1083 lines
53 KiB
Python
1083 lines
53 KiB
Python
from typing import Dict, Any, List, Optional
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import logging
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import uuid
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import asyncio
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import json
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from modules.datamodels.datamodelChat import (
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UserInputRequest,
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ChatMessage,
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ChatWorkflow,
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ChatDocument,
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WorkflowModeEnum
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)
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from modules.datamodels.datamodelChat import TaskContext
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from modules.workflows.processing.workflowProcessor import WorkflowProcessor
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from modules.workflows.processing.shared.stateTools import WorkflowStoppedException, checkWorkflowStopped
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logger = logging.getLogger(__name__)
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class WorkflowManager:
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"""Manager for workflow processing and coordination"""
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def __init__(self, services):
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self.services = services
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self.workflowProcessor = None
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# Exported functions
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async def workflowStart(self, userInput: UserInputRequest, workflowMode: WorkflowModeEnum, workflowId: Optional[str] = None) -> ChatWorkflow:
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"""Starts a new workflow or continues an existing one, then launches processing."""
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try:
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# Debug log to check workflowMode parameter
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logger.info(f"WorkflowManager received workflowMode: {workflowMode}")
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currentTime = self.services.utils.timestampGetUtc()
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if workflowId:
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workflow = self.services.chat.getWorkflow(workflowId)
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if not workflow:
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raise ValueError(f"Workflow {workflowId} not found")
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# Store workflow in services for reference (this is the ChatWorkflow object)
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self.services.workflow = workflow
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# CRITICAL: Update all method instances to use the current Services object with the correct workflow
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from modules.workflows.processing.shared.methodDiscovery import discoverMethods
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discoverMethods(self.services)
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logger.debug(f"Updated method instances to use workflow {self.services.workflow.id}")
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if workflow.status == "running":
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logger.info(f"Stopping running workflow {workflowId} before processing new prompt")
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workflow.status = "stopped"
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workflow.lastActivity = currentTime
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self.services.chat.updateWorkflow(workflowId, {
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"status": "stopped",
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"lastActivity": currentTime
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})
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self.services.chat.storeLog(workflow, {
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"message": "Workflow stopped for new prompt",
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"type": "info",
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"status": "stopped",
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"progress": 1.0
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})
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newRound = workflow.currentRound + 1
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self.services.chat.updateWorkflow(workflowId, {
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"status": "running",
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"lastActivity": currentTime,
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"currentRound": newRound,
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"workflowMode": workflowMode # Update workflow mode for existing workflows
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})
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# Reflect updates on the in-memory object without reloading
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workflow.status = "running"
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workflow.lastActivity = currentTime
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workflow.currentRound = newRound
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workflow.workflowMode = workflowMode
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self.services.chat.storeLog(workflow, {
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"message": f"Workflow resumed (round {workflow.currentRound}) with mode: {workflowMode}",
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"type": "info",
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"status": "running",
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"progress": 0
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})
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else:
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workflowData = {
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"name": "New Workflow",
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"status": "running",
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"startedAt": currentTime,
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"lastActivity": currentTime,
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"currentRound": 1,
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"currentTask": 0,
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"currentAction": 0,
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"totalTasks": 0,
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"totalActions": 0,
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"mandateId": self.services.user.mandateId,
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"messageIds": [],
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"workflowMode": workflowMode,
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"maxSteps": 5 if workflowMode == WorkflowModeEnum.WORKFLOW_DYNAMIC else 1, # Set maxSteps for Dynamic mode
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}
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workflow = self.services.chat.createWorkflow(workflowData)
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logger.info(f"Created workflow with mode: {getattr(workflow, 'workflowMode', 'NOT_SET')}")
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logger.info(f"Workflow data passed: {workflowData.get('workflowMode', 'NOT_IN_DATA')}")
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# Store workflow in services (this is the ChatWorkflow object)
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self.services.workflow = workflow
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# CRITICAL: Update all method instances to use the current Services object with the correct workflow
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# This ensures cached method instances don't use stale workflow IDs from previous workflows
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from modules.workflows.processing.shared.methodDiscovery import discoverMethods
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discoverMethods(self.services)
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logger.debug(f"Updated method instances to use workflow {self.services.workflow.id}")
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# Start workflow processing asynchronously
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asyncio.create_task(self._workflowProcess(userInput))
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return workflow
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except Exception as e:
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logger.error(f"Error starting workflow: {str(e)}")
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raise
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async def workflowStop(self, workflowId: str) -> ChatWorkflow:
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"""Stops a running workflow."""
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try:
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workflow = self.services.chat.getWorkflow(workflowId)
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if not workflow:
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raise ValueError(f"Workflow {workflowId} not found")
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# Store workflow in services (this is the ChatWorkflow object)
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self.services.workflow = workflow
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workflow.status = "stopped"
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workflow.lastActivity = self.services.utils.timestampGetUtc()
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self.services.chat.updateWorkflow(workflowId, {
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"status": "stopped",
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"lastActivity": workflow.lastActivity
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})
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self.services.chat.storeLog(workflow, {
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"message": "Workflow stopped",
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"type": "warning",
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"status": "stopped",
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"progress": 1.0
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})
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return workflow
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except Exception as e:
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logger.error(f"Error stopping workflow: {str(e)}")
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raise
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# Main processor
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async def _workflowProcess(self, userInput: UserInputRequest) -> None:
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"""Process a workflow with user input"""
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try:
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# Store the current user prompt in services for easy access throughout the workflow
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self.services.rawUserPrompt = userInput.prompt
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self.services.currentUserPrompt = userInput.prompt
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# Reset progress logger for new workflow
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self.services.chat._progressLogger = None
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self.workflowProcessor = WorkflowProcessor(self.services)
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# Process user-uploaded documents from userInput for complexity detection
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# This is the correct way: use the input data directly, not workflow state
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documents = []
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if userInput.listFileId:
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try:
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documents = await self._processFileIds(userInput.listFileId, None)
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except Exception as e:
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logger.warning(f"Failed to process user fileIds for complexity detection: {e}")
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# Detect complexity (AI-based semantic understanding) using user input documents
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complexity = await self.workflowProcessor.detectComplexity(userInput.prompt, documents)
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logger.info(f"Request complexity detected: {complexity}")
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# Now send the first message (which will also process the documents again, but that's fine)
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await self._sendFirstMessage(userInput)
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# Route to fast path for simple requests
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if complexity == "simple":
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logger.info("Routing to fast path for simple request")
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await self._executeFastPath(userInput, documents)
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return # Fast path completes the workflow
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# Route to full workflow for moderate/complex requests
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logger.info(f"Routing to full workflow for {complexity} request")
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taskPlan = await self._planTasks(userInput)
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await self._executeTasks(taskPlan)
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await self._processWorkflowResults()
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except WorkflowStoppedException:
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self._handleWorkflowStop()
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except Exception as e:
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self._handleWorkflowError(e)
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# Helper functions
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async def _executeFastPath(self, userInput: UserInputRequest, documents: List[ChatDocument]) -> None:
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"""Execute fast path for simple requests and deliver result to user"""
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try:
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workflow = self.services.workflow
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checkWorkflowStopped(self.services)
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# Get user language if available
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userLanguage = getattr(self.services, 'currentUserLanguage', None)
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# Execute fast path
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result = await self.workflowProcessor.fastPathExecute(
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prompt=userInput.prompt,
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documents=documents,
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userLanguage=userLanguage
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)
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if not result.success:
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# Fast path failed, fall back to full workflow
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logger.warning(f"Fast path failed: {result.error}, falling back to full workflow")
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taskPlan = await self._planTasks(userInput)
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await self._executeTasks(taskPlan)
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await self._processWorkflowResults()
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return
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# Extract response text from ActionResult
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responseText = ""
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chatDocuments = []
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if result.documents and len(result.documents) > 0:
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# Get response text from first document
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firstDoc = result.documents[0]
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if hasattr(firstDoc, 'documentData'):
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docData = firstDoc.documentData
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if isinstance(docData, bytes):
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responseText = docData.decode('utf-8')
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else:
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responseText = str(docData)
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# Convert ActionDocuments to ChatDocuments for persistence
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for actionDoc in result.documents:
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if hasattr(actionDoc, 'documentData') and actionDoc.documentData:
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# Create file in component storage
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fileItem = self.services.interfaceDbComponent.createFile(
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name=actionDoc.documentName if hasattr(actionDoc, 'documentName') else "fast_path_response.txt",
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mimeType=actionDoc.mimeType if hasattr(actionDoc, 'mimeType') else "text/plain",
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content=actionDoc.documentData if isinstance(actionDoc.documentData, bytes) else actionDoc.documentData.encode('utf-8')
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)
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# Persist file data
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self.services.interfaceDbComponent.createFileData(fileItem.id, actionDoc.documentData if isinstance(actionDoc.documentData, bytes) else actionDoc.documentData.encode('utf-8'))
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# Get file info
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fileInfo = self.services.chat.getFileInfo(fileItem.id)
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# Create ChatDocument as dict (messageId will be assigned by createMessage)
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# Don't create ChatDocument object directly - it requires messageId which doesn't exist yet
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chatDoc = {
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"fileId": fileItem.id,
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"fileName": fileInfo.get("fileName", actionDoc.documentName) if fileInfo else actionDoc.documentName,
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"fileSize": fileInfo.get("size", len(actionDoc.documentData) if isinstance(actionDoc.documentData, bytes) else len(actionDoc.documentData.encode('utf-8'))) if fileInfo else (len(actionDoc.documentData) if isinstance(actionDoc.documentData, bytes) else len(actionDoc.documentData.encode('utf-8'))),
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"mimeType": fileInfo.get("mimeType", actionDoc.mimeType) if fileInfo else actionDoc.mimeType,
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"roundNumber": workflow.currentRound,
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"taskNumber": 0, # Fast path doesn't have tasks
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"actionNumber": 0
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}
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chatDocuments.append(chatDoc)
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# Create ChatMessage with fast path response (in user's language)
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messageData = {
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"workflowId": workflow.id,
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"role": "assistant",
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"message": responseText or "Fast path response completed",
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"status": "last", # Fast path completes the workflow
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"sequenceNr": len(workflow.messages) + 1,
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"publishedAt": self.services.utils.timestampGetUtc(),
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"documentsLabel": "fast_path_response",
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"documents": [],
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# Add workflow context fields
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"roundNumber": workflow.currentRound,
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"taskNumber": 0, # Fast path doesn't have tasks
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"actionNumber": 0,
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# Add progress status
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"taskProgress": "success",
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"actionProgress": "success"
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}
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# Store message with documents
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self.services.chat.storeMessageWithDocuments(workflow, messageData, chatDocuments)
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# Mark workflow as completed
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workflow.status = "completed"
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workflow.lastActivity = self.services.utils.timestampGetUtc()
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self.services.chat.updateWorkflow(workflow.id, {
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"status": "completed",
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"lastActivity": workflow.lastActivity
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})
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logger.info(f"Fast path completed successfully, response length: {len(responseText)} chars")
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except Exception as e:
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logger.error(f"Error in _executeFastPath: {str(e)}")
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# Fall back to full workflow on error
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logger.info("Falling back to full workflow due to fast path error")
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taskPlan = await self._planTasks(userInput)
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await self._executeTasks(taskPlan)
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await self._processWorkflowResults()
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async def _sendFirstMessage(self, userInput: UserInputRequest) -> None:
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"""Send first message to start workflow"""
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try:
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workflow = self.services.workflow
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checkWorkflowStopped(self.services)
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# Create initial message using interface
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# For first user message, include round info in the user context label
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roundNum = workflow.currentRound
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contextLabel = f"round{roundNum}_usercontext"
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messageData = {
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"workflowId": workflow.id,
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"role": "user",
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"message": userInput.prompt,
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"status": "first",
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"sequenceNr": 1,
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"publishedAt": self.services.utils.timestampGetUtc(),
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"documentsLabel": contextLabel,
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"documents": [],
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# Add workflow context fields
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"roundNumber": workflow.currentRound,
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"taskNumber": 0,
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"actionNumber": 0,
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# Add progress status
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"taskProgress": "pending",
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"actionProgress": "pending"
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}
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# Analyze the user's input to detect language, normalize request, extract intent, and offload bulky context into documents
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# SKIP user intention analysis for AUTOMATION mode - it uses predefined JSON plans
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createdDocs = []
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workflowMode = getattr(workflow, 'workflowMode', None)
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skipIntentionAnalysis = (workflowMode == WorkflowModeEnum.WORKFLOW_AUTOMATION)
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if skipIntentionAnalysis:
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logger.info("Skipping user intention analysis for AUTOMATION mode - using direct user input")
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# For automation mode, use user input directly without AI analysis
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self.services.currentUserPrompt = userInput.prompt
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# Always set currentUserPromptNormalized - use user input directly for automation mode
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self.services.currentUserPromptNormalized = userInput.prompt
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detectedLanguage = None
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normalizedRequest = None
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intentText = userInput.prompt
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contextItems = []
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else:
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try:
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analyzerPrompt = (
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"You are an input analyzer. From the user's message, perform ALL of the following in one pass:\n"
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"1) detectedLanguage: detect ISO 639-1 language code (e.g., de, en).\n"
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"2) normalizedRequest: full, explicit restatement of the user's request in the detected language; do NOT summarize; preserve ALL constraints and details.\n"
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"3) intent: concise single-paragraph core request in the detected language for high-level routing.\n"
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"4) contextItems: supportive data blocks to attach as separate documents if significantly larger than the intent (large literal content, long lists/tables, code/JSON blocks, transcripts, CSV fragments, detailed specs). Keep URLs in the intent unless they embed large pasted content.\n"
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"5) primaryGoal: The main objective the user wants to achieve.\n"
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"6) dataType: What type of data/content they want (numbers|text|documents|analysis|code|unknown).\n"
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"7) expectedFormats: What file format(s) they expect - provide matching file format extensions list (e.g., [\"xlsx\", \"pdf\"]). If format is unclear or not specified, use empty list [].\n"
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"8) qualityRequirements: Quality requirements they have (accuracy, completeness) as {accuracyThreshold: 0.0-1.0, completenessThreshold: 0.0-1.0}.\n"
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"9) successCriteria: Specific success criteria that define completion (array of strings).\n\n"
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"Rules:\n"
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"- If total content (intent + data) is < 10% of model max tokens, do not extract; return empty contextItems and keep intent compact and self-contained.\n"
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"- If content exceeds that threshold, move bulky parts into contextItems; keep intent short and clear.\n"
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"- Preserve critical references (URLs, filenames) in intent.\n"
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"- Normalize to the primary detected language if mixed-language.\n\n"
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"Return ONLY JSON (no markdown) with this shape:\n"
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"{\n"
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" \"detectedLanguage\": \"de|en|fr|it|...\",\n"
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" \"normalizedRequest\": \"Full explicit instruction in detected language\",\n"
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" \"intent\": \"Concise normalized request...\",\n"
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" \"contextItems\": [\n"
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" {\n"
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" \"title\": \"User context 1\",\n"
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" \"mimeType\": \"text/plain\",\n"
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" \"content\": \"Full extracted content block here\"\n"
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" }\n"
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" ],\n"
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" \"primaryGoal\": \"The main objective the user wants to achieve\",\n"
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" \"dataType\": \"numbers|text|documents|analysis|code|unknown\",\n"
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" \"expectedFormats\": [\"pdf\", \"docx\", \"xlsx\", \"txt\", \"json\", \"csv\", \"html\", \"md\"],\n"
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" \"qualityRequirements\": {\n"
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" \"accuracyThreshold\": 0.0-1.0,\n"
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" \"completenessThreshold\": 0.0-1.0\n"
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" },\n"
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" \"successCriteria\": [\"specific criterion 1\", \"specific criterion 2\"]\n"
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"}\n\n"
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f"User message:\n{self.services.utils.sanitizePromptContent(userInput.prompt, 'userinput')}"
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)
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# Call AI analyzer (planning call - will use static parameters)
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aiResponse = await self.services.ai.callAiPlanning(
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prompt=analyzerPrompt,
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placeholders=None,
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debugType="userintention"
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)
|
|
|
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detectedLanguage = None
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normalizedRequest = None
|
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intentText = userInput.prompt
|
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contextItems = []
|
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workflowIntent = None
|
|
|
|
# Parse analyzer response (JSON expected)
|
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try:
|
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jsonStart = aiResponse.find('{') if aiResponse else -1
|
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jsonEnd = aiResponse.rfind('}') + 1 if aiResponse else 0
|
|
if jsonStart != -1 and jsonEnd > jsonStart:
|
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parsed = json.loads(aiResponse[jsonStart:jsonEnd])
|
|
detectedLanguage = parsed.get('detectedLanguage') or None
|
|
normalizedRequest = parsed.get('normalizedRequest') or None
|
|
if parsed.get('intent'):
|
|
intentText = parsed.get('intent')
|
|
contextItems = parsed.get('contextItems') or []
|
|
|
|
# Extract intent analysis fields and store as workflowIntent
|
|
workflowIntent = {
|
|
'primaryGoal': parsed.get('primaryGoal'),
|
|
'dataType': parsed.get('dataType', 'unknown'),
|
|
'expectedFormats': parsed.get('expectedFormats', []),
|
|
'qualityRequirements': parsed.get('qualityRequirements', {}),
|
|
'successCriteria': parsed.get('successCriteria', []),
|
|
'languageUserDetected': detectedLanguage
|
|
}
|
|
|
|
# Store workflowIntent in workflow object for reuse
|
|
if hasattr(self.services, 'workflow') and self.services.workflow:
|
|
self.services.workflow._workflowIntent = workflowIntent
|
|
except Exception:
|
|
contextItems = []
|
|
workflowIntent = None
|
|
|
|
# Update services state
|
|
if detectedLanguage and isinstance(detectedLanguage, str):
|
|
self._setUserLanguage(detectedLanguage)
|
|
try:
|
|
setattr(self.services, 'currentUserLanguage', detectedLanguage)
|
|
except Exception:
|
|
pass
|
|
self.services.currentUserPrompt = intentText or userInput.prompt
|
|
# Always set currentUserPromptNormalized - use normalizedRequest if available, otherwise fallback to currentUserPrompt
|
|
normalizedValue = normalizedRequest or intentText or userInput.prompt
|
|
self.services.currentUserPromptNormalized = normalizedValue
|
|
if contextItems is not None:
|
|
self.services.currentUserContextItems = contextItems
|
|
|
|
# Create documents for context items
|
|
if contextItems and isinstance(contextItems, list):
|
|
for idx, item in enumerate(contextItems):
|
|
try:
|
|
title = item.get('title') if isinstance(item, dict) else None
|
|
mime = item.get('mimeType') if isinstance(item, dict) else None
|
|
content = item.get('content') if isinstance(item, dict) else None
|
|
if not content:
|
|
continue
|
|
fileName = (title or f"user_context_{idx+1}.txt").strip()
|
|
mimeType = (mime or "text/plain").strip()
|
|
|
|
# Neutralize content before storing if neutralization is enabled
|
|
contentBytes = content.encode('utf-8')
|
|
contentBytes = await self._neutralizeContentIfEnabled(contentBytes, mimeType)
|
|
|
|
# Create file in component storage
|
|
fileItem = self.services.interfaceDbComponent.createFile(
|
|
name=fileName,
|
|
mimeType=mimeType,
|
|
content=contentBytes
|
|
)
|
|
# Persist file data
|
|
self.services.interfaceDbComponent.createFileData(fileItem.id, contentBytes)
|
|
|
|
# Collect file info
|
|
fileInfo = self.services.chat.getFileInfo(fileItem.id)
|
|
from modules.datamodels.datamodelChat import ChatDocument
|
|
doc = ChatDocument(
|
|
fileId=fileItem.id,
|
|
fileName=fileInfo.get("fileName", fileName) if fileInfo else fileName,
|
|
fileSize=fileInfo.get("size", len(contentBytes)) if fileInfo else len(contentBytes),
|
|
mimeType=fileInfo.get("mimeType", mimeType) if fileInfo else mimeType
|
|
)
|
|
createdDocs.append(doc)
|
|
except Exception:
|
|
continue
|
|
except Exception as e:
|
|
logger.warning(f"Prompt analysis failed or skipped: {str(e)}")
|
|
|
|
# Process user-uploaded documents (fileIds) and combine with context documents
|
|
if userInput.listFileId:
|
|
try:
|
|
userDocs = await self._processFileIds(userInput.listFileId, None)
|
|
if userDocs:
|
|
createdDocs.extend(userDocs)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to process user fileIds: {e}")
|
|
|
|
# Finally, persist and bind the first message with combined documents (context + user)
|
|
self.services.chat.storeMessageWithDocuments(workflow, messageData, createdDocs)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error sending first message: {str(e)}")
|
|
raise
|
|
|
|
async def _planTasks(self, userInput: UserInputRequest):
|
|
"""Generate task plan for workflow execution"""
|
|
workflow = self.services.workflow
|
|
handling = self.workflowProcessor
|
|
# Generate task plan first (shared for both modes)
|
|
taskPlan = await handling.generateTaskPlan(userInput.prompt, workflow)
|
|
if not taskPlan or not taskPlan.tasks:
|
|
raise Exception("No tasks generated in task plan.")
|
|
workflowMode = getattr(workflow, 'workflowMode')
|
|
logger.info(f"Workflow object attributes: {workflow.__dict__ if hasattr(workflow, '__dict__') else 'No __dict__'}")
|
|
logger.info(f"Executing workflow mode={workflowMode} with {len(taskPlan.tasks)} tasks")
|
|
return taskPlan
|
|
|
|
async def _executeTasks(self, taskPlan) -> None:
|
|
"""Execute all tasks in the task plan and update workflow status."""
|
|
workflow = self.services.workflow
|
|
handling = self.workflowProcessor
|
|
totalTasks = len(taskPlan.tasks)
|
|
allTaskResults: List = []
|
|
previousResults: List[str] = []
|
|
|
|
for idx, taskStep in enumerate(taskPlan.tasks):
|
|
currentTaskIndex = idx + 1
|
|
logger.info(f"Task {currentTaskIndex}/{totalTasks}: {taskStep.objective}")
|
|
|
|
# Update workflow state before executing task (fixes "Task 0" issue)
|
|
handling.updateWorkflowBeforeExecutingTask(currentTaskIndex)
|
|
|
|
# Build TaskContext (mode-specific behavior is inside WorkflowProcessor)
|
|
taskContext = TaskContext(
|
|
taskStep=taskStep,
|
|
workflow=workflow,
|
|
workflowId=workflow.id,
|
|
availableDocuments=None,
|
|
availableConnections=None,
|
|
previousResults=previousResults,
|
|
previousHandover=None,
|
|
improvements=[],
|
|
retryCount=0,
|
|
previousActionResults=[],
|
|
previousReviewResult=None,
|
|
isRegeneration=False,
|
|
failurePatterns=[],
|
|
failedActions=[],
|
|
successfulActions=[],
|
|
criteriaProgress={
|
|
'met_criteria': set(),
|
|
'unmet_criteria': set(),
|
|
'attempt_history': []
|
|
}
|
|
)
|
|
|
|
taskResult = await handling.executeTask(taskStep, workflow, taskContext)
|
|
|
|
# Persist task result for cross-task/round document references
|
|
# Convert ChatTaskResult to WorkflowTaskResult for persistence
|
|
from modules.datamodels.datamodelWorkflow import TaskResult as WorkflowTaskResult
|
|
from modules.datamodels.datamodelChat import ActionResult
|
|
|
|
# Get final ActionResult from task execution (last action result)
|
|
finalActionResult = None
|
|
if hasattr(taskResult, 'actionResult'):
|
|
finalActionResult = taskResult.actionResult
|
|
elif taskContext.previousActionResults and len(taskContext.previousActionResults) > 0:
|
|
# Use last action result from context
|
|
finalActionResult = taskContext.previousActionResults[-1]
|
|
|
|
# Create WorkflowTaskResult for persistence
|
|
if finalActionResult:
|
|
workflowTaskResult = WorkflowTaskResult(
|
|
taskId=taskStep.id,
|
|
actionResult=finalActionResult
|
|
)
|
|
# Persist task result (creates ChatMessage + ChatDocuments)
|
|
await handling.persistTaskResult(workflowTaskResult, workflow, taskContext)
|
|
|
|
handoverData = await handling.prepareTaskHandover(taskStep, [], taskResult, workflow)
|
|
allTaskResults.append({
|
|
'taskStep': taskStep,
|
|
'taskResult': taskResult,
|
|
'handoverData': handoverData
|
|
})
|
|
if taskResult.success and taskResult.feedback:
|
|
previousResults.append(taskResult.feedback)
|
|
|
|
# Mark workflow as completed; error/stop cases update status elsewhere
|
|
workflow.status = "completed"
|
|
return None
|
|
|
|
async def _processWorkflowResults(self) -> None:
|
|
"""Process workflow results based on workflow status and create appropriate messages"""
|
|
try:
|
|
workflow = self.services.workflow
|
|
try:
|
|
checkWorkflowStopped(self.services)
|
|
except WorkflowStoppedException:
|
|
logger.info(f"Workflow {workflow.id} was stopped during result processing")
|
|
|
|
# Create final stopped message
|
|
stoppedMessage = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": "🛑 Workflow stopped by user",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_stopped",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "stopped",
|
|
"actionProgress": "stopped"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, stoppedMessage, [])
|
|
|
|
# Update workflow status to stopped
|
|
workflow.status = "stopped"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "stopped",
|
|
"lastActivity": workflow.lastActivity
|
|
})
|
|
return
|
|
|
|
if workflow.status == 'stopped':
|
|
# Create stopped message
|
|
stopped_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": "🛑 Workflow stopped by user",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_stopped",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "stopped",
|
|
"actionProgress": "stopped"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, stopped_message, [])
|
|
|
|
# Update workflow status to stopped
|
|
workflow.status = "stopped"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "stopped",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
# Add stopped log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": "Workflow stopped by user",
|
|
"type": "warning",
|
|
"status": "stopped",
|
|
"progress": 1.0
|
|
})
|
|
return
|
|
elif workflow.status == 'failed':
|
|
# Create error message
|
|
errorMessage = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": f"Workflow failed: {'Unknown error'}",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_failure",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "fail",
|
|
"actionProgress": "fail"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, errorMessage, [])
|
|
|
|
# Update workflow status to failed
|
|
workflow.status = "failed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "failed",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
# Add failed log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": "Workflow failed: Unknown error",
|
|
"type": "error",
|
|
"status": "failed",
|
|
"progress": 1.0
|
|
})
|
|
return
|
|
|
|
# For successful workflows, send detailed completion message
|
|
await self._sendLastMessage()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error processing workflow results: {str(e)}")
|
|
# Create error message
|
|
error_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": f"Error processing workflow results: {str(e)}",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_error",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "fail",
|
|
"actionProgress": "fail"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, error_message, [])
|
|
|
|
# Update workflow status to failed
|
|
workflow.status = "failed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "failed",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
async def _sendLastMessage(self) -> None:
|
|
"""Send last message to complete workflow (only for successful workflows)"""
|
|
try:
|
|
workflow = self.services.workflow
|
|
# Safety check: ensure this is only called for successful workflows
|
|
if workflow.status in ['stopped', 'failed']:
|
|
logger.warning(f"Attempted to send last message for {workflow.status} workflow {workflow.id}")
|
|
return
|
|
|
|
# Generate feedback
|
|
feedback = await self._generateWorkflowFeedback()
|
|
|
|
# Create last message using interface
|
|
messageData = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": feedback,
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_feedback",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "success",
|
|
"actionProgress": "success"
|
|
}
|
|
|
|
# Create message using interface
|
|
self.services.chat.storeMessageWithDocuments(workflow, messageData, [])
|
|
|
|
# Update workflow status to completed
|
|
workflow.status = "completed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
|
|
# Update workflow in database
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "completed",
|
|
"lastActivity": workflow.lastActivity
|
|
})
|
|
|
|
# Add completion log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": "Workflow completed",
|
|
"type": "success",
|
|
"status": "completed",
|
|
"progress": 1.0
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error sending last message: {str(e)}")
|
|
raise
|
|
|
|
async def _generateWorkflowFeedback(self) -> str:
|
|
"""Generate feedback message for workflow completion"""
|
|
try:
|
|
workflow = self.services.workflow
|
|
checkWorkflowStopped(self.services)
|
|
|
|
# Count messages by role
|
|
userMessages = [msg for msg in workflow.messages if msg.role == 'user']
|
|
assistantMessages = [msg for msg in workflow.messages if msg.role == 'assistant']
|
|
|
|
# Generate summary feedback
|
|
feedback = f"Workflow completed.\n\n"
|
|
feedback += f"Processed {len(userMessages)} user inputs and generated {len(assistantMessages)} responses.\n"
|
|
|
|
# Add final status
|
|
if workflow.status == "completed":
|
|
feedback += "All tasks completed successfully."
|
|
elif workflow.status == "partial":
|
|
feedback += "Some tasks completed with partial success."
|
|
else:
|
|
feedback += f"Workflow status: {workflow.status}"
|
|
|
|
return feedback
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating workflow feedback: {str(e)}")
|
|
return "Workflow processing completed."
|
|
|
|
def _handleWorkflowStop(self) -> None:
|
|
"""Handle workflow stop exception"""
|
|
workflow = self.services.workflow
|
|
logger.info("Workflow stopped by user")
|
|
|
|
# Update workflow status to stopped
|
|
workflow.status = "stopped"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "stopped",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
# Create final stopped message
|
|
stopped_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": "🛑 Workflow stopped by user",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_stopped",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "pending",
|
|
"actionProgress": "pending"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, stopped_message, [])
|
|
|
|
# Add log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": "Workflow stopped by user",
|
|
"type": "warning",
|
|
"status": "stopped",
|
|
"progress": 1.0
|
|
})
|
|
|
|
def _handleWorkflowError(self, error: Exception) -> None:
|
|
"""Handle workflow error exception"""
|
|
workflow = self.services.workflow
|
|
logger.error(f"Workflow processing error: {str(error)}")
|
|
|
|
# Update workflow status to failed
|
|
workflow.status = "failed"
|
|
workflow.lastActivity = self.services.utils.timestampGetUtc()
|
|
self.services.chat.updateWorkflow(workflow.id, {
|
|
"status": "failed",
|
|
"lastActivity": workflow.lastActivity,
|
|
"totalTasks": workflow.totalTasks,
|
|
"totalActions": workflow.totalActions
|
|
})
|
|
|
|
# Create error message
|
|
error_message = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": f"Workflow processing failed: {str(error)}",
|
|
"status": "last",
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.timestampGetUtc(),
|
|
"documentsLabel": "workflow_error",
|
|
"documents": [],
|
|
# Add workflow context fields
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": 0,
|
|
"actionNumber": 0,
|
|
# Add progress status
|
|
"taskProgress": "fail",
|
|
"actionProgress": "fail"
|
|
}
|
|
self.services.chat.storeMessageWithDocuments(workflow, error_message, [])
|
|
|
|
# Add error log entry
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": f"Workflow failed: {str(error)}",
|
|
"type": "error",
|
|
"status": "failed",
|
|
"progress": 1.0
|
|
})
|
|
|
|
raise
|
|
|
|
async def _processFileIds(self, fileIds: List[str], messageId: str = None) -> List[ChatDocument]:
|
|
"""Process file IDs from existing files and return ChatDocument objects.
|
|
If neutralization is enabled, files are neutralized and new files are created with neutralized content.
|
|
If neutralization fails, the document is not included and an error is logged to ChatLog."""
|
|
documents = []
|
|
|
|
# Check if neutralization is enabled
|
|
neutralizationEnabled = False
|
|
try:
|
|
config = self.services.neutralization.getConfig()
|
|
neutralizationEnabled = config and config.enabled
|
|
except Exception as e:
|
|
logger.debug(f"Could not check neutralization config: {str(e)}")
|
|
|
|
workflow = self.services.workflow
|
|
|
|
for fileId in fileIds:
|
|
try:
|
|
# Get file info from chat service
|
|
fileInfo = self.services.chat.getFileInfo(fileId)
|
|
if not fileInfo:
|
|
logger.warning(f"No file info found for file ID {fileId}")
|
|
continue
|
|
|
|
originalFileName = fileInfo.get("fileName", "unknown")
|
|
originalMimeType = fileInfo.get("mimeType", "application/octet-stream")
|
|
fileIdToUse = fileId
|
|
fileNameToUse = originalFileName
|
|
fileSizeToUse = fileInfo.get("size", 0)
|
|
neutralizationFailed = False
|
|
|
|
# Neutralize file if enabled
|
|
if neutralizationEnabled:
|
|
try:
|
|
# Neutralize the file using the neutralization service
|
|
neutralizationResult = self.services.neutralization.processFile(fileId)
|
|
|
|
# Check if file is binary (not neutralized)
|
|
if neutralizationResult.get('is_binary', False):
|
|
# Binary file - log INFO and use original file
|
|
infoMsg = f"File '{originalFileName}' (MIME type: {neutralizationResult.get('mime_type', 'unknown')}) is a binary file. Binary file neutralization will be implemented in the future. Using original file without neutralization."
|
|
logger.info(infoMsg)
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": infoMsg,
|
|
"type": "info",
|
|
"status": "running",
|
|
"progress": 50
|
|
})
|
|
# Use original file (fileIdToUse already set to fileId)
|
|
elif neutralizationResult and 'neutralized_text' in neutralizationResult:
|
|
neutralizedText = neutralizationResult['neutralized_text']
|
|
|
|
# Create new file with neutralized content
|
|
neutralizedFileName = neutralizationResult.get('neutralized_file_name', f"neutralized_{originalFileName}")
|
|
neutralizedContentBytes = neutralizedText.encode('utf-8')
|
|
|
|
# Create file in component storage
|
|
neutralizedFileItem = self.services.interfaceDbComponent.createFile(
|
|
name=neutralizedFileName,
|
|
mimeType=originalMimeType,
|
|
content=neutralizedContentBytes
|
|
)
|
|
# Persist file data
|
|
self.services.interfaceDbComponent.createFileData(neutralizedFileItem.id, neutralizedContentBytes)
|
|
|
|
# Use the neutralized file ID and actual size
|
|
fileIdToUse = neutralizedFileItem.id
|
|
fileNameToUse = neutralizedFileName
|
|
fileSizeToUse = len(neutralizedContentBytes)
|
|
|
|
logger.info(f"Neutralized file {fileId} -> {fileIdToUse} ({fileNameToUse})")
|
|
else:
|
|
neutralizationFailed = True
|
|
errorMsg = f"Neutralization did not return neutralized_text for file '{originalFileName}' (ID: {fileId})"
|
|
logger.warning(errorMsg)
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": errorMsg,
|
|
"type": "error",
|
|
"status": "error",
|
|
"progress": -1
|
|
})
|
|
except Exception as e:
|
|
neutralizationFailed = True
|
|
errorMsg = f"Failed to neutralize file '{originalFileName}' (ID: {fileId}): {str(e)}"
|
|
logger.error(errorMsg)
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": errorMsg,
|
|
"type": "error",
|
|
"status": "error",
|
|
"progress": -1
|
|
})
|
|
|
|
# Only skip document if neutralization failed (not for binary files)
|
|
if not neutralizationFailed:
|
|
# Create document with file ID (neutralized or original)
|
|
document = ChatDocument(
|
|
id=str(uuid.uuid4()),
|
|
messageId=messageId or "",
|
|
fileId=fileIdToUse,
|
|
fileName=fileNameToUse,
|
|
fileSize=fileSizeToUse,
|
|
mimeType=originalMimeType
|
|
)
|
|
documents.append(document)
|
|
logger.info(f"Processed file ID {fileId} -> {document.fileName} (using fileId: {fileIdToUse})")
|
|
else:
|
|
logger.warning(f"Skipping document for file ID {fileId} due to neutralization failure")
|
|
except Exception as e:
|
|
errorMsg = f"Error processing file ID {fileId}: {str(e)}"
|
|
logger.error(errorMsg)
|
|
self.services.chat.storeLog(workflow, {
|
|
"message": errorMsg,
|
|
"type": "error",
|
|
"status": "error",
|
|
"progress": -1
|
|
})
|
|
return documents
|
|
|
|
|
|
def _setUserLanguage(self, language: str) -> None:
|
|
"""Set user language for the service center"""
|
|
self.services.user.language = language
|
|
|
|
async def _neutralizeContentIfEnabled(self, contentBytes: bytes, mimeType: str) -> bytes:
|
|
"""Neutralize content if neutralization is enabled in user settings"""
|
|
try:
|
|
# Check if neutralization is enabled
|
|
config = self.services.neutralization.getConfig()
|
|
if not config or not config.enabled:
|
|
return contentBytes
|
|
|
|
# Decode content to text for neutralization
|
|
try:
|
|
textContent = contentBytes.decode('utf-8')
|
|
except UnicodeDecodeError:
|
|
# Try alternative encodings
|
|
for enc in ['latin-1', 'cp1252', 'iso-8859-1']:
|
|
try:
|
|
textContent = contentBytes.decode(enc)
|
|
break
|
|
except UnicodeDecodeError:
|
|
continue
|
|
else:
|
|
# If unable to decode, return original bytes (binary content)
|
|
logger.debug(f"Unable to decode content for neutralization, skipping: {mimeType}")
|
|
return contentBytes
|
|
|
|
# Neutralize the text content
|
|
# Note: The neutralization service should use names from config when processing
|
|
result = self.services.neutralization.processText(textContent)
|
|
if result and 'neutralized_text' in result:
|
|
neutralizedText = result['neutralized_text']
|
|
# Encode back to bytes using the same encoding
|
|
try:
|
|
return neutralizedText.encode('utf-8')
|
|
except Exception as e:
|
|
logger.warning(f"Error encoding neutralized text: {str(e)}")
|
|
return contentBytes
|
|
else:
|
|
logger.warning("Neutralization did not return neutralized_text")
|
|
return contentBytes
|
|
except Exception as e:
|
|
logger.error(f"Error during content neutralization: {str(e)}")
|
|
# Return original content on error
|
|
return contentBytes
|