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292 lines
11 KiB
Python
292 lines
11 KiB
Python
import json
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from typing import Optional, Union
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from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
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from core.app.entities.app_invoke_entities import InvokeFrom
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from core.llm_generator.llm_generator import LLMGenerator
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from core.memory.token_buffer_memory import TokenBufferMemory
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from core.model_manager import ModelManager
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from core.model_runtime.entities.model_entities import ModelType
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from core.ops.entities.trace_entity import TraceTaskName
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from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
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from core.ops.utils import measure_time
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from extensions.ext_database import db
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from libs.infinite_scroll_pagination import InfiniteScrollPagination
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from models.account import Account
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from models.model import App, AppMode, AppModelConfig, EndUser, Message, MessageFeedback
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from services.conversation_service import ConversationService
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from services.errors.conversation import ConversationCompletedError, ConversationNotExistsError
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from services.errors.message import (
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FirstMessageNotExistsError,
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LastMessageNotExistsError,
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MessageNotExistsError,
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SuggestedQuestionsAfterAnswerDisabledError,
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)
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from services.workflow_service import WorkflowService
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class MessageService:
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@classmethod
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def pagination_by_first_id(
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cls,
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app_model: App,
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user: Optional[Union[Account, EndUser]],
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conversation_id: str,
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first_id: Optional[str],
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limit: int,
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) -> InfiniteScrollPagination:
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if not user:
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return InfiniteScrollPagination(data=[], limit=limit, has_more=False)
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if not conversation_id:
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return InfiniteScrollPagination(data=[], limit=limit, has_more=False)
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conversation = ConversationService.get_conversation(
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app_model=app_model, user=user, conversation_id=conversation_id
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)
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if first_id:
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first_message = (
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db.session.query(Message)
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.filter(Message.conversation_id == conversation.id, Message.id == first_id)
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.first()
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)
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if not first_message:
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raise FirstMessageNotExistsError()
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history_messages = (
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db.session.query(Message)
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.filter(
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Message.conversation_id == conversation.id,
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Message.created_at < first_message.created_at,
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Message.id != first_message.id,
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)
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.order_by(Message.created_at.desc())
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.limit(limit)
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.all()
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)
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else:
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history_messages = (
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db.session.query(Message)
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.filter(Message.conversation_id == conversation.id)
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.order_by(Message.created_at.desc())
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.limit(limit)
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.all()
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)
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has_more = False
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if len(history_messages) == limit:
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current_page_first_message = history_messages[-1]
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rest_count = (
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db.session.query(Message)
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.filter(
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Message.conversation_id == conversation.id,
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Message.created_at < current_page_first_message.created_at,
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Message.id != current_page_first_message.id,
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)
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.count()
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)
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if rest_count > 0:
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has_more = True
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history_messages = list(reversed(history_messages))
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return InfiniteScrollPagination(data=history_messages, limit=limit, has_more=has_more)
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@classmethod
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def pagination_by_last_id(
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cls,
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app_model: App,
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user: Optional[Union[Account, EndUser]],
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last_id: Optional[str],
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limit: int,
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conversation_id: Optional[str] = None,
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include_ids: Optional[list] = None,
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) -> InfiniteScrollPagination:
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if not user:
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return InfiniteScrollPagination(data=[], limit=limit, has_more=False)
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base_query = db.session.query(Message)
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if conversation_id is not None:
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conversation = ConversationService.get_conversation(
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app_model=app_model, user=user, conversation_id=conversation_id
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)
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base_query = base_query.filter(Message.conversation_id == conversation.id)
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if include_ids is not None:
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base_query = base_query.filter(Message.id.in_(include_ids))
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if last_id:
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last_message = base_query.filter(Message.id == last_id).first()
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if not last_message:
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raise LastMessageNotExistsError()
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history_messages = (
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base_query.filter(Message.created_at < last_message.created_at, Message.id != last_message.id)
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.order_by(Message.created_at.desc())
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.limit(limit)
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.all()
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)
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else:
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history_messages = base_query.order_by(Message.created_at.desc()).limit(limit).all()
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has_more = False
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if len(history_messages) == limit:
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current_page_first_message = history_messages[-1]
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rest_count = base_query.filter(
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Message.created_at < current_page_first_message.created_at, Message.id != current_page_first_message.id
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).count()
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if rest_count > 0:
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has_more = True
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return InfiniteScrollPagination(data=history_messages, limit=limit, has_more=has_more)
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@classmethod
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def create_feedback(
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cls, app_model: App, message_id: str, user: Optional[Union[Account, EndUser]], rating: Optional[str]
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) -> MessageFeedback:
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if not user:
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raise ValueError("user cannot be None")
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message = cls.get_message(app_model=app_model, user=user, message_id=message_id)
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feedback = message.user_feedback if isinstance(user, EndUser) else message.admin_feedback
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if not rating and feedback:
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db.session.delete(feedback)
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elif rating and feedback:
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feedback.rating = rating
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elif not rating and not feedback:
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raise ValueError("rating cannot be None when feedback not exists")
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else:
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feedback = MessageFeedback(
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app_id=app_model.id,
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conversation_id=message.conversation_id,
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message_id=message.id,
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rating=rating,
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from_source=("user" if isinstance(user, EndUser) else "admin"),
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from_end_user_id=(user.id if isinstance(user, EndUser) else None),
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from_account_id=(user.id if isinstance(user, Account) else None),
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)
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db.session.add(feedback)
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db.session.commit()
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return feedback
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@classmethod
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def get_message(cls, app_model: App, user: Optional[Union[Account, EndUser]], message_id: str):
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message = (
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db.session.query(Message)
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.filter(
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Message.id == message_id,
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Message.app_id == app_model.id,
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Message.from_source == ("api" if isinstance(user, EndUser) else "console"),
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Message.from_end_user_id == (user.id if isinstance(user, EndUser) else None),
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Message.from_account_id == (user.id if isinstance(user, Account) else None),
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)
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.first()
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)
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if not message:
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raise MessageNotExistsError()
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return message
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@classmethod
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def get_suggested_questions_after_answer(
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cls, app_model: App, user: Optional[Union[Account, EndUser]], message_id: str, invoke_from: InvokeFrom
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) -> list[Message]:
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if not user:
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raise ValueError("user cannot be None")
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message = cls.get_message(app_model=app_model, user=user, message_id=message_id)
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conversation = ConversationService.get_conversation(
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app_model=app_model, conversation_id=message.conversation_id, user=user
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)
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if not conversation:
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raise ConversationNotExistsError()
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if conversation.status != "normal":
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raise ConversationCompletedError()
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model_manager = ModelManager()
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if app_model.mode == AppMode.ADVANCED_CHAT.value:
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workflow_service = WorkflowService()
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if invoke_from == InvokeFrom.DEBUGGER:
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workflow = workflow_service.get_draft_workflow(app_model=app_model)
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else:
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workflow = workflow_service.get_published_workflow(app_model=app_model)
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if workflow is None:
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return []
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app_config = AdvancedChatAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
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if not app_config.additional_features.suggested_questions_after_answer:
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raise SuggestedQuestionsAfterAnswerDisabledError()
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model_instance = model_manager.get_default_model_instance(
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tenant_id=app_model.tenant_id, model_type=ModelType.LLM
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)
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else:
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if not conversation.override_model_configs:
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app_model_config = (
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db.session.query(AppModelConfig)
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.filter(
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AppModelConfig.id == conversation.app_model_config_id, AppModelConfig.app_id == app_model.id
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)
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.first()
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)
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else:
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conversation_override_model_configs = json.loads(conversation.override_model_configs)
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app_model_config = AppModelConfig(
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id=conversation.app_model_config_id,
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app_id=app_model.id,
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)
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app_model_config = app_model_config.from_model_config_dict(conversation_override_model_configs)
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suggested_questions_after_answer = app_model_config.suggested_questions_after_answer_dict
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if suggested_questions_after_answer.get("enabled", False) is False:
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raise SuggestedQuestionsAfterAnswerDisabledError()
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model_instance = model_manager.get_model_instance(
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tenant_id=app_model.tenant_id,
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provider=app_model_config.model_dict["provider"],
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model_type=ModelType.LLM,
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model=app_model_config.model_dict["name"],
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)
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# get memory of conversation (read-only)
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memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
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histories = memory.get_history_prompt_text(
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max_token_limit=3000,
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message_limit=3,
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)
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with measure_time() as timer:
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questions = LLMGenerator.generate_suggested_questions_after_answer(
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tenant_id=app_model.tenant_id, histories=histories
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)
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# get tracing instance
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trace_manager = TraceQueueManager(app_id=app_model.id)
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trace_manager.add_trace_task(
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TraceTask(
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TraceTaskName.SUGGESTED_QUESTION_TRACE, message_id=message_id, suggested_question=questions, timer=timer
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)
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)
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return questions
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