FEAT: NEW WORKFLOW ENGINE (#3160)
Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: Yeuoly <admin@srmxy.cn> Co-authored-by: JzoNg <jzongcode@gmail.com> Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: jyong <jyong@dify.ai> Co-authored-by: nite-knite <nkCoding@gmail.com> Co-authored-by: jyong <718720800@qq.com>
This commit is contained in:
takatost
685
api/services/workflow/workflow_converter.py
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685
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.app.app_config.entities import (
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DatasetEntity,
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DatasetRetrieveConfigEntity,
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EasyUIBasedAppConfig,
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ExternalDataVariableEntity,
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FileExtraConfig,
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ModelConfigEntity,
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PromptTemplateEntity,
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VariableEntity,
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)
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from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfigManager
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from core.app.apps.chat.app_config_manager import ChatAppConfigManager
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from core.app.apps.completion.app_config_manager import CompletionAppConfigManager
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from core.helper import encrypter
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from core.model_runtime.entities.llm_entities import LLMMode
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from core.model_runtime.utils.encoders import jsonable_encoder
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from core.prompt.simple_prompt_transform import SimplePromptTransform
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from core.workflow.entities.node_entities import NodeType
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from events.app_event import app_was_created
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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.api_based_extension import APIBasedExtension, APIBasedExtensionPoint
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from models.model import App, AppMode, AppModelConfig
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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,
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account: Account,
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name: str,
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icon: str,
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icon_background: str) -> App:
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"""
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Convert app to workflow
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- basic mode of chatbot app
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- expert mode of chatbot app
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- completion app
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:param app_model: App instance
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:param account: Account
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:param name: new app name
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:param icon: new app icon
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:param icon_background: new app icon background
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:return: new App instance
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"""
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# convert app model config
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workflow = self.convert_app_model_config_to_workflow(
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app_model=app_model,
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app_model_config=app_model.app_model_config,
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account_id=account.id
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)
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# create new app
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new_app = App()
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new_app.tenant_id = app_model.tenant_id
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new_app.name = name if name else app_model.name + '(workflow)'
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new_app.mode = AppMode.ADVANCED_CHAT.value \
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if app_model.mode == AppMode.CHAT.value else AppMode.WORKFLOW.value
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new_app.icon = icon if icon else app_model.icon
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new_app.icon_background = icon_background if icon_background else app_model.icon_background
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new_app.enable_site = app_model.enable_site
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new_app.enable_api = app_model.enable_api
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new_app.api_rpm = app_model.api_rpm
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new_app.api_rph = app_model.api_rph
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new_app.is_demo = False
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new_app.is_public = app_model.is_public
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db.session.add(new_app)
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db.session.flush()
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db.session.commit()
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workflow.app_id = new_app.id
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db.session.commit()
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app_was_created.send(new_app, account=account)
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return new_app
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def convert_app_model_config_to_workflow(self, app_model: App,
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app_model_config: AppModelConfig,
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account_id: str) -> Workflow:
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"""
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Convert app model config to workflow mode
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:param app_model: App instance
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:param app_model_config: AppModelConfig instance
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:param account_id: Account ID
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:return:
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"""
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# get new app mode
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new_app_mode = self._get_new_app_mode(app_model)
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# convert app model config
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app_config = self._convert_to_app_config(
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app_model=app_model,
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app_model_config=app_model_config
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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_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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external_data_variable_node_mapping = {}
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if app_config.external_data_variables:
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http_request_nodes, external_data_variable_node_mapping = self._convert_to_http_request_node(
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app_model=app_model,
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variables=app_config.variables,
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external_data_variables=app_config.external_data_variables
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)
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for http_request_node in http_request_nodes:
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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_config.dataset:
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knowledge_retrieval_node = self._convert_to_knowledge_retrieval_node(
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new_app_mode=new_app_mode,
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dataset_config=app_config.dataset,
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model_config=app_config.model
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)
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if knowledge_retrieval_node:
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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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original_app_mode=AppMode.value_of(app_model.mode),
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new_app_mode=new_app_mode,
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graph=graph,
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model_config=app_config.model,
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prompt_template=app_config.prompt_template,
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file_upload=app_config.additional_features.file_upload,
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external_data_variable_node_mapping=external_data_variable_node_mapping
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)
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graph = self._append_node(graph, llm_node)
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if new_app_mode == AppMode.WORKFLOW:
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# convert to end node by app mode
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end_node = self._convert_to_end_node()
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graph = self._append_node(graph, end_node)
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else:
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answer_node = self._convert_to_answer_node()
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graph = self._append_node(graph, answer_node)
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app_model_config_dict = app_config.app_model_config_dict
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# features
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if new_app_mode == AppMode.ADVANCED_CHAT:
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features = {
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"opening_statement": app_model_config_dict.get("opening_statement"),
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"suggested_questions": app_model_config_dict.get("suggested_questions"),
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"suggested_questions_after_answer": app_model_config_dict.get("suggested_questions_after_answer"),
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"speech_to_text": app_model_config_dict.get("speech_to_text"),
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"text_to_speech": app_model_config_dict.get("text_to_speech"),
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"file_upload": app_model_config_dict.get("file_upload"),
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"sensitive_word_avoidance": app_model_config_dict.get("sensitive_word_avoidance"),
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"retriever_resource": app_model_config_dict.get("retriever_resource"),
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}
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else:
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features = {
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"text_to_speech": app_model_config_dict.get("text_to_speech"),
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"file_upload": app_model_config_dict.get("file_upload"),
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"sensitive_word_avoidance": app_model_config_dict.get("sensitive_word_avoidance"),
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}
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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(new_app_mode).value,
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version='draft',
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graph=json.dumps(graph),
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features=json.dumps(features),
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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.commit()
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return workflow
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def _convert_to_app_config(self, app_model: App,
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app_model_config: AppModelConfig) -> EasyUIBasedAppConfig:
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app_mode = AppMode.value_of(app_model.mode)
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if app_mode == AppMode.AGENT_CHAT or app_model.is_agent:
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app_model.mode = AppMode.AGENT_CHAT.value
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app_config = AgentChatAppConfigManager.get_app_config(
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app_model=app_model,
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app_model_config=app_model_config
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)
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elif app_mode == AppMode.CHAT:
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app_config = ChatAppConfigManager.get_app_config(
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app_model=app_model,
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app_model_config=app_model_config
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)
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elif app_mode == AppMode.COMPLETION:
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app_config = CompletionAppConfigManager.get_app_config(
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app_model=app_model,
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app_model_config=app_model_config
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)
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else:
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raise ValueError("Invalid app mode")
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return app_config
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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": [jsonable_encoder(v) for v in variables]
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}
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}
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def _convert_to_http_request_node(self, app_model: App,
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variables: list[VariableEntity],
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external_data_variables: list[ExternalDataVariableEntity]) \
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-> tuple[list[dict], dict[str, str]]:
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"""
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Convert API Based Extension to HTTP Request Node
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:param app_model: App instance
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:param variables: list of variables
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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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index = 1
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nodes = []
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external_data_variable_node_mapping = {}
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tenant_id = app_model.tenant_id
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for external_data_variable in external_data_variables:
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tool_type = external_data_variable.type
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if tool_type != "api":
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continue
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tool_variable = external_data_variable.variable
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tool_config = external_data_variable.config
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# get params from config
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api_based_extension_id = tool_config.get("api_based_extension_id")
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# get api_based_extension
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api_based_extension = self._get_api_based_extension(
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tenant_id=tenant_id,
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api_based_extension_id=api_based_extension_id
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)
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if not api_based_extension:
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raise ValueError("[External data tool] API query failed, variable: {}, "
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"error: api_based_extension_id is invalid"
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.format(tool_variable))
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# decrypt api_key
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api_key = encrypter.decrypt_token(
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tenant_id=tenant_id,
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token=api_based_extension.api_key
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)
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inputs = {}
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for v in variables:
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inputs[v.variable] = '{{#start.' + v.variable + '#}}'
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request_body = {
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'point': APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY.value,
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'params': {
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'app_id': app_model.id,
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'tool_variable': tool_variable,
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'inputs': inputs,
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'query': '{{#sys.query#}}' if app_model.mode == AppMode.CHAT.value else ''
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}
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}
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request_body_json = json.dumps(request_body)
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request_body_json = request_body_json.replace('\{\{', '{{').replace('\}\}', '}}')
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http_request_node = {
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"id": f"http_request_{index}",
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"position": None,
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"data": {
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"title": f"HTTP REQUEST {api_based_extension.name}",
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"type": NodeType.HTTP_REQUEST.value,
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"method": "post",
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"url": api_based_extension.api_endpoint,
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"authorization": {
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"type": "api-key",
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"config": {
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"type": "bearer",
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"api_key": api_key
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}
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},
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"headers": "",
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"params": "",
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"body": {
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"type": "json",
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"data": request_body_json
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}
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}
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}
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nodes.append(http_request_node)
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# append code node for response body parsing
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code_node = {
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"id": f"code_{index}",
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"position": None,
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"data": {
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"title": f"Parse {api_based_extension.name} Response",
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"type": NodeType.CODE.value,
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"variables": [{
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"variable": "response_json",
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"value_selector": [http_request_node['id'], "body"]
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}],
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"code_language": "python3",
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"code": "import json\n\ndef main(response_json: str) -> str:\n response_body = json.loads("
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"response_json)\n return {\n \"result\": response_body[\"result\"]\n }",
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"outputs": {
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"result": {
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"type": "string"
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}
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}
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}
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}
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nodes.append(code_node)
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external_data_variable_node_mapping[external_data_variable.variable] = code_node['id']
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index += 1
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return nodes, external_data_variable_node_mapping
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def _convert_to_knowledge_retrieval_node(self, new_app_mode: AppMode,
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dataset_config: DatasetEntity,
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model_config: ModelConfigEntity) \
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-> Optional[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_config: dataset
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:param model_config: model config
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:return:
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"""
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retrieve_config = dataset_config.retrieve_config
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if new_app_mode == AppMode.ADVANCED_CHAT:
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query_variable_selector = ["sys", "query"]
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elif retrieve_config.query_variable:
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# fetch query variable
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query_variable_selector = ["start", retrieve_config.query_variable]
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else:
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return None
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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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"query_variable_selector": query_variable_selector,
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"dataset_ids": dataset_config.dataset_ids,
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"retrieval_mode": retrieve_config.retrieve_strategy.value,
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"single_retrieval_config": {
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"model": {
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"provider": model_config.provider,
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"name": model_config.model,
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"mode": model_config.mode,
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"completion_params": {
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**model_config.parameters,
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"stop": model_config.stop,
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}
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}
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}
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if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE
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else None,
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"multiple_retrieval_config": {
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"top_k": retrieve_config.top_k,
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"score_threshold": retrieve_config.score_threshold,
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"reranking_model": retrieve_config.reranking_model
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}
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if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE
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else None,
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}
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}
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def _convert_to_llm_node(self, original_app_mode: AppMode,
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new_app_mode: AppMode,
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graph: dict,
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model_config: ModelConfigEntity,
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prompt_template: PromptTemplateEntity,
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file_upload: Optional[FileExtraConfig] = None,
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external_data_variable_node_mapping: dict[str, str] = None) -> dict:
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"""
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Convert to LLM Node
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:param original_app_mode: original app mode
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:param new_app_mode: new app mode
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:param graph: graph
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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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:param external_data_variable_node_mapping: external data variable node mapping
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"""
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# fetch start and knowledge retrieval node
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start_node = next(filter(lambda n: n['data']['type'] == NodeType.START.value, graph['nodes']))
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knowledge_retrieval_node = next(filter(
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lambda n: n['data']['type'] == NodeType.KNOWLEDGE_RETRIEVAL.value,
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graph['nodes']
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), None)
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role_prefix = None
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# Chat Model
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if model_config.mode == LLMMode.CHAT.value:
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if prompt_template.prompt_type == PromptTemplateEntity.PromptType.SIMPLE:
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# get prompt template
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prompt_transform = SimplePromptTransform()
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prompt_template_config = prompt_transform.get_prompt_template(
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app_mode=original_app_mode,
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provider=model_config.provider,
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model=model_config.model,
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pre_prompt=prompt_template.simple_prompt_template,
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has_context=knowledge_retrieval_node is not None,
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query_in_prompt=False
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)
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template = prompt_template_config['prompt_template'].template
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if not template:
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prompts = []
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else:
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template = self._replace_template_variables(
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template,
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start_node['data']['variables'],
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external_data_variable_node_mapping
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)
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||||
prompts = [
|
||||
{
|
||||
"role": 'user',
|
||||
"text": template
|
||||
}
|
||||
]
|
||||
else:
|
||||
advanced_chat_prompt_template = prompt_template.advanced_chat_prompt_template
|
||||
|
||||
prompts = []
|
||||
for m in advanced_chat_prompt_template.messages:
|
||||
if advanced_chat_prompt_template:
|
||||
text = m.text
|
||||
text = self._replace_template_variables(
|
||||
text,
|
||||
start_node['data']['variables'],
|
||||
external_data_variable_node_mapping
|
||||
)
|
||||
|
||||
prompts.append({
|
||||
"role": m.role.value,
|
||||
"text": text
|
||||
})
|
||||
# Completion Model
|
||||
else:
|
||||
if prompt_template.prompt_type == PromptTemplateEntity.PromptType.SIMPLE:
|
||||
# get prompt template
|
||||
prompt_transform = SimplePromptTransform()
|
||||
prompt_template_config = prompt_transform.get_prompt_template(
|
||||
app_mode=original_app_mode,
|
||||
provider=model_config.provider,
|
||||
model=model_config.model,
|
||||
pre_prompt=prompt_template.simple_prompt_template,
|
||||
has_context=knowledge_retrieval_node is not None,
|
||||
query_in_prompt=False
|
||||
)
|
||||
|
||||
template = prompt_template_config['prompt_template'].template
|
||||
template = self._replace_template_variables(
|
||||
template,
|
||||
start_node['data']['variables'],
|
||||
external_data_variable_node_mapping
|
||||
)
|
||||
|
||||
prompts = {
|
||||
"text": template
|
||||
}
|
||||
|
||||
prompt_rules = prompt_template_config['prompt_rules']
|
||||
role_prefix = {
|
||||
"user": prompt_rules['human_prefix'] if 'human_prefix' in prompt_rules else 'Human',
|
||||
"assistant": prompt_rules['assistant_prefix'] if 'assistant_prefix' in prompt_rules else 'Assistant'
|
||||
}
|
||||
else:
|
||||
advanced_completion_prompt_template = prompt_template.advanced_completion_prompt_template
|
||||
if advanced_completion_prompt_template:
|
||||
text = advanced_completion_prompt_template.prompt
|
||||
text = self._replace_template_variables(
|
||||
text,
|
||||
start_node['data']['variables'],
|
||||
external_data_variable_node_mapping
|
||||
)
|
||||
else:
|
||||
text = ""
|
||||
|
||||
text = text.replace('{{#query#}}', '{{#sys.query#}}')
|
||||
|
||||
prompts = {
|
||||
"text": text,
|
||||
}
|
||||
|
||||
if advanced_completion_prompt_template.role_prefix:
|
||||
role_prefix = {
|
||||
"user": advanced_completion_prompt_template.role_prefix.user,
|
||||
"assistant": advanced_completion_prompt_template.role_prefix.assistant
|
||||
}
|
||||
|
||||
memory = None
|
||||
if new_app_mode == AppMode.ADVANCED_CHAT:
|
||||
memory = {
|
||||
"role_prefix": role_prefix,
|
||||
"window": {
|
||||
"enabled": False
|
||||
}
|
||||
}
|
||||
|
||||
completion_params = model_config.parameters
|
||||
completion_params.update({"stop": model_config.stop})
|
||||
return {
|
||||
"id": "llm",
|
||||
"position": None,
|
||||
"data": {
|
||||
"title": "LLM",
|
||||
"type": NodeType.LLM.value,
|
||||
"model": {
|
||||
"provider": model_config.provider,
|
||||
"name": model_config.model,
|
||||
"mode": model_config.mode,
|
||||
"completion_params": completion_params
|
||||
},
|
||||
"prompt_template": prompts,
|
||||
"memory": memory,
|
||||
"context": {
|
||||
"enabled": knowledge_retrieval_node is not None,
|
||||
"variable_selector": ["knowledge_retrieval", "result"]
|
||||
if knowledge_retrieval_node is not None else None
|
||||
},
|
||||
"vision": {
|
||||
"enabled": file_upload is not None,
|
||||
"variable_selector": ["sys", "files"] if file_upload is not None else None,
|
||||
"configs": {
|
||||
"detail": file_upload.image_config['detail']
|
||||
} if file_upload is not None else None
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def _replace_template_variables(self, template: str,
|
||||
variables: list[dict],
|
||||
external_data_variable_node_mapping: dict[str, str] = None) -> str:
|
||||
"""
|
||||
Replace Template Variables
|
||||
:param template: template
|
||||
:param variables: list of variables
|
||||
:return:
|
||||
"""
|
||||
for v in variables:
|
||||
template = template.replace('{{' + v['variable'] + '}}', '{{#start.' + v['variable'] + '#}}')
|
||||
|
||||
if external_data_variable_node_mapping:
|
||||
for variable, code_node_id in external_data_variable_node_mapping.items():
|
||||
template = template.replace('{{' + variable + '}}',
|
||||
'{{#' + code_node_id + '.result#}}')
|
||||
|
||||
return template
|
||||
|
||||
def _convert_to_end_node(self) -> dict:
|
||||
"""
|
||||
Convert to End Node
|
||||
:return:
|
||||
"""
|
||||
# for original completion app
|
||||
return {
|
||||
"id": "end",
|
||||
"position": None,
|
||||
"data": {
|
||||
"title": "END",
|
||||
"type": NodeType.END.value,
|
||||
"outputs": [{
|
||||
"variable": "result",
|
||||
"value_selector": ["llm", "text"]
|
||||
}]
|
||||
}
|
||||
}
|
||||
|
||||
def _convert_to_answer_node(self) -> dict:
|
||||
"""
|
||||
Convert to Answer Node
|
||||
:return:
|
||||
"""
|
||||
# for original chat app
|
||||
return {
|
||||
"id": "answer",
|
||||
"position": None,
|
||||
"data": {
|
||||
"title": "ANSWER",
|
||||
"type": NodeType.ANSWER.value,
|
||||
"answer": "{{#llm.text#}}"
|
||||
}
|
||||
}
|
||||
|
||||
def _create_edge(self, source: str, target: str) -> dict:
|
||||
"""
|
||||
Create Edge
|
||||
:param source: source node id
|
||||
:param target: target node id
|
||||
:return:
|
||||
"""
|
||||
return {
|
||||
"id": f"{source}-{target}",
|
||||
"source": source,
|
||||
"target": target
|
||||
}
|
||||
|
||||
def _append_node(self, graph: dict, node: dict) -> dict:
|
||||
"""
|
||||
Append Node to Graph
|
||||
|
||||
:param graph: Graph, include: nodes, edges
|
||||
:param node: Node to append
|
||||
:return:
|
||||
"""
|
||||
previous_node = graph['nodes'][-1]
|
||||
graph['nodes'].append(node)
|
||||
graph['edges'].append(self._create_edge(previous_node['id'], node['id']))
|
||||
return graph
|
||||
|
||||
def _get_new_app_mode(self, app_model: App) -> AppMode:
|
||||
"""
|
||||
Get new app mode
|
||||
:param app_model: App instance
|
||||
:return: AppMode
|
||||
"""
|
||||
if app_model.mode == AppMode.COMPLETION.value:
|
||||
return AppMode.WORKFLOW
|
||||
else:
|
||||
return AppMode.ADVANCED_CHAT
|
||||
|
||||
def _get_api_based_extension(self, tenant_id: str, api_based_extension_id: str) -> APIBasedExtension:
|
||||
"""
|
||||
Get API Based Extension
|
||||
:param tenant_id: tenant id
|
||||
:param api_based_extension_id: api based extension id
|
||||
:return:
|
||||
"""
|
||||
return db.session.query(APIBasedExtension).filter(
|
||||
APIBasedExtension.tenant_id == tenant_id,
|
||||
APIBasedExtension.id == api_based_extension_id
|
||||
).first()
|
||||
Reference in New Issue
Block a user