refactor: move workflow package to dify_graph (#32844)

api/dify_graph/nodes/base/entities.py

@@ -0,0 +1,212 @@
+from __future__ import annotations
+
+import json
+from abc import ABC
+from builtins import type as type_
+from collections.abc import Sequence
+from enum import StrEnum
+from typing import Any, Union
+
+from pydantic import BaseModel, field_validator, model_validator
+
+from dify_graph.enums import ErrorStrategy
+
+from .exc import DefaultValueTypeError
+
+_NumberType = Union[int, float]
+
+
+class RetryConfig(BaseModel):
+    """node retry config"""
+
+    max_retries: int = 0  # max retry times
+    retry_interval: int = 0  # retry interval in milliseconds
+    retry_enabled: bool = False  # whether retry is enabled
+
+    @property
+    def retry_interval_seconds(self) -> float:
+        return self.retry_interval / 1000
+
+
+class VariableSelector(BaseModel):
+    """
+    Variable Selector.
+    """
+
+    variable: str
+    value_selector: Sequence[str]
+
+
+class OutputVariableType(StrEnum):
+    STRING = "string"
+    NUMBER = "number"
+    INTEGER = "integer"
+    SECRET = "secret"
+    BOOLEAN = "boolean"
+    OBJECT = "object"
+    FILE = "file"
+    ARRAY = "array"
+    ARRAY_STRING = "array[string]"
+    ARRAY_NUMBER = "array[number]"
+    ARRAY_OBJECT = "array[object]"
+    ARRAY_BOOLEAN = "array[boolean]"
+    ARRAY_FILE = "array[file]"
+    ANY = "any"
+    ARRAY_ANY = "array[any]"
+
+
+class OutputVariableEntity(BaseModel):
+    """
+    Output Variable Entity.
+    """
+
+    variable: str
+    value_type: OutputVariableType = OutputVariableType.ANY
+    value_selector: Sequence[str]
+
+    @field_validator("value_type", mode="before")
+    @classmethod
+    def normalize_value_type(cls, v: Any) -> Any:
+        """
+        Normalize value_type to handle case-insensitive array types.
+        Converts 'Array[...]' to 'array[...]' for backward compatibility.
+        """
+        if isinstance(v, str) and v.startswith("Array["):
+            return v.lower()
+        return v
+
+
+class DefaultValueType(StrEnum):
+    STRING = "string"
+    NUMBER = "number"
+    OBJECT = "object"
+    ARRAY_NUMBER = "array[number]"
+    ARRAY_STRING = "array[string]"
+    ARRAY_OBJECT = "array[object]"
+    ARRAY_FILES = "array[file]"
+
+
+class DefaultValue(BaseModel):
+    value: Any = None
+    type: DefaultValueType
+    key: str
+
+    @staticmethod
+    def _parse_json(value: str):
+        """Unified JSON parsing handler"""
+        try:
+            return json.loads(value)
+        except json.JSONDecodeError:
+            raise DefaultValueTypeError(f"Invalid JSON format for value: {value}")
+
+    @staticmethod
+    def _validate_array(value: Any, element_type: type_ | tuple[type_, ...]) -> bool:
+        """Unified array type validation"""
+        return isinstance(value, list) and all(isinstance(x, element_type) for x in value)
+
+    @staticmethod
+    def _convert_number(value: str) -> float:
+        """Unified number conversion handler"""
+        try:
+            return float(value)
+        except ValueError:
+            raise DefaultValueTypeError(f"Cannot convert to number: {value}")
+
+    @model_validator(mode="after")
+    def validate_value_type(self) -> DefaultValue:
+        # Type validation configuration
+        type_validators: dict[DefaultValueType, dict[str, Any]] = {
+            DefaultValueType.STRING: {
+                "type": str,
+                "converter": lambda x: x,
+            },
+            DefaultValueType.NUMBER: {
+                "type": _NumberType,
+                "converter": self._convert_number,
+            },
+            DefaultValueType.OBJECT: {
+                "type": dict,
+                "converter": self._parse_json,
+            },
+            DefaultValueType.ARRAY_NUMBER: {
+                "type": list,
+                "element_type": _NumberType,
+                "converter": self._parse_json,
+            },
+            DefaultValueType.ARRAY_STRING: {
+                "type": list,
+                "element_type": str,
+                "converter": self._parse_json,
+            },
+            DefaultValueType.ARRAY_OBJECT: {
+                "type": list,
+                "element_type": dict,
+                "converter": self._parse_json,
+            },
+        }
+
+        validator: dict[str, Any] = type_validators.get(self.type, {})
+        if not validator:
+            if self.type == DefaultValueType.ARRAY_FILES:
+                # Handle files type
+                return self
+            raise DefaultValueTypeError(f"Unsupported type: {self.type}")
+
+        # Handle string input cases
+        if isinstance(self.value, str) and self.type != DefaultValueType.STRING:
+            self.value = validator["converter"](self.value)
+
+        # Validate base type
+        if not isinstance(self.value, validator["type"]):
+            raise DefaultValueTypeError(f"Value must be {validator['type'].__name__} type for {self.value}")
+
+        # Validate array element types
+        if validator["type"] == list and not self._validate_array(self.value, validator["element_type"]):
+            raise DefaultValueTypeError(f"All elements must be {validator['element_type'].__name__} for {self.value}")
+
+        return self
+
+
+class BaseNodeData(ABC, BaseModel):
+    title: str
+    desc: str | None = None
+    version: str = "1"
+    error_strategy: ErrorStrategy | None = None
+    default_value: list[DefaultValue] | None = None
+    retry_config: RetryConfig = RetryConfig()
+
+    @property
+    def default_value_dict(self) -> dict[str, Any]:
+        if self.default_value:
+            return {item.key: item.value for item in self.default_value}
+        return {}
+
+
+class BaseIterationNodeData(BaseNodeData):
+    start_node_id: str | None = None
+
+
+class BaseIterationState(BaseModel):
+    iteration_node_id: str
+    index: int
+    inputs: dict
+
+    class MetaData(BaseModel):
+        pass
+
+    metadata: MetaData
+
+
+class BaseLoopNodeData(BaseNodeData):
+    start_node_id: str | None = None
+
+
+class BaseLoopState(BaseModel):
+    loop_node_id: str
+    index: int
+    inputs: dict
+
+    class MetaData(BaseModel):
+        pass
+
+    metadata: MetaData