mirror of
https://github.com/langgenius/dify.git
synced 2026-05-06 02:18:08 +08:00
feat: queue-based graph engine
Signed-off-by: -LAN- <laipz8200@outlook.com>
This commit is contained in:
-LAN-
@ -1,48 +1,35 @@
|
||||
import contextvars
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
from collections.abc import Generator, Mapping, Sequence
|
||||
from concurrent.futures import Future, wait
|
||||
from datetime import datetime
|
||||
from queue import Empty, Queue
|
||||
from typing import TYPE_CHECKING, Any, Optional, cast
|
||||
from datetime import UTC, datetime
|
||||
from typing import TYPE_CHECKING, Any, Optional, Union, cast
|
||||
|
||||
from flask import Flask, current_app
|
||||
|
||||
from configs import dify_config
|
||||
from core.variables import ArrayVariable, IntegerVariable, NoneVariable
|
||||
from core.variables.segments import ArrayAnySegment, ArraySegment
|
||||
from core.workflow.entities.node_entities import (
|
||||
NodeRunResult,
|
||||
from core.workflow.entities import VariablePool
|
||||
from core.workflow.enums import (
|
||||
ErrorStrategy,
|
||||
NodeExecutionType,
|
||||
NodeType,
|
||||
WorkflowNodeExecutionMetadataKey,
|
||||
WorkflowNodeExecutionStatus,
|
||||
)
|
||||
from core.workflow.entities.variable_pool import VariablePool
|
||||
from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
|
||||
from core.workflow.graph_engine.entities.event import (
|
||||
BaseGraphEvent,
|
||||
BaseNodeEvent,
|
||||
BaseParallelBranchEvent,
|
||||
from core.workflow.graph_events import (
|
||||
GraphNodeEventBase,
|
||||
GraphRunFailedEvent,
|
||||
InNodeEvent,
|
||||
IterationRunFailedEvent,
|
||||
IterationRunNextEvent,
|
||||
IterationRunStartedEvent,
|
||||
IterationRunSucceededEvent,
|
||||
NodeInIterationFailedEvent,
|
||||
NodeRunFailedEvent,
|
||||
NodeRunStartedEvent,
|
||||
NodeRunStreamChunkEvent,
|
||||
NodeRunSucceededEvent,
|
||||
GraphRunSucceededEvent,
|
||||
)
|
||||
from core.workflow.node_events import (
|
||||
IterationFailedEvent,
|
||||
IterationNextEvent,
|
||||
IterationStartedEvent,
|
||||
IterationSucceededEvent,
|
||||
NodeRunResult,
|
||||
StreamCompletedEvent,
|
||||
)
|
||||
from core.workflow.graph_engine.entities.graph import Graph
|
||||
from core.workflow.nodes.base import BaseNode
|
||||
from core.workflow.nodes.base.entities import BaseNodeData, RetryConfig
|
||||
from core.workflow.nodes.enums import ErrorStrategy, NodeType
|
||||
from core.workflow.nodes.event import NodeEvent, RunCompletedEvent
|
||||
from core.workflow.nodes.base.node import Node
|
||||
from core.workflow.nodes.iteration.entities import ErrorHandleMode, IterationNodeData
|
||||
from factories.variable_factory import build_segment
|
||||
from libs.datetime_utils import naive_utc_now
|
||||
from libs.flask_utils import preserve_flask_contexts
|
||||
|
||||
from .exc import (
|
||||
InvalidIteratorValueError,
|
||||
@ -54,17 +41,18 @@ from .exc import (
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from core.workflow.graph_engine.graph_engine import GraphEngine
|
||||
from core.workflow.graph_engine import GraphEngine
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class IterationNode(BaseNode):
|
||||
class IterationNode(Node):
|
||||
"""
|
||||
Iteration Node.
|
||||
"""
|
||||
|
||||
_node_type = NodeType.ITERATION
|
||||
|
||||
node_type = NodeType.ITERATION
|
||||
execution_type = NodeExecutionType.CONTAINER
|
||||
_node_data: IterationNodeData
|
||||
|
||||
def init_node_data(self, data: Mapping[str, Any]) -> None:
|
||||
@ -103,10 +91,7 @@ class IterationNode(BaseNode):
|
||||
def version(cls) -> str:
|
||||
return "1"
|
||||
|
||||
def _run(self) -> Generator[NodeEvent | InNodeEvent, None, None]:
|
||||
"""
|
||||
Run the node.
|
||||
"""
|
||||
def _run(self) -> Generator:
|
||||
variable = self.graph_runtime_state.variable_pool.get(self._node_data.iterator_selector)
|
||||
|
||||
if not variable:
|
||||
@ -121,8 +106,8 @@ class IterationNode(BaseNode):
|
||||
output = variable.model_copy(update={"value": []})
|
||||
else:
|
||||
output = ArrayAnySegment(value=[])
|
||||
yield RunCompletedEvent(
|
||||
run_result=NodeRunResult(
|
||||
yield StreamCompletedEvent(
|
||||
node_run_result=NodeRunResult(
|
||||
status=WorkflowNodeExecutionStatus.SUCCEEDED,
|
||||
# TODO(QuantumGhost): is it possible to compute the type of `output`
|
||||
# from graph definition?
|
||||
@ -138,190 +123,76 @@ class IterationNode(BaseNode):
|
||||
|
||||
inputs = {"iterator_selector": iterator_list_value}
|
||||
|
||||
graph_config = self.graph_config
|
||||
|
||||
if not self._node_data.start_node_id:
|
||||
raise StartNodeIdNotFoundError(f"field start_node_id in iteration {self.node_id} not found")
|
||||
raise StartNodeIdNotFoundError(f"field start_node_id in iteration {self._node_id} not found")
|
||||
|
||||
root_node_id = self._node_data.start_node_id
|
||||
|
||||
# init graph
|
||||
iteration_graph = Graph.init(graph_config=graph_config, root_node_id=root_node_id)
|
||||
|
||||
if not iteration_graph:
|
||||
raise IterationGraphNotFoundError("iteration graph not found")
|
||||
|
||||
variable_pool = self.graph_runtime_state.variable_pool
|
||||
|
||||
# append iteration variable (item, index) to variable pool
|
||||
variable_pool.add([self.node_id, "index"], 0)
|
||||
variable_pool.add([self.node_id, "item"], iterator_list_value[0])
|
||||
|
||||
# init graph engine
|
||||
from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
|
||||
from core.workflow.graph_engine.graph_engine import GraphEngine, GraphEngineThreadPool
|
||||
|
||||
graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
|
||||
|
||||
graph_engine = GraphEngine(
|
||||
tenant_id=self.tenant_id,
|
||||
app_id=self.app_id,
|
||||
workflow_type=self.workflow_type,
|
||||
workflow_id=self.workflow_id,
|
||||
user_id=self.user_id,
|
||||
user_from=self.user_from,
|
||||
invoke_from=self.invoke_from,
|
||||
call_depth=self.workflow_call_depth,
|
||||
graph=iteration_graph,
|
||||
graph_config=graph_config,
|
||||
graph_runtime_state=graph_runtime_state,
|
||||
max_execution_steps=dify_config.WORKFLOW_MAX_EXECUTION_STEPS,
|
||||
max_execution_time=dify_config.WORKFLOW_MAX_EXECUTION_TIME,
|
||||
thread_pool_id=self.thread_pool_id,
|
||||
)
|
||||
|
||||
start_at = naive_utc_now()
|
||||
|
||||
yield IterationRunStartedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
start_at=start_at,
|
||||
inputs=inputs,
|
||||
metadata={"iterator_length": len(iterator_list_value)},
|
||||
predecessor_node_id=self.previous_node_id,
|
||||
)
|
||||
|
||||
yield IterationRunNextEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
index=0,
|
||||
pre_iteration_output=None,
|
||||
duration=None,
|
||||
)
|
||||
started_at = naive_utc_now()
|
||||
iter_run_map: dict[str, float] = {}
|
||||
outputs: list[Any] = [None] * len(iterator_list_value)
|
||||
outputs: list[Any] = []
|
||||
|
||||
yield IterationStartedEvent(
|
||||
start_at=started_at,
|
||||
inputs=inputs,
|
||||
metadata={"iteration_length": len(iterator_list_value)},
|
||||
)
|
||||
|
||||
try:
|
||||
if self._node_data.is_parallel:
|
||||
futures: list[Future] = []
|
||||
q: Queue = Queue()
|
||||
thread_pool = GraphEngineThreadPool(
|
||||
max_workers=self._node_data.parallel_nums, max_submit_count=dify_config.MAX_SUBMIT_COUNT
|
||||
for index, item in enumerate(iterator_list_value):
|
||||
iter_start_at = datetime.now(UTC).replace(tzinfo=None)
|
||||
yield IterationNextEvent(index=index)
|
||||
|
||||
graph_engine = self._create_graph_engine(index, item)
|
||||
|
||||
# Run the iteration
|
||||
yield from self._run_single_iter(
|
||||
variable_pool=graph_engine.graph_runtime_state.variable_pool,
|
||||
outputs=outputs,
|
||||
graph_engine=graph_engine,
|
||||
)
|
||||
for index, item in enumerate(iterator_list_value):
|
||||
future: Future = thread_pool.submit(
|
||||
self._run_single_iter_parallel,
|
||||
flask_app=current_app._get_current_object(), # type: ignore
|
||||
q=q,
|
||||
context=contextvars.copy_context(),
|
||||
iterator_list_value=iterator_list_value,
|
||||
inputs=inputs,
|
||||
outputs=outputs,
|
||||
start_at=start_at,
|
||||
graph_engine=graph_engine,
|
||||
iteration_graph=iteration_graph,
|
||||
index=index,
|
||||
item=item,
|
||||
iter_run_map=iter_run_map,
|
||||
)
|
||||
future.add_done_callback(thread_pool.task_done_callback)
|
||||
futures.append(future)
|
||||
succeeded_count = 0
|
||||
while True:
|
||||
try:
|
||||
event = q.get(timeout=1)
|
||||
if event is None:
|
||||
break
|
||||
if isinstance(event, IterationRunNextEvent):
|
||||
succeeded_count += 1
|
||||
if succeeded_count == len(futures):
|
||||
q.put(None)
|
||||
yield event
|
||||
if isinstance(event, RunCompletedEvent):
|
||||
q.put(None)
|
||||
for f in futures:
|
||||
if not f.done():
|
||||
f.cancel()
|
||||
yield event
|
||||
if isinstance(event, IterationRunFailedEvent):
|
||||
q.put(None)
|
||||
yield event
|
||||
except Empty:
|
||||
continue
|
||||
|
||||
# wait all threads
|
||||
wait(futures)
|
||||
else:
|
||||
for _ in range(len(iterator_list_value)):
|
||||
yield from self._run_single_iter(
|
||||
iterator_list_value=iterator_list_value,
|
||||
variable_pool=variable_pool,
|
||||
inputs=inputs,
|
||||
outputs=outputs,
|
||||
start_at=start_at,
|
||||
graph_engine=graph_engine,
|
||||
iteration_graph=iteration_graph,
|
||||
iter_run_map=iter_run_map,
|
||||
)
|
||||
if self._node_data.error_handle_mode == ErrorHandleMode.REMOVE_ABNORMAL_OUTPUT:
|
||||
outputs = [output for output in outputs if output is not None]
|
||||
# Update the total tokens from this iteration
|
||||
self.graph_runtime_state.total_tokens += graph_engine.graph_runtime_state.total_tokens
|
||||
iter_run_map[str(index)] = (datetime.now(UTC).replace(tzinfo=None) - iter_start_at).total_seconds()
|
||||
|
||||
# Flatten the list of lists
|
||||
if isinstance(outputs, list) and all(isinstance(output, list) for output in outputs):
|
||||
outputs = [item for sublist in outputs for item in sublist]
|
||||
output_segment = build_segment(outputs)
|
||||
|
||||
yield IterationRunSucceededEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
start_at=start_at,
|
||||
yield IterationSucceededEvent(
|
||||
start_at=started_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": outputs},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
metadata={
|
||||
WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: self.graph_runtime_state.total_tokens,
|
||||
WorkflowNodeExecutionMetadataKey.ITERATION_DURATION_MAP: iter_run_map,
|
||||
},
|
||||
)
|
||||
|
||||
yield RunCompletedEvent(
|
||||
run_result=NodeRunResult(
|
||||
# Yield final success event
|
||||
yield StreamCompletedEvent(
|
||||
node_run_result=NodeRunResult(
|
||||
status=WorkflowNodeExecutionStatus.SUCCEEDED,
|
||||
outputs={"output": output_segment},
|
||||
outputs={"output": outputs},
|
||||
metadata={
|
||||
WorkflowNodeExecutionMetadataKey.ITERATION_DURATION_MAP: iter_run_map,
|
||||
WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
|
||||
WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: self.graph_runtime_state.total_tokens,
|
||||
},
|
||||
)
|
||||
)
|
||||
except IterationNodeError as e:
|
||||
# iteration run failed
|
||||
logger.warning("Iteration run failed")
|
||||
yield IterationRunFailedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
start_at=start_at,
|
||||
yield IterationFailedEvent(
|
||||
start_at=started_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": outputs},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
metadata={
|
||||
WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: self.graph_runtime_state.total_tokens,
|
||||
WorkflowNodeExecutionMetadataKey.ITERATION_DURATION_MAP: iter_run_map,
|
||||
},
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
yield RunCompletedEvent(
|
||||
run_result=NodeRunResult(
|
||||
yield StreamCompletedEvent(
|
||||
node_run_result=NodeRunResult(
|
||||
status=WorkflowNodeExecutionStatus.FAILED,
|
||||
error=str(e),
|
||||
)
|
||||
)
|
||||
finally:
|
||||
# remove iteration variable (item, index) from variable pool after iteration run completed
|
||||
variable_pool.remove([self.node_id, "index"])
|
||||
variable_pool.remove([self.node_id, "item"])
|
||||
|
||||
@classmethod
|
||||
def _extract_variable_selector_to_variable_mapping(
|
||||
@ -339,12 +210,45 @@ class IterationNode(BaseNode):
|
||||
}
|
||||
|
||||
# init graph
|
||||
iteration_graph = Graph.init(graph_config=graph_config, root_node_id=typed_node_data.start_node_id)
|
||||
from core.workflow.entities import GraphInitParams, GraphRuntimeState
|
||||
from core.workflow.graph import Graph
|
||||
from core.workflow.nodes.node_factory import DifyNodeFactory
|
||||
|
||||
# Create minimal GraphInitParams for static analysis
|
||||
graph_init_params = GraphInitParams(
|
||||
tenant_id="",
|
||||
app_id="",
|
||||
workflow_id="",
|
||||
graph_config=graph_config,
|
||||
user_id="",
|
||||
user_from="",
|
||||
invoke_from="",
|
||||
call_depth=0,
|
||||
)
|
||||
|
||||
# Create minimal GraphRuntimeState for static analysis
|
||||
from core.workflow.entities import VariablePool
|
||||
|
||||
graph_runtime_state = GraphRuntimeState(
|
||||
variable_pool=VariablePool(),
|
||||
start_at=0,
|
||||
)
|
||||
|
||||
# Create node factory for static analysis
|
||||
node_factory = DifyNodeFactory(graph_init_params=graph_init_params, graph_runtime_state=graph_runtime_state)
|
||||
|
||||
iteration_graph = Graph.init(
|
||||
graph_config=graph_config,
|
||||
node_factory=node_factory,
|
||||
root_node_id=typed_node_data.start_node_id,
|
||||
)
|
||||
|
||||
if not iteration_graph:
|
||||
raise IterationGraphNotFoundError("iteration graph not found")
|
||||
|
||||
for sub_node_id, sub_node_config in iteration_graph.node_id_config_mapping.items():
|
||||
# Get node configs from graph_config instead of non-existent node_id_config_mapping
|
||||
node_configs = {node["id"]: node for node in graph_config.get("nodes", []) if "id" in node}
|
||||
for sub_node_id, sub_node_config in node_configs.items():
|
||||
if sub_node_config.get("data", {}).get("iteration_id") != node_id:
|
||||
continue
|
||||
|
||||
@ -382,297 +286,120 @@ class IterationNode(BaseNode):
|
||||
|
||||
return variable_mapping
|
||||
|
||||
def _handle_event_metadata(
|
||||
def _append_iteration_info_to_event(
|
||||
self,
|
||||
*,
|
||||
event: BaseNodeEvent | InNodeEvent,
|
||||
event: GraphNodeEventBase,
|
||||
iter_run_index: int,
|
||||
parallel_mode_run_id: str | None,
|
||||
) -> NodeRunStartedEvent | BaseNodeEvent | InNodeEvent:
|
||||
"""
|
||||
add iteration metadata to event.
|
||||
ensures iteration context (ID, index/parallel_run_id) is added to metadata,
|
||||
"""
|
||||
if not isinstance(event, BaseNodeEvent):
|
||||
return event
|
||||
if self._node_data.is_parallel and isinstance(event, NodeRunStartedEvent):
|
||||
event.parallel_mode_run_id = parallel_mode_run_id
|
||||
|
||||
):
|
||||
event.in_iteration_id = self._node_id
|
||||
iter_metadata = {
|
||||
WorkflowNodeExecutionMetadataKey.ITERATION_ID: self.node_id,
|
||||
WorkflowNodeExecutionMetadataKey.ITERATION_ID: self._node_id,
|
||||
WorkflowNodeExecutionMetadataKey.ITERATION_INDEX: iter_run_index,
|
||||
}
|
||||
if parallel_mode_run_id:
|
||||
# for parallel, the specific branch ID is more important than the sequential index
|
||||
iter_metadata[WorkflowNodeExecutionMetadataKey.PARALLEL_MODE_RUN_ID] = parallel_mode_run_id
|
||||
|
||||
if event.route_node_state.node_run_result:
|
||||
current_metadata = event.route_node_state.node_run_result.metadata or {}
|
||||
if WorkflowNodeExecutionMetadataKey.ITERATION_ID not in current_metadata:
|
||||
event.route_node_state.node_run_result.metadata = {**current_metadata, **iter_metadata}
|
||||
|
||||
return event
|
||||
current_metadata = event.node_run_result.metadata
|
||||
if WorkflowNodeExecutionMetadataKey.ITERATION_ID not in current_metadata:
|
||||
event.node_run_result.metadata = {**current_metadata, **iter_metadata}
|
||||
|
||||
def _run_single_iter(
|
||||
self,
|
||||
*,
|
||||
iterator_list_value: Sequence[str],
|
||||
variable_pool: VariablePool,
|
||||
inputs: Mapping[str, list],
|
||||
outputs: list,
|
||||
start_at: datetime,
|
||||
graph_engine: "GraphEngine",
|
||||
iteration_graph: Graph,
|
||||
iter_run_map: dict[str, float],
|
||||
parallel_mode_run_id: Optional[str] = None,
|
||||
) -> Generator[NodeEvent | InNodeEvent, None, None]:
|
||||
"""
|
||||
run single iteration
|
||||
"""
|
||||
iter_start_at = naive_utc_now()
|
||||
) -> Generator[Union[GraphNodeEventBase, StreamCompletedEvent], None, None]:
|
||||
rst = graph_engine.run()
|
||||
# get current iteration index
|
||||
index_variable = variable_pool.get([self._node_id, "index"])
|
||||
if not isinstance(index_variable, IntegerVariable):
|
||||
raise IterationIndexNotFoundError(f"iteration {self._node_id} current index not found")
|
||||
current_index = index_variable.value
|
||||
for event in rst:
|
||||
if isinstance(event, GraphNodeEventBase) and event.node_type == NodeType.ITERATION_START:
|
||||
continue
|
||||
|
||||
try:
|
||||
rst = graph_engine.run()
|
||||
# get current iteration index
|
||||
index_variable = variable_pool.get([self.node_id, "index"])
|
||||
if not isinstance(index_variable, IntegerVariable):
|
||||
raise IterationIndexNotFoundError(f"iteration {self.node_id} current index not found")
|
||||
current_index = index_variable.value
|
||||
iteration_run_id = parallel_mode_run_id if parallel_mode_run_id is not None else f"{current_index}"
|
||||
next_index = int(current_index) + 1
|
||||
for event in rst:
|
||||
if isinstance(event, (BaseNodeEvent | BaseParallelBranchEvent)) and not event.in_iteration_id:
|
||||
event.in_iteration_id = self.node_id
|
||||
|
||||
if (
|
||||
isinstance(event, BaseNodeEvent)
|
||||
and event.node_type == NodeType.ITERATION_START
|
||||
and not isinstance(event, NodeRunStreamChunkEvent)
|
||||
):
|
||||
continue
|
||||
|
||||
if isinstance(event, NodeRunSucceededEvent):
|
||||
yield self._handle_event_metadata(
|
||||
event=event, iter_run_index=current_index, parallel_mode_run_id=parallel_mode_run_id
|
||||
)
|
||||
elif isinstance(event, BaseGraphEvent):
|
||||
if isinstance(event, GraphRunFailedEvent):
|
||||
# iteration run failed
|
||||
if self._node_data.is_parallel:
|
||||
yield IterationRunFailedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
parallel_mode_run_id=parallel_mode_run_id,
|
||||
start_at=start_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": outputs},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
error=event.error,
|
||||
)
|
||||
else:
|
||||
yield IterationRunFailedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
start_at=start_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": outputs},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
error=event.error,
|
||||
)
|
||||
yield RunCompletedEvent(
|
||||
run_result=NodeRunResult(
|
||||
status=WorkflowNodeExecutionStatus.FAILED,
|
||||
error=event.error,
|
||||
)
|
||||
)
|
||||
if isinstance(event, GraphNodeEventBase):
|
||||
self._append_iteration_info_to_event(event=event, iter_run_index=current_index)
|
||||
yield event
|
||||
elif isinstance(event, GraphRunSucceededEvent):
|
||||
result = variable_pool.get(self._node_data.output_selector)
|
||||
if result is None:
|
||||
outputs.append(None)
|
||||
else:
|
||||
outputs.append(result.to_object())
|
||||
return
|
||||
elif isinstance(event, GraphRunFailedEvent):
|
||||
match self._node_data.error_handle_mode:
|
||||
case ErrorHandleMode.TERMINATED:
|
||||
raise IterationNodeError(event.error)
|
||||
case ErrorHandleMode.CONTINUE_ON_ERROR:
|
||||
outputs.append(None)
|
||||
return
|
||||
case ErrorHandleMode.REMOVE_ABNORMAL_OUTPUT:
|
||||
return
|
||||
elif isinstance(event, InNodeEvent):
|
||||
# event = cast(InNodeEvent, event)
|
||||
metadata_event = self._handle_event_metadata(
|
||||
event=event, iter_run_index=current_index, parallel_mode_run_id=parallel_mode_run_id
|
||||
)
|
||||
if isinstance(event, NodeRunFailedEvent):
|
||||
if self._node_data.error_handle_mode == ErrorHandleMode.CONTINUE_ON_ERROR:
|
||||
yield NodeInIterationFailedEvent(
|
||||
**metadata_event.model_dump(),
|
||||
)
|
||||
outputs[current_index] = None
|
||||
variable_pool.add([self.node_id, "index"], next_index)
|
||||
if next_index < len(iterator_list_value):
|
||||
variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
|
||||
duration = (naive_utc_now() - iter_start_at).total_seconds()
|
||||
iter_run_map[iteration_run_id] = duration
|
||||
yield IterationRunNextEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
index=next_index,
|
||||
parallel_mode_run_id=parallel_mode_run_id,
|
||||
pre_iteration_output=None,
|
||||
duration=duration,
|
||||
)
|
||||
return
|
||||
elif self._node_data.error_handle_mode == ErrorHandleMode.REMOVE_ABNORMAL_OUTPUT:
|
||||
yield NodeInIterationFailedEvent(
|
||||
**metadata_event.model_dump(),
|
||||
)
|
||||
variable_pool.add([self.node_id, "index"], next_index)
|
||||
|
||||
if next_index < len(iterator_list_value):
|
||||
variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
|
||||
duration = (naive_utc_now() - iter_start_at).total_seconds()
|
||||
iter_run_map[iteration_run_id] = duration
|
||||
yield IterationRunNextEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
index=next_index,
|
||||
parallel_mode_run_id=parallel_mode_run_id,
|
||||
pre_iteration_output=None,
|
||||
duration=duration,
|
||||
)
|
||||
return
|
||||
elif self._node_data.error_handle_mode == ErrorHandleMode.TERMINATED:
|
||||
yield NodeInIterationFailedEvent(
|
||||
**metadata_event.model_dump(),
|
||||
)
|
||||
outputs[current_index] = None
|
||||
def _create_graph_engine(self, index: int, item: Any):
|
||||
# Import dependencies
|
||||
from core.workflow.entities import GraphInitParams, GraphRuntimeState
|
||||
from core.workflow.graph import Graph
|
||||
from core.workflow.graph_engine import GraphEngine
|
||||
from core.workflow.graph_engine.command_channels import InMemoryChannel
|
||||
from core.workflow.nodes.node_factory import DifyNodeFactory
|
||||
|
||||
# clean nodes resources
|
||||
for node_id in iteration_graph.node_ids:
|
||||
variable_pool.remove([node_id])
|
||||
# Create GraphInitParams from node attributes
|
||||
graph_init_params = GraphInitParams(
|
||||
tenant_id=self.tenant_id,
|
||||
app_id=self.app_id,
|
||||
workflow_id=self.workflow_id,
|
||||
graph_config=self.graph_config,
|
||||
user_id=self.user_id,
|
||||
user_from=self.user_from.value,
|
||||
invoke_from=self.invoke_from.value,
|
||||
call_depth=self.workflow_call_depth,
|
||||
)
|
||||
# Create a deep copy of the variable pool for each iteration
|
||||
variable_pool_copy = self.graph_runtime_state.variable_pool.model_copy(deep=True)
|
||||
|
||||
# iteration run failed
|
||||
if self._node_data.is_parallel:
|
||||
yield IterationRunFailedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
parallel_mode_run_id=parallel_mode_run_id,
|
||||
start_at=start_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": outputs},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
error=event.error,
|
||||
)
|
||||
else:
|
||||
yield IterationRunFailedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
start_at=start_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": outputs},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
error=event.error,
|
||||
)
|
||||
# append iteration variable (item, index) to variable pool
|
||||
variable_pool_copy.add([self._node_id, "index"], index)
|
||||
variable_pool_copy.add([self._node_id, "item"], item)
|
||||
|
||||
# stop the iterator
|
||||
yield RunCompletedEvent(
|
||||
run_result=NodeRunResult(
|
||||
status=WorkflowNodeExecutionStatus.FAILED,
|
||||
error=event.error,
|
||||
)
|
||||
)
|
||||
return
|
||||
yield metadata_event
|
||||
# Create a new GraphRuntimeState for this iteration
|
||||
graph_runtime_state_copy = GraphRuntimeState(
|
||||
variable_pool=variable_pool_copy,
|
||||
start_at=self.graph_runtime_state.start_at,
|
||||
total_tokens=0,
|
||||
node_run_steps=0,
|
||||
)
|
||||
|
||||
current_output_segment = variable_pool.get(self._node_data.output_selector)
|
||||
if current_output_segment is None:
|
||||
raise IterationNodeError("iteration output selector not found")
|
||||
current_iteration_output = current_output_segment.value
|
||||
outputs[current_index] = current_iteration_output
|
||||
# remove all nodes outputs from variable pool
|
||||
for node_id in iteration_graph.node_ids:
|
||||
variable_pool.remove([node_id])
|
||||
# Create a new node factory with the new GraphRuntimeState
|
||||
node_factory = DifyNodeFactory(
|
||||
graph_init_params=graph_init_params, graph_runtime_state=graph_runtime_state_copy
|
||||
)
|
||||
|
||||
# move to next iteration
|
||||
variable_pool.add([self.node_id, "index"], next_index)
|
||||
# Initialize the iteration graph with the new node factory
|
||||
iteration_graph = Graph.init(
|
||||
graph_config=self.graph_config, node_factory=node_factory, root_node_id=self._node_data.start_node_id
|
||||
)
|
||||
|
||||
if next_index < len(iterator_list_value):
|
||||
variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
|
||||
duration = (naive_utc_now() - iter_start_at).total_seconds()
|
||||
iter_run_map[iteration_run_id] = duration
|
||||
yield IterationRunNextEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
index=next_index,
|
||||
parallel_mode_run_id=parallel_mode_run_id,
|
||||
pre_iteration_output=current_iteration_output or None,
|
||||
duration=duration,
|
||||
)
|
||||
if not iteration_graph:
|
||||
raise IterationGraphNotFoundError("iteration graph not found")
|
||||
|
||||
except IterationNodeError as e:
|
||||
logger.warning("Iteration run failed:%s", str(e))
|
||||
yield IterationRunFailedEvent(
|
||||
iteration_id=self.id,
|
||||
iteration_node_id=self.node_id,
|
||||
iteration_node_type=self.type_,
|
||||
iteration_node_data=self._node_data,
|
||||
start_at=start_at,
|
||||
inputs=inputs,
|
||||
outputs={"output": None},
|
||||
steps=len(iterator_list_value),
|
||||
metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
|
||||
error=str(e),
|
||||
)
|
||||
yield RunCompletedEvent(
|
||||
run_result=NodeRunResult(
|
||||
status=WorkflowNodeExecutionStatus.FAILED,
|
||||
error=str(e),
|
||||
)
|
||||
)
|
||||
# Create a new GraphEngine for this iteration
|
||||
graph_engine = GraphEngine(
|
||||
tenant_id=self.tenant_id,
|
||||
app_id=self.app_id,
|
||||
workflow_id=self.workflow_id,
|
||||
user_id=self.user_id,
|
||||
user_from=self.user_from,
|
||||
invoke_from=self.invoke_from,
|
||||
call_depth=self.workflow_call_depth,
|
||||
graph=iteration_graph,
|
||||
graph_config=self.graph_config,
|
||||
graph_runtime_state=graph_runtime_state_copy,
|
||||
max_execution_steps=10000, # Use default or config value
|
||||
max_execution_time=600, # Use default or config value
|
||||
command_channel=InMemoryChannel(), # Use InMemoryChannel for sub-graphs
|
||||
)
|
||||
|
||||
def _run_single_iter_parallel(
|
||||
self,
|
||||
*,
|
||||
flask_app: Flask,
|
||||
context: contextvars.Context,
|
||||
q: Queue,
|
||||
iterator_list_value: Sequence[str],
|
||||
inputs: Mapping[str, list],
|
||||
outputs: list,
|
||||
start_at: datetime,
|
||||
graph_engine: "GraphEngine",
|
||||
iteration_graph: Graph,
|
||||
index: int,
|
||||
item: Any,
|
||||
iter_run_map: dict[str, float],
|
||||
):
|
||||
"""
|
||||
run single iteration in parallel mode
|
||||
"""
|
||||
|
||||
with preserve_flask_contexts(flask_app, context_vars=context):
|
||||
parallel_mode_run_id = uuid.uuid4().hex
|
||||
graph_engine_copy = graph_engine.create_copy()
|
||||
variable_pool_copy = graph_engine_copy.graph_runtime_state.variable_pool
|
||||
variable_pool_copy.add([self.node_id, "index"], index)
|
||||
variable_pool_copy.add([self.node_id, "item"], item)
|
||||
for event in self._run_single_iter(
|
||||
iterator_list_value=iterator_list_value,
|
||||
variable_pool=variable_pool_copy,
|
||||
inputs=inputs,
|
||||
outputs=outputs,
|
||||
start_at=start_at,
|
||||
graph_engine=graph_engine_copy,
|
||||
iteration_graph=iteration_graph,
|
||||
iter_run_map=iter_run_map,
|
||||
parallel_mode_run_id=parallel_mode_run_id,
|
||||
):
|
||||
q.put(event)
|
||||
graph_engine.graph_runtime_state.total_tokens += graph_engine_copy.graph_runtime_state.total_tokens
|
||||
return graph_engine
|
||||
|
||||
@ -1,20 +1,19 @@
|
||||
from collections.abc import Mapping
|
||||
from typing import Any, Optional
|
||||
|
||||
from core.workflow.entities.node_entities import NodeRunResult
|
||||
from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
|
||||
from core.workflow.nodes.base import BaseNode
|
||||
from core.workflow.enums import ErrorStrategy, NodeType, WorkflowNodeExecutionStatus
|
||||
from core.workflow.node_events import NodeRunResult
|
||||
from core.workflow.nodes.base.entities import BaseNodeData, RetryConfig
|
||||
from core.workflow.nodes.enums import ErrorStrategy, NodeType
|
||||
from core.workflow.nodes.base.node import Node
|
||||
from core.workflow.nodes.iteration.entities import IterationStartNodeData
|
||||
|
||||
|
||||
class IterationStartNode(BaseNode):
|
||||
class IterationStartNode(Node):
|
||||
"""
|
||||
Iteration Start Node.
|
||||
"""
|
||||
|
||||
_node_type = NodeType.ITERATION_START
|
||||
node_type = NodeType.ITERATION_START
|
||||
|
||||
_node_data: IterationStartNodeData
|
||||
|
||||
|
||||
Reference in New Issue
Block a user