mirror of
https://github.com/langgenius/dify.git
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629 lines
25 KiB
Python
629 lines
25 KiB
Python
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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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.file.models import FileExtraConfig
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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.nodes 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(
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self, app_model: App, account: Account, name: str, icon_type: str, icon: str, icon_background: str
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):
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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_type: new app icon type
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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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if not app_model.app_model_config:
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raise ValueError("App model config is required")
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workflow = self.convert_app_model_config_to_workflow(
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app_model=app_model, app_model_config=app_model.app_model_config, 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 or app_model.name + "(workflow)"
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new_app.mode = AppMode.ADVANCED_CHAT.value if app_model.mode == AppMode.CHAT.value else AppMode.WORKFLOW.value
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new_app.icon_type = icon_type or app_model.icon_type
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new_app.icon = icon or app_model.icon
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new_app.icon_background = icon_background or 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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new_app.created_by = account.id
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new_app.updated_by = account.id
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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, app_model_config: AppModelConfig, account_id: str):
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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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"""
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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(app_model=app_model, app_model_config=app_model_config)
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# init workflow graph
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graph = {"nodes": [], "edges": []}
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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(variables=app_config.variables)
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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, dataset_config=app_config.dataset, 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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environment_variables=[],
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conversation_variables=[],
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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, 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, 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(app_model=app_model, app_model_config=app_model_config)
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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, 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(
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self, app_model: App, variables: list[VariableEntity], 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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if not api_based_extension_id:
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continue
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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, api_based_extension_id=api_based_extension_id
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)
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# decrypt api_key
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api_key = encrypter.decrypt_token(tenant_id=tenant_id, token=api_based_extension.api_key)
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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(r"\{\{", "{{").replace(r"\}\}", "}}")
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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": {"type": "api-key", "config": {"type": "bearer", "api_key": api_key}},
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"headers": "",
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"params": "",
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"body": {"type": "json", "data": request_body_json},
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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": [{"variable": "response_json", "value_selector": [http_request_node["id"], "body"]}],
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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": {"result": {"type": "string"}},
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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(
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self, new_app_mode: AppMode, dataset_config: DatasetEntity, 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(
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self,
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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 = None,
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) -> 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(
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filter(lambda n: n["data"]["type"] == NodeType.KNOWLEDGE_RETRIEVAL.value, graph["nodes"]), None
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)
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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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if not prompt_template.simple_prompt_template:
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raise ValueError("Simple prompt template is required")
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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, start_node["data"]["variables"], external_data_variable_node_mapping
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)
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prompts = [{"role": "user", "text": template}]
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else:
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advanced_chat_prompt_template = prompt_template.advanced_chat_prompt_template
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prompts = []
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if advanced_chat_prompt_template:
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for m in advanced_chat_prompt_template.messages:
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text = m.text
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text = self._replace_template_variables(
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text, start_node["data"]["variables"], external_data_variable_node_mapping
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)
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prompts.append({"role": m.role.value, "text": text})
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# Completion Model
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else:
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if prompt_template.prompt_type == PromptTemplateEntity.PromptType.SIMPLE:
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if not prompt_template.simple_prompt_template:
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raise ValueError("Simple prompt template is required")
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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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template = self._replace_template_variables(
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template=template,
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variables=start_node["data"]["variables"],
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external_data_variable_node_mapping=external_data_variable_node_mapping,
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)
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prompts = {"text": template}
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prompt_rules = prompt_template_config["prompt_rules"]
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role_prefix = {
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"user": prompt_rules.get("human_prefix", "Human"),
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"assistant": prompt_rules.get("assistant_prefix", "Assistant"),
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}
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else:
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advanced_completion_prompt_template = prompt_template.advanced_completion_prompt_template
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if advanced_completion_prompt_template:
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text = advanced_completion_prompt_template.prompt
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text = self._replace_template_variables(
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template=text,
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variables=start_node["data"]["variables"],
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external_data_variable_node_mapping=external_data_variable_node_mapping,
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)
|
|
else:
|
|
text = ""
|
|
|
|
text = text.replace("{{#query#}}", "{{#sys.query#}}")
|
|
|
|
prompts = {
|
|
"text": text,
|
|
}
|
|
|
|
if advanced_completion_prompt_template and 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 and file_upload.image_config is not None
|
|
else None,
|
|
},
|
|
},
|
|
}
|
|
|
|
def _replace_template_variables(
|
|
self, template: str, variables: list[dict], external_data_variable_node_mapping: dict[str, str] | None = None
|
|
) -> str:
|
|
"""
|
|
Replace Template Variables
|
|
:param template: template
|
|
:param variables: list of variables
|
|
:param external_data_variable_node_mapping: external data variable node mapping
|
|
: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):
|
|
"""
|
|
Get API Based Extension
|
|
:param tenant_id: tenant id
|
|
:param api_based_extension_id: api based extension id
|
|
:return:
|
|
"""
|
|
api_based_extension = (
|
|
db.session.query(APIBasedExtension)
|
|
.filter(APIBasedExtension.tenant_id == tenant_id, APIBasedExtension.id == api_based_extension_id)
|
|
.first()
|
|
)
|
|
|
|
if not api_based_extension:
|
|
raise ValueError(f"API Based Extension not found, id: {api_based_extension_id}")
|
|
|
|
return api_based_extension
|