907 lines
42 KiB
Python
907 lines
42 KiB
Python
# reactMode.py
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# React mode implementation for workflows
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import json
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import logging
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import re
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import time
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from datetime import datetime, timezone
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from typing import List, Dict, Any
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from modules.datamodels.datamodelWorkflow import (
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TaskStep, TaskContext, TaskResult, TaskAction, TaskStatus,
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ActionResult
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)
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from modules.datamodels.datamodelChat import ChatWorkflow
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from modules.datamodels.datamodelAi import AiCallOptions, OperationType, ProcessingMode, Priority
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from modules.workflows.processing.modes.baseMode import BaseMode
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from modules.workflows.processing.shared.executionState import TaskExecutionState, should_continue
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from modules.workflows.processing.shared.promptFactoryPlaceholders import (
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createActionSelectionPromptTemplate,
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createActionParameterPromptTemplate,
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createRefinementPromptTemplate,
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extractUserPrompt,
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extractAvailableDocuments,
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extractUserLanguage,
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extractAvailableMethods,
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extractReviewContent
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)
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from modules.workflows.processing.shared.promptFactory import getConnectionReferenceList
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from modules.workflows.processing.adaptive import IntentAnalyzer, ContentValidator, LearningEngine, ProgressTracker
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logger = logging.getLogger(__name__)
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class ReactMode(BaseMode):
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"""React mode implementation - iterative plan-act-observe-refine loop"""
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def __init__(self, services, workflow):
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super().__init__(services, workflow)
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# Initialize adaptive components
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self.intentAnalyzer = IntentAnalyzer()
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self.contentValidator = ContentValidator()
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self.learningEngine = LearningEngine()
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self.progressTracker = ProgressTracker()
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self.currentIntent = None
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async def generateTaskActions(self, taskStep: TaskStep, workflow: ChatWorkflow,
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previousResults: List = None, enhancedContext: TaskContext = None) -> List[TaskAction]:
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"""React mode doesn't use batch action generation - actions are generated iteratively"""
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# React mode generates actions one at a time in the execution loop
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return []
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async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext,
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taskIndex: int = None, totalTasks: int = None) -> TaskResult:
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"""Execute task using React mode - iterative plan-act-observe-refine loop"""
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logger.info(f"=== STARTING TASK {taskIndex or '?'}: {taskStep.objective} ===")
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# NEW: Analyze user intent
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self.currentIntent = self.intentAnalyzer.analyzeUserIntent(taskStep.objective, context)
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logger.info(f"Intent analysis: {self.currentIntent}")
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# NEW: Reset progress tracking for new task
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self.progressTracker.reset()
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# Update workflow object before executing task
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if taskIndex is not None:
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self._updateWorkflowBeforeExecutingTask(taskIndex)
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# Update workflow context for this task
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if taskIndex is not None:
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self.services.workflow.setWorkflowContext(task_number=taskIndex)
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# Create task start message
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await self.messageCreator.createTaskStartMessage(taskStep, workflow, taskIndex, totalTasks)
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state = TaskExecutionState(taskStep)
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# React mode uses max_steps instead of max_retries
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state.max_steps = max(1, int(getattr(workflow, 'maxSteps', 5)))
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logger.info(f"Using React mode execution with max_steps: {state.max_steps}")
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step = 1
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lastReviewDict = None
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while step <= state.max_steps:
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self._checkWorkflowStopped(workflow)
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# Update workflow[currentAction] for UI
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self._updateWorkflowBeforeExecutingAction(step)
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self.services.workflow.setWorkflowContext(action_number=step)
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try:
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t0 = time.time()
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selection = await self._planSelect(context)
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logger.info(f"React step {step}: Selected action: {selection}")
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# Create user-friendly message BEFORE action execution
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# Action intention message is now handled by the standard message creator in _actExecute
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result = await self._actExecute(context, selection, taskStep, workflow, step)
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observation = self._observeBuild(result)
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# Attach deterministic label for clarity
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observation['resultLabel'] = result.resultLabel
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# NEW: Add content validation
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if self.currentIntent and result.documents:
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validationResult = self.contentValidator.validateContent(result.documents, self.currentIntent)
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observation['contentValidation'] = validationResult
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logger.info(f"Content validation: {validationResult['overallSuccess']} (quality: {validationResult['qualityScore']:.2f})")
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# NEW: Learn from feedback
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feedback = self._collectFeedback(result, validationResult, self.currentIntent)
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self.learningEngine.learnFromFeedback(feedback, context, self.currentIntent)
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# NEW: Update progress
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self.progressTracker.updateProgress(result, validationResult, self.currentIntent)
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decision = await self._refineDecide(context, observation)
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# Telemetry: simple duration per step
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duration = time.time() - t0
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self.services.interfaceDbChat.createLog({
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"workflowId": workflow.id,
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"message": f"react_step_duration_sec={duration:.3f}",
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"type": "info"
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})
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lastReviewDict = decision
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# Create user-friendly message AFTER action execution
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# Action completion message is now handled by the standard message creator in _actExecute
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except Exception as e:
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logger.error(f"React step {step} error: {e}")
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break
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# NEW: Use adaptive stopping logic
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progressState = self.progressTracker.getCurrentProgress()
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shouldContinue = self.progressTracker.shouldContinue(progressState, observation.get('contentValidation', {}))
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if not shouldContinue or not should_continue(observation, lastReviewDict, step, state.max_steps):
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logger.info(f"Stopping at step {step}: shouldContinue={shouldContinue}, should_continue={should_continue(observation, lastReviewDict, step, state.max_steps)}")
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break
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step += 1
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# Summarize task result for react mode
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status = TaskStatus.COMPLETED
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success = True
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feedback = lastReviewDict.get('reason') if isinstance(lastReviewDict, dict) else 'Completed'
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if isinstance(lastReviewDict, dict) and lastReviewDict.get('decision') == 'stop':
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success = True
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# Create task completion message
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await self.messageCreator.createTaskCompletionMessage(taskStep, workflow, taskIndex, totalTasks,
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type('ReviewResult', (), {'reason': feedback, 'met_criteria': [], 'quality_score': 8})())
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return TaskResult(
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taskId=taskStep.id,
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status=status,
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success=success,
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feedback=feedback,
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error=None if success else feedback
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)
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async def _planSelect(self, context: TaskContext) -> Dict[str, Any]:
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"""Plan: select exactly one action. Returns {"action": {method, name}}"""
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promptTemplate = createActionSelectionPromptTemplate()
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# Extract content for placeholders
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userPrompt = extractUserPrompt(context)
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# Use same pattern as taskplan mode - extractAvailableDocuments with proper context
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availableDocuments = extractAvailableDocuments(context)
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userLanguage = extractUserLanguage(self.services)
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availableMethods = extractAvailableMethods(self.services)
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# Create placeholders dictionary
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placeholders = {
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"USER_PROMPT": userPrompt,
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"AVAILABLE_DOCUMENTS": availableDocuments,
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"USER_LANGUAGE": userLanguage,
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"AVAILABLE_METHODS": availableMethods
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}
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self._writeTraceLog("React Plan Selection Prompt", promptTemplate)
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self._writeTraceLog("React Plan Selection Placeholders", placeholders)
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# Centralized AI call for plan selection (use plan generation quality)
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options = AiCallOptions(
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operationType=OperationType.GENERATE_PLAN,
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priority=Priority.QUALITY,
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compressPrompt=False,
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compressContext=False,
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processingMode=ProcessingMode.DETAILED,
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maxCost=0.10,
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maxProcessingTime=30
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)
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response = await self.services.ai.callAi(
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prompt=promptTemplate,
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placeholders=placeholders,
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options=options
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)
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self._writeTraceLog("React Plan Selection Response", response)
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jsonStart = response.find('{') if response else -1
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jsonEnd = response.rfind('}') + 1 if response else 0
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if jsonStart == -1 or jsonEnd == 0:
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raise ValueError("No JSON in selection response")
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selection = json.loads(response[jsonStart:jsonEnd])
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if 'action' not in selection or not isinstance(selection['action'], dict):
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raise ValueError("Selection missing 'action'")
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return selection
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async def _actExecute(self, context: TaskContext, selection: Dict[str, Any], taskStep: TaskStep,
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workflow: ChatWorkflow, stepIndex: int) -> ActionResult:
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"""Act: request minimal parameters then execute selected action"""
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action = selection.get('action', {})
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# Check if parameters are already provided in the action selection
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if 'parameters' in action and action['parameters']:
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logger.info("Using parameters from action selection")
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parameters = action['parameters']
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else:
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logger.info("No parameters in action selection, requesting from AI")
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promptTemplate = createActionParameterPromptTemplate()
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# Extract content for placeholders
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userPrompt = extractUserPrompt(context)
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# Use same pattern as taskplan mode - extractAvailableDocuments with proper context
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availableDocuments = extractAvailableDocuments(context)
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userLanguage = extractUserLanguage(self.services)
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# Get available connections for React mode
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availableConnections = getConnectionReferenceList(self.services)
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availableConnectionsStr = '\n'.join(f"- {conn}" for conn in availableConnections) if availableConnections else "No connections available"
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# Get action parameter description (not function signature)
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method = action.get('method', '')
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name = action.get('name', '')
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actionParameters = ""
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from modules.workflows.processing.shared.promptFactory import methods
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if self.services and method in methods:
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methodInstance = methods[method]['instance']
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if name in methodInstance.actions:
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action_info = methodInstance.actions[name]
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# Extract parameter descriptions from docstring
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docstring = action_info.get('description', '')
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paramDescriptions, paramTypes = methodInstance._extractParameterDetails(docstring)
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param_list = []
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for paramName, paramDesc in paramDescriptions.items():
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paramType = paramTypes.get(paramName, 'Any')
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if paramDesc:
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param_list.append(f"- {paramName} ({paramType}): {paramDesc}")
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else:
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param_list.append(f"- {paramName} ({paramType})")
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actionParameters = "Required parameters:\n" + "\n".join(param_list)
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selectedAction = f"{method}.{name}"
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# Create placeholders dictionary
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placeholders = {
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"USER_PROMPT": userPrompt,
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"AVAILABLE_DOCUMENTS": availableDocuments,
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"AVAILABLE_CONNECTIONS": availableConnectionsStr,
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"USER_LANGUAGE": userLanguage,
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"SELECTED_ACTION": selectedAction,
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"ACTION_SIGNATURE": actionParameters
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}
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self._writeTraceLog("React Parameters Prompt", promptTemplate)
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self._writeTraceLog("React Parameters Placeholders", placeholders)
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# Centralized AI call for parameter suggestion (balanced analysis)
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options = AiCallOptions(
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operationType=OperationType.ANALYSE_CONTENT,
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priority=Priority.BALANCED,
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compressPrompt=True,
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compressContext=False,
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processingMode=ProcessingMode.ADVANCED,
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maxCost=0.05,
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maxProcessingTime=30
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)
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paramsResp = await self.services.ai.callAi(
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prompt=promptTemplate,
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placeholders=placeholders,
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options=options
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)
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self._writeTraceLog("React Parameters Response", paramsResp)
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# Parse JSON response
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js = paramsResp[paramsResp.find('{'):paramsResp.rfind('}')+1] if paramsResp else '{}'
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try:
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paramObj = json.loads(js)
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parameters = paramObj.get('parameters', {}) if isinstance(paramObj, dict) else {}
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except Exception as e:
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logger.error(f"Failed to parse AI parameters response as JSON: {str(e)}")
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logger.error(f"Response was: {paramsResp}")
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parameters = {}
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# Apply minimal defaults in-code (language)
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if 'language' not in parameters and hasattr(self.services, 'user') and getattr(self.services.user, 'language', None):
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parameters['language'] = self.services.user.language
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# Build a synthetic TaskAction for execution routing and labels
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currentRound = getattr(self.workflow, 'currentRound', 0)
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currentTask = getattr(self.workflow, 'currentTask', 0)
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resultLabel = f"round{currentRound}_task{currentTask}_action{stepIndex}_results"
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taskAction = self._createTaskAction({
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"execMethod": action.get('method', ''),
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"execAction": action.get('name', ''),
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"execParameters": parameters,
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"execResultLabel": resultLabel,
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"status": TaskStatus.PENDING
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})
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# Execute using existing single action flow (message creation is handled internally)
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result = await self.actionExecutor.executeSingleAction(taskAction, workflow, taskStep, currentTask, stepIndex, 1)
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return result
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def _observeBuild(self, actionResult: ActionResult) -> Dict[str, Any]:
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"""Observe: build compact observation object from ActionResult with full document metadata"""
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previews = []
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notes = []
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if actionResult and actionResult.documents:
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# Process all documents and show full metadata
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for doc in actionResult.documents:
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# Extract all available metadata without content
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docMetadata = {
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"name": getattr(doc, 'documentName', 'Unknown'),
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"mimeType": getattr(doc, 'mimeType', 'Unknown'),
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"size": getattr(doc, 'size', 'Unknown'),
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"created": getattr(doc, 'created', 'Unknown'),
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"modified": getattr(doc, 'modified', 'Unknown'),
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"typeGroup": getattr(doc, 'typeGroup', 'Unknown'),
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"documentId": getattr(doc, 'documentId', 'Unknown'),
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"reference": getattr(doc, 'reference', 'Unknown')
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}
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# Remove 'Unknown' values to keep it clean
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docMetadata = {k: v for k, v in docMetadata.items() if v != 'Unknown'}
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# Add content size indicator instead of actual content
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if hasattr(doc, 'documentData') and doc.documentData:
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if isinstance(doc.documentData, dict) and 'content' in doc.documentData:
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contentLength = len(str(doc.documentData['content']))
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docMetadata['contentSize'] = f"{contentLength} characters"
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else:
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contentLength = len(str(doc.documentData))
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docMetadata['contentSize'] = f"{contentLength} characters"
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# Extract comment if available
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if hasattr(doc, 'documentData') and doc.documentData:
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data = getattr(doc, 'documentData', None)
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if isinstance(data, dict):
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comment = data.get("comment", "")
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if comment:
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notes.append(f"Document '{docMetadata.get('name', 'Unknown')}': {comment}")
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previews.append(docMetadata)
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observation = {
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"success": bool(actionResult.success),
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"resultLabel": actionResult.resultLabel or "",
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"documentsCount": len(actionResult.documents) if actionResult.documents else 0,
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"previews": previews,
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"notes": notes
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}
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# NEW: Add content analysis if intent is available
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if self.currentIntent and actionResult.documents:
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contentAnalysis = self._analyzeContent(actionResult.documents)
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observation['contentAnalysis'] = contentAnalysis
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return observation
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def _analyzeContent(self, documents: List[Any]) -> Dict[str, Any]:
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"""Analyzes content of documents for adaptive learning"""
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try:
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if not documents:
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return {"contentType": "none", "contentSnippet": "", "intentMatch": False}
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# Extract content from first document
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firstDoc = documents[0]
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content = ""
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if hasattr(firstDoc, 'documentData'):
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data = firstDoc.documentData
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if isinstance(data, dict) and 'content' in data:
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content = str(data['content'])
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else:
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content = str(data)
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# Classify content type
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contentType = self._classifyContent(content)
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# Create content snippet
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contentSnippet = content[:200] + "..." if len(content) > 200 else content
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# Assess intent match
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intentMatch = self._assessIntentMatch(content, self.currentIntent)
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return {
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"contentType": contentType,
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"contentSnippet": contentSnippet,
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"intentMatch": intentMatch
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}
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except Exception as e:
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logger.error(f"Error analyzing content: {str(e)}")
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return {"contentType": "error", "contentSnippet": "", "intentMatch": False}
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def _classifyContent(self, content: str) -> str:
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"""Classifies the type of content"""
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if not content:
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return "empty"
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# Check for code
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codeIndicators = ['def ', 'function', 'import ', 'class ', 'for ', 'while ', 'if ']
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if any(indicator in content.lower() for indicator in codeIndicators):
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return "code"
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# Check for numbers
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if re.search(r'\b\d+\b', content):
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return "numbers"
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# Check for structured content
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if any(indicator in content for indicator in ['\n', '\t', '|', '-', '*', '1.', '2.']):
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return "structured"
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# Default to text
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return "text"
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def _assessIntentMatch(self, content: str, intent: Dict[str, Any]) -> bool:
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"""Assesses if content matches the user intent"""
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if not intent:
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return False
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dataType = intent.get("dataType", "unknown")
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if dataType == "numbers":
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# Check if content contains actual numbers, not code
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hasNumbers = bool(re.search(r'\b\d+\b', content))
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isNotCode = not any(keyword in content.lower() for keyword in ['def ', 'function', 'import '])
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return hasNumbers and isNotCode
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elif dataType == "text":
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# Check if content is readable text
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words = re.findall(r'\b\w+\b', content)
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return len(words) > 5
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elif dataType == "documents":
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# Check if content is suitable for document creation
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hasStructure = any(indicator in content for indicator in ['\n', '\t', '|', '-', '*'])
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hasContent = len(content.strip()) > 50
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return hasStructure and hasContent
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return True # Default to match for unknown types
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def _collectFeedback(self, result: Any, validation: Dict[str, Any], intent: Dict[str, Any]) -> Dict[str, Any]:
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"""Collects comprehensive feedback from action execution"""
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try:
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# Extract content summary
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contentDelivered = ""
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if result.documents:
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firstDoc = result.documents[0]
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if hasattr(firstDoc, 'documentData'):
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data = firstDoc.documentData
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if isinstance(data, dict) and 'content' in data:
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content = str(data['content'])
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contentDelivered = content[:100] + "..." if len(content) > 100 else content
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else:
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contentDelivered = str(data)[:100] + "..." if len(str(data)) > 100 else str(data)
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return {
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"actionAttempted": result.resultLabel or "unknown",
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"parametersUsed": {}, # Would be extracted from action context
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"contentDelivered": contentDelivered,
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"intentMatchScore": validation.get('qualityScore', 0),
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"qualityScore": validation.get('qualityScore', 0),
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"issuesFound": validation.get('improvementSuggestions', []),
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"learningOpportunities": validation.get('improvementSuggestions', []),
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"userSatisfaction": None, # Would be collected from user feedback
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"timestamp": datetime.now(timezone.utc).timestamp()
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}
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except Exception as e:
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logger.error(f"Error collecting feedback: {str(e)}")
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return {
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"actionAttempted": "unknown",
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"parametersUsed": {},
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"contentDelivered": "",
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"intentMatchScore": 0,
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"qualityScore": 0,
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"issuesFound": [],
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"learningOpportunities": [],
|
|
"userSatisfaction": None,
|
|
"timestamp": datetime.now(timezone.utc).timestamp()
|
|
}
|
|
|
|
async def _refineDecide(self, context: TaskContext, observation: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Refine: decide continue or stop, with reason"""
|
|
promptTemplate = createRefinementPromptTemplate()
|
|
|
|
# Extract content for placeholders
|
|
userPrompt = extractUserPrompt(context)
|
|
|
|
# Create proper ReviewContext for extractReviewContent
|
|
from modules.datamodels.datamodelWorkflow import ReviewContext
|
|
reviewContext = ReviewContext(
|
|
task_step=context.task_step,
|
|
task_actions=[],
|
|
action_results=[], # React mode doesn't have action results in this context
|
|
step_result={'observation': observation},
|
|
workflow_id=context.workflow_id,
|
|
previous_results=[]
|
|
)
|
|
reviewContent = extractReviewContent(reviewContext)
|
|
|
|
# NEW: Add content validation to review content
|
|
enhancedReviewContent = reviewContent
|
|
if 'contentValidation' in observation:
|
|
validation = observation['contentValidation']
|
|
enhancedReviewContent += f"\n\nCONTENT VALIDATION:\n"
|
|
enhancedReviewContent += f"Overall Success: {validation['overallSuccess']}\n"
|
|
enhancedReviewContent += f"Quality Score: {validation['qualityScore']:.2f}\n"
|
|
if validation['improvementSuggestions']:
|
|
enhancedReviewContent += f"Improvement Suggestions: {', '.join(validation['improvementSuggestions'])}\n"
|
|
|
|
# NEW: Add content analysis to review content
|
|
if 'contentAnalysis' in observation:
|
|
analysis = observation['contentAnalysis']
|
|
enhancedReviewContent += f"\nCONTENT ANALYSIS:\n"
|
|
enhancedReviewContent += f"Content Type: {analysis['contentType']}\n"
|
|
enhancedReviewContent += f"Intent Match: {analysis['intentMatch']}\n"
|
|
if analysis['contentSnippet']:
|
|
enhancedReviewContent += f"Content Preview: {analysis['contentSnippet']}\n"
|
|
|
|
# NEW: Add progress state to review content
|
|
progressState = self.progressTracker.getCurrentProgress()
|
|
enhancedReviewContent += f"\nPROGRESS STATE:\n"
|
|
enhancedReviewContent += f"Completed Objectives: {len(progressState['completedObjectives'])}\n"
|
|
enhancedReviewContent += f"Partial Achievements: {len(progressState['partialAchievements'])}\n"
|
|
enhancedReviewContent += f"Failed Attempts: {len(progressState['failedAttempts'])}\n"
|
|
enhancedReviewContent += f"Current Phase: {progressState['currentPhase']}\n"
|
|
if progressState['nextActionsSuggested']:
|
|
enhancedReviewContent += f"Next Action Suggestions: {', '.join(progressState['nextActionsSuggested'])}\n"
|
|
|
|
# Create placeholders dictionary
|
|
placeholders = {
|
|
"USER_PROMPT": userPrompt,
|
|
"REVIEW_CONTENT": enhancedReviewContent
|
|
}
|
|
|
|
self._writeTraceLog("React Refinement Prompt", promptTemplate)
|
|
self._writeTraceLog("React Refinement Placeholders", placeholders)
|
|
|
|
# Centralized AI call for refinement decision (balanced analysis)
|
|
options = AiCallOptions(
|
|
operationType=OperationType.ANALYSE_CONTENT,
|
|
priority=Priority.BALANCED,
|
|
compressPrompt=True,
|
|
compressContext=False,
|
|
processingMode=ProcessingMode.ADVANCED,
|
|
maxCost=0.05,
|
|
maxProcessingTime=30
|
|
)
|
|
|
|
resp = await self.services.ai.callAi(
|
|
prompt=promptTemplate,
|
|
placeholders=placeholders,
|
|
options=options
|
|
)
|
|
self._writeTraceLog("React Refinement Response", resp)
|
|
js = resp[resp.find('{'):resp.rfind('}')+1] if resp else '{}'
|
|
try:
|
|
decision = json.loads(js)
|
|
except Exception:
|
|
decision = {"decision": "continue", "reason": "default"}
|
|
return decision
|
|
|
|
async def _createReactActionMessage(self, workflow: ChatWorkflow, selection: Dict[str, Any],
|
|
step: int, maxSteps: int, taskIndex: int, messageType: str,
|
|
result: ActionResult = None, observation: Dict[str, Any] = None):
|
|
"""Create user-friendly messages for React workflow actions"""
|
|
try:
|
|
action = selection.get('action', {})
|
|
method = action.get('method', '')
|
|
actionName = action.get('name', '')
|
|
|
|
# Get user language
|
|
userLanguage = self.services.user.language if self.services and self.services.user else 'en'
|
|
|
|
if messageType == "before":
|
|
# Message BEFORE action execution
|
|
userMessage = await self._generateActionIntentionMessage(method, actionName, userLanguage)
|
|
messageContent = f"🔄 **Step {step}/{maxSteps}**\n\n{userMessage}"
|
|
status = "step"
|
|
actionProgress = "pending"
|
|
documentsLabel = f"action_{step}_intention"
|
|
|
|
elif messageType == "after":
|
|
# Message AFTER action execution
|
|
userMessage = await self._generateActionResultMessage(method, actionName, result, observation, userLanguage)
|
|
successIcon = "✅" if result and result.success else "❌"
|
|
messageContent = f"{successIcon} **Step {step}/{maxSteps} Complete**\n\n{userMessage}"
|
|
status = "step"
|
|
actionProgress = "success" if result and result.success else "fail"
|
|
documentsLabel = observation.get('resultLabel') if observation else f"action_{step}_result"
|
|
else:
|
|
return
|
|
|
|
# Create workflow message
|
|
messageData = {
|
|
"workflowId": workflow.id,
|
|
"role": "assistant",
|
|
"message": messageContent,
|
|
"status": status,
|
|
"sequenceNr": len(workflow.messages) + 1,
|
|
"publishedAt": self.services.utils.getUtcTimestamp(),
|
|
"documentsLabel": documentsLabel,
|
|
"documents": [],
|
|
"roundNumber": workflow.currentRound,
|
|
"taskNumber": taskIndex,
|
|
"actionNumber": step,
|
|
"actionProgress": actionProgress
|
|
}
|
|
|
|
message = self.services.interfaceDbChat.createMessage(messageData)
|
|
if message:
|
|
workflow.messages.append(message)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating React action message: {str(e)}")
|
|
|
|
async def _generateActionIntentionMessage(self, method: str, actionName: str, userLanguage: str):
|
|
"""Generate user-friendly message explaining what action will do"""
|
|
try:
|
|
# Create a simple AI prompt to generate user-friendly action descriptions
|
|
prompt = f"""Generate a brief, user-friendly message explaining what the {method}.{actionName} action will do.
|
|
|
|
User language: {userLanguage}
|
|
|
|
Examples:
|
|
- For ai.process: "I'll analyze the content and provide insights"
|
|
- For document.extract: "I'll extract the key information from the documents"
|
|
- For document.generate: "I'll create a formatted report from the documents"
|
|
- For outlook.composeEmail: "I'll compose an email based on your requirements"
|
|
- For outlook.sendEmail: "I'll send the composed email"
|
|
- For sharepoint.findDocumentPath: "I'll search for the requested documents"
|
|
- For sharepoint.readDocuments: "I'll read the document contents"
|
|
|
|
Return only the user-friendly message, no technical details."""
|
|
|
|
# Call AI to generate user-friendly message
|
|
response = await self.services.ai.callAi(
|
|
prompt=prompt,
|
|
options=AiCallOptions(
|
|
operationType=OperationType.GENERATE_CONTENT,
|
|
priority=Priority.SPEED,
|
|
compressPrompt=True,
|
|
maxCost=0.01,
|
|
maxProcessingTime=5
|
|
)
|
|
)
|
|
|
|
return response.strip() if response else f"Executing {method}.{actionName} action..."
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating action intention message: {str(e)}")
|
|
return f"Executing {method}.{actionName} action..."
|
|
|
|
async def _generateActionResultMessage(self, method: str, actionName: str, result: ActionResult,
|
|
observation: Dict[str, Any], userLanguage: str):
|
|
"""Generate user-friendly message explaining action results"""
|
|
try:
|
|
# Build result context
|
|
resultContext = ""
|
|
if result and result.documents:
|
|
docCount = len(result.documents)
|
|
resultContext = f"Generated {docCount} document(s)"
|
|
elif observation and observation.get('documentsCount', 0) > 0:
|
|
docCount = observation.get('documentsCount', 0)
|
|
resultContext = f"Generated {docCount} document(s)"
|
|
|
|
# Create AI prompt for result message
|
|
prompt = f"""Generate a brief, user-friendly message explaining the result of the {method}.{actionName} action.
|
|
|
|
User language: {userLanguage}
|
|
Success: {result.success if result else 'Unknown'}
|
|
Result context: {resultContext}
|
|
|
|
Examples:
|
|
- For successful ai.process: "Analysis complete! I've processed the content and generated insights."
|
|
- For successful document.extract: "Extraction complete! I've extracted the key information from the documents."
|
|
- For successful document.generate: "Report generated! I've created a formatted document with the requested content."
|
|
- For successful outlook.composeEmail: "Email composed! I've prepared the email content for sending."
|
|
- For successful outlook.sendEmail: "Email sent! The message has been delivered successfully."
|
|
- For failed actions: "The action encountered an issue. Please check the details."
|
|
|
|
Return only the user-friendly message, no technical details."""
|
|
|
|
# Call AI to generate user-friendly result message
|
|
response = await self.services.ai.callAi(
|
|
prompt=prompt,
|
|
options=AiCallOptions(
|
|
operationType=OperationType.GENERATE_CONTENT,
|
|
priority=Priority.SPEED,
|
|
compressPrompt=True,
|
|
maxCost=0.01,
|
|
maxProcessingTime=5
|
|
)
|
|
)
|
|
|
|
return response.strip() if response else f"{method}.{actionName} action completed"
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating action result message: {str(e)}")
|
|
return f"{method}.{actionName} action completed"
|
|
|
|
def _createTaskAction(self, actionData: Dict[str, Any]) -> TaskAction:
|
|
"""Creates a new task action for React mode"""
|
|
try:
|
|
import uuid
|
|
|
|
# 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
|
|
simpleFields, objectFields = self.services.interfaceDbChat._separate_object_fields(TaskAction, actionData)
|
|
|
|
# Create action in database
|
|
createdAction = self.services.interfaceDbChat.db.recordCreate(TaskAction, simpleFields)
|
|
|
|
# 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", self.services.utils.getUtcTimestamp())),
|
|
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
|
|
|
|
def _updateWorkflowBeforeExecutingTask(self, taskNumber: int):
|
|
"""Update workflow object before executing a task"""
|
|
try:
|
|
updateData = {
|
|
"currentTask": taskNumber,
|
|
"currentAction": 0,
|
|
"totalActions": 0
|
|
}
|
|
|
|
# Update workflow object
|
|
self.workflow.currentTask = taskNumber
|
|
self.workflow.currentAction = 0
|
|
self.workflow.totalActions = 0
|
|
|
|
# Update in database
|
|
self.services.interfaceDbChat.updateWorkflow(self.workflow.id, updateData)
|
|
logger.info(f"Updated workflow {self.workflow.id} before executing task {taskNumber}: {updateData}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating workflow before executing task: {str(e)}")
|
|
|
|
def _updateWorkflowBeforeExecutingAction(self, actionNumber: int):
|
|
"""Update workflow object before executing an action"""
|
|
try:
|
|
updateData = {
|
|
"currentAction": actionNumber
|
|
}
|
|
|
|
# Update workflow object
|
|
self.workflow.currentAction = actionNumber
|
|
|
|
# Update in database
|
|
self.services.interfaceDbChat.updateWorkflow(self.workflow.id, updateData)
|
|
logger.info(f"Updated workflow {self.workflow.id} before executing action {actionNumber}: {updateData}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error updating workflow before executing action: {str(e)}")
|
|
|
|
def _createTaskAction(self, actionData: Dict[str, Any]) -> TaskAction:
|
|
"""Creates a new task action for React mode"""
|
|
try:
|
|
import uuid
|
|
|
|
# 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
|
|
simpleFields, objectFields = self.services.interfaceDbChat._separate_object_fields(TaskAction, actionData)
|
|
|
|
# Create action in database
|
|
createdAction = self.services.interfaceDbChat.db.recordCreate(TaskAction, simpleFields)
|
|
|
|
# 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", self.services.utils.getUtcTimestamp())),
|
|
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
|
|
|
|
def _writeTraceLog(self, contextText: str, data: Any) -> None:
|
|
"""Write trace data to configured trace file if in debug mode"""
|
|
try:
|
|
import os
|
|
import json
|
|
from datetime import datetime, UTC
|
|
|
|
# Only write if logger is in debug mode
|
|
if logger.level > logging.DEBUG:
|
|
return
|
|
|
|
# Get log directory from configuration
|
|
logDir = self.services.utils.configGet("APP_LOGGING_LOG_DIR", "./")
|
|
if not os.path.isabs(logDir):
|
|
# If relative path, make it relative to the gateway directory
|
|
gatewayDir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
|
logDir = os.path.join(gatewayDir, logDir)
|
|
|
|
# Ensure log directory exists
|
|
os.makedirs(logDir, exist_ok=True)
|
|
|
|
# Create trace file path
|
|
traceFile = os.path.join(logDir, "log_trace.log")
|
|
|
|
# Format the trace entry
|
|
timestamp = datetime.fromtimestamp(self.services.utils.getUtcTimestamp(), UTC).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
|
|
traceEntry = f"[{timestamp}] {contextText}\n"
|
|
|
|
# Add data if provided - show full content without truncation
|
|
if data is not None:
|
|
if isinstance(data, (dict, list)):
|
|
# Use ensure_ascii=False to preserve Unicode characters and indent=2 for readability
|
|
traceEntry += f"Data: {json.dumps(data, indent=2, default=str, ensure_ascii=False)}\n"
|
|
else:
|
|
# For string data, show full content without truncation
|
|
traceEntry += f"Data: {str(data)}\n"
|
|
|
|
traceEntry += "-" * 80 + "\n\n"
|
|
|
|
# Write to trace file
|
|
with open(traceFile, "a", encoding="utf-8") as f:
|
|
f.write(traceEntry)
|
|
|
|
except Exception as e:
|
|
# Don't log trace errors to avoid recursion
|
|
pass
|