feat(modules): 帮助中心、工单、工作流与多渠道接入
补齐 FAQ、帮助中心、工单、可视化工作流及 Telegram/WhatsApp/Slack 等渠道模块。
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"""
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Copyright 2024-2026 ReplyPier
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import asyncio
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from app.core.logger import get_logger
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from app.agents.chat_agent import ChatAgent, ChatResponse
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from app.repositories.chat import ChatRepository
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from app.repositories.session_to_agent import SessionToAgentRepository
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from app.services.workflow_execution import WorkflowExecutionService
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from app.core.security import decrypt_api_key
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from app.models.schemas.chat import TransferReasonType
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logger = get_logger(__name__)
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class WorkflowChatService:
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"""Service for handling workflow-based chat processing"""
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def __init__(self, db):
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self.db = db
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self.chat_repo = ChatRepository(db)
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self.session_repo = SessionToAgentRepository(db)
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self.workflow_service = WorkflowExecutionService(db)
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async def handle_workflow_chat(
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self,
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active_session,
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message: str,
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session_id: str,
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org_id: str,
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customer_id: str,
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session: dict,
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sio,
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namespace: str = '/widget',
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source: str = None
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) -> ChatResponse:
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"""
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Handle chat processing for workflow-enabled agents
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Args:
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active_session: The active session object
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message: User message
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session_id: Session ID
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org_id: Organization ID
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customer_id: Customer ID
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session: Session data containing agent and AI config
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sio: SocketIO instance for emitting events
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namespace: SocketIO namespace
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Returns:
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ChatResponse: The response from workflow execution
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"""
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# Store user message in chat history
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self.chat_repo.create_message({
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"message": message,
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"message_type": "user",
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"session_id": session_id,
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"organization_id": org_id,
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"agent_id": session['agent_id'],
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"customer_id": customer_id,
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})
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# Execute workflow
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workflow_result = await self.workflow_service.execute_workflow(
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session_id=session_id,
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user_message=message,
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workflow_id=active_session.workflow_id,
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current_node_id=active_session.current_node_id,
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workflow_state=active_session.workflow_state,
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api_key=decrypt_api_key(session['ai_config'].encrypted_api_key),
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model_name=session['ai_config'].model_name,
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model_type=session['ai_config'].model_type,
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base_url=(session['ai_config'].settings or {}).get('base_url'),
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org_id=org_id,
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agent_id=session['agent_id'],
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customer_id=customer_id,
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source=source
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)
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if workflow_result.success:
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return await self._handle_successful_workflow(
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workflow_result, active_session, session_id, org_id,
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customer_id, session, sio, namespace
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)
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else:
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return await self._handle_failed_workflow(
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workflow_result, session_id, org_id, session, customer_id
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)
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async def _handle_successful_workflow(
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self,
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workflow_result,
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active_session,
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session_id: str,
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org_id: str,
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customer_id: str,
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session: dict,
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sio,
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namespace: str
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) -> ChatResponse:
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"""Handle successful workflow execution"""
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# Handle intermediate messages from MESSAGE nodes during automatic execution
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if workflow_result.intermediate_messages:
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await self._handle_intermediate_messages(
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workflow_result.intermediate_messages, session_id, org_id,
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session, customer_id, active_session, sio, namespace
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)
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# Check if this is a form node that needs to display a form
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if workflow_result.form_data:
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await self._handle_form_display(workflow_result, session_id, sio, namespace)
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return None # Don't send a regular chat response for form display
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# Create ChatResponse object from workflow result
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response = ChatResponse(
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message=workflow_result.message,
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transfer_to_human=workflow_result.transfer_to_human,
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transfer_reason=None,
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transfer_description=None,
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end_chat=workflow_result.end_chat,
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request_rating=workflow_result.request_rating,
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create_ticket=False
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)
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# Handle transfer to human if requested by workflow LLM
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if workflow_result.transfer_to_human:
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transfer_response = await self._handle_workflow_transfer(
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workflow_result, session_id, org_id, session, customer_id, response
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)
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if transfer_response:
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return transfer_response
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# Handle end chat if requested by workflow
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if workflow_result.end_chat:
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response = await self._handle_workflow_end_chat(
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response, session_id, org_id, session, customer_id
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)
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# Only store message if there's actual content
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if workflow_result.message:
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self._store_workflow_response(
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response, session_id, org_id, session, customer_id,
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active_session, workflow_result
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)
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return response
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async def _handle_intermediate_messages(
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self,
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intermediate_messages,
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session_id: str,
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org_id: str,
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session: dict,
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customer_id: str,
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active_session,
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sio,
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namespace: str
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):
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"""Handle intermediate messages from MESSAGE nodes"""
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logger.info(f"Found {len(intermediate_messages)} intermediate messages from MESSAGE nodes")
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for intermediate_message in intermediate_messages:
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logger.debug(f"Emitting intermediate message: {intermediate_message}")
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# Store intermediate message in chat history
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self.chat_repo.create_message({
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"message": intermediate_message,
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"message_type": "bot",
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"session_id": session_id,
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"organization_id": org_id,
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"agent_id": session['agent_id'],
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"customer_id": customer_id,
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"attributes": {
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"workflow_execution": True,
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"workflow_id": str(active_session.workflow_id),
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"intermediate_message": True,
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"message_node": True
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}
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})
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# Emit intermediate message to client immediately
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await sio.emit('chat_response', {
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'message': intermediate_message,
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'type': 'chat_response',
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'transfer_to_human': False,
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'end_chat': False,
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'request_rating': False,
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'shopify_output': None
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}, room=session_id, namespace=namespace)
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async def _handle_form_display(self, workflow_result, session_id: str, sio, namespace: str):
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"""Handle form display for form nodes"""
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logger.info(f"Form data detected: {workflow_result.form_data}")
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logger.info("Emitting display_form event")
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# Emit form display to client
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await sio.emit('display_form', {
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'form_data': workflow_result.form_data,
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'session_id': session_id
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}, room=session_id, namespace=namespace)
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logger.info("Form display event emitted successfully")
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async def _handle_workflow_transfer(
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self,
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workflow_result,
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session_id: str,
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org_id: str,
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session: dict,
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customer_id: str,
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response: ChatResponse
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) -> ChatResponse:
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"""Handle transfer to human requested by workflow"""
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logger.info(f"Workflow LLM requested transfer to human for session {session_id}")
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if not workflow_result.transfer_group_id:
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logger.warning(
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f"No transfer_group_id provided for workflow transfer in session {session_id}; "
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"falling back to the agent's default group")
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chat_agent = await ChatAgent.create_async(
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api_key=decrypt_api_key(session['ai_config'].encrypted_api_key),
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model_name=session['ai_config'].model_name,
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model_type=session['ai_config'].model_type,
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base_url=(session['ai_config'].settings or {}).get('base_url'),
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org_id=org_id,
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agent_id=session['agent_id'],
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customer_id=customer_id,
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session_id=session_id
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)
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try:
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# Create ChatResponse object with transfer details from workflow
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llm_response = ChatResponse(
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message=workflow_result.message,
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transfer_to_human=True,
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transfer_reason=TransferReasonType(workflow_result.transfer_reason) if workflow_result.transfer_reason else None,
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transfer_description=workflow_result.transfer_description,
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end_chat=False,
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request_rating=False,
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create_ticket=False
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)
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# A missing group id is passed through rather than short-circuited:
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# the transfer path then falls back to the agent's own default
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# group, as a plain agent transfer does. Setting the status alone
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# left group_id NULL, and an inbox matches on user_id or group_id —
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# so the handover was invisible to every agent it was meant for.
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transfer_response = await chat_agent.handle_workflow_transfer(
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session_id=session_id,
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org_id=org_id,
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agent_id=session['agent_id'],
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customer_id=customer_id,
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transfer_group_id=workflow_result.transfer_group_id,
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db=self.db,
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chat_repo=self.chat_repo,
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llm_response=llm_response
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)
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logger.info(f"Transfer completed for session {session_id} to group {workflow_result.transfer_group_id}")
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# Mark this as a transfer response so widget_chat.py can handle it properly
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transfer_response._is_transfer_response = True
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return transfer_response
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except ValueError as e:
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# The agent has no groups at all, so there is nobody to route to.
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# Mark it transferred anyway — an admin can still see and claim it.
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logger.error(
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f"Workflow transfer for session {session_id} could not be routed to a group: {e}")
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self.session_repo.update_session_status(session_id, "TRANSFERRED")
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return response
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finally:
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await chat_agent.safe_cleanup_mcp_tools()
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async def _handle_workflow_end_chat(
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self,
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response: ChatResponse,
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session_id: str,
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org_id: str,
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session: dict,
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customer_id: str
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) -> ChatResponse:
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"""Handle end chat requested by workflow"""
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logger.info(f"Workflow requested end chat for session {session_id}")
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# Create a ChatAgent instance to use the _handle_end_chat method
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chat_agent = await ChatAgent.create_async(
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api_key=decrypt_api_key(session['ai_config'].encrypted_api_key),
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model_name=session['ai_config'].model_name,
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model_type=session['ai_config'].model_type,
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base_url=(session['ai_config'].settings or {}).get('base_url'),
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org_id=org_id,
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agent_id=session['agent_id'],
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customer_id=customer_id,
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session_id=session_id
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)
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try:
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# Handle end chat using the agent's method with rating from response
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# The response.request_rating value comes from the workflow execution config
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return await chat_agent._handle_end_chat(response, session_id, self.db, response.request_rating)
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finally:
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await chat_agent.safe_cleanup_mcp_tools()
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def _store_workflow_response(
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self,
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response: ChatResponse,
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session_id: str,
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org_id: str,
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session: dict,
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customer_id: str,
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active_session,
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workflow_result
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):
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"""Store workflow response in chat history"""
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self.chat_repo.create_message({
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"message": response.message,
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"message_type": "bot",
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"session_id": session_id,
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"organization_id": org_id,
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"agent_id": session['agent_id'],
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"customer_id": customer_id,
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"attributes": {
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"workflow_execution": True,
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"workflow_id": str(active_session.workflow_id),
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"current_node_id": str(workflow_result.next_node_id) if workflow_result.next_node_id else None,
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"transfer_to_human": response.transfer_to_human,
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"end_chat": response.end_chat,
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"request_rating": response.request_rating,
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}
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})
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async def _handle_failed_workflow(
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self,
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workflow_result,
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session_id: str,
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org_id: str,
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session: dict,
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customer_id: str
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) -> ChatResponse:
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"""Handle failed workflow execution"""
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logger.error(f"Workflow execution failed: {workflow_result.error}")
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response = ChatResponse(
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message=workflow_result.message or "I apologize, but I'm having trouble processing your request right now.",
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transfer_to_human=False,
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transfer_reason=None,
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transfer_description=None,
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end_chat=False,
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request_rating=False,
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create_ticket=False
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)
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# Store error response in chat history
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self.chat_repo.create_message({
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"message": response.message,
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"message_type": "bot",
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"session_id": session_id,
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"organization_id": org_id,
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"agent_id": session['agent_id'],
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"customer_id": customer_id,
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"attributes": {
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"workflow_execution": True,
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"workflow_error": workflow_result.error,
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"transfer_to_human": response.transfer_to_human,
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"end_chat": response.end_chat,
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"request_rating": response.request_rating,
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}
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})
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return response
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