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Typer_Body
280
src/langbot/pkg/workflow/node.py
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280
src/langbot/pkg/workflow/node.py
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"""Workflow node base class and decorators"""
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from __future__ import annotations
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import abc
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from typing import Any, Callable, Optional, TYPE_CHECKING
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import pydantic
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if TYPE_CHECKING:
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from .entities import ExecutionContext
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from ..core import app
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class NodePort(pydantic.BaseModel):
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"""Node port definition"""
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name: str
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type: str = "any" # any, string, number, boolean, object, array
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description: str = ""
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required: bool = True
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class NodeConfig(pydantic.BaseModel):
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"""Node configuration field definition"""
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name: str
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type: str # string, integer, number, boolean, select, json, secret, etc.
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required: bool = False
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default: Any = None
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description: str = ""
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options: Optional[list[str]] = None # For select type
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# Validation
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min_value: Optional[float] = None
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max_value: Optional[float] = None
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min_length: Optional[int] = None
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max_length: Optional[int] = None
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pattern: Optional[str] = None # Regex pattern
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# UI hints
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placeholder: str = ""
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show_if: Optional[dict] = None # Conditional display
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# Pipeline config source (for reusing Pipeline config metadata)
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pipeline_config_source: Optional[str] = None # e.g., "pipeline:trigger"
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# i18n support for label
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label: Optional[dict[str, str]] = None # e.g., {"en_US": "Name", "zh_Hans": "名称"}
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label_zh: Optional[str] = None # Chinese label
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label_en: Optional[str] = None # English label
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class WorkflowNode(abc.ABC):
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"""Base class for all workflow nodes"""
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# Node metadata
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type_name: str = ""
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name: str = ""
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description: str = ""
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category: str = "misc" # trigger, process, control, action, integration
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icon: str = ""
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# Port definitions
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inputs: list[NodePort] = []
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outputs: list[NodePort] = []
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# Configuration schema
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config_schema: list[NodeConfig] = []
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# Pipeline config reuse
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config_schema_source: Optional[str] = None # e.g., "pipeline:ai"
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config_stages: list[str] = [] # Specific stages to reuse
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def __init__(self, node_id: str, config: dict[str, Any], ap: Optional['app.Application'] = None):
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"""Initialize node with ID and configuration"""
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self.node_id = node_id
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self.config = config
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self.ap = ap # Reference to the application instance for accessing services
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@abc.abstractmethod
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async def execute(
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self,
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inputs: dict[str, Any],
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context: ExecutionContext
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) -> dict[str, Any]:
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"""
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Execute the node logic.
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Args:
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inputs: Input data from connected nodes
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context: Execution context with workflow state
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Returns:
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Dictionary of output values
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"""
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pass
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async def validate_inputs(self, inputs: dict[str, Any]) -> list[str]:
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"""
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Validate input data against port definitions.
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Returns:
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List of validation error messages (empty if valid)
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"""
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errors = []
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for port in self.inputs:
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if port.required and port.name not in inputs:
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errors.append(f"Missing required input: {port.name}")
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return errors
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async def validate_config(self) -> list[str]:
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"""
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Validate node configuration.
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Returns:
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List of validation error messages (empty if valid)
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"""
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errors = []
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for cfg in self.config_schema:
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if cfg.required and cfg.name not in self.config:
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errors.append(f"Missing required config: {cfg.name}")
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elif cfg.name in self.config:
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value = self.config[cfg.name]
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# Type validation
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if cfg.type == "integer" and not isinstance(value, int):
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errors.append(f"Config {cfg.name} must be an integer")
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elif cfg.type == "number" and not isinstance(value, (int, float)):
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errors.append(f"Config {cfg.name} must be a number")
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elif cfg.type == "boolean" and not isinstance(value, bool):
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errors.append(f"Config {cfg.name} must be a boolean")
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# Range validation
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if cfg.min_value is not None and isinstance(value, (int, float)):
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if value < cfg.min_value:
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errors.append(f"Config {cfg.name} must be >= {cfg.min_value}")
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if cfg.max_value is not None and isinstance(value, (int, float)):
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if value > cfg.max_value:
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errors.append(f"Config {cfg.name} must be <= {cfg.max_value}")
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return errors
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# Type mapping from backend to frontend DynamicFormItemType
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_TYPE_MAP = {
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'string': 'string',
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'integer': 'integer',
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'number': 'float',
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'boolean': 'boolean',
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'select': 'select',
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'json': 'text',
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'textarea': 'text',
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'secret': 'secret',
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'llm-model-selector': 'llm-model-selector',
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'embedding-model-selector': 'embedding-model-selector',
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'rerank-model-selector': 'rerank-model-selector',
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'knowledge-base-selector': 'knowledge-base-selector',
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'knowledge-base-multi-selector': 'knowledge-base-multi-selector',
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'bot-selector': 'bot-selector',
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'tools-selector': 'tools-selector',
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'model-fallback-selector': 'model-fallback-selector',
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'prompt-editor': 'prompt-editor',
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}
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def get_config(self, key: str, default: Any = None) -> Any:
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"""Get configuration value with default"""
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return self.config.get(key, default)
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@classmethod
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def _config_to_schema_item(cls, cfg: NodeConfig) -> dict[str, Any]:
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"""Convert a NodeConfig to frontend-compatible schema item"""
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# Map type to frontend type
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frontend_type = cls._TYPE_MAP.get(cfg.type, 'string')
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# Build i18n label from name
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label = {
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'zh_Hans': cfg.name,
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'en_US': cfg.name,
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}
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# Build i18n description
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desc = cfg.description or ''
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description = {
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'zh_Hans': desc,
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'en_US': desc,
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}
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result = {
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'id': cfg.name,
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'name': cfg.name,
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'type': frontend_type,
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'label': label,
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'description': description,
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'required': cfg.required,
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'default': cfg.default,
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}
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# Add placeholder if present
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if cfg.placeholder:
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result['placeholder'] = cfg.placeholder
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# Add options if present
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if cfg.options:
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result['options'] = [
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{
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'name': opt,
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'label': {
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'zh_Hans': opt,
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'en_US': opt,
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}
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}
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for opt in cfg.options
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]
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# Add show_if if present
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if cfg.show_if:
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result['show_if'] = cfg.show_if
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return result
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@classmethod
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def to_schema(cls) -> dict[str, Any]:
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"""
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Convert node class to JSON schema for frontend.
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Returns:
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Node schema dictionary
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"""
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# Build label dict for i18n support
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# Use underscore format to match frontend I18nObject interface
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name_zh = getattr(cls, 'name_zh', None) or cls.name
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name_en = getattr(cls, 'name_en', None) or cls.name
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desc_zh = getattr(cls, 'description_zh', None) or cls.description
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desc_en = getattr(cls, 'description_en', None) or cls.description
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label = {
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'zh_Hans': name_zh,
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'en_US': name_en,
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}
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description = {
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'zh_Hans': desc_zh,
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'en_US': desc_en,
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}
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return {
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'type': f'{cls.category}.{cls.type_name}',
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'name': cls.name,
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'label': label,
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'description': description,
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'category': cls.category,
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'icon': cls.icon,
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'inputs': [port.model_dump() for port in cls.inputs],
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'outputs': [port.model_dump() for port in cls.outputs],
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'config_schema': [cls._config_to_schema_item(cfg) for cfg in cls.config_schema],
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'config_schema_source': cls.config_schema_source,
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'config_stages': cls.config_stages,
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}
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# Registry for node type decorator
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_pending_registrations: list[tuple[str, type[WorkflowNode]]] = []
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def workflow_node(type_name: str) -> Callable[[type[WorkflowNode]], type[WorkflowNode]]:
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"""
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Decorator to register a workflow node type.
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Usage:
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@workflow_node('llm_call')
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class LLMCallNode(WorkflowNode):
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...
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"""
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def decorator(cls: type[WorkflowNode]) -> type[WorkflowNode]:
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cls.type_name = type_name
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_pending_registrations.append((type_name, cls))
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return cls
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return decorator
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def get_pending_registrations() -> list[tuple[str, type[WorkflowNode]]]:
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"""Get pending node registrations"""
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return _pending_registrations.copy()
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def clear_pending_registrations():
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"""Clear pending registrations after they're processed"""
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_pending_registrations.clear()
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