feat(workflow): domain model for workflow node execution (#19430)

Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

api/core/workflow/entities/node_execution_entities.py

@@ -0,0 +1,98 @@
+"""
+Domain entities for workflow node execution.
+
+This module contains the domain model for workflow node execution, which is used
+by the core workflow module. These models are independent of the storage mechanism
+and don't contain implementation details like tenant_id, app_id, etc.
+"""
+
+from collections.abc import Mapping
+from datetime import datetime
+from enum import StrEnum
+from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
+from core.workflow.entities.node_entities import NodeRunMetadataKey
+from core.workflow.nodes.enums import NodeType
+
+
+class NodeExecutionStatus(StrEnum):
+    """
+    Node Execution Status Enum.
+    """
+
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+    EXCEPTION = "exception"
+    RETRY = "retry"
+
+
+class NodeExecution(BaseModel):
+    """
+    Domain model for workflow node execution.
+
+    This model represents the core business entity of a node execution,
+    without implementation details like tenant_id, app_id, etc.
+
+    Note: User/context-specific fields (triggered_from, created_by, created_by_role)
+    have been moved to the repository implementation to keep the domain model clean.
+    These fields are still accepted in the constructor for backward compatibility,
+    but they are not stored in the model.
+    """
+
+    # Core identification fields
+    id: str  # Unique identifier for this execution record
+    node_execution_id: Optional[str] = None  # Optional secondary ID for cross-referencing
+    workflow_id: str  # ID of the workflow this node belongs to
+    workflow_run_id: Optional[str] = None  # ID of the specific workflow run (null for single-step debugging)
+
+    # Execution positioning and flow
+    index: int  # Sequence number for ordering in trace visualization
+    predecessor_node_id: Optional[str] = None  # ID of the node that executed before this one
+    node_id: str  # ID of the node being executed
+    node_type: NodeType  # Type of node (e.g., start, llm, knowledge)
+    title: str  # Display title of the node
+
+    # Execution data
+    inputs: Optional[Mapping[str, Any]] = None  # Input variables used by this node
+    process_data: Optional[Mapping[str, Any]] = None  # Intermediate processing data
+    outputs: Optional[Mapping[str, Any]] = None  # Output variables produced by this node
+
+    # Execution state
+    status: NodeExecutionStatus = NodeExecutionStatus.RUNNING  # Current execution status
+    error: Optional[str] = None  # Error message if execution failed
+    elapsed_time: float = Field(default=0.0)  # Time taken for execution in seconds
+
+    # Additional metadata
+    metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None  # Execution metadata (tokens, cost, etc.)
+
+    # Timing information
+    created_at: datetime  # When execution started
+    finished_at: Optional[datetime] = None  # When execution completed
+
+    def update_from_mapping(
+        self,
+        inputs: Optional[Mapping[str, Any]] = None,
+        process_data: Optional[Mapping[str, Any]] = None,
+        outputs: Optional[Mapping[str, Any]] = None,
+        metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None,
+    ) -> None:
+        """
+        Update the model from mappings.
+
+        Args:
+            inputs: The inputs to update
+            process_data: The process data to update
+            outputs: The outputs to update
+            metadata: The metadata to update
+        """
+        if inputs is not None:
+            self.inputs = dict(inputs)
+        if process_data is not None:
+            self.process_data = dict(process_data)
+        if outputs is not None:
+            self.outputs = dict(outputs)
+        if metadata is not None:
+            self.metadata = dict(metadata)