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add workflow logics
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takatost
259
api/services/workflow/workflow_converter.py
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259
api/services/workflow/workflow_converter.py
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import json
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from typing import Optional
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from core.application_manager import ApplicationManager
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from core.entities.application_entities import ModelConfigEntity, PromptTemplateEntity, FileUploadEntity, \
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ExternalDataVariableEntity, DatasetEntity, VariableEntity
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from core.model_runtime.utils import helper
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from core.workflow.entities.NodeEntities import NodeType
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from core.workflow.nodes.end.entities import EndNodeOutputType
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from extensions.ext_database import db
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from models.account import Account
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from models.model import App, AppMode, ChatbotAppEngine
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from models.workflow import Workflow, WorkflowType
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class WorkflowConverter:
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"""
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App Convert to Workflow Mode
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"""
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def convert_to_workflow(self, app_model: App, account: Account) -> Workflow:
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"""
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Convert to workflow mode
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- basic mode of chatbot app
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- advanced mode of assistant app (for migration)
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- completion app (for migration)
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:param app_model: App instance
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:param account: Account instance
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:return: workflow instance
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"""
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# get original app config
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app_model_config = app_model.app_model_config
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# convert app model config
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application_manager = ApplicationManager()
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application_manager.convert_from_app_model_config_dict(
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tenant_id=app_model.tenant_id,
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app_model_config_dict=app_model_config.to_dict()
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)
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# init workflow graph
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graph = {
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"nodes": [],
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"edges": []
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}
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# Convert list:
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# - variables -> start
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# - model_config -> llm
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# - prompt_template -> llm
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# - file_upload -> llm
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# - external_data_variables -> http-request
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# - dataset -> knowledge-retrieval
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# - show_retrieve_source -> knowledge-retrieval
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# convert to start node
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start_node = self._convert_to_start_node(
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variables=app_model_config.variables
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)
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graph['nodes'].append(start_node)
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# convert to http request node
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if app_model_config.external_data_variables:
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http_request_node = self._convert_to_http_request_node(
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external_data_variables=app_model_config.external_data_variables
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)
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graph = self._append_node(graph, http_request_node)
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# convert to knowledge retrieval node
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if app_model_config.dataset:
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knowledge_retrieval_node = self._convert_to_knowledge_retrieval_node(
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dataset=app_model_config.dataset,
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show_retrieve_source=app_model_config.show_retrieve_source
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)
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graph = self._append_node(graph, knowledge_retrieval_node)
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# convert to llm node
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llm_node = self._convert_to_llm_node(
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model_config=app_model_config.model_config,
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prompt_template=app_model_config.prompt_template,
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file_upload=app_model_config.file_upload
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)
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graph = self._append_node(graph, llm_node)
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# convert to end node by app mode
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end_node = self._convert_to_end_node(app_model=app_model)
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graph = self._append_node(graph, end_node)
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# get new app mode
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app_mode = self._get_new_app_mode(app_model)
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# create workflow record
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workflow = Workflow(
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tenant_id=app_model.tenant_id,
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app_id=app_model.id,
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type=WorkflowType.from_app_mode(app_mode).value,
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version='draft',
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graph=json.dumps(graph),
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created_by=account.id
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)
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db.session.add(workflow)
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db.session.flush()
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# create new app model config record
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new_app_model_config = app_model_config.copy()
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new_app_model_config.external_data_tools = ''
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new_app_model_config.model = ''
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new_app_model_config.user_input_form = ''
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new_app_model_config.dataset_query_variable = None
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new_app_model_config.pre_prompt = None
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new_app_model_config.agent_mode = ''
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new_app_model_config.prompt_type = 'simple'
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new_app_model_config.chat_prompt_config = ''
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new_app_model_config.completion_prompt_config = ''
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new_app_model_config.dataset_configs = ''
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new_app_model_config.chatbot_app_engine = ChatbotAppEngine.WORKFLOW.value \
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if app_mode == AppMode.CHAT else ChatbotAppEngine.NORMAL.value
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new_app_model_config.workflow_id = workflow.id
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db.session.add(new_app_model_config)
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db.session.commit()
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return workflow
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def _convert_to_start_node(self, variables: list[VariableEntity]) -> dict:
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"""
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Convert to Start Node
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:param variables: list of variables
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:return:
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"""
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return {
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"id": "start",
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"position": None,
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"data": {
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"title": "START",
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"type": NodeType.START.value,
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"variables": [helper.dump_model(v) for v in variables]
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}
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}
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def _convert_to_http_request_node(self, external_data_variables: list[ExternalDataVariableEntity]) -> dict:
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"""
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Convert API Based Extension to HTTP Request Node
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:param external_data_variables: list of external data variables
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:return:
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"""
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# TODO: implement
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pass
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def _convert_to_knowledge_retrieval_node(self, new_app_mode: AppMode, dataset: DatasetEntity) -> dict:
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"""
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Convert datasets to Knowledge Retrieval Node
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:param new_app_mode: new app mode
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:param dataset: dataset
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:return:
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"""
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# TODO: implement
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if new_app_mode == AppMode.CHAT:
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query_variable_selector = ["start", "sys.query"]
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else:
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pass
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return {
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"id": "knowledge-retrieval",
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"position": None,
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"data": {
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"title": "KNOWLEDGE RETRIEVAL",
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"type": NodeType.KNOWLEDGE_RETRIEVAL.value,
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}
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}
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def _convert_to_llm_node(self, model_config: ModelConfigEntity,
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prompt_template: PromptTemplateEntity,
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file_upload: Optional[FileUploadEntity] = None) -> dict:
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"""
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Convert to LLM Node
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:param model_config: model config
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:param prompt_template: prompt template
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:param file_upload: file upload config (optional)
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"""
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# TODO: implement
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pass
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def _convert_to_end_node(self, app_model: App) -> dict:
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"""
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Convert to End Node
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:param app_model: App instance
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:return:
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"""
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if app_model.mode == AppMode.CHAT.value:
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return {
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"id": "end",
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"position": None,
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"data": {
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"title": "END",
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"type": NodeType.END.value,
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}
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}
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elif app_model.mode == "completion":
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# for original completion app
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return {
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"id": "end",
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"position": None,
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"data": {
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"title": "END",
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"type": NodeType.END.value,
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"outputs": {
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"type": EndNodeOutputType.PLAIN_TEXT.value,
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"plain_text_selector": ["llm", "text"]
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}
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}
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}
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def _create_edge(self, source: str, target: str) -> dict:
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"""
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Create Edge
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:param source: source node id
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:param target: target node id
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:return:
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"""
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return {
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"id": f"{source}-{target}",
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"source": source,
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"target": target
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}
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def _append_node(self, graph: dict, node: dict) -> dict:
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"""
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Append Node to Graph
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:param graph: Graph, include: nodes, edges
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:param node: Node to append
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:return:
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"""
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previous_node = graph['nodes'][-1]
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graph['nodes'].append(node)
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graph['edges'].append(self._create_edge(previous_node['id'], node['id']))
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return graph
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def _get_new_app_mode(self, app_model: App) -> AppMode:
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"""
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Get new app mode
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:param app_model: App instance
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:return: AppMode
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"""
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if app_model.mode == "completion":
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return AppMode.WORKFLOW
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else:
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return AppMode.value_of(app_model.mode)
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