment_variables,
)
# init graph
graph = Graph.init(
graph_config=workflow.graph_dict
)
# init workflow run state
node_instance = node_cls(
tenant_id=workflow.tenant_id,
app_id=workflow.app_id,
workflow_id=workflow.id,
user_id=user_id,
user_from=UserFrom.ACCOUNT,
invoke_from=InvokeFrom.DEBUGGER,
node_instance: BaseNode = node_cls(
id=str(uuid.uuid4()),
config=node_config,
workflow_call_depth=0
graph_init_params=GraphInitParams(
tenant_id=workflow.tenant_id,
app_id=workflow.app_id,
workflow_type=WorkflowType.value_of(workflow.type),
workflow_id=workflow.id,
graph_config=workflow.graph_dict,
user_id=user_id,
user_from=UserFrom.ACCOUNT,
invoke_from=InvokeFrom.DEBUGGER,
call_depth=0
),
graph=graph,
graph_runtime_state=GraphRuntimeState(
variable_pool=variable_pool,
start_at=time.perf_counter()
)
)
try:
# init variable pool
variable_pool = VariablePool(
system_variables={},
user_inputs={},
environment_variables=workflow.environment_variables,
)
# variable selector to variable mapping
try:
variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(node_config)
variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
graph_config=workflow.graph_dict,
config=node_config
)
except NotImplementedError:
variable_mapping = {}
self._mapping_user_inputs_to_variable_pool(
cls._mapping_user_inputs_to_variable_pool(
variable_mapping=variable_mapping,
user_inputs=user_inputs,
variable_pool=variable_pool,
tenant_id=workflow.tenant_id,
node_instance=node_instance
node_type=node_type,
node_data=node_instance.node_data
)
# run node
node_run_result = node_instance.run(
variable_pool=variable_pool
)
generator = node_instance.run()
# sign output files
node_run_result.outputs = self.handle_special_values(node_run_result.outputs)
return node_instance, generator
except Exception as e:
raise WorkflowNodeRunFailedError(
node_id=node_instance.node_id,
node_type=node_instance.node_type,
node_title=node_instance.node_data.title,
node_instance=node_instance,
error=str(e)
)
return node_instance, node_run_result
@classmethod
def handle_special_values(cls, value: Optional[Mapping[str, Any]]) -> Optional[dict]:
"""
@ -250,33 +412,49 @@ class WorkflowEntry:
return new_value
def _mapping_user_inputs_to_variable_pool(self,
variable_mapping: dict,
user_inputs: dict,
variable_pool: VariablePool,
tenant_id: str,
node_instance: BaseNode):
for variable_key, variable_selector in variable_mapping.items():
if variable_key not in user_inputs and not variable_pool.get(variable_selector):
raise ValueError(f'Variable key {variable_key} not found in user inputs.')
@classmethod
def _mapping_user_inputs_to_variable_pool(
cls,
variable_mapping: Mapping[str, Sequence[str]],
user_inputs: dict,
variable_pool: VariablePool,
tenant_id: str,
node_type: NodeType,
node_data: BaseNodeData
) -> None:
for node_variable, variable_selector in variable_mapping.items():
# fetch node id and variable key from node_variable
node_variable_list = node_variable.split('.')
if len(node_variable_list) < 1:
raise ValueError(f'Invalid node variable {node_variable}')
node_variable_key = node_variable_list[1:]
if (
node_variable_key not in user_inputs
or node_variable not in user_inputs
) and not variable_pool.get(variable_selector):
raise ValueError(f'Variable key {node_variable} not found in user inputs.')
# fetch variable node id from variable selector
variable_node_id = variable_selector[0]
variable_key_list = variable_selector[1:]
variable_key_list = cast(list[str], variable_key_list)
# get value
value = user_inputs.get(variable_key)
# get input value
input_value = user_inputs.get(node_variable)
if not input_value:
input_value = user_inputs.get(node_variable_key)
# FIXME: temp fix for image type
if node_instance.node_type == NodeType.LLM:
if node_type == NodeType.LLM:
new_value = []
if isinstance(value, list):
node_data = node_instance.node_data
if isinstance(input_value, list):
node_data = cast(LLMNodeData, node_data)
detail = node_data.vision.configs.detail if node_data.vision.configs else None
for item in value:
for item in input_value:
if isinstance(item, dict) and 'type' in item and item['type'] == 'image':
transfer_method = FileTransferMethod.value_of(item.get('transfer_method'))
file = FileVar(
@ -294,4 +472,4 @@ class WorkflowEntry:
value = new_value
# append variable and value to variable pool
variable_pool.add([variable_node_id] + variable_key_list, value)
variable_pool.add([variable_node_id] + variable_key_list, input_value)

6
api/services/app_generate_service.py
View File

@ -10,6 +10,7 @@ from core.app.apps.workflow.app_generator import WorkflowAppGenerator
from core.app.entities.app_invoke_entities import InvokeFrom
from core.app.features.rate_limiting import RateLimit
from models.model import Account, App, AppMode, EndUser
from models.workflow import Workflow
from services.workflow_service import WorkflowService
@ -95,11 +96,10 @@ class AppGenerateService:
@classmethod
def generate_single_iteration(cls, app_model: App,
user: Union[Account, EndUser],
user: Account,
node_id: str,
args: Any,
streaming: bool = True):
# TODO
if app_model.mode == AppMode.ADVANCED_CHAT.value:
workflow = cls._get_workflow(app_model, InvokeFrom.DEBUGGER)
return AdvancedChatAppGenerator().single_iteration_generate(
@ -145,7 +145,7 @@ class AppGenerateService:
)
@classmethod
def _get_workflow(cls, app_model: App, invoke_from: InvokeFrom) -> Any:
def _get_workflow(cls, app_model: App, invoke_from: InvokeFrom) -> Workflow:
"""
Get workflow
:param app_model: app model

106
api/services/workflow_service.py
View File

@ -8,8 +8,9 @@ from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
from core.app.apps.workflow.app_config_manager import WorkflowAppConfigManager
from core.app.segments import Variable
from core.model_runtime.utils.encoders import jsonable_encoder
from core.workflow.entities.node_entities import NodeType
from core.workflow.entities.node_entities import NodeRunResult, NodeType
from core.workflow.errors import WorkflowNodeRunFailedError
from core.workflow.nodes.event import RunCompletedEvent
from core.workflow.nodes.node_mapping import node_classes
from core.workflow.workflow_entry import WorkflowEntry
from events.app_event import app_draft_workflow_was_synced, app_published_workflow_was_updated
@ -213,81 +214,62 @@ class WorkflowService:
raise ValueError('Workflow not initialized')
# run draft workflow node
workflow_entry = WorkflowEntry()
start_at = time.perf_counter()
try:
node_instance, node_run_result = workflow_entry.single_step_run(
node_instance, generator = WorkflowEntry.single_step_run(
workflow=draft_workflow,
node_id=node_id,
user_inputs=user_inputs,
user_id=account.id,
)
node_run_result: NodeRunResult | None = None
for event in generator:
if isinstance(event, RunCompletedEvent):
node_run_result = event.run_result
# sign output files
node_run_result.outputs = WorkflowEntry.handle_special_values(node_run_result.outputs)
break
if not node_run_result:
raise ValueError('Node run failed with no run result')
run_succeeded = True if node_run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED else False
error = node_run_result.error if not run_succeeded else None
except WorkflowNodeRunFailedError as e:
workflow_node_execution = WorkflowNodeExecution(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
workflow_id=draft_workflow.id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
index=1,
node_id=e.node_id,
node_type=e.node_type.value,
title=e.node_title,
status=WorkflowNodeExecutionStatus.FAILED.value,
error=e.error,
elapsed_time=time.perf_counter() - start_at,
created_by_role=CreatedByRole.ACCOUNT.value,
created_by=account.id,
created_at=datetime.now(timezone.utc).replace(tzinfo=None),
finished_at=datetime.now(timezone.utc).replace(tzinfo=None)
)
db.session.add(workflow_node_execution)
db.session.commit()
node_instance = e.node_instance
run_succeeded = False
node_run_result = None
error = e.error
return workflow_node_execution
workflow_node_execution = WorkflowNodeExecution()
workflow_node_execution.tenant_id = app_model.tenant_id
workflow_node_execution.app_id = app_model.id
workflow_node_execution.workflow_id = draft_workflow.id
workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value
workflow_node_execution.index = 1
workflow_node_execution.node_id = node_id
workflow_node_execution.node_type = node_instance.node_type.value
workflow_node_execution.title = node_instance.node_data.title
workflow_node_execution.elapsed_time = time.perf_counter() - start_at
workflow_node_execution.created_by_role = CreatedByRole.ACCOUNT.value
workflow_node_execution.created_by = account.id
workflow_node_execution.created_at = datetime.now(timezone.utc).replace(tzinfo=None)
workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
if node_run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED:
if run_succeeded and node_run_result:
# create workflow node execution
workflow_node_execution = WorkflowNodeExecution(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
workflow_id=draft_workflow.id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
index=1,
node_id=node_id,
node_type=node_instance.node_type.value,
title=node_instance.node_data.title,
inputs=json.dumps(node_run_result.inputs) if node_run_result.inputs else None,
process_data=json.dumps(node_run_result.process_data) if node_run_result.process_data else None,
outputs=json.dumps(jsonable_encoder(node_run_result.outputs)) if node_run_result.outputs else None,
execution_metadata=(json.dumps(jsonable_encoder(node_run_result.metadata))
if node_run_result.metadata else None),
status=WorkflowNodeExecutionStatus.SUCCEEDED.value,
elapsed_time=time.perf_counter() - start_at,
created_by_role=CreatedByRole.ACCOUNT.value,
created_by=account.id,
created_at=datetime.now(timezone.utc).replace(tzinfo=None),
finished_at=datetime.now(timezone.utc).replace(tzinfo=None)
)
workflow_node_execution.inputs = json.dumps(node_run_result.inputs) if node_run_result.inputs else None
workflow_node_execution.process_data = json.dumps(node_run_result.process_data) if node_run_result.process_data else None
workflow_node_execution.outputs = json.dumps(jsonable_encoder(node_run_result.outputs)) if node_run_result.outputs else None
workflow_node_execution.execution_metadata = json.dumps(jsonable_encoder(node_run_result.metadata)) if node_run_result.metadata else None
workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
else:
# create workflow node execution
workflow_node_execution = WorkflowNodeExecution(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
workflow_id=draft_workflow.id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
index=1,
node_id=node_id,
node_type=node_instance.node_type.value,
title=node_instance.node_data.title,
status=node_run_result.status.value,
error=node_run_result.error,
elapsed_time=time.perf_counter() - start_at,
created_by_role=CreatedByRole.ACCOUNT.value,
created_by=account.id,
created_at=datetime.now(timezone.utc).replace(tzinfo=None),
finished_at=datetime.now(timezone.utc).replace(tzinfo=None)
)
workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
workflow_node_execution.error = error
db.session.add(workflow_node_execution)
db.session.commit()

1
api/tests/integration_tests/workflow/nodes/test_code.py
View File

@ -22,6 +22,7 @@ def test_execute_code(setup_code_executor_mock):
# trim first 4 spaces at the beginning of each line
code = '\n'.join([line[4:] for line in code.split('\n')])
node = CodeNode(
id='test',
tenant_id='1',
app_id='1',
workflow_id='1',