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refactor: Unify NodeConfigDict.data and BaseNodeData (#32780)
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
盐粒 Yanli
GitHub
@ -1,5 +1,5 @@
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from collections.abc import Mapping
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from typing import TYPE_CHECKING, Any, cast, final
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from collections.abc import Callable, Mapping
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from typing import TYPE_CHECKING, Any, TypeAlias, cast, final
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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@ -22,7 +22,8 @@ from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
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from core.rag.summary_index.summary_index import SummaryIndex
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from core.repositories.human_input_repository import HumanInputFormRepositoryImpl
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from core.tools.tool_file_manager import ToolFileManager
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from dify_graph.entities.graph_config import NodeConfigDict
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from dify_graph.entities.base_node_data import BaseNodeData
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from dify_graph.entities.graph_config import NodeConfigDict, NodeConfigDictAdapter
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from dify_graph.entities.graph_init_params import DIFY_RUN_CONTEXT_KEY
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from dify_graph.enums import NodeType, SystemVariableKey
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from dify_graph.file.file_manager import file_manager
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@ -31,26 +32,19 @@ from dify_graph.model_runtime.entities.model_entities import ModelType
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from dify_graph.model_runtime.memory import PromptMessageMemory
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from dify_graph.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from dify_graph.nodes.base.node import Node
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from dify_graph.nodes.code.code_node import CodeNode, WorkflowCodeExecutor
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from dify_graph.nodes.code.code_node import WorkflowCodeExecutor
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from dify_graph.nodes.code.entities import CodeLanguage
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from dify_graph.nodes.code.limits import CodeNodeLimits
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from dify_graph.nodes.datasource import DatasourceNode
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from dify_graph.nodes.document_extractor import DocumentExtractorNode, UnstructuredApiConfig
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from dify_graph.nodes.http_request import HttpRequestNode, build_http_request_config
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from dify_graph.nodes.human_input.human_input_node import HumanInputNode
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from dify_graph.nodes.knowledge_index.knowledge_index_node import KnowledgeIndexNode
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from dify_graph.nodes.knowledge_retrieval.knowledge_retrieval_node import KnowledgeRetrievalNode
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from dify_graph.nodes.llm.entities import ModelConfig
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from dify_graph.nodes.document_extractor import UnstructuredApiConfig
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from dify_graph.nodes.http_request import build_http_request_config
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from dify_graph.nodes.llm.entities import LLMNodeData
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from dify_graph.nodes.llm.exc import LLMModeRequiredError, ModelNotExistError
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from dify_graph.nodes.llm.node import LLMNode
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from dify_graph.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
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from dify_graph.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
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from dify_graph.nodes.question_classifier.question_classifier_node import QuestionClassifierNode
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from dify_graph.nodes.parameter_extractor.entities import ParameterExtractorNodeData
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from dify_graph.nodes.question_classifier.entities import QuestionClassifierNodeData
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from dify_graph.nodes.template_transform.template_renderer import (
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CodeExecutorJinja2TemplateRenderer,
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)
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from dify_graph.nodes.template_transform.template_transform_node import TemplateTransformNode
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from dify_graph.nodes.tool.tool_node import ToolNode
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from dify_graph.variables.segments import StringSegment
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from extensions.ext_database import db
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from models.model import Conversation
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@ -60,6 +54,9 @@ if TYPE_CHECKING:
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from dify_graph.runtime import GraphRuntimeState
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LLMCompatibleNodeData: TypeAlias = LLMNodeData | QuestionClassifierNodeData | ParameterExtractorNodeData
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def fetch_memory(
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*,
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conversation_id: str | None,
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@ -157,178 +154,128 @@ class DifyNodeFactory(NodeFactory):
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return DifyRunContext.model_validate(raw_ctx)
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@override
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def create_node(self, node_config: NodeConfigDict) -> Node:
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def create_node(self, node_config: dict[str, Any] | NodeConfigDict) -> Node:
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"""
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Create a Node instance from node configuration data using the traditional mapping.
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:param node_config: node configuration dictionary containing type and other data
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:return: initialized Node instance
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:raises ValueError: if node type is unknown or configuration is invalid
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:raises ValueError: if node_config fails NodeConfigDict/BaseNodeData validation
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(including pydantic ValidationError, which subclasses ValueError),
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if node type is unknown, or if no implementation exists for the resolved version
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"""
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# Get node_id from config
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node_id = node_config["id"]
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typed_node_config = NodeConfigDictAdapter.validate_python(node_config)
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node_id = typed_node_config["id"]
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node_data = typed_node_config["data"]
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node_class = self._resolve_node_class(node_type=node_data.type, node_version=str(node_data.version))
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node_type = node_data.type
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node_init_kwargs_factories: Mapping[NodeType, Callable[[], dict[str, object]]] = {
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NodeType.CODE: lambda: {
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"code_executor": self._code_executor,
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"code_limits": self._code_limits,
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},
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NodeType.TEMPLATE_TRANSFORM: lambda: {
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"template_renderer": self._template_renderer,
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"max_output_length": self._template_transform_max_output_length,
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},
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NodeType.HTTP_REQUEST: lambda: {
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"http_request_config": self._http_request_config,
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"http_client": self._http_request_http_client,
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"tool_file_manager_factory": self._http_request_tool_file_manager_factory,
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"file_manager": self._http_request_file_manager,
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},
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NodeType.HUMAN_INPUT: lambda: {
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"form_repository": HumanInputFormRepositoryImpl(tenant_id=self._dify_context.tenant_id),
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},
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NodeType.KNOWLEDGE_INDEX: lambda: {
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"index_processor": IndexProcessor(),
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"summary_index_service": SummaryIndex(),
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},
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NodeType.LLM: lambda: self._build_llm_compatible_node_init_kwargs(
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node_class=node_class,
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node_data=node_data,
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include_http_client=True,
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),
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NodeType.DATASOURCE: lambda: {
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"datasource_manager": DatasourceManager,
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},
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NodeType.KNOWLEDGE_RETRIEVAL: lambda: {
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"rag_retrieval": self._rag_retrieval,
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},
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NodeType.DOCUMENT_EXTRACTOR: lambda: {
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"unstructured_api_config": self._document_extractor_unstructured_api_config,
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"http_client": self._http_request_http_client,
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},
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NodeType.QUESTION_CLASSIFIER: lambda: self._build_llm_compatible_node_init_kwargs(
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node_class=node_class,
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node_data=node_data,
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include_http_client=True,
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),
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NodeType.PARAMETER_EXTRACTOR: lambda: self._build_llm_compatible_node_init_kwargs(
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node_class=node_class,
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node_data=node_data,
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include_http_client=False,
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),
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NodeType.TOOL: lambda: {
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"tool_file_manager_factory": self._http_request_tool_file_manager_factory(),
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},
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}
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node_init_kwargs = node_init_kwargs_factories.get(node_type, lambda: {})()
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return node_class(
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id=node_id,
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config=typed_node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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**node_init_kwargs,
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)
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# Get node type from config
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node_data = node_config["data"]
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try:
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node_type = NodeType(node_data["type"])
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except ValueError:
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raise ValueError(f"Unknown node type: {node_data['type']}")
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@staticmethod
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def _validate_resolved_node_data(node_class: type[Node], node_data: BaseNodeData) -> BaseNodeData:
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"""
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Re-validate the permissive graph payload with the concrete NodeData model declared by the resolved node class.
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"""
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return node_class.validate_node_data(node_data)
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# Get node class
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@staticmethod
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def _resolve_node_class(*, node_type: NodeType, node_version: str) -> type[Node]:
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node_mapping = NODE_TYPE_CLASSES_MAPPING.get(node_type)
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if not node_mapping:
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raise ValueError(f"No class mapping found for node type: {node_type}")
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latest_node_class = node_mapping.get(LATEST_VERSION)
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node_version = str(node_data.get("version", "1"))
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matched_node_class = node_mapping.get(node_version)
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node_class = matched_node_class or latest_node_class
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if not node_class:
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raise ValueError(f"No latest version class found for node type: {node_type}")
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return node_class
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# Create node instance
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if node_type == NodeType.CODE:
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return CodeNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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code_executor=self._code_executor,
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code_limits=self._code_limits,
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)
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if node_type == NodeType.TEMPLATE_TRANSFORM:
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return TemplateTransformNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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template_renderer=self._template_renderer,
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max_output_length=self._template_transform_max_output_length,
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)
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if node_type == NodeType.HTTP_REQUEST:
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return HttpRequestNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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http_request_config=self._http_request_config,
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http_client=self._http_request_http_client,
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tool_file_manager_factory=self._http_request_tool_file_manager_factory,
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file_manager=self._http_request_file_manager,
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)
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if node_type == NodeType.HUMAN_INPUT:
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return HumanInputNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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form_repository=HumanInputFormRepositoryImpl(tenant_id=self._dify_context.tenant_id),
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)
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if node_type == NodeType.KNOWLEDGE_INDEX:
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return KnowledgeIndexNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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index_processor=IndexProcessor(),
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summary_index_service=SummaryIndex(),
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)
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if node_type == NodeType.LLM:
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model_instance = self._build_model_instance_for_llm_node(node_data)
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memory = self._build_memory_for_llm_node(node_data=node_data, model_instance=model_instance)
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return LLMNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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credentials_provider=self._llm_credentials_provider,
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model_factory=self._llm_model_factory,
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model_instance=model_instance,
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memory=memory,
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http_client=self._http_request_http_client,
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)
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if node_type == NodeType.DATASOURCE:
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return DatasourceNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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datasource_manager=DatasourceManager,
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)
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if node_type == NodeType.KNOWLEDGE_RETRIEVAL:
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return KnowledgeRetrievalNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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rag_retrieval=self._rag_retrieval,
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)
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if node_type == NodeType.DOCUMENT_EXTRACTOR:
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return DocumentExtractorNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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unstructured_api_config=self._document_extractor_unstructured_api_config,
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http_client=self._http_request_http_client,
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)
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if node_type == NodeType.QUESTION_CLASSIFIER:
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model_instance = self._build_model_instance_for_llm_node(node_data)
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memory = self._build_memory_for_llm_node(node_data=node_data, model_instance=model_instance)
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return QuestionClassifierNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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credentials_provider=self._llm_credentials_provider,
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model_factory=self._llm_model_factory,
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model_instance=model_instance,
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memory=memory,
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http_client=self._http_request_http_client,
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)
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if node_type == NodeType.PARAMETER_EXTRACTOR:
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model_instance = self._build_model_instance_for_llm_node(node_data)
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memory = self._build_memory_for_llm_node(node_data=node_data, model_instance=model_instance)
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return ParameterExtractorNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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credentials_provider=self._llm_credentials_provider,
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model_factory=self._llm_model_factory,
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model_instance=model_instance,
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memory=memory,
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)
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if node_type == NodeType.TOOL:
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return ToolNode(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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tool_file_manager_factory=self._http_request_tool_file_manager_factory(),
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)
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return node_class(
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id=node_id,
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config=node_config,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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def _build_llm_compatible_node_init_kwargs(
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self,
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*,
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node_class: type[Node],
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node_data: BaseNodeData,
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include_http_client: bool,
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) -> dict[str, object]:
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validated_node_data = cast(
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LLMCompatibleNodeData,
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self._validate_resolved_node_data(node_class=node_class, node_data=node_data),
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)
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model_instance = self._build_model_instance_for_llm_node(validated_node_data)
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node_init_kwargs: dict[str, object] = {
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"credentials_provider": self._llm_credentials_provider,
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"model_factory": self._llm_model_factory,
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"model_instance": model_instance,
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"memory": self._build_memory_for_llm_node(
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node_data=validated_node_data,
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model_instance=model_instance,
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),
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}
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if include_http_client:
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node_init_kwargs["http_client"] = self._http_request_http_client
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return node_init_kwargs
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def _build_model_instance_for_llm_node(self, node_data: Mapping[str, Any]) -> ModelInstance:
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node_data_model = ModelConfig.model_validate(node_data["model"])
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def _build_model_instance_for_llm_node(self, node_data: LLMCompatibleNodeData) -> ModelInstance:
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node_data_model = node_data.model
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if not node_data_model.mode:
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raise LLMModeRequiredError("LLM mode is required.")
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@ -364,14 +311,12 @@ class DifyNodeFactory(NodeFactory):
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def _build_memory_for_llm_node(
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self,
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*,
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node_data: Mapping[str, Any],
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node_data: LLMCompatibleNodeData,
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model_instance: ModelInstance,
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) -> PromptMessageMemory | None:
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raw_memory_config = node_data.get("memory")
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if raw_memory_config is None:
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if node_data.memory is None:
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return None
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node_memory = MemoryConfig.model_validate(raw_memory_config)
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conversation_id_variable = self.graph_runtime_state.variable_pool.get(
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["sys", SystemVariableKey.CONVERSATION_ID]
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)
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@ -381,6 +326,6 @@ class DifyNodeFactory(NodeFactory):
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return fetch_memory(
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conversation_id=conversation_id,
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app_id=self._dify_context.app_id,
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node_data_memory=node_memory,
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node_data_memory=node_data.memory,
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model_instance=model_instance,
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)
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@ -11,7 +11,7 @@ from core.app.workflow.layers.observability import ObservabilityLayer
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from core.workflow.node_factory import DifyNodeFactory
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from dify_graph.constants import ENVIRONMENT_VARIABLE_NODE_ID
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from dify_graph.entities import GraphInitParams
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from dify_graph.entities.graph_config import NodeConfigData, NodeConfigDict
|
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from dify_graph.entities.graph_config import NodeConfigDictAdapter
|
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from dify_graph.errors import WorkflowNodeRunFailedError
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from dify_graph.file.models import File
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from dify_graph.graph import Graph
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@ -212,7 +212,7 @@ class WorkflowEntry:
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node_config_data = node_config["data"]
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# Get node type
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node_type = NodeType(node_config_data["type"])
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node_type = node_config_data.type
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# init graph init params and runtime state
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graph_init_params = GraphInitParams(
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@ -234,8 +234,7 @@ class WorkflowEntry:
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graph_init_params=graph_init_params,
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graph_runtime_state=graph_runtime_state,
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)
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typed_node_config = cast(dict[str, object], node_config)
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node = cast(Any, node_factory).create_node(typed_node_config)
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node = node_factory.create_node(node_config)
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node_cls = type(node)
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|
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try:
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@ -371,10 +370,7 @@ class WorkflowEntry:
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graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
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# init workflow run state
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node_config: NodeConfigDict = {
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"id": node_id,
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"data": cast(NodeConfigData, node_data),
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}
|
||||
node_config = NodeConfigDictAdapter.validate_python({"id": node_id, "data": node_data})
|
||||
node_factory = DifyNodeFactory(
|
||||
graph_init_params=graph_init_params,
|
||||
graph_runtime_state=graph_runtime_state,
|
||||
|
||||
Reference in New Issue
Block a user