refactor: move workflow package to dify_graph (#32844)

api/dify_graph/nodes/trigger_plugin/__init__.py

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+from .trigger_event_node import TriggerEventNode
+
+__all__ = ["TriggerEventNode"]

api/dify_graph/nodes/trigger_plugin/entities.py

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+from collections.abc import Mapping
+from typing import Any, Literal, Union
+
+from pydantic import BaseModel, Field, ValidationInfo, field_validator
+
+from core.trigger.entities.entities import EventParameter
+from dify_graph.nodes.base.entities import BaseNodeData
+from dify_graph.nodes.trigger_plugin.exc import TriggerEventParameterError
+
+
+class TriggerEventNodeData(BaseNodeData):
+    """Plugin trigger node data"""
+
+    class TriggerEventInput(BaseModel):
+        value: Union[Any, list[str]]
+        type: Literal["mixed", "variable", "constant"]
+
+        @field_validator("type", mode="before")
+        @classmethod
+        def check_type(cls, value, validation_info: ValidationInfo):
+            type = value
+            value = validation_info.data.get("value")
+
+            if value is None:
+                return type
+
+            if type == "mixed" and not isinstance(value, str):
+                raise ValueError("value must be a string")
+
+            if type == "variable":
+                if not isinstance(value, list):
+                    raise ValueError("value must be a list")
+                for val in value:
+                    if not isinstance(val, str):
+                        raise ValueError("value must be a list of strings")
+
+            if type == "constant" and not isinstance(value, str | int | float | bool | dict | list):
+                raise ValueError("value must be a string, int, float, bool or dict")
+            return type
+
+    title: str
+    desc: str | None = None
+    plugin_id: str = Field(..., description="Plugin ID")
+    provider_id: str = Field(..., description="Provider ID")
+    event_name: str = Field(..., description="Event name")
+    subscription_id: str = Field(..., description="Subscription ID")
+    plugin_unique_identifier: str = Field(..., description="Plugin unique identifier")
+    event_parameters: Mapping[str, TriggerEventInput] = Field(default_factory=dict, description="Trigger parameters")
+
+    def resolve_parameters(
+        self,
+        *,
+        parameter_schemas: Mapping[str, EventParameter],
+    ) -> Mapping[str, Any]:
+        """
+        Generate parameters based on the given plugin trigger parameters.
+
+        Args:
+            parameter_schemas (Mapping[str, EventParameter]): The mapping of parameter schemas.
+
+        Returns:
+            Mapping[str, Any]: A dictionary containing the generated parameters.
+
+        """
+        result: dict[str, Any] = {}
+        for parameter_name in self.event_parameters:
+            parameter: EventParameter | None = parameter_schemas.get(parameter_name)
+            if not parameter:
+                result[parameter_name] = None
+                continue
+            event_input = self.event_parameters[parameter_name]
+
+            # trigger node only supports constant input
+            if event_input.type != "constant":
+                raise TriggerEventParameterError(f"Unknown plugin trigger input type '{event_input.type}'")
+            result[parameter_name] = event_input.value
+        return result

api/dify_graph/nodes/trigger_plugin/exc.py

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+class TriggerEventNodeError(ValueError):
+    """Base exception for plugin trigger node errors."""
+
+    pass
+
+
+class TriggerEventParameterError(TriggerEventNodeError):
+    """Exception raised for errors in plugin trigger parameters."""
+
+    pass

api/dify_graph/nodes/trigger_plugin/trigger_event_node.py

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+from collections.abc import Mapping
+
+from dify_graph.constants import SYSTEM_VARIABLE_NODE_ID
+from dify_graph.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from dify_graph.enums import NodeExecutionType, NodeType
+from dify_graph.node_events import NodeRunResult
+from dify_graph.nodes.base.node import Node
+
+from .entities import TriggerEventNodeData
+
+
+class TriggerEventNode(Node[TriggerEventNodeData]):
+    node_type = NodeType.TRIGGER_PLUGIN
+    execution_type = NodeExecutionType.ROOT
+
+    @classmethod
+    def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:
+        return {
+            "type": "plugin",
+            "config": {
+                "title": "",
+                "plugin_id": "",
+                "provider_id": "",
+                "event_name": "",
+                "subscription_id": "",
+                "plugin_unique_identifier": "",
+                "event_parameters": {},
+            },
+        }
+
+    @classmethod
+    def version(cls) -> str:
+        return "1"
+
+    def _run(self) -> NodeRunResult:
+        """
+        Run the plugin trigger node.
+
+        This node invokes the trigger to convert request data into events
+        and makes them available to downstream nodes.
+        """
+
+        # Get trigger data passed when workflow was triggered
+        metadata = {
+            WorkflowNodeExecutionMetadataKey.TRIGGER_INFO: {
+                "provider_id": self.node_data.provider_id,
+                "event_name": self.node_data.event_name,
+                "plugin_unique_identifier": self.node_data.plugin_unique_identifier,
+            },
+        }
+        node_inputs = dict(self.graph_runtime_state.variable_pool.user_inputs)
+        system_inputs = self.graph_runtime_state.variable_pool.system_variables.to_dict()
+
+        # TODO: System variables should be directly accessible, no need for special handling
+        # Set system variables as node outputs.
+        for var in system_inputs:
+            node_inputs[SYSTEM_VARIABLE_NODE_ID + "." + var] = system_inputs[var]
+        outputs = dict(node_inputs)
+        return NodeRunResult(
+            status=WorkflowNodeExecutionStatus.SUCCEEDED,
+            inputs=node_inputs,
+            outputs=outputs,
+            metadata=metadata,
+        )