# handlingTasks.py
# Refactored for clarity and consolidation

import asyncio
import logging
import json
import time
from typing import Dict, Any, Optional, List, Union
from datetime import datetime, UTC
from modules.interfaces.interfaceChatModel import (
    TaskStatus, TaskStep, TaskContext, TaskAction, ReviewResult, TaskPlan, WorkflowResult, TaskResult, ReviewContext, ActionResult
)
from modules.shared.timezoneUtils import get_utc_timestamp
from .executionState import TaskExecutionState
from .promptFactory import (
    createTaskPlanningPrompt, 
    createActionDefinitionPrompt, 
    createResultReviewPrompt
)
from modules.chat.documents.documentGeneration import DocumentGenerator
import uuid

logger = logging.getLogger(__name__)

class WorkflowStoppedException(Exception):
    """Exception raised when a workflow is stopped by the user."""
    pass

class HandlingTasks:
    def __init__(self, chatInterface, service, workflow=None):
        self.chatInterface = chatInterface
        self.service = service
        self.workflow = workflow
        self.documentGenerator = DocumentGenerator(service)
    
    def _checkWorkflowStopped(self):
        """
        Check if workflow has been stopped by user and raise exception if so.
        This function centralizes all workflow stop checking logic to avoid code duplication.
        """
        try:
            # Get the current workflow status from the database to avoid stale data
            current_workflow = self.chatInterface.getWorkflow(self.service.workflow.id)
            if current_workflow and current_workflow.status == "stopped":
                logger.info("Workflow stopped by user, aborting execution")
                raise WorkflowStoppedException("Workflow was stopped by user")
        except WorkflowStoppedException:
            # Re-raise the WorkflowStoppedException immediately
            raise
        except Exception as e:
            # If we can't get the current status due to other database issues, fall back to the in-memory object
            logger.warning(f"Could not check current workflow status from database: {str(e)}")
            if self.service.workflow.status == "stopped":
                logger.info("Workflow stopped by user (from in-memory object), aborting execution")
                raise WorkflowStoppedException("Workflow was stopped by user")

    async def generateTaskPlan(self, userInput: str, workflow) -> TaskPlan:
        """Generate a high-level task plan for the workflow."""
        try:
            # Check workflow status before generating task plan
            self._checkWorkflowStopped()
            
            logger.info(f"=== STARTING TASK PLAN GENERATION ===")
            logger.info(f"Workflow ID: {workflow.id}")
            logger.info(f"User Input: {userInput}")
            available_docs = self.service.getAvailableDocuments(workflow)

            # Check workflow status before calling AI service
            self._checkWorkflowStopped()
            
            # Create proper context object for task planning
            # For task planning, we need to create a minimal TaskStep since TaskContext requires it
            from modules.interfaces.interfaceChatModel import TaskStep
            planning_task_step = TaskStep(
                id="planning",
                objective=userInput,
                dependencies=[],
                success_criteria=[],
                estimated_complexity="medium"
            )
            
            task_planning_context = TaskContext(
                task_step=planning_task_step,
                workflow=workflow,
                workflow_id=workflow.id,
                available_documents=available_docs,
                available_connections=[],
                previous_results=[],
                previous_handover=None,
                improvements=[],
                retry_count=0,
                previous_action_results=[],
                previous_review_result=None,
                is_regeneration=False,
                failure_patterns=[],
                failed_actions=[],
                successful_actions=[],
                criteria_progress={
                    'met_criteria': set(),
                    'unmet_criteria': set(),
                    'attempt_history': []
                }
            )
            
            # Generate the task planning prompt
            task_planning_prompt = createTaskPlanningPrompt(task_planning_context, self.service)
            
            # Log the full task planning prompt being sent to AI for debugging
            logger.info("=== TASK PLANNING PROMPT SENT TO AI ===")
            logger.info(f"User Input: {userInput}")
            logger.info(f"Available Documents: {available_docs}")
            logger.info("=== FULL TASK PLANNING PROMPT ===")
            logger.info(task_planning_prompt)
            logger.info("=== END TASK PLANNING PROMPT ===")
            
            prompt = await self.service.callAiTextAdvanced(task_planning_prompt)
            
            # Check if AI response is valid
            if not prompt:
                raise ValueError("AI service returned no response for task planning")
            
            # Log the full AI response for task planning
            logger.info("=== TASK PLANNING AI RESPONSE RECEIVED ===")
            logger.info(f"Response length: {len(prompt) if prompt else 0}")
            logger.debug("=== FULL TASK PLANNING AI RESPONSE ===")
            logger.debug(prompt)
            logger.debug("=== END TASK PLANNING AI RESPONSE ===")
                
            # Inline _parseTaskPlanResponse logic
            try:
                json_start = prompt.find('{')
                json_end = prompt.rfind('}') + 1
                if json_start == -1 or json_end == 0:
                    raise ValueError("No JSON found in response")
                json_str = prompt[json_start:json_end]
                task_plan_dict = json.loads(json_str)
                
                if 'tasks' not in task_plan_dict:
                    raise ValueError("Task plan missing 'tasks' field")
            except Exception as e:
                logger.error(f"Error parsing task plan response: {str(e)}")
                task_plan_dict = {'tasks': []}
            
            if not self._validateTaskPlan(task_plan_dict):
                logger.error("Generated task plan failed validation")
                logger.error(f"AI Response: {prompt}")
                logger.error(f"Parsed Task Plan: {json.dumps(task_plan_dict, indent=2)}")
                raise Exception("AI-generated task plan failed validation - AI is required for task planning")
            
            if not task_plan_dict.get('tasks'):
                raise ValueError("Task plan contains no tasks")
            
            # LANGUAGE DETECTION: Determine user language once for the entire workflow
            # Priority: 1. languageUserDetected from AI response, 2. service.user.language, 3. "en"
            detected_language = task_plan_dict.get('languageUserDetected', '').strip()
            service_user_language = getattr(self.service.user, 'language', '') if self.service and self.service.user else ''
            
            if detected_language and len(detected_language) == 2:  # Valid language code like "en", "de", "fr"
                user_language = detected_language
                logger.info(f"Using detected language from AI response: {user_language}")
            elif service_user_language and len(service_user_language) == 2:
                user_language = service_user_language
                logger.info(f"Using language from service user object: {user_language}")
            else:
                user_language = "en"
                logger.info(f"Using default language: {user_language}")
            
            # Set the detected language in the service for use throughout the workflow
            if self.service and self.service.user:
                self.service.user.language = user_language
                logger.info(f"Set workflow user language to: {user_language}")
            
            tasks = []
            for i, task_dict in enumerate(task_plan_dict.get('tasks', [])):
                if not isinstance(task_dict, dict):
                    logger.warning(f"Skipping invalid task {i+1}: not a dictionary")
                    continue
                    
                # Map old 'description' field to new 'objective' field
                if 'description' in task_dict and 'objective' not in task_dict:
                    task_dict['objective'] = task_dict.pop('description')
                
                try:
                    task = TaskStep(**task_dict)
                    tasks.append(task)
                except Exception as e:
                    logger.warning(f"Skipping invalid task {i+1}: {str(e)}")
                    continue
            
            if not tasks:
                raise ValueError("No valid tasks could be created from AI response")
                
            task_plan = TaskPlan(
                overview=task_plan_dict.get('overview', ''),
                tasks=tasks,
                userMessage=task_plan_dict.get('userMessage', '')
            )
            
            # Set workflow totals for progress tracking
            total_tasks = len(tasks)
            if total_tasks == 0:
                raise ValueError("Task plan contains no valid tasks")
                
            self.setWorkflowTotals(total_tasks=total_tasks)
            
            logger.info(f"Task plan generated successfully with {len(tasks)} tasks")
            logger.info(f"Workflow user language set to: {user_language}")
            
            # PHASE 3: Create chat message containing the task plan
            await self.createTaskPlanMessage(task_plan, workflow)
            
            return task_plan
        except Exception as e:
            logger.error(f"Error in generateTaskPlan: {str(e)}")
            raise

    async def createTaskPlanMessage(self, task_plan: TaskPlan, workflow):
        """Create a chat message containing the task plan with user-friendly messages"""
        try:
            # Build task plan summary
            task_summary = f"📋 **Task Plan**\n\n"

            # Get overall user message from task plan if available
            overall_message = task_plan.userMessage
            if overall_message:
                task_summary += f"{overall_message}\n\n"

            # Add each task with its user message
            for i, task in enumerate(task_plan.tasks):
                if task.userMessage:
                    task_summary += f"💬 {task.userMessage}\n"
                task_summary += "\n"
        
            
            # Create workflow message
            message_data = {
                "workflowId": workflow.id,
                "role": "assistant",
                "message": task_summary,
                "status": "step",
                "sequenceNr": len(workflow.messages) + 1,
                "publishedAt": get_utc_timestamp(),
                "documentsLabel": "task_plan",
                "documents": [],
                # Add workflow context fields - use current workflow round instead of hardcoded 1
                "roundNumber": workflow.currentRound,  # Use current workflow round
                "taskNumber": 1,  # Task plan is before individual tasks; to keep 1, that UI not filtering the message
                "actionNumber": 0,
                # Add task progress status
                "taskProgress": "pending"
            }
            
            message = self.chatInterface.createMessage(message_data)
            if message:
                workflow.messages.append(message)
                
                # PHASE 4: Update workflow object after task plan created
                # Set currentTask=1, currentAction=0, totalTasks=len(task_plan.tasks), totalActions=0
                self.updateWorkflowAfterTaskPlanCreated(len(task_plan.tasks))
                
                logger.info(f"Task plan message created with {len(task_plan.tasks)} tasks")
            else:
                logger.error("Failed to create task plan message")
                
        except Exception as e:
            logger.error(f"Error creating task plan message: {str(e)}")

    async def generateTaskActions(self, task_step, workflow, previous_results=None, enhanced_context=None) -> List[TaskAction]:
        """Generate actions for a given task step."""
        try:
            # Check workflow status before generating actions
            self._checkWorkflowStopped()
            
            retry_info = f" (Retry #{enhanced_context.retry_count})" if enhanced_context and enhanced_context.retry_count > 0 else ""
            logger.info(f"Generating actions for task: {task_step.objective}{retry_info}")
            
            # Log criteria progress if this is a retry
            if enhanced_context and hasattr(enhanced_context, 'criteria_progress') and enhanced_context.criteria_progress is not None:
                progress = enhanced_context.criteria_progress
                logger.info(f"Retry attempt {enhanced_context.retry_count} - Criteria progress:")
                if progress.get('met_criteria'):
                    logger.info(f"  Met criteria: {', '.join(progress['met_criteria'])}")
                if progress.get('unmet_criteria'):
                    logger.warning(f"  Unmet criteria: {', '.join(progress['unmet_criteria'])}")
                
                # Show improvement trends
                if progress.get('attempt_history'):
                    recent_attempts = progress['attempt_history'][-2:]  # Last 2 attempts
                    if len(recent_attempts) >= 2:
                        prev_score = recent_attempts[0].get('quality_score', 0)
                        curr_score = recent_attempts[1].get('quality_score', 0)
                        if curr_score > prev_score:
                            logger.info(f"  Quality improving: {prev_score} -> {curr_score}")
                        elif curr_score < prev_score:
                            logger.warning(f"  Quality declining: {prev_score} -> {curr_score}")
                        else:
                            logger.info(f"  Quality stable: {curr_score}")
            
            # Enhanced retry context logging
            if enhanced_context and enhanced_context.retry_count > 0:
                logger.info("=== RETRY CONTEXT FOR ACTION GENERATION ===")
                logger.info(f"Retry Count: {enhanced_context.retry_count}")
                logger.info(f"Previous Improvements: {enhanced_context.improvements}")
                logger.info(f"Previous Review Result: {enhanced_context.previous_review_result}")
                logger.info(f"Failure Patterns: {enhanced_context.failure_patterns}")
                logger.info(f"Failed Actions: {enhanced_context.failed_actions}")
                logger.info(f"Successful Actions: {enhanced_context.successful_actions}")
                logger.info("=== END RETRY CONTEXT ===")
            
            available_docs = self.service.getAvailableDocuments(workflow)
            available_connections = self.service.getConnectionReferenceList()

            # Log available resources for debugging
            logger.info("=== AVAILABLE RESOURCES FOR ACTION GENERATION ===")
            logger.info(f"Available Documents: {available_docs}")
            # Note: available_docs is now a string description, not a list
            logger.info(f"Available Connections: {len(available_connections) if available_connections else 0}")
            if available_connections:
                for i, conn in enumerate(available_connections[:5]):  # Show first 5
                    logger.info(f"  Conn {i+1}: {conn}")
                if len(available_connections) > 5:
                    logger.info(f"  ... and {len(available_connections) - 5} more connections")
            logger.info("=== END AVAILABLE RESOURCES ===")

            # Create proper context object for action definition
            if enhanced_context and isinstance(enhanced_context, TaskContext):
                # Use existing TaskContext if provided
                action_context = TaskContext(
                    task_step=enhanced_context.task_step,
                    workflow=enhanced_context.workflow,
                    workflow_id=enhanced_context.workflow_id,
                    available_documents=enhanced_context.available_documents or available_docs,
                    available_connections=enhanced_context.available_connections or available_connections,
                    previous_results=enhanced_context.previous_results or previous_results or [],
                    previous_handover=enhanced_context.previous_handover,
                    improvements=enhanced_context.improvements or [],
                    retry_count=enhanced_context.retry_count or 0,
                    previous_action_results=enhanced_context.previous_action_results or [],
                    previous_review_result=enhanced_context.previous_review_result,
                    is_regeneration=enhanced_context.is_regeneration or False,
                    failure_patterns=enhanced_context.failure_patterns or [],
                    failed_actions=enhanced_context.failed_actions or [],
                    successful_actions=enhanced_context.successful_actions or [],
                    criteria_progress=enhanced_context.criteria_progress
                )
            else:
                # Create new context from scratch
                action_context = TaskContext(
                    task_step=task_step,
                    workflow=workflow,
                    workflow_id=workflow.id,
                    available_documents=available_docs,
                    available_connections=available_connections,
                    previous_results=previous_results or [],
                    previous_handover=None,
                    improvements=[],
                    retry_count=0,
                    previous_action_results=[],
                    previous_review_result=None,
                    is_regeneration=False,
                    failure_patterns=[],
                    failed_actions=[],
                    successful_actions=[],
                    criteria_progress=None
                )
            
            # Check workflow status before calling AI service
            self._checkWorkflowStopped()
            
            # Log the final action context being sent to AI
            logger.info("=== FINAL ACTION CONTEXT FOR AI ===")
            logger.info(f"Task Step ID: {action_context.task_step.id if action_context.task_step else 'None'}")
            logger.info(f"Task Step Objective: {action_context.task_step.objective if action_context.task_step else 'None'}")
            logger.info(f"Workflow ID: {action_context.workflow_id}")
            logger.info(f"Available Documents: {action_context.available_documents or 'No documents available'}")
            logger.info(f"Available Connections Count: {len(action_context.available_connections) if action_context.available_connections else 0}")
            logger.info(f"Previous Results Count: {len(action_context.previous_results) if action_context.previous_results else 0}")
            logger.info(f"Retry Count: {action_context.retry_count}")
            logger.info(f"Is Regeneration: {action_context.is_regeneration}")
            logger.info("=== END ACTION CONTEXT ===")
            
            # Generate the action definition prompt
            action_prompt = await createActionDefinitionPrompt(action_context, self.service)
            prompt = await self.service.callAiTextAdvanced(action_prompt)
            
            # Check if AI response is valid
            if not prompt:
                raise ValueError("AI service returned no response")
            
            # Log the full AI response for debugging
            logger.debug("=== FULL AI RESPONSE ===")
            logger.debug(prompt)
            logger.debug("=== END AI RESPONSE ===")
                
            # Inline parseActionResponse logic here
            json_start = prompt.find('{')
            json_end = prompt.rfind('}') + 1
            if json_start == -1 or json_end == 0:
                raise ValueError("No JSON found in response")
            json_str = prompt[json_start:json_end]
            
            try:
                action_data = json.loads(json_str)
            except Exception as e:
                logger.error(f"Error parsing action response JSON: {str(e)}")
                action_data = {}
                
            if 'actions' not in action_data:
                raise ValueError("Action response missing 'actions' field")
            
            actions = action_data['actions']
            if not actions:
                raise ValueError("Action response contains empty actions list")
            
            if not isinstance(actions, list):
                raise ValueError(f"Action response 'actions' field is not a list: {type(actions)}")
                
            if not self._validateActions(actions, action_context):
                logger.error("Generated actions failed validation")
                raise Exception("AI-generated actions failed validation - AI is required for action generation")
            
            # Convert to TaskAction objects
            task_actions = []
            for i, a in enumerate(actions):
                if not isinstance(a, dict):
                    logger.warning(f"Skipping invalid action {i+1}: not a dictionary")
                    continue
                    
                task_action = self.createTaskAction({
                    "execMethod": a.get('method', 'unknown'),
                    "execAction": a.get('action', 'unknown'),
                    "execParameters": a.get('parameters', {}),
                    "execResultLabel": a.get('resultLabel', ''),
                    "expectedDocumentFormats": a.get('expectedDocumentFormats', None),
                    "status": TaskStatus.PENDING,
                    # Extract user-friendly message if available
                    "userMessage": a.get('userMessage', None)
                })
                
                if task_action:
                    task_actions.append(task_action)
                else:
                    logger.warning(f"Skipping invalid action {i+1}: failed to create TaskAction")
            
            valid_actions = [ta for ta in task_actions if ta]
            
            if not valid_actions:
                raise ValueError("No valid actions could be created from AI response")
                
            return valid_actions
        except Exception as e:
            logger.error(f"Error in generateTaskActions: {str(e)}")
            return []

    async def executeTask(self, task_step, workflow, context, task_index=None, total_tasks=None) -> TaskResult:
        """Execute all actions for a task step, with state management and retries."""
        logger.info(f"=== STARTING TASK {task_index or '?'}: {task_step.objective} ===")
        
        # PHASE 4: Update workflow object before executing task
        # Set currentTask=task_number, currentAction=0, totalActions=0
        if task_index is not None:
            self.updateWorkflowBeforeExecutingTask(task_index)
        
        # Update workflow context for this task
        if task_index is not None:
            self.service.setWorkflowContext(task_number=task_index)
            # Remove the increment call that causes double-increment bug
        
        # Create database log entry for task start in format expected by frontend
        if task_index is not None:

            # Create a task start message for the user
            task_progress = f"{task_index}/{total_tasks}" if total_tasks is not None else str(task_index)
            task_start_message = {
                "workflowId": workflow.id,
                "role": "assistant",
                "message": f"🚀 **Task {task_progress}**",
                "status": "step",
                "sequenceNr": len(workflow.messages) + 1,
                "publishedAt": get_utc_timestamp(),
                "documentsLabel": f"task_{task_index}_start",
                "documents": [],
                # Add workflow context fields
                "roundNumber": workflow.currentRound,  # Use current workflow round
                "taskNumber": task_index,
                "actionNumber": 0,
                # Add task progress status
                "taskProgress": "running"
            }
            
            # Add user-friendly message if available
            if task_step.userMessage:
                task_start_message["message"] += f"\n\n💬 {task_step.userMessage}"
            
            message = self.chatInterface.createMessage(task_start_message)
            if message:
                workflow.messages.append(message)
                logger.info(f"Task start message created for task {task_index}")
        
        state = TaskExecutionState(task_step)
        retry_context = context
        max_retries = state.max_retries
        for attempt in range(max_retries):
            logger.info(f"Task execution attempt {attempt+1}/{max_retries}")
            
            # Check workflow status before starting task execution
            self._checkWorkflowStopped()
            
            # Update retry context with current attempt information
            if retry_context:
                retry_context.retry_count = attempt + 1
            
            actions = await self.generateTaskActions(task_step, workflow, previous_results=retry_context.previous_results, enhanced_context=retry_context)
            
            # Log total actions count for this task
            total_actions = len(actions) if actions else 0
            logger.info(f"Task {task_index or '?'} has {total_actions} actions")
            
            # PHASE 4: Update workflow object after action planning
            # Set totalActions=extracted_total_actions for THIS task
            self.updateWorkflowAfterActionPlanning(total_actions)
            
            # Set workflow action total for this task (0 if no actions generated)
            self.setWorkflowTotals(total_actions=total_actions)
            
            if not actions:
                logger.error("No actions defined for task step, aborting task execution")
                break
            
            action_results = []
            for action_idx, action in enumerate(actions):
                # Check workflow status before each action execution
                self._checkWorkflowStopped()
                
                # PHASE 4: Update workflow object before executing action
                # Set currentAction=action_number
                action_number = action_idx + 1
                self.updateWorkflowBeforeExecutingAction(action_number)
                
                # Update workflow context for this action
                self.service.setWorkflowContext(action_number=action_number)
                # Remove the increment call that causes double-increment bug
                
                # Log action start in format expected by frontend
                logger.info(f"Task {task_index} - Starting action {action_number}/{total_actions}")
                
                # Create an action start message for the user
                action_start_message = {
                    "workflowId": workflow.id,
                    "role": "assistant",
                    "message": f"⚡ **Action {action_number}/{total_actions}** (Method {action.execMethod}.{action.execAction})",
                    "status": "step",
                    "sequenceNr": len(workflow.messages) + 1,
                    "publishedAt": get_utc_timestamp(),
                    "documentsLabel": f"action_{action_number}_start",
                    "documents": [],
                    # Add action progress status
                    "actionProgress": "running"
                }
                
                # Add user-friendly message if available
                if action.userMessage:
                    action_start_message["message"] += f"\n\n💬 {action.userMessage}"
                
                # Add workflow context fields - use current workflow round instead of hardcoded 1
                action_start_message.update({
                    "roundNumber": workflow.currentRound,  # Use current workflow round
                    "taskNumber": task_index,
                    "actionNumber": action_number
                })
                
                message = self.chatInterface.createMessage(action_start_message)
                if message:
                    workflow.messages.append(message)
                    logger.info(f"Action start message created for action {action_number}")
                
                # Pass action index to executeSingleAction with task context
                result = await self.executeSingleAction(action, workflow, task_step, task_index, action_number, total_actions)
                action_results.append(result)
                if result.success:
                    state.addSuccessfulAction(result)
                else:
                    state.addFailedAction(result)
            
            # Check workflow status before review
            self._checkWorkflowStopped()
            
            review_result = await self.reviewTaskCompletion(task_step, actions, action_results, workflow)
            success = review_result.status == 'success'
            feedback = review_result.reason
            error = None if success else review_result.reason
            if success:
                logger.info(f"=== TASK {task_index or '?'} COMPLETED SUCCESSFULLY: {task_step.objective} ===")
                
                # Create a task completion message for the user
                task_progress = f"{task_index}/{total_tasks}" if total_tasks is not None else str(task_index)
                
                # Enhanced completion message with criteria details
                completion_message = f"🎯 **Task {task_progress}**\n\n✅ {feedback or 'Task completed successfully'}"
                
                # Add criteria status if available
                if hasattr(review_result, 'met_criteria') and review_result.met_criteria:
                    for criterion in review_result.met_criteria:
                        completion_message += f"\n• {criterion}"
                
                if hasattr(review_result, 'quality_score'):
                    completion_message += f"\n📊 Score {review_result.quality_score}/10"
                
                task_completion_message = {
                    "workflowId": workflow.id,
                    "role": "assistant",
                    "message": completion_message,
                    "status": "step",
                    "sequenceNr": len(workflow.messages) + 1,
                    "publishedAt": get_utc_timestamp(),
                    "documentsLabel": f"task_{task_index}_completion",
                    "documents": [],
                    # Add workflow context fields
                    "roundNumber": workflow.currentRound,  # Use current workflow round
                    "taskNumber": task_index,
                    "actionNumber": 0,
                    # Add task progress status
                    "taskProgress": "success"
                }
                
                message = self.chatInterface.createMessage(task_completion_message)
                if message:
                    workflow.messages.append(message)
                    logger.info(f"Task completion message created for task {task_index}")
                
                return TaskResult(
                    taskId=task_step.id,
                    status=TaskStatus.COMPLETED,
                    success=True,
                    feedback=feedback,
                    error=None
                )

            elif review_result.status == 'retry' and state.canRetry():
                logger.warning(f"Task step '{task_step.objective}' requires retry: {review_result.improvements}")
                
                # Enhanced logging of criteria status
                if review_result.met_criteria:
                    logger.info(f"Met criteria: {', '.join(review_result.met_criteria)}")
                if review_result.unmet_criteria:
                    logger.warning(f"Unmet criteria: {', '.join(review_result.unmet_criteria)}")
                
                state.incrementRetryCount()
                
                # Update retry context with retry information and criteria tracking
                if retry_context:
                    retry_context.retry_count = state.retry_count
                    retry_context.improvements = review_result.improvements
                    retry_context.previous_action_results = action_results
                    retry_context.previous_review_result = review_result
                    retry_context.is_regeneration = True
                    retry_context.failure_patterns = state.getFailurePatterns()
                    retry_context.failed_actions = state.failed_actions
                    retry_context.successful_actions = state.successful_actions
                    
                # Track criteria progress across retries
                if not hasattr(retry_context, 'criteria_progress'):
                    retry_context.criteria_progress = {
                        'met_criteria': set(),
                        'unmet_criteria': set(),
                        'attempt_history': []
                    }
                
                # Update criteria progress - convert lists to sets for deduplication
                if review_result.met_criteria:
                    retry_context.criteria_progress['met_criteria'].update(review_result.met_criteria)
                if review_result.unmet_criteria:
                    retry_context.criteria_progress['unmet_criteria'].update(review_result.unmet_criteria)
                
                # Record this attempt's criteria status
                attempt_record = {
                    'attempt': state.retry_count,
                    'met_criteria': review_result.met_criteria or [],
                    'unmet_criteria': review_result.unmet_criteria or [],
                    'quality_score': review_result.quality_score,
                    'improvements': review_result.improvements or []
                }
                retry_context.criteria_progress['attempt_history'].append(attempt_record)
                
                logger.info(f"Criteria progress after {state.retry_count} attempts:")
                logger.info(f"  Total met: {len(retry_context.criteria_progress['met_criteria'])}")
                logger.info(f"  Total unmet: {len(retry_context.criteria_progress['unmet_criteria'])}")
                if retry_context.criteria_progress['met_criteria']:
                    logger.info(f"  Met criteria: {', '.join(retry_context.criteria_progress['met_criteria'])}")
                if retry_context.criteria_progress['unmet_criteria']:
                    logger.info(f"  Unmet criteria: {', '.join(retry_context.criteria_progress['unmet_criteria'])}")
                
                # Log retry summary for debugging
                logger.info(f"=== RETRY #{state.retry_count} SUMMARY ===")
                logger.info(f"Task: {task_step.objective}")
                logger.info(f"Quality Score: {review_result.quality_score}/10")
                logger.info(f"Status: {review_result.status}")
                logger.info(f"Improvements Needed: {review_result.improvements}")
                logger.info(f"Reason: {review_result.reason}")
                logger.info("=== END RETRY SUMMARY ===")
                
                # Create retry message for user
                retry_message = {
                    "workflowId": workflow.id,
                    "role": "assistant",
                    "message": f"🔄 **Task {task_index}** needs retry: {review_result.improvements}",
                    "status": "step",
                    "sequenceNr": len(workflow.messages) + 1,
                    "publishedAt": get_utc_timestamp(),
                    "documentsLabel": f"task_{task_index}_retry",
                    "documents": [],
                    "roundNumber": workflow.currentRound,
                    "taskNumber": task_index,
                    "actionNumber": 0,
                    "taskProgress": "retry"
                }
                
                message = self.chatInterface.createMessage(retry_message)
                if message:
                    workflow.messages.append(message)
                
                continue
            else:
                logger.error(f"=== TASK {task_index or '?'} FAILED: {task_step.objective} after {attempt+1} attempts ===")
                task_progress = f"{task_index}/{total_tasks}" if total_tasks is not None else str(task_index)

                # Create user-facing error message for task failure
                error_message = f"**Task {task_progress}**\n\n❌ '{task_step.objective}' {attempt+1}x failed\n\n"
                
                # Add specific error details if available
                if review_result and hasattr(review_result, 'reason') and review_result.reason:
                    error_message += f"{review_result.reason}\n\n"
                
                # Add criteria progress information if available
                if retry_context and hasattr(retry_context, 'criteria_progress'):
                    progress = retry_context.criteria_progress
                    error_message += f"📊 **Details**\n"
                    if progress.get('met_criteria'):
                        error_message += f"✅ Met criteria: {', '.join(progress['met_criteria'])}\n"
                    if progress.get('unmet_criteria'):
                        error_message += f"❌ Unmet criteria: {', '.join(progress['unmet_criteria'])}\n"
                    error_message += "\n"
                
                # Add retry information
                error_message += f"Attempts: {attempt+1}\n"
                error_message += f"Status: Will retry automatically\n\n"
                error_message += "The system will attempt to retry this task. Please wait..."
                
                # Create workflow message for user
                message_data = {
                    "workflowId": workflow.id,
                    "role": "assistant",
                    "message": error_message,
                    "status": "step",
                    "sequenceNr": len(workflow.messages) + 1,
                    "publishedAt": get_utc_timestamp(),
                    "actionId": None,
                    "actionMethod": "task",
                    "actionName": "task_retry",
                    "documentsLabel": None,
                    "documents": [],
                    # Add workflow context fields
                    "roundNumber": workflow.currentRound,  # Use current workflow round
                    "taskNumber": task_index,
                    "actionNumber": 0,
                    # Add task progress status
                    "taskProgress": "retry"
                }
                
                try:
                    message = self.chatInterface.createMessage(message_data)
                    if message:
                        workflow.messages.append(message)
                        logger.info(f"Created user-facing retry message for failed task: {task_step.objective}")
                    else:
                        logger.error(f"Failed to create user-facing retry message for failed task: {task_step.objective}")
                except Exception as e:
                        logger.error(f"Error creating user-facing retry message: {str(e)}")
                
                return TaskResult(
                    taskId=task_step.id,
                    status=TaskStatus.FAILED,
                    success=False,
                    feedback=feedback,
                    error=review_result.reason if review_result and hasattr(review_result, 'reason') else "Task failed after retry attempts"
                )
        logger.error(f"=== TASK {task_index or '?'} FAILED AFTER ALL RETRIES: {task_step.objective} ===")
        
        # Create user-facing error message for task failure
        error_message = f"**Task {task_index or '?'}**\n\n❌ '{task_step.objective}' failed after all retries\n\n"
        error_message += f"{task_step.objective}\n\n"
        
        # Add specific error details if available
        if retry_context and hasattr(retry_context, 'previous_review_result') and retry_context.previous_review_result:
            reason = retry_context.previous_review_result.reason or ''
            if reason and reason != "Task failed after all retries.":
                error_message += f"{reason}\n\n"
        
        # Add retry information
        error_message += f"Retries attempted: {retry_context.retry_count if retry_context else 'Unknown'}\n"
        error_message += f"Status: Task failed permanently"
        
        # Create workflow message for user
        message_data = {
            "workflowId": workflow.id,
            "role": "assistant",
            "message": error_message,
            "status": "step",
            "sequenceNr": len(workflow.messages) + 1,
            "publishedAt": get_utc_timestamp(),
            "actionId": None,
            "actionMethod": "task",
            "actionName": "task_failure",
            "documentsLabel": None,
            "documents": [],
            # Add workflow context fields
            "roundNumber": workflow.currentRound,  # Use current workflow round
            "taskNumber": task_index,
            "actionNumber": 0,
            # NEW: Add task progress status
            "taskProgress": "fail"
        }
        
        try:
            message = self.chatInterface.createMessage(message_data)
            if message:
                workflow.messages.append(message)
                logger.info(f"Created user-facing error message for failed task: {task_step.objective}")
            else:
                logger.error(f"Failed to create user-facing error message for failed task: {task_step.objective}")
        except Exception as e:
            logger.error(f"Error creating user-facing error message: {str(e)}")
        
        return TaskResult(
            taskId=task_step.id,
            status=TaskStatus.FAILED,
            success=False,
            feedback="Task failed after all retries.",
            error="Task failed after all retries."
        )

    async def reviewTaskCompletion(self, task_step, task_actions, action_results, workflow):
        try:
            # Check workflow status before reviewing task completion
            self._checkWorkflowStopped()
            
            logger.info(f"=== STARTING TASK COMPLETION REVIEW ===")
            logger.info(f"Task: {task_step.objective}")
            logger.info(f"Actions executed: {len(task_actions) if task_actions else 0}")
            logger.info(f"Action results: {len(action_results) if action_results else 0}")
            
            # Create proper context object for result review
            review_context = ReviewContext(
                task_step=task_step,
                task_actions=task_actions,
                action_results=action_results,
                step_result={
                    'successful_actions': sum(1 for result in action_results if result.success),
                    'total_actions': len(action_results),
                    'results': [self._extractResultText(result) for result in action_results if result.success],
                    'errors': [result.error for result in action_results if not result.success],
                    'documents': [
                        {
                            'action_index': i,
                            'documents_count': len(result.documents) if result.documents else 0,
                            'documents': result.documents if result.documents else []
                        }
                        for i, result in enumerate(action_results)
                    ]
                },
                workflow_id=workflow.id,
                previous_results=[]
            )
            
            # Check workflow status before calling AI service
            self._checkWorkflowStopped()
            
            # Use promptFactory for review prompt
            prompt = createResultReviewPrompt(review_context, self.service)
            
            # Log the full result review prompt being sent to AI for debugging
            logger.info("=== RESULT REVIEW PROMPT SENT TO AI ===")
            logger.info(f"Task: {task_step.objective}")
            logger.info(f"Action Results Count: {len(review_context.action_results) if review_context.action_results else 0}")
            logger.info(f"Task Actions Count: {len(review_context.task_actions) if review_context.task_actions else 0}")
            logger.info("=== FULL RESULT REVIEW PROMPT ===")
            logger.info(prompt)
            logger.info("=== END RESULT REVIEW PROMPT ===")
            
            response = await self.service.callAiTextAdvanced(prompt)
            
            # Log the full AI response for result review
            logger.info("=== RESULT REVIEW AI RESPONSE RECEIVED ===")
            logger.info(f"Response length: {len(response) if response else 0}")
            logger.debug("=== FULL RESULT REVIEW AI RESPONSE ===")
            logger.debug(response)
            logger.debug("=== END RESULT REVIEW AI RESPONSE ===")
            
            # Inline parseReviewResponse logic here
            json_start = response.find('{')
            json_end = response.rfind('}') + 1
            if json_start == -1 or json_end == 0:
                raise ValueError("No JSON found in review response")
            json_str = response[json_start:json_end]
            
            try:
                review = json.loads(json_str)
            except Exception as e:
                logger.error(f"Error parsing review response JSON: {str(e)}")
                review = {}
            if 'status' not in review:
                raise ValueError("Review response missing 'status' field")
            review.setdefault('status', 'unknown')
            review.setdefault('reason', 'No reason provided')
            review.setdefault('quality_score', 5)
            
            # Ensure improvements is a list
            improvements = review.get('improvements', [])
            if isinstance(improvements, str):
                # Split string into list if it's a single improvement
                improvements = [improvements.strip()] if improvements.strip() else []
            elif not isinstance(improvements, list):
                improvements = []
            
            # Ensure all list fields are properly typed
            met_criteria = review.get('met_criteria', [])
            if not isinstance(met_criteria, list):
                met_criteria = []
                
            unmet_criteria = review.get('unmet_criteria', [])
            if not isinstance(unmet_criteria, list):
                unmet_criteria = []
            
            review_result = ReviewResult(
                status=review.get('status', 'unknown'),
                reason=review.get('reason', 'No reason provided'),
                improvements=improvements,
                quality_score=review.get('quality_score', 5),
                missing_outputs=[],
                met_criteria=met_criteria,
                unmet_criteria=unmet_criteria,
                confidence=review.get('confidence', 0.5),
                # Extract user-friendly message if available
                userMessage=review.get('userMessage', None)
            )
            
            # Enhanced validation logging
            logger.info(f"VALIDATION RESULT - Task: '{task_step.objective}' - Status: {review_result.status.upper()}, Quality: {review_result.quality_score}/10")
            if review_result.status == 'success':
                logger.info(f"VALIDATION SUCCESS - Task completed successfully")
                if review_result.met_criteria:
                    logger.info(f"Met criteria: {', '.join(review_result.met_criteria)}")
            elif review_result.status == 'retry':
                logger.warning(f"VALIDATION RETRY - Task requires retry: {review_result.improvements}")
                if review_result.unmet_criteria:
                    logger.warning(f"Unmet criteria: {', '.join(review_result.unmet_criteria)}")
            else:
                logger.error(f"VALIDATION FAILED - Task failed: {review_result.reason}")
            
            logger.info(f"=== TASK COMPLETION REVIEW FINISHED ===")
            logger.info(f"Final Status: {review_result.status}")
            logger.info(f"Quality Score: {review_result.quality_score}/10")
            logger.info(f"Improvements: {review_result.improvements}")
            logger.info("=== END REVIEW ===")
            
            return review_result
        except Exception as e:
            logger.error(f"Error in reviewTaskCompletion: {str(e)}")
            return ReviewResult(
                status='failed',
                reason=str(e),
                quality_score=0
            )

    async def prepareTaskHandover(self, task_step, task_actions, task_result, workflow):
        try:
            # Check workflow status before preparing task handover
            self._checkWorkflowStopped()
            
            # Log handover status summary
            status = task_result.status if task_result else 'unknown'
            
            # Handle both TaskResult and ReviewResult objects
            if hasattr(task_result, 'met_criteria'):
                # This is a ReviewResult object
                met = task_result.met_criteria if task_result.met_criteria else []
                review_result = task_result.to_dict()
            else:
                # This is a TaskResult object
                met = []
                review_result = {
                    'status': task_result.status if task_result else 'unknown',
                    'reason': task_result.error if task_result and hasattr(task_result, 'error') else None,
                    'success': task_result.success if task_result else False
                }
            
            handover_data = {
                'task_id': task_step.id,
                'task_description': task_step.objective,
                'actions': [action.to_dict() for action in task_actions],
                'review_result': review_result,
                'workflow_id': workflow.id,
                'handover_time': get_utc_timestamp()
            }
            logger.info(f"Prepared handover for task {task_step.id} in workflow {workflow.id}")
            return handover_data
        except Exception as e:
            logger.error(f"Error in prepareTaskHandover: {str(e)}")
            return {'error': str(e)}

    def createTaskAction(self, actionData: Dict[str, Any]) -> 'TaskAction':
        """Creates a new task action."""
        try:
            # Ensure ID is present
            if "id" not in actionData or not actionData["id"]:
                actionData["id"] = f"action_{uuid.uuid4()}"
            
            # Ensure required fields
            if "status" not in actionData:
                actionData["status"] = TaskStatus.PENDING
            
            if "execMethod" not in actionData:
                logger.error("execMethod is required for task action")
                return None
            
            if "execAction" not in actionData:
                logger.error("execAction is required for task action")
                return None
            
            if "execParameters" not in actionData:
                actionData["execParameters"] = {}
            
            # Use generic field separation based on TaskAction model
            simple_fields, object_fields = self.chatInterface._separate_object_fields(TaskAction, actionData)
            
            # Create action in database
            createdAction = self.chatInterface.db.recordCreate(TaskAction, simple_fields)
            
            # Convert to TaskAction model
            return TaskAction(
                id=createdAction["id"],
                execMethod=createdAction["execMethod"],
                execAction=createdAction["execAction"],
                execParameters=createdAction.get("execParameters", {}),
                execResultLabel=createdAction.get("execResultLabel"),
                expectedDocumentFormats=createdAction.get("expectedDocumentFormats"),
                status=createdAction.get("status", TaskStatus.PENDING),
                error=createdAction.get("error"),
                retryCount=createdAction.get("retryCount", 0),
                retryMax=createdAction.get("retryMax", 3),
                processingTime=createdAction.get("processingTime"),
                timestamp=float(createdAction.get("timestamp", get_utc_timestamp())),
                result=createdAction.get("result"),
                resultDocuments=createdAction.get("resultDocuments", []),
                userMessage=createdAction.get("userMessage")
            )
            
        except Exception as e:
            logger.error(f"Error creating task action: {str(e)}")
            return None

    # --- Helper action handling methods ---

    async def executeSingleAction(self, action, workflow, task_step, task_index=None, action_index=None, total_actions=None):
        """Execute a single action and return ActionResult with enhanced document processing"""
        try:
            # Check workflow status before executing action
            self._checkWorkflowStopped()
            
            # Use passed indices or fallback to '?'
            task_num = task_index if task_index is not None else '?'
            action_num = action_index if action_index is not None else '?'
            
            logger.info(f"=== TASK {task_num} ACTION {action_num}: {action.execMethod}.{action.execAction} ===")
            
            # Log input parameters
            input_docs = action.execParameters.get('documentList', [])
            input_connections = action.execParameters.get('connections', [])
            logger.info(f"Input documents: {input_docs} (type: {type(input_docs)})")
            if input_connections:
                logger.info(f"Input connections: {input_connections}")
            
            # Log all action parameters for debugging
            logger.info(f"All action parameters: {action.execParameters}")
            
            enhanced_parameters = action.execParameters.copy()
            if action.expectedDocumentFormats:
                enhanced_parameters['expectedDocumentFormats'] = action.expectedDocumentFormats
                logger.info(f"Expected formats: {action.expectedDocumentFormats}")
            
            # Check workflow status before executing the action
            self._checkWorkflowStopped()
            
            result = await self.service.executeAction(
                methodName=action.execMethod,
                actionName=action.execAction,
                parameters=enhanced_parameters
            )
            result_label = action.execResultLabel
            
                # Process documents from the action result
                created_documents = []
                if result.success:
                    action.setSuccess()
                    # Extract result text from documents if available, otherwise use empty string
                    action.result = ""
                    if result.documents and len(result.documents) > 0:
                        # Try to get text content from the first document
                        first_doc = result.documents[0]
                        if isinstance(first_doc.documentData, dict):
                            action.result = first_doc.documentData.get("result", "")
                        elif isinstance(first_doc.documentData, str):
                            action.result = first_doc.documentData
                    # Preserve the action's execResultLabel for document routing
                    # Action methods should NOT return resultLabel - this is managed by the action handler
                    if not action.execResultLabel:
                        logger.warning(f"Action {action.execMethod}.{action.execAction} has no execResultLabel set")
                    # Always use the action's execResultLabel for message creation to ensure proper document routing
                    message_result_label = action.execResultLabel
                    
                    # Create message first to get messageId, then create documents with messageId
                    message = await self.createActionMessage(action, result, workflow, message_result_label, [], task_step, task_index)
                    if message:
                        # Now create documents with the messageId
                        created_documents = self.documentGenerator.createDocumentsFromActionResult(result, action, workflow, message.id)
                        # Update the message with the created documents
                        if created_documents:
                            message.documents = created_documents
                            # Update the message in the database
                            self.chatInterface.updateMessage(message.id, {"documents": [doc.to_dict() for doc in created_documents]})
                
                # Log action results
                logger.info(f"Action completed successfully")
                
                if created_documents:
                    logger.info(f"Output documents ({len(created_documents)}):")
                    for i, doc in enumerate(created_documents):
                        logger.info(f"  {i+1}. {doc.fileName}")
                    
                    # Log document details for debugging
                    logger.info("Document details:")
                    for i, doc in enumerate(created_documents):
                        logger.info(f"  Doc {i+1}: fileName={doc.fileName}, type={type(doc)}")
                        logger.info(f"    ID: {doc.id}")
                        logger.info(f"    File ID: {doc.fileId}")
                else:
                    logger.info("Output: No documents created")
            else:
                action.setError(result.error or "Action execution failed")
                logger.error(f"Action failed: {result.error}")
                
                # ⚠️ IMPORTANT: Create error message for failed actions so user can see what went wrong
                message = await self.createActionMessage(action, result, workflow, result_label, [], task_step, task_index)
                
                # Create database log entry for action failure
                self.chatInterface.createLog({
                    "workflowId": workflow.id,
                    "message": f"❌ **Task {task_num}**\n\n❌ **Action {action_num}/{total_actions}** failed: {result.error}",
                    "type": "error"
                })

            # Log action summary
            logger.info(f"=== TASK {task_num} ACTION {action_num} COMPLETED ===")
            
            # Preserve the original documents field from the method result
            # This ensures the standard document format is maintained
            original_documents = result.documents
            
            # Extract result text from documents if available
            result_text = self._extractResultText(result)
            
            return ActionResult(
                success=result.success,
                documents=original_documents,  # Preserve original documents field from method result
                resultLabel=action.execResultLabel,  # Always use action's execResultLabel
                error=result.error or ""
            )
        except Exception as e:
            logger.error(f"Error executing single action: {str(e)}")
            action.setError(str(e))
            return ActionResult(
                success=False,
                documents=[],  # Empty documents for error case
                resultLabel=action.execResultLabel,
                error=str(e)
            )

    async def createActionMessage(self, action, result, workflow, result_label=None, created_documents=None, task_step=None, task_index=None):
        """Create and store a message for the action result in the workflow with enhanced document processing"""
        try:
            # Check workflow status before creating action message
            self._checkWorkflowStopped()
            
            if result_label is None:
                result_label = action.execResultLabel
            
            # Log delivered documents
            if created_documents:
                logger.info(f"Result label: {result_label} - {len(created_documents)} documents")
            else:
                logger.info(f"Result label: {result_label} - No documents")
            
            # Get current workflow context and stats
            workflow_context = self.service.getWorkflowContext()
            workflow_stats = self.service.getWorkflowStats()
            
            # Create a more meaningful message that includes task context
            task_objective = task_step.objective if task_step else 'Unknown task'

            # Add comprehensive workflow context
            current_round = workflow_context.get('currentRound', 0)
            current_task = workflow_context.get('currentTask', 0)
            total_tasks = workflow_stats.get('totalTasks', 0)
            current_action = workflow_context.get('currentAction', 0)
            total_actions = workflow_stats.get('totalActions', 0)

            # Build a user-friendly message based on success/failure
            if result.success:
                message_text = f"**Action {current_action}/{total_actions} ({action.execMethod}.{action.execAction})**\n\n"
                message_text += f"✅ {task_objective}\n\n"
            else:
                # ⚠️ FAILURE MESSAGE - Show error details to user
                error_details = result.error if result.error else "Unknown error occurred"
                message_text = f"**Action {current_action}/{total_actions} ({action.execMethod}.{action.execAction})**\n\n"
                message_text += f"❌ {task_objective}\n\n"
                message_text += f"{error_details}\n\n"
                            
            message_data = {
                "workflowId": workflow.id,
                "role": "assistant",
                "message": message_text,
                "status": "step",
                "sequenceNr": len(workflow.messages) + 1,
                "publishedAt": get_utc_timestamp(),
                "actionId": action.id,
                "actionMethod": action.execMethod,
                "actionName": action.execAction,
                "documentsLabel": result_label,
                "documents": created_documents,
                # Add workflow context fields - extract from result_label to match document reference
                "roundNumber": current_round,
                "taskNumber": current_task,
                "actionNumber": current_action,
                "actionProgress": "success" if result.success else "fail"
            }
            
            # Add debugging for error messages
            if not result.success:
                logger.info(f"Creating ERROR message: {message_text}")
                logger.info(f"Message data: {message_data}")
            
            message = self.chatInterface.createMessage(message_data)
            if message:
                workflow.messages.append(message)
                logger.info(f"Message created: {action.execMethod}.{action.execAction}")
                return message
            else:
                logger.error(f"Failed to create workflow message for action {action.execMethod}.{action.execAction}")
                return None
        except Exception as e:
            logger.error(f"Error creating action message: {str(e)}")
            return None

    # --- Helper validation methods ---

    def _validateTaskPlan(self, task_plan: Dict[str, Any]) -> bool:
        try:

            
            if not isinstance(task_plan, dict):
                logger.error("Task plan is not a dictionary")
                return False
                
            if 'tasks' not in task_plan or not isinstance(task_plan['tasks'], list):
                logger.error(f"Task plan missing 'tasks' field or not a list. Found: {type(task_plan.get('tasks', 'MISSING'))}")
                return False
                
            # First pass: collect all task IDs to validate dependencies
            task_ids = set()
            for task in task_plan['tasks']:
                if not isinstance(task, dict):
                    logger.error(f"Task is not a dictionary: {type(task)}")
                    return False
                if 'id' not in task:
                    logger.error(f"Task missing 'id' field: {task}")
                    return False
                task_ids.add(task['id'])
            
            # Second pass: validate each task
            for i, task in enumerate(task_plan['tasks']):
    
                
                if not isinstance(task, dict):
                    logger.error(f"Task {i} is not a dictionary: {type(task)}")
                    return False
                    
                required_fields = ['id', 'objective', 'success_criteria']
                missing_fields = [field for field in required_fields if field not in task]
                if missing_fields:
                    logger.error(f"Task {i} missing required fields: {missing_fields}")
                    return False
                    
                # Check for duplicate IDs (shouldn't happen after first pass, but safety check)
                if task['id'] in task_ids and list(task_plan['tasks']).count(task['id']) > 1:
                    logger.error(f"Task {i} has duplicate ID: {task['id']}")
                    return False
                
                dependencies = task.get('dependencies', [])
                if not isinstance(dependencies, list):
                    logger.error(f"Task {i} dependencies is not a list: {type(dependencies)}")
                    return False
                    
                for dep in dependencies:
                    if dep not in task_ids and dep != 'task_0':
                        logger.error(f"Task {i} has invalid dependency: {dep} (available: {list(task_ids) + ['task_0']})")
                        return False
                        
            logger.info(f"Task plan validation successful with {len(task_ids)} tasks")
            return True
            
        except Exception as e:
            logger.error(f"Error validating task plan: {str(e)}")
            return False

    def _extractActionNumberFromLabel(self, label: str) -> int:
        """Extract action number from a document label like 'round1_task1_action1_diagram_analysis'"""
        try:
            if not label or not isinstance(label, str):
                return 0
            
            # Parse label format: round{round}_task{task}_action{action}_{context}
            if '_action' in label:
                action_part = label.split('_action')[1]
                if action_part and '_' in action_part:
                    action_number = action_part.split('_')[0]
                    return int(action_number)
            
            return 0
        except Exception as e:
            logger.warning(f"Could not extract action number from label '{label}': {str(e)}")
            return 0

    def _validateActions(self, actions: List[Dict[str, Any]], context) -> bool:
        try:
            if not isinstance(actions, list):
                logger.error("Actions must be a list")
                return False
            if len(actions) == 0:
                logger.warning("No actions generated")
                return False
            for i, action in enumerate(actions):
                if not isinstance(action, dict):
                    logger.error(f"Action {i} must be a dictionary")
                    return False
                required_fields = ['method', 'action', 'parameters', 'resultLabel']
                missing_fields = []
                for field in required_fields:
                    if field not in action or not action[field]:
                        missing_fields.append(field)
                if missing_fields:
                    logger.error(f"Action {i} missing required fields: {missing_fields}")
                    return False
                result_label = action.get('resultLabel', '')
                if not result_label.startswith('round'):
                    logger.error(f"Action {i} result label must start with 'round': {result_label}")
                    return False
                parameters = action.get('parameters', {})
                if not isinstance(parameters, dict):
                    logger.error(f"Action {i} parameters must be a dictionary")
                    return False
            logger.info(f"Successfully validated {len(actions)} actions")
            return True
        except Exception as e:
            logger.error(f"Error validating actions: {str(e)}")
            return False

    def _extractResultText(self, result: ActionResult) -> str:
        """Extract result text from ActionResult documents"""
        if not result.success or not result.documents:
            return ""
        
        # Try to get text content from the first document
        first_doc = result.documents[0]
        if isinstance(first_doc.documentData, dict):
            return first_doc.documentData.get("result", "")
        elif isinstance(first_doc.documentData, str):
            return first_doc.documentData
        return ""

    # PHASE 4: Workflow Object Update Rules Implementation
    
    def updateWorkflowAfterTaskPlanCreated(self, total_tasks: int):
        """
        Update workflow object after task plan is created.
        Rule: Set currentTask=1, currentAction=0, totalTasks=extracted_total_tasks, totalActions=0
        """
        try:
            update_data = {
                "currentTask": 1,
                "currentAction": 0,
                "totalTasks": total_tasks,
                "totalActions": 0
            }
            
            # Update workflow object
            self.workflow.currentTask = 1
            self.workflow.currentAction = 0
            self.workflow.totalTasks = total_tasks
            self.workflow.totalActions = 0
            
            # Update in database
            self.chatInterface.updateWorkflow(self.workflow.id, update_data)
            logger.info(f"Updated workflow {self.workflow.id} after task plan created: {update_data}")
            
        except Exception as e:
            logger.error(f"Error updating workflow after task plan created: {str(e)}")

    def updateWorkflowBeforeExecutingTask(self, task_number: int):
        """
        Update workflow object before executing a task.
        Rule: Set currentTask=task_number, currentAction=0, totalActions=0
        """
        try:
            update_data = {
                "currentTask": task_number,
                "currentAction": 0,
                "totalActions": 0
            }
            
            # Update workflow object
            self.workflow.currentTask = task_number
            self.workflow.currentAction = 0
            self.workflow.totalActions = 0
            
            # Update in database
            self.chatInterface.updateWorkflow(self.workflow.id, update_data)
            logger.info(f"Updated workflow {self.workflow.id} before executing task {task_number}: {update_data}")
            
        except Exception as e:
            logger.error(f"Error updating workflow before executing task: {str(e)}")

    def updateWorkflowAfterActionPlanning(self, total_actions: int):
        """
        Update workflow object after action planning for current task.
        Rule: Set totalActions=extracted_total_actions for THIS task
        """
        try:
            update_data = {
                "totalActions": total_actions
            }
            
            # Update workflow object
            self.workflow.totalActions = total_actions
            
            # Update in database
            self.chatInterface.updateWorkflow(self.workflow.id, update_data)
            logger.info(f"Updated workflow {self.workflow.id} after action planning: {update_data}")
            
        except Exception as e:
            logger.error(f"Error updating workflow after action planning: {str(e)}")

    def updateWorkflowBeforeExecutingAction(self, action_number: int):
        """
        Update workflow object before executing an action.
        Rule: Set currentAction=action_number
        """
        try:
            update_data = {
                "currentAction": action_number
            }
            
            # Update workflow object
            self.workflow.currentAction = action_number
            
            # Update in database
            self.chatInterface.updateWorkflow(self.workflow.id, update_data)
            logger.info(f"Updated workflow {self.workflow.id} before executing action {action_number}: {update_data}")
            
        except Exception as e:
            logger.error(f"Error updating workflow before executing action: {str(e)}")

    def setWorkflowTotals(self, total_tasks: int = None, total_actions: int = None):
        """Set total counts for workflow progress tracking and update database"""
        try:
            update_data = {}
            
            if total_tasks is not None:
                self.workflow.totalTasks = total_tasks
                update_data["totalTasks"] = total_tasks
                
            if total_actions is not None:
                self.workflow.totalActions = total_actions
                update_data["totalActions"] = total_actions
            
            # Update workflow object in database if we have changes
            if update_data:
                self.chatInterface.updateWorkflow(self.workflow.id, update_data)
                logger.info(f"Updated workflow {self.workflow.id} totals in database: {update_data}")
            
            logger.debug(f"Updated workflow totals: Tasks {self.workflow.totalTasks if hasattr(self.workflow, 'totalTasks') else 'N/A'}, Actions {self.workflow.totalActions if hasattr(self.workflow, 'totalActions') else 'N/A'}")
        except Exception as e:
            logger.error(f"Error setting workflow totals: {str(e)}")

    def resetWorkflowForNewSession(self):
        """Reset workflow values for a new workflow session"""
        try:
            # Reset all workflow progress values to initial state
            self.workflow.currentRound = 0
            self.workflow.currentTask = 0
            self.workflow.currentAction = 0
            self.workflow.totalTasks = 0
            self.workflow.totalActions = 0
            self.workflow.status = 'ready'
            
            # Update workflow object in database with reset values
            self.chatInterface.updateWorkflow(self.workflow.id, {
                "currentRound": 0,
                "currentTask": 0,
                "currentAction": 0,
                "totalTasks": 0,
                "totalActions": 0,
                "status": "ready"
            })
            
            logger.info("Workflow reset for new session - all values set to initial state and updated in database")
        except Exception as e:
            logger.error(f"Error resetting workflow for new session: {str(e)}")