mirror of
https://github.com/langgenius/dify.git
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feat: add backwards invoke node api
This commit is contained in:
Yeuoly
114
api/core/plugin/backwards_invocation/node.py
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114
api/core/plugin/backwards_invocation/node.py
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@ -0,0 +1,114 @@
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from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
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from core.workflow.nodes.parameter_extractor.entities import (
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ModelConfig as ParameterExtractorModelConfig,
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)
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from core.workflow.nodes.parameter_extractor.entities import (
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ParameterConfig,
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ParameterExtractorNodeData,
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)
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from core.workflow.nodes.question_classifier.entities import (
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ClassConfig,
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QuestionClassifierNodeData,
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)
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from core.workflow.nodes.question_classifier.entities import (
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ModelConfig as QuestionClassifierModelConfig,
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)
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from services.workflow_service import WorkflowService
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class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
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@classmethod
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def invoke_parameter_extractor(
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cls,
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tenant_id: str,
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user_id: str,
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parameters: list[ParameterConfig],
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model_config: ParameterExtractorModelConfig,
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instruction: str,
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query: str,
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) -> dict:
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"""
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Invoke parameter extractor node.
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:param tenant_id: str
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:param user_id: str
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:param parameters: list[ParameterConfig]
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:param model_config: ModelConfig
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:param instruction: str
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:param query: str
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:return: dict with __reason, __is_success, and other parameters
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"""
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workflow_service = WorkflowService()
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node_id = "1919810"
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node_data = ParameterExtractorNodeData(
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title="parameter_extractor",
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desc="parameter_extractor",
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parameters=parameters,
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reasoning_mode="function_call",
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query=[node_id, "query"],
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model=model_config,
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instruction=instruction, # instruct with variables are not supported
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)
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node_data_dict = node_data.model_dump()
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execution = workflow_service.run_free_workflow_node(
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node_data_dict,
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tenant_id=tenant_id,
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user_id=user_id,
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node_id=node_id,
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user_inputs={
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f"{node_id}.query": query,
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},
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)
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output = execution.outputs_dict
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return output or {
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"__reason": "No parameters extracted",
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"__is_success": False,
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}
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@classmethod
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def invoke_question_classifier(
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cls,
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tenant_id: str,
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user_id: str,
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model_config: QuestionClassifierModelConfig,
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classes: list[ClassConfig],
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instruction: str,
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query: str,
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) -> dict:
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"""
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Invoke question classifier node.
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:param tenant_id: str
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:param user_id: str
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:param model_config: ModelConfig
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:param classes: list[ClassConfig]
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:param instruction: str
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:param query: str
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:return: dict with class_name
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"""
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workflow_service = WorkflowService()
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node_id = "1919810"
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node_data = QuestionClassifierNodeData(
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title="question_classifier",
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desc="question_classifier",
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query_variable_selector=[node_id, "query"],
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model=model_config,
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classes=classes,
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instruction=instruction, # instruct with variables are not supported
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)
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node_data_dict = node_data.model_dump()
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execution = workflow_service.run_free_workflow_node(
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node_data_dict,
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tenant_id=tenant_id,
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user_id=user_id,
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node_id=node_id,
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user_inputs={
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f"{node_id}.query": query,
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},
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)
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output = execution.outputs_dict
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return output or {
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"class_name": classes[0].name,
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}
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@ -14,6 +14,16 @@ from core.model_runtime.entities.message_entities import (
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UserPromptMessage,
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)
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from core.model_runtime.entities.model_entities import ModelType
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from core.workflow.nodes.question_classifier.entities import (
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ClassConfig,
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ModelConfig as QuestionClassifierModelConfig,
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)
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from core.workflow.nodes.parameter_extractor.entities import (
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ModelConfig as ParameterExtractorModelConfig,
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)
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from core.workflow.nodes.parameter_extractor.entities import (
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ParameterConfig,
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)
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class RequestInvokeTool(BaseModel):
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@ -92,11 +102,27 @@ class RequestInvokeModeration(BaseModel):
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"""
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class RequestInvokeNode(BaseModel):
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class RequestInvokeParameterExtractorNode(BaseModel):
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"""
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Request to invoke node
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Request to invoke parameter extractor node
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"""
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parameters: list[ParameterConfig]
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model: ParameterExtractorModelConfig
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instruction: str
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query: str
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class RequestInvokeQuestionClassifierNode(BaseModel):
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"""
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Request to invoke question classifier node
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"""
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query: str
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model: QuestionClassifierModelConfig
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classes: list[ClassConfig]
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instruction: str
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class RequestInvokeApp(BaseModel):
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"""
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@ -205,6 +205,88 @@ class WorkflowEntry:
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except Exception as e:
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raise WorkflowNodeRunFailedError(node_instance=node_instance, error=str(e))
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@classmethod
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def run_free_node(
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cls, node_data: dict, node_id: str, tenant_id: str, user_id: str, user_inputs: dict[str, Any]
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) -> tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]:
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"""
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Run free node
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NOTE: only parameter_extractor/question_classifier are supported
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:param node_data: node data
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:param user_id: user id
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:param user_inputs: user inputs
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:return:
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"""
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# generate a fake graph
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node_config = {"id": node_id, "width": 114, "height": 514, "type": "custom", "data": node_data}
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graph_dict = {
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"nodes": [node_config],
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}
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node_type = NodeType.value_of(node_data.get("type", ""))
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if node_type not in {NodeType.PARAMETER_EXTRACTOR, NodeType.QUESTION_CLASSIFIER}:
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raise ValueError(f"Node type {node_type} not supported")
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node_cls = node_classes.get(node_type)
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if not node_cls:
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raise ValueError(f"Node class not found for node type {node_type}")
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graph = Graph.init(graph_config=graph_dict)
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# init variable pool
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variable_pool = VariablePool(
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system_variables={},
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user_inputs={},
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environment_variables=[],
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)
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node_cls = cast(type[BaseNode], node_cls)
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# init workflow run state
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node_instance: BaseNode = node_cls(
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id=str(uuid.uuid4()),
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config=node_config,
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graph_init_params=GraphInitParams(
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tenant_id=tenant_id,
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app_id="",
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workflow_type=WorkflowType.WORKFLOW,
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workflow_id="",
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graph_config=graph_dict,
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user_id=user_id,
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user_from=UserFrom.ACCOUNT,
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invoke_from=InvokeFrom.DEBUGGER,
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call_depth=0,
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),
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graph=graph,
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graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
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)
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try:
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# variable selector to variable mapping
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try:
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variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
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graph_config=graph_dict, config=node_config
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)
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except NotImplementedError:
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variable_mapping = {}
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cls.mapping_user_inputs_to_variable_pool(
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variable_mapping=variable_mapping,
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user_inputs=user_inputs,
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variable_pool=variable_pool,
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tenant_id=tenant_id,
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node_type=node_type,
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node_data=node_instance.node_data,
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)
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# run node
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generator = node_instance.run()
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return node_instance, generator
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except Exception as e:
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raise WorkflowNodeRunFailedError(node_instance=node_instance, error=str(e))
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@classmethod
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def handle_special_values(cls, value: Optional[Mapping[str, Any]]) -> Optional[dict]:
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"""
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