""" Placeholder Factory Centralized placeholder extraction functions for all workflow modes. Each function corresponds to a {{KEY:PLACEHOLDER_NAME}} in prompt templates. NAMING CONVENTION: - All functions follow pattern: extract{PlaceholderName}() - Placeholder names are in UPPER_CASE with underscores - Function names are in camelCase MAPPING TABLE (keys → function) with usage [global | react | actionplan]: {{KEY:USER_PROMPT}} -> extractUserPrompt() [global, react, actionplan] {{KEY:USER_LANGUAGE}} -> extractUserLanguage() [react, actionplan] {{KEY:AVAILABLE_DOCUMENTS_SUMMARY}} -> extractAvailableDocumentsSummary() [react, actionplan] {{KEY:AVAILABLE_DOCUMENTS_INDEX}} -> extractAvailableDocumentsIndex() [react] {{KEY:AVAILABLE_CONNECTIONS_INDEX}} -> extractAvailableConnectionsIndex() [react, actionplan] {{KEY:AVAILABLE_CONNECTIONS_SUMMARY}} -> extractAvailableConnectionsSummary() [unused] {{KEY:WORKFLOW_HISTORY}} -> extractWorkflowHistory() [actionplan] {{KEY:AVAILABLE_METHODS}} -> extractAvailableMethods() [react, actionplan] {{KEY:REVIEW_CONTENT}} -> extractReviewContent() [react, actionplan] {{KEY:PREVIOUS_ACTION_RESULTS}} -> extractPreviousActionResults() [react] {{KEY:LEARNINGS_AND_IMPROVEMENTS}} -> extractLearningsAndImprovements() [react] {{KEY:LATEST_REFINEMENT_FEEDBACK}} -> extractLatestRefinementFeedback() [react] Following placeholders are populated directly by prompt builders with according context in promptGenerationActionsReact module: - ACTION_OBJECTIVE, - SELECTED_ACTION, - ACTION_SIGNATURE """ import json import logging from typing import Dict, Any, List from modules.datamodels.datamodelChat import ChatDocument logger = logging.getLogger(__name__) from modules.workflows.processing.shared.methodDiscovery import (methods, discoverMethods) def extractUserPrompt(context: Any) -> str: """Extract user prompt from context. Maps to {{KEY:USER_PROMPT}}. Prefer the cleaned intent stored on the services object if available via context. Fallback to the task_step objective. """ try: # Prefer services.currentUserPrompt when accessible through context services = getattr(context, 'services', None) if services and getattr(services, 'currentUserPrompt', None): return services.currentUserPrompt except Exception: pass if hasattr(context, 'task_step') and context.task_step: return context.task_step.objective or 'No request specified' return 'No request specified' def extractWorkflowHistory(service: Any, context: Any) -> str: """Extract workflow history from context. Maps to {{KEY:WORKFLOW_HISTORY}}""" if hasattr(context, 'workflow') and context.workflow: return getPreviousRoundContext(service, context.workflow) or "No previous workflow rounds - this is the first round." return "No previous workflow rounds - this is the first round." def extractAvailableMethods(service: Any) -> str: """Extract available methods for action planning. Maps to {{KEY:AVAILABLE_METHODS}}""" try: # Get the methods dictionary directly from the global methods variable if not methods: discoverMethods(service) # Create a flat JSON format with compound action names for better AI parsing available_actions_json = {} for methodName, methodInfo in methods.items(): # Convert MethodAi -> ai, MethodDocument -> document, etc. shortName = methodName.replace('Method', '').lower() for actionName, actionInfo in methodInfo['actions'].items(): # Create compound action name: method.action compoundActionName = f"{shortName}.{actionName}" # Get the action description action_description = actionInfo.get('description', f"Execute {actionName} action") available_actions_json[compoundActionName] = action_description return json.dumps(available_actions_json, indent=2, ensure_ascii=False) except Exception as e: logger.error(f"Error extracting available methods: {str(e)}") return json.dumps({}, indent=2, ensure_ascii=False) def extractUserLanguage(service: Any) -> str: """Extract user language from service. Maps to {{KEY:USER_LANGUAGE}}""" return service.user.language if service and service.user else 'en' def getConnectionReferenceList(services) -> List[str]: """Get list of available connections""" try: # Get connections from the database if hasattr(services, 'interfaceDbApp') and hasattr(services, 'user'): userId = services.user.id connections = services.interfaceDbApp.getUserConnections(userId) if connections: # Format connections as reference strings connectionRefs = [] for conn in connections: # Create reference string in format: conn_{authority}_{id} ref = f"conn_{conn.authority.value}_{conn.id}" connectionRefs.append(ref) return connectionRefs return [] except Exception as e: logger.error(f"Error getting connection reference list: {str(e)}") return [] def getPreviousRoundContext(services, context: Any) -> str: """Get previous round context for prompt""" try: if not context or not hasattr(context, 'workflow_id'): return "No previous round context available" workflowId = context.workflow_id if not workflowId: return "No previous round context available" # Get previous round results previousResults = getattr(context, 'previous_results', []) if not previousResults: return "No previous round context available" contextList = [] for i, result in enumerate(previousResults, 1): if hasattr(result, 'success') and hasattr(result, 'resultLabel'): status = "Success" if result.success else "Failed" contextList.append(f"{i}. {result.resultLabel} - {status}") elif isinstance(result, dict): status = "Success" if result.get('success', False) else "Failed" label = result.get('resultLabel', 'Unknown') contextList.append(f"{i}. {label} - {status}") else: contextList.append(f"{i}. {str(result)}") return "\n".join(contextList) if contextList else "No previous round context available" except Exception as e: logger.error(f"Error getting previous round context: {str(e)}") return "Error retrieving previous round context" def extractReviewContent(context: Any) -> str: """Extract review content for result validation. Maps to {{KEY:REVIEW_CONTENT}}""" try: if hasattr(context, 'action_results') and context.action_results: # Build result summary result_summary = "" for i, result in enumerate(context.action_results): result_summary += f"\nRESULT {i+1}:\n" result_summary += f" Success: {result.success}\n" if result.error: result_summary += f" Error: {result.error}\n" if result.documents: result_summary += f" Documents: {len(result.documents)} document(s)\n" for doc in result.documents: # Extract all available metadata without content doc_metadata = { "name": getattr(doc, 'documentName', 'Unknown'), "mimeType": getattr(doc, 'mimeType', 'Unknown'), "size": getattr(doc, 'size', 'Unknown'), "created": getattr(doc, 'created', 'Unknown'), "modified": getattr(doc, 'modified', 'Unknown'), "typeGroup": getattr(doc, 'typeGroup', 'Unknown'), "documentId": getattr(doc, 'documentId', 'Unknown'), "reference": getattr(doc, 'reference', 'Unknown') } # Remove 'Unknown' values to keep it clean doc_metadata = {k: v for k, v in doc_metadata.items() if v != 'Unknown'} result_summary += f" - {json.dumps(doc_metadata, indent=6, ensure_ascii=False)}\n" else: result_summary += f" Documents: None\n" return result_summary elif hasattr(context, 'observation') and context.observation: # For observation data, show full content but handle documents specially if isinstance(context.observation, dict): # Create a copy to modify obs_copy = context.observation.copy() # If there are previews with documents, show only metadata if 'previews' in obs_copy and isinstance(obs_copy['previews'], list): for preview in obs_copy['previews']: if isinstance(preview, dict) and 'snippet' in preview: # Replace snippet with metadata indicator preview['snippet'] = f"[Content: {len(preview.get('snippet', ''))} characters]" return json.dumps(obs_copy, indent=2, ensure_ascii=False) else: return json.dumps(context.observation, ensure_ascii=False) elif hasattr(context, 'step_result') and context.step_result and 'observation' in context.step_result: # For observation data in step_result, show full content but handle documents specially observation = context.step_result['observation'] if isinstance(observation, dict): # Create a copy to modify obs_copy = observation.copy() # If there are previews with documents, show only metadata if 'previews' in obs_copy and isinstance(obs_copy['previews'], list): for preview in obs_copy['previews']: if isinstance(preview, dict) and 'snippet' in preview: # Replace snippet with metadata indicator preview['snippet'] = f"[Content: {len(preview.get('snippet', ''))} characters]" return json.dumps(obs_copy, indent=2, ensure_ascii=False) else: return json.dumps(observation, ensure_ascii=False) else: return "No review content available" except Exception as e: logger.error(f"Error extracting review content: {str(e)}") return "No review content available" def extractPreviousActionResults(context: Any) -> str: """Extract previous action results for learning context. Maps to {{KEY:PREVIOUS_ACTION_RESULTS}}""" try: if not hasattr(context, 'previous_action_results') or not context.previous_action_results: return "No previous actions executed yet" results = [] for i, result in enumerate(context.previous_action_results[-5:], 1): # Last 5 results if hasattr(result, 'resultLabel') and hasattr(result, 'status'): status = "SUCCESS" if result.status == "completed" else "FAILED" results.append(f"Action {i}: {result.resultLabel} - {status}") if hasattr(result, 'error') and result.error: results.append(f" Error: {result.error}") return "\n".join(results) if results else "No previous actions executed yet" except Exception as e: logger.error(f"Error extracting previous action results: {str(e)}") return "No previous actions executed yet" def extractLearningsAndImprovements(context: Any) -> str: """Extract learnings and improvements from previous actions. Maps to {{KEY:LEARNINGS_AND_IMPROVEMENTS}}""" try: learnings = [] # Get improvements from context if hasattr(context, 'improvements') and context.improvements and isinstance(context.improvements, list): learnings.append("IMPROVEMENTS:") for improvement in context.improvements[-3:]: # Last 3 improvements learnings.append(f"- {improvement}") # Get failure patterns if hasattr(context, 'failure_patterns') and context.failure_patterns and isinstance(context.failure_patterns, list): learnings.append("FAILURE PATTERNS TO AVOID:") for pattern in context.failure_patterns[-3:]: # Last 3 patterns learnings.append(f"- {pattern}") # Get successful actions if hasattr(context, 'successful_actions') and context.successful_actions and isinstance(context.successful_actions, list): learnings.append("SUCCESSFUL APPROACHES:") for action in context.successful_actions[-3:]: # Last 3 successful learnings.append(f"- {action}") return "\n".join(learnings) if learnings else "No learnings available yet" except Exception as e: logger.error(f"Error extracting learnings and improvements: {str(e)}") return "No learnings available yet" def extractLatestRefinementFeedback(context: Any) -> str: """Extract the latest refinement feedback. Maps to {{KEY:LATEST_REFINEMENT_FEEDBACK}}""" try: if not hasattr(context, 'previous_review_result') or not context.previous_review_result or not isinstance(context.previous_review_result, list): return "No previous refinement feedback available" # Get the most recent refinement decision latest_decision = context.previous_review_result[-1] if not isinstance(latest_decision, dict): return "No previous refinement feedback available" feedback_parts = [] # Add decision and reason decision = latest_decision.get('decision', 'unknown') reason = latest_decision.get('reason', 'No reason provided') feedback_parts.append(f"Latest Decision: {decision}") feedback_parts.append(f"Reason: {reason}") # Add any specific feedback or suggestions if 'feedback' in latest_decision: feedback_parts.append(f"Feedback: {latest_decision['feedback']}") if 'suggestions' in latest_decision: feedback_parts.append(f"Suggestions: {latest_decision['suggestions']}") return "\n".join(feedback_parts) except Exception as e: logger.error(f"Error extracting latest refinement feedback: {str(e)}") return "No previous refinement feedback available" def extractAvailableDocumentsSummary(service: Any, context: Any) -> str: """Summary of available documents (count only).""" try: if hasattr(context, 'workflow') and context.workflow: documents = service.workflow.getAvailableDocuments(context.workflow) if documents and documents != "No documents available": doc_count = documents.count("docList:") + documents.count("docItem:") return f"{doc_count} documents available from previous tasks" return "No documents available" return "No documents available" except Exception as e: logger.error(f"Error getting document summary: {str(e)}") return "No documents available" def extractAvailableDocumentsIndex(service: Any, context: Any) -> str: """Index of available documents with detailed references for parameter generation.""" try: if hasattr(context, 'workflow') and context.workflow: return service.workflow.getAvailableDocuments(context.workflow) return "No documents available" except Exception as e: logger.error(f"Error getting document index: {str(e)}") return "No documents available" def extractAvailableConnectionsSummary(service: Any) -> str: """Summary of available connections (count only).""" try: connections = getConnectionReferenceList(service) if connections: return f"{len(connections)} connections available" return "No connections available" except Exception as e: logger.error(f"Error getting connection summary: {str(e)}") return "No connections available" def extractAvailableConnectionsIndex(service: Any) -> str: """Index of available connections with detailed references for parameter generation.""" try: connections = getConnectionReferenceList(service) if connections: return '\n'.join(f"- {conn}" for conn in connections) return "No connections available" except Exception as e: logger.error(f"Error getting connection index: {str(e)}") return "No connections available"