import json import logging from typing import Dict, Any, List, Optional, Tuple, Union from modules.datamodels.datamodelChat import PromptPlaceholder, ChatDocument from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum from modules.services.serviceAi.subSharedAiUtils import ( buildPromptWithPlaceholders, extractTextFromContentParts, reduceText, determineCallType ) from modules.shared.jsonUtils import ( extractJsonString, repairBrokenJson, extractSectionsFromDocument, buildContinuationContext ) logger = logging.getLogger(__name__) # Repair-based looping system - no longer needs LOOP_INSTRUCTION_TEXT # Sections are accumulated and repair mechanism handles broken JSON automatically # Rebuild the model to resolve forward references AiCallRequest.model_rebuild() class SubCoreAi: """Core AI operations including image analysis, text generation, and planning calls.""" def __init__(self, services, aiObjects): """Initialize core AI operations. Args: services: Service center instance for accessing other services aiObjects: Initialized AiObjects instance """ self.services = services self.aiObjects = aiObjects async def _analyzePromptAndCreateOptions(self, prompt: str) -> AiCallOptions: """Analyze prompt to determine appropriate AiCallOptions parameters.""" try: # Get dynamic enum values from Pydantic models operation_types = [e.value for e in OperationTypeEnum] priorities = [e.value for e in PriorityEnum] processing_modes = [e.value for e in ProcessingModeEnum] # Create analysis prompt for AI to determine operation type and parameters analysisPrompt = f""" You are an AI operation analyzer. Analyze the following prompt and determine the most appropriate operation type and parameters. PROMPT TO ANALYZE: {self.services.ai.sanitizePromptContent(prompt, 'userinput')} Based on the prompt content, determine: 1. operationType: Choose the most appropriate from: {', '.join(operation_types)} 2. priority: Choose from: {', '.join(priorities)} 3. processingMode: Choose from: {', '.join(processing_modes)} 4. compressPrompt: true/false (true for story-like prompts, false for structured prompts with JSON/schemas) 5. compressContext: true/false (true to summarize context, false to process fully) Respond with ONLY a JSON object in this exact format: {{ "operationType": "dataAnalyse", "priority": "balanced", "processingMode": "basic", "compressPrompt": true, "compressContext": true }} """ # Use AI to analyze the prompt request = AiCallRequest( prompt=analysisPrompt, options=AiCallOptions( operationType=OperationTypeEnum.DATA_ANALYSE, priority=PriorityEnum.SPEED, processingMode=ProcessingModeEnum.BASIC, compressPrompt=True, compressContext=False ) ) response = await self.aiObjects.call(request) # Parse AI response try: import json json_start = response.content.find('{') json_end = response.content.rfind('}') + 1 if json_start != -1 and json_end > json_start: analysis = json.loads(response.content[json_start:json_end]) # Map string values to enums operation_type = OperationTypeEnum(analysis.get('operationType', 'dataAnalyse')) priority = PriorityEnum(analysis.get('priority', 'balanced')) processing_mode = ProcessingModeEnum(analysis.get('processingMode', 'basic')) return AiCallOptions( operationType=operation_type, priority=priority, processingMode=processing_mode, compressPrompt=analysis.get('compressPrompt', True), compressContext=analysis.get('compressContext', True) ) except Exception as e: logger.warning(f"Failed to parse AI analysis response: {e}") except Exception as e: logger.warning(f"Prompt analysis failed: {e}") # Fallback to default options return AiCallOptions( operationType=OperationTypeEnum.DATA_ANALYSE, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.BASIC ) # Shared Core Function for AI Calls with Looping and Repair async def _callAiWithLooping( self, prompt: str, options: AiCallOptions, debugPrefix: str = "ai_call", promptBuilder: Optional[callable] = None, promptArgs: Optional[Dict[str, Any]] = None, operationId: Optional[str] = None ) -> str: """ Shared core function for AI calls with repair-based looping system. Automatically repairs broken JSON and continues generation seamlessly. Args: prompt: The prompt to send to AI options: AI call configuration options debugPrefix: Prefix for debug file names promptBuilder: Optional function to rebuild prompts for continuation promptArgs: Optional arguments for prompt builder operationId: Optional operation ID for progress tracking Returns: Complete AI response after all iterations """ max_iterations = 50 # Prevent infinite loops iteration = 0 allSections = [] # Accumulate all sections across iterations lastRawResponse = None # Store last raw JSON response for continuation while iteration < max_iterations: iteration += 1 # Update progress for iteration start if operationId: if iteration == 1: self.services.workflow.progressLogUpdate(operationId, 0.5, f"Starting AI call iteration {iteration}") else: # For continuation iterations, show progress incrementally base_progress = 0.5 + (min(iteration - 1, max_iterations) / max_iterations * 0.4) # Progress from 0.5 to 0.9 over max_iterations iterations self.services.workflow.progressLogUpdate(operationId, base_progress, f"Continuing generation (iteration {iteration})") # Build iteration prompt if len(allSections) > 0 and promptBuilder and promptArgs: # This is a continuation - build continuation context with raw JSON and rebuild prompt continuationContext = buildContinuationContext(allSections, lastRawResponse) if not lastRawResponse: logger.warning(f"Iteration {iteration}: No previous response available for continuation!") # Rebuild prompt with continuation context using the provided prompt builder iterationPrompt = await promptBuilder(**promptArgs, continuationContext=continuationContext) else: # First iteration - use original prompt iterationPrompt = prompt # Make AI call try: if operationId and iteration == 1: self.services.workflow.progressLogUpdate(operationId, 0.51, "Calling AI model") from modules.datamodels.datamodelAi import AiCallRequest request = AiCallRequest( prompt=iterationPrompt, context="", options=options ) # Write the ACTUAL prompt sent to AI if iteration == 1: self.services.utils.writeDebugFile(iterationPrompt, f"{debugPrefix}_prompt") else: self.services.utils.writeDebugFile(iterationPrompt, f"{debugPrefix}_prompt_iteration_{iteration}") response = await self.aiObjects.call(request) result = response.content # Update progress after AI call if operationId: if iteration == 1: self.services.workflow.progressLogUpdate(operationId, 0.6, f"AI response received (iteration {iteration})") else: progress = 0.6 + (min(iteration - 1, 10) * 0.03) self.services.workflow.progressLogUpdate(operationId, progress, f"Processing response (iteration {iteration})") # Write raw AI response to debug file if iteration == 1: self.services.utils.writeDebugFile(result, f"{debugPrefix}_response") else: self.services.utils.writeDebugFile(result, f"{debugPrefix}_response_iteration_{iteration}") # Emit stats for this iteration self.services.workflow.storeWorkflowStat( self.services.currentWorkflow, response, f"ai.call.{debugPrefix}.iteration_{iteration}" ) if not result or not result.strip(): logger.warning(f"Iteration {iteration}: Empty response, stopping") break # Store raw response for continuation (even if broken) lastRawResponse = result # Check for complete_response flag in raw response (before parsing) import re if re.search(r'"complete_response"\s*:\s*true', result, re.IGNORECASE): pass # Flag detected, will stop in _shouldContinueGeneration # Extract sections from response (handles both valid and broken JSON) extractedSections, wasJsonComplete = self._extractSectionsFromResponse(result, iteration, debugPrefix) # Update progress after parsing if operationId: if extractedSections: self.services.workflow.progressLogUpdate(operationId, 0.65 + (min(iteration - 1, 10) * 0.025), f"Extracted {len(extractedSections)} sections (iteration {iteration})") if not extractedSections: # If we're in continuation mode and JSON was incomplete, don't stop - continue to allow retry if iteration > 1 and not wasJsonComplete: logger.warning(f"Iteration {iteration}: No sections extracted from continuation fragment, continuing for another attempt") continue # Otherwise, stop if no sections logger.warning(f"Iteration {iteration}: No sections extracted, stopping") break # Add new sections to accumulator allSections.extend(extractedSections) # Check if we should continue (completion detection) if self._shouldContinueGeneration(allSections, iteration, wasJsonComplete, result): continue else: # Done - build final result if operationId: self.services.workflow.progressLogUpdate(operationId, 0.95, f"Generation complete ({iteration} iterations, {len(allSections)} sections)") break except Exception as e: logger.error(f"Error in AI call iteration {iteration}: {str(e)}") break if iteration >= max_iterations: logger.warning(f"AI call stopped after maximum iterations ({max_iterations})") # Build final result from accumulated sections final_result = self._buildFinalResultFromSections(allSections) # Write final result to debug file self.services.utils.writeDebugFile(final_result, f"{debugPrefix}_final_result") return final_result def _extractSectionsFromResponse( self, result: str, iteration: int, debugPrefix: str ) -> Tuple[List[Dict[str, Any]], bool]: """ Extract sections from AI response, handling both valid and broken JSON. Uses repair mechanism for broken JSON. Checks for "complete_response": true flag to determine completion. Returns (sections, wasJsonComplete) """ # First, try to parse as valid JSON try: extracted = extractJsonString(result) parsed_result = json.loads(extracted) # Check if AI marked response as complete isComplete = parsed_result.get("complete_response", False) == True # Extract sections from parsed JSON sections = extractSectionsFromDocument(parsed_result) # If AI marked as complete, always return as complete if isComplete: return sections, True # If in continuation mode (iteration > 1), continuation responses are expected to be fragments # A fragment with 0 extractable sections means JSON is incomplete - need another iteration if len(sections) == 0 and iteration > 1: return sections, False # Mark as incomplete so loop continues # First iteration with 0 sections means empty response - stop if len(sections) == 0: return sections, True # Complete but empty return sections, True # JSON was complete with sections except json.JSONDecodeError as e: # Broken JSON - try repair mechanism (normal in iterative generation) self.services.utils.writeDebugFile(result, f"{debugPrefix}_broken_json_iteration_{iteration}") # Try to repair repaired_json = repairBrokenJson(result) if repaired_json: # Extract sections from repaired JSON sections = extractSectionsFromDocument(repaired_json) return sections, False # JSON was broken but repaired else: # Repair failed - log error logger.error(f"Iteration {iteration}: All repair strategies failed") return [], False except Exception as e: logger.error(f"Iteration {iteration}: Unexpected error during parsing: {str(e)}") return [], False def _shouldContinueGeneration( self, allSections: List[Dict[str, Any]], iteration: int, wasJsonComplete: bool, rawResponse: str = None ) -> bool: """ Determine if generation should continue based on JSON completeness and complete_response flag. Returns True if we should continue, False if done. """ if len(allSections) == 0: return True # No sections yet, continue # Check for complete_response flag in raw response if rawResponse: import re if re.search(r'"complete_response"\s*:\s*true', rawResponse, re.IGNORECASE): return False # If JSON was complete (and no complete_response flag), we're done # If JSON was broken and repaired, continue to get more content if wasJsonComplete: return False else: return True def _buildFinalResultFromSections( self, allSections: List[Dict[str, Any]] ) -> str: """ Build final JSON result from accumulated sections. """ if not allSections: return "" # Build documents structure # Assuming single document for now documents = [{ "id": "doc_1", "title": "Generated Document", # This should come from prompt "filename": "document.json", "sections": allSections }] result = { "metadata": { "split_strategy": "single_document", "source_documents": [], "extraction_method": "ai_generation" }, "documents": documents } return json.dumps(result, indent=2) # Old _buildContinuationPrompt and _mergeJsonContent methods removed # Now handled by repair mechanism in jsonUtils.py and section accumulation # Planning AI Call async def callAiPlanning( self, prompt: str, placeholders: Optional[List[PromptPlaceholder]] = None ) -> str: """ Planning AI call for task planning, action planning, action selection, etc. Always uses static parameters optimized for planning tasks. Args: prompt: The planning prompt placeholders: Optional list of placeholder replacements Returns: Planning JSON response """ # Planning calls always use static parameters options = AiCallOptions( operationType=OperationTypeEnum.PLAN, priority=PriorityEnum.QUALITY, processingMode=ProcessingModeEnum.DETAILED, compressPrompt=False, compressContext=False ) # Build full prompt with placeholders if placeholders: placeholders_dict = {p.label: p.content for p in placeholders} full_prompt = buildPromptWithPlaceholders(prompt, placeholders_dict) else: full_prompt = prompt # Use shared core function with planning-specific debug prefix return await self._callAiWithLooping(full_prompt, options, "plan") # Document Generation AI Call async def callAiDocuments( self, prompt: str, documents: Optional[List[ChatDocument]] = None, options: Optional[AiCallOptions] = None, outputFormat: Optional[str] = None, title: Optional[str] = None ) -> Union[str, Dict[str, Any]]: """ Document generation AI call for all non-planning calls. Uses the current unified path with extraction and generation. Args: prompt: The main prompt for the AI call documents: Optional list of documents to process options: AI call configuration options outputFormat: Optional output format for document generation title: Optional title for generated documents Returns: AI response as string, or dict with documents if outputFormat is specified """ # Create separate operationId for detailed progress tracking import time import uuid workflowId = self.services.currentWorkflow.id if self.services.currentWorkflow else f"no-workflow-{int(time.time())}" aiOperationId = f"ai_documents_{workflowId}_{int(time.time())}" # Start progress tracking for this operation self.services.workflow.progressLogStart( aiOperationId, "AI call with documents", "Document Generation", f"Format: {outputFormat or 'text'}" ) try: if options is None or (hasattr(options, 'operationType') and options.operationType is None): # Use AI to determine parameters ONLY when truly needed (options=None OR operationType=None) self.services.workflow.progressLogUpdate(aiOperationId, 0.1, "Analyzing prompt parameters") options = await self._analyzePromptAndCreateOptions(prompt) # CRITICAL: For document generation with JSON templates, NEVER compress the prompt # Compressing would truncate the template structure and confuse the AI if outputFormat: # Document generation with structured output if not options: options = AiCallOptions() options.compressPrompt = False # JSON templates must NOT be truncated options.compressContext = False # Context also should not be compressed # Handle document generation with specific output format using unified approach if outputFormat: # Use unified generation method for all document generation if documents and len(documents) > 0: self.services.workflow.progressLogUpdate(aiOperationId, 0.2, f"Extracting content from {len(documents)} documents") extracted_content = await self.services.ai.documentProcessor.callAiText(prompt, documents, options, aiOperationId) else: self.services.workflow.progressLogUpdate(aiOperationId, 0.2, "Preparing for direct generation") extracted_content = None self.services.workflow.progressLogUpdate(aiOperationId, 0.3, "Building generation prompt") from modules.services.serviceGeneration.subPromptBuilderGeneration import buildGenerationPrompt # First call without continuation context generation_prompt = await buildGenerationPrompt(outputFormat, prompt, title, extracted_content, None) # Prepare prompt builder arguments for continuation promptArgs = { "outputFormat": outputFormat, "userPrompt": prompt, "title": title, "extracted_content": extracted_content } self.services.workflow.progressLogUpdate(aiOperationId, 0.4, "Calling AI for content generation") generated_json = await self._callAiWithLooping( generation_prompt, options, "document_generation", buildGenerationPrompt, promptArgs, aiOperationId ) self.services.workflow.progressLogUpdate(aiOperationId, 0.7, "Parsing generated JSON") # Parse the generated JSON (extract fenced/embedded JSON first) try: extracted_json = self.services.utils.jsonExtractString(generated_json) generated_data = json.loads(extracted_json) except json.JSONDecodeError as e: logger.error(f"Failed to parse generated JSON: {str(e)}") logger.error(f"JSON content length: {len(generated_json)}") logger.error(f"JSON content preview (last 200 chars): ...{generated_json[-200:]}") logger.error(f"JSON content around error position: {generated_json[max(0, e.pos-50):e.pos+50]}") # Write the problematic JSON to debug file self.services.utils.writeDebugFile(generated_json, "failed_json_parsing") self.services.workflow.progressLogFinish(aiOperationId, False) return {"success": False, "error": f"Generated content is not valid JSON: {str(e)}"} self.services.workflow.progressLogUpdate(aiOperationId, 0.8, f"Rendering to {outputFormat} format") # Render to final format using the existing renderer try: from modules.services.serviceGeneration.mainServiceGeneration import GenerationService generationService = GenerationService(self.services) rendered_content, mime_type = await generationService.renderReport( generated_data, outputFormat, title or "Generated Document", prompt, self ) # Build result in the expected format result = { "success": True, "content": generated_data, "documents": [{ "documentName": f"generated.{outputFormat}", "documentData": rendered_content, "mimeType": mime_type, "title": title or "Generated Document" }], "is_multi_file": False, "format": outputFormat, "title": title, "split_strategy": "single", "total_documents": 1, "processed_documents": 1 } # Log AI response for debugging self.services.utils.writeDebugFile(str(result), "document_generation_response", documents) self.services.workflow.progressLogFinish(aiOperationId, True) return result except Exception as e: logger.error(f"Error rendering document: {str(e)}") self.services.workflow.progressLogFinish(aiOperationId, False) return {"success": False, "error": f"Rendering failed: {str(e)}"} # Handle text calls (no output format specified) self.services.workflow.progressLogUpdate(aiOperationId, 0.5, "Processing text call") if documents: # Use document processing for text calls with documents result = await self.services.ai.documentProcessor.callAiText(prompt, documents, options, aiOperationId) else: # Use shared core function for direct text calls result = await self._callAiWithLooping(prompt, options, "text", None, None, aiOperationId) self.services.workflow.progressLogFinish(aiOperationId, True) return result except Exception as e: logger.error(f"Error in callAiDocuments: {str(e)}") self.services.workflow.progressLogFinish(aiOperationId, False) raise # AI Image Analysis async def readImage( self, prompt: str, imageData: Union[str, bytes], mimeType: str = None, options: Optional[AiCallOptions] = None, ) -> str: """Call AI for image analysis using interface.call() with contentParts.""" try: # Check if imageData is valid if not imageData: error_msg = "No image data provided" logger.error(f"Error in AI image analysis: {error_msg}") return f"Error: {error_msg}" # Always use IMAGE_ANALYSE operation type for image processing if options is None: options = AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE) else: # Override the operation type to ensure image analysis options.operationType = OperationTypeEnum.IMAGE_ANALYSE # Create content parts with image data from modules.datamodels.datamodelExtraction import ContentPart import base64 # ContentPart.data must be a string - convert bytes to base64 if needed if isinstance(imageData, bytes): imageDataStr = base64.b64encode(imageData).decode('utf-8') else: # Already a base64 string imageDataStr = imageData imagePart = ContentPart( id="image_0", parentId=None, label="Image", typeGroup="image", mimeType=mimeType or "image/jpeg", data=imageDataStr, # Must be a string (base64 encoded) metadata={"imageAnalysis": True} ) # Create request with content parts from modules.datamodels.datamodelAi import AiCallRequest request = AiCallRequest( prompt=prompt, context="", options=options, contentParts=[imagePart] ) response = await self.aiObjects.call(request) result = response.content # Check if result is valid if not result or (isinstance(result, str) and not result.strip()): error_msg = f"No response from AI image analysis (result: {repr(result)})" logger.error(f"Error in AI image analysis: {error_msg}") return f"Error: {error_msg}" return result except Exception as e: logger.error(f"Error in AI image analysis: {str(e)}") return f"Error: {str(e)}" # AI Image Generation async def generateImage( self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid", options: Optional[AiCallOptions] = None, ) -> Dict[str, Any]: """Generate an image using AI using interface.generateImage().""" try: response = await self.aiObjects.generateImage(prompt, size, quality, style, options) # Emit stats for image generation self.services.workflow.storeWorkflowStat( self.services.currentWorkflow, response, f"ai.generate.image" ) # Convert response to dict format for backward compatibility if hasattr(response, 'content'): return { "success": True, "content": response.content, "modelName": response.modelName, "priceUsd": response.priceUsd, "processingTime": response.processingTime } else: return response except Exception as e: logger.error(f"Error in AI image generation: {str(e)}") return {"success": False, "error": str(e)}