# Copyright (c) 2025 Patrick Motsch # All rights reserved. """ Structure Filling Module Handles filling document structure with content, including: - Filling sections with content parts - Building section generation prompts - Aggregation logic """ import json import logging import copy import asyncio from typing import Dict, Any, List, Optional, Tuple from modules.datamodels.datamodelExtraction import ContentPart from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum from modules.workflows.processing.shared.stateTools import checkWorkflowStopped logger = logging.getLogger(__name__) class StructureFiller: """Handles filling document structure with content.""" # Default concurrency limit for parallel generation (chapters/sections) DEFAULT_MAX_CONCURRENT_GENERATION = 16 def __init__(self, services, aiService): """Initialize StructureFiller with service center and AI service access.""" self.services = services self.aiService = aiService def _getMaxConcurrentGeneration(self, options: Optional[AiCallOptions] = None) -> int: """Get max concurrent generation limit, configurable via options.""" if options and hasattr(options, 'maxConcurrentGeneration'): return options.maxConcurrentGeneration return self.DEFAULT_MAX_CONCURRENT_GENERATION def _getUserLanguage(self) -> str: """Get user language for document generation""" try: if self.services: # Prefer detected language if available (from user intention analysis) if hasattr(self.services, 'currentUserLanguage') and self.services.currentUserLanguage: return self.services.currentUserLanguage # Fallback to user's preferred language elif hasattr(self.services, 'user') and self.services.user and hasattr(self.services.user, 'language'): return self.services.user.language except Exception: pass return 'en' # Default fallback def _getDocumentLanguage(self, structure: Dict[str, Any], documentId: str) -> str: """ Get language for a specific document from structure. Falls back to user language if not specified. Args: structure: The document structure with documents array documentId: The ID of the document to get language for Returns: ISO 639-1 language code (e.g., "de", "en", "fr") """ # Try to find document in structure for doc in structure.get("documents", []): if doc.get("id") == documentId: docLanguage = doc.get("language") if docLanguage: return docLanguage # Fallback to metadata language metadataLanguage = structure.get("metadata", {}).get("language") if metadataLanguage: return metadataLanguage # Fallback to user language return self._getUserLanguage() def _extractContentPartInfo(self, chapter: Dict[str, Any]) -> Tuple[List[str], Dict[str, Any]]: """ Extract contentPartIds and contentPartInstructions from chapter's contentParts structure. Returns: tuple: (contentPartIds list, contentPartInstructions dict) """ contentParts = chapter.get("contentParts", {}) contentPartIds = list(contentParts.keys()) # Extract instructions (entries with "instruction" field) and captions (entries with "caption" field) contentPartInstructions = {} for partId, partInfo in contentParts.items(): if isinstance(partInfo, dict): if "instruction" in partInfo: contentPartInstructions[partId] = {"instruction": partInfo["instruction"]} elif "caption" in partInfo: # For entries with only caption (no instruction), still add to dict so it's available contentPartInstructions[partId] = {"caption": partInfo["caption"]} return contentPartIds, contentPartInstructions def _getContentPartCaption(self, chapter: Dict[str, Any], partId: str) -> Optional[str]: """ Get caption for a contentPart from chapter's contentParts structure. Returns None if no caption is available. Args: chapter: Chapter dict partId: ContentPart ID Returns: Caption string or None """ if "contentParts" in chapter: contentParts = chapter.get("contentParts", {}) partInfo = contentParts.get(partId) if isinstance(partInfo, dict) and "caption" in partInfo: return partInfo["caption"] return None async def fillStructure( self, structure: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, parentOperationId: str, language: Optional[str] = None ) -> Dict[str, Any]: """ Phase 5D: Chapter-Content-Generierung (Zwei-Phasen-Ansatz). Phase 5D.1: Generiert Sections-Struktur für jedes Chapter Phase 5D.2: Füllt Sections mit ContentParts Args: structure: Struktur-Dict mit documents und chapters (nicht sections!) contentParts: Alle vorbereiteten ContentParts userPrompt: User-Anfrage parentOperationId: Parent Operation-ID für ChatLog-Hierarchie language: Language identified from user intention analysis (e.g., "de", "en", "fr") Returns: Gefüllte Struktur mit elements in jeder Section (nach Flattening) """ # Erstelle Operation-ID für Struktur-Abfüllen fillOperationId = f"{parentOperationId}_structure_filling" # Validate structure has chapters hasChapters = False for doc in structure.get("documents", []): if "chapters" in doc: hasChapters = True break if not hasChapters: error_msg = "Structure must have chapters. Legacy section-based structure is not supported." logger.error(error_msg) raise ValueError(error_msg) # Get language from services (user intention analysis) or parameter if language is None: language = self._getUserLanguage() logger.debug(f"Using language from services (user intention analysis): {language}") else: logger.debug(f"Using provided language parameter: {language}") # Starte ChatLog mit Parent-Referenz chapterCount = sum(len(doc.get("chapters", [])) for doc in structure.get("documents", [])) self.services.chat.progressLogStart( fillOperationId, "Chapter Content Generation", "Filling", f"Processing {chapterCount} chapters", parentOperationId=parentOperationId ) try: filledStructure = copy.deepcopy(structure) # Get options from AI service if available (for concurrency control) # Default concurrency limit (16) will be used if options is None options = None # Note: Options can be passed via fillStructure if needed in the future # Phase 5D.1: Sections-Struktur für jedes Chapter generieren filledStructure = await self._generateChapterSectionsStructure( filledStructure, contentParts, userPrompt, fillOperationId, language, options ) # Phase 5D.2: Sections mit ContentParts füllen filledStructure = await self._fillChapterSections( filledStructure, contentParts, userPrompt, fillOperationId, language, options ) # Flattening: Chapters zu Sections konvertieren flattenedStructure = self._flattenChaptersToSections(filledStructure) # Füge ContentParts-Metadaten zur Struktur hinzu (für Validierung) flattenedStructure = self._addContentPartsMetadata(flattenedStructure, contentParts) # State 4 Validation: Validate and auto-fix filled structure # Validation 4.1: Filled structure missing 'documents' field if "documents" not in flattenedStructure: raise ValueError("Filled structure missing 'documents' field - cannot auto-fix") for doc in flattenedStructure["documents"]: # Validation 4.4: Verify language is preserved from input structure # Language MUST be preserved from Phase 3 structure (validated in State 3) if "language" not in doc: raise ValueError(f"Document {doc.get('id')} missing language in filled structure - should have been preserved from Phase 3") # Validate language format if not isinstance(doc["language"], str) or len(doc["language"]) != 2: raise ValueError(f"Document {doc.get('id')} has invalid language format in filled structure: {doc['language']} - should be 2-character ISO 639-1 code") # CRITICAL: flattenedStructure has sections, not chapters! # After flattening, chapters are converted to sections, so we need to validate sections directly for section in doc.get("sections", []): # Validation 4.2: Section missing 'elements' field if "elements" not in section: section["elements"] = [] logger.info(f"Section {section.get('id')} missing 'elements' - created empty list") # Validation 4.3: Section has empty elements list - ALLOW (intentionally empty is OK) # No action needed - empty elements are allowed # ChatLog abschließen self.services.chat.progressLogFinish(fillOperationId, True) return flattenedStructure except Exception as e: self.services.chat.progressLogFinish(fillOperationId, False) logger.error(f"Error in fillStructure: {str(e)}") raise async def _generateSingleChapterSectionsStructure( self, chapter: Dict[str, Any], chapterIndex: int, chapterId: str, chapterLevel: int, chapterTitle: str, generationHint: str, contentPartIds: List[str], contentPartInstructions: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, language: str, outputFormat: str, parentOperationId: str, totalChapters: int ) -> None: """ Generate sections structure for a single chapter (used for parallel processing). Modifies chapter dict in place. """ try: # Update progress for chapter structure generation progress = chapterIndex / totalChapters if totalChapters > 0 else 1.0 self.services.chat.progressLogUpdate( parentOperationId, progress, f"Generating sections for Chapter {chapterIndex}/{totalChapters}: {chapterTitle}" ) chapterPrompt = self._buildChapterSectionsStructurePrompt( chapterId=chapterId, chapterLevel=chapterLevel, chapterTitle=chapterTitle, generationHint=generationHint, contentPartIds=contentPartIds, contentPartInstructions=contentPartInstructions, contentParts=contentParts, userPrompt=userPrompt, language=language, outputFormat=outputFormat ) # AI-Call für Chapter-Struktur-Generierung # Note: Debug logging is handled by callAiPlanning checkWorkflowStopped(self.services) aiResponse = await self.aiService.callAiPlanning( prompt=chapterPrompt, debugType=f"chapter_structure_{chapterId}" ) sectionsStructure = json.loads( self.services.utils.jsonExtractString(aiResponse) ) chapter["sections"] = sectionsStructure.get("sections", []) # Setze useAiCall Flag (falls nicht von AI gesetzt) # WICHTIG: useAiCall kann nur true sein, wenn mindestens ein ContentPart Format "extracted" hat! # "object" und "reference" Formate werden direkt als Elemente hinzugefügt, benötigen kein AI. for section in chapter["sections"]: if "useAiCall" not in section: contentType = section.get("content_type", "paragraph") sectionContentPartIds = section.get("contentPartIds", []) # Prüfe ob mindestens ein ContentPart Format "extracted" hat hasExtractedPart = False for partId in sectionContentPartIds: part = self._findContentPartById(partId, contentParts) if part: contentFormat = part.metadata.get("contentFormat", "unknown") if contentFormat == "extracted": hasExtractedPart = True break # useAiCall kann nur true sein, wenn extracted Parts vorhanden sind useAiCall = False if hasExtractedPart: # Prüfe ob Transformation nötig ist useAiCall = contentType != "paragraph" # Prüfe contentPartInstructions für Transformation if not useAiCall: for partId in sectionContentPartIds: instruction = contentPartInstructions.get(partId, {}).get("instruction", "") if instruction and instruction.lower() not in ["include full text", "include all content", "use full extracted text"]: useAiCall = True break section["useAiCall"] = useAiCall logger.debug(f"Section {section.get('id')}: useAiCall={useAiCall} (hasExtractedPart={hasExtractedPart}, contentType={contentType})") # Update progress after chapter completion progress = chapterIndex / totalChapters if totalChapters > 0 else 1.0 self.services.chat.progressLogUpdate( parentOperationId, progress, f"Chapter {chapterIndex}/{totalChapters} completed: {chapterTitle}" ) except Exception as e: logger.error(f"Error generating sections structure for chapter {chapterId}: {str(e)}") # Set empty sections on error chapter["sections"] = [] # Update progress even on error progress = chapterIndex / totalChapters if totalChapters > 0 else 1.0 self.services.chat.progressLogUpdate( parentOperationId, progress, f"Chapter {chapterIndex}/{totalChapters} error: {chapterTitle}" ) raise async def _generateChapterSectionsStructure( self, chapterStructure: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, parentOperationId: str, language: str, options: Optional[AiCallOptions] = None ) -> Dict[str, Any]: """ Phase 5D.1: Generiert Sections-Struktur für jedes Chapter (ohne Content) in parallel. Sections enthalten: content_type, contentPartIds, generationHint, useAiCall """ # Count total chapters for progress tracking totalChapters = sum(len(doc.get("chapters", [])) for doc in chapterStructure.get("documents", [])) # Get concurrency limit maxConcurrent = self._getMaxConcurrentGeneration(options) semaphore = asyncio.Semaphore(maxConcurrent) # Collect all chapters with their indices for parallel processing chapterTasks = [] chapterIndex = 0 for doc in chapterStructure.get("documents", []): docId = doc.get("id", "unknown") # Get language for this specific document docLanguage = self._getDocumentLanguage(chapterStructure, docId) # Get output format for this specific document docFormat = doc.get("outputFormat", "txt") for chapter in doc.get("chapters", []): chapterIndex += 1 chapterId = chapter.get("id", "unknown") chapterLevel = chapter.get("level", 1) chapterTitle = chapter.get("title", "Untitled Chapter") generationHint = chapter.get("generationHint", "") contentPartIds, contentPartInstructions = self._extractContentPartInfo(chapter) # Create task for parallel processing with semaphore async def processChapterWithSemaphore(chapter, chapterIndex, chapterId, chapterLevel, chapterTitle, generationHint, contentPartIds, contentPartInstructions, docLanguage, docFormat): checkWorkflowStopped(self.services) async with semaphore: return await self._generateSingleChapterSectionsStructure( chapter=chapter, chapterIndex=chapterIndex, chapterId=chapterId, chapterLevel=chapterLevel, chapterTitle=chapterTitle, generationHint=generationHint, contentPartIds=contentPartIds, contentPartInstructions=contentPartInstructions, contentParts=contentParts, userPrompt=userPrompt, language=docLanguage, # Use document-specific language outputFormat=docFormat, # Use document-specific format parentOperationId=parentOperationId, totalChapters=totalChapters ) task = processChapterWithSemaphore( chapter, chapterIndex, chapterId, chapterLevel, chapterTitle, generationHint, contentPartIds, contentPartInstructions, docLanguage, docFormat ) chapterTasks.append((chapterIndex, chapter, task)) # Execute all chapter tasks in parallel with concurrency control if chapterTasks: # Create list of tasks (without indices for gather) tasks = [task for _, _, task in chapterTasks] # Execute in parallel with error handling results = await asyncio.gather(*tasks, return_exceptions=True) # Process results in order and handle errors for (originalIndex, originalChapter, _), result in zip(chapterTasks, results): if isinstance(result, Exception): logger.error(f"Error processing chapter {originalChapter.get('id')}: {str(result)}") # Chapter already has empty sections set by _generateSingleChapterSectionsStructure # Continue processing other chapters return chapterStructure async def _processAiResponseForSection( self, aiResponse: Any, contentType: str, operationType: OperationTypeEnum, sectionId: str, generationHint: str, generatedElements: List[Dict[str, Any]], section: Dict[str, Any] ) -> List[Dict[str, Any]]: """ Helper method to process AI response and extract elements. Handles both IMAGE_GENERATE and DATA_ANALYSE operation types. """ elements = [] # Handle IMAGE_GENERATE differently - returns image data directly if contentType == "image" and operationType == OperationTypeEnum.IMAGE_GENERATE: import base64 base64Data = "" # Convert image data to base64 string if needed if isinstance(aiResponse.content, bytes): base64Data = base64.b64encode(aiResponse.content).decode('utf-8') elif isinstance(aiResponse.content, str): # Check if it's already a JSON structure try: jsonContent = json.loads(self.services.utils.jsonExtractString(aiResponse.content)) if isinstance(jsonContent, dict) and jsonContent.get("type") == "image": elements.append(jsonContent) logger.debug("AI returned proper JSON image structure") base64Data = None # Signal that image was already processed elif isinstance(jsonContent, list) and len(jsonContent) > 0: if isinstance(jsonContent[0], dict) and jsonContent[0].get("type") == "image": elements.extend(jsonContent) logger.debug("AI returned proper JSON image structure in list") base64Data = None # Signal that image was already processed else: base64Data = "" # Continue with normal processing else: base64Data = "" # Continue with normal processing except (json.JSONDecodeError, ValueError, AttributeError): base64Data = "" # Will be processed below # Process base64 if not already handled above if base64Data is None: # Already processed as JSON, skip base64 processing pass elif aiResponse.content.startswith("data:image/"): # Extract base64 from data URI base64Data = aiResponse.content.split(",", 1)[1] else: content_stripped = aiResponse.content.strip() if len(content_stripped) > 100 and all(c in "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=\n\r\t " for c in content_stripped[:200]): base64Data = content_stripped.replace("\n", "").replace("\r", "").replace("\t", "").replace(" ", "") else: base64Data = aiResponse.content else: base64Data = "" # Always create proper JSON structure for images (if not already processed) if base64Data is None: # Image already processed as JSON, skip pass elif base64Data: # Get caption from section if available caption = section.get("caption") or section.get("metadata", {}).get("caption") or "" elements.append({ "type": "image", "content": { "base64Data": base64Data, "altText": generationHint or "Generated image", "caption": caption # Use caption from section if available }, "caption": caption # Also at element level for compatibility }) logger.debug(f"Created proper JSON image structure with base64Data length: {len(base64Data)}") else: logger.warning(f"IMAGE_GENERATE returned empty or invalid content for section {sectionId}") elements.append({ "type": "error", "message": f"Image generation returned empty or invalid content", "sectionId": sectionId }) else: # For non-image content: Use already parsed elements from _callAiWithLooping if generatedElements: elements.extend(generatedElements) else: # Fallback: Try to parse JSON response directly with repair logic try: from modules.shared.jsonUtils import tryParseJson, repairBrokenJson # Use tryParseJson which handles extraction and basic parsing fallbackElements, parseError, cleanedStr = tryParseJson(aiResponse.content) # If parsing failed, try repair if parseError and isinstance(aiResponse.content, str): logger.warning(f"Initial JSON parse failed for section {sectionId}, attempting repair: {str(parseError)}") repairedJson = repairBrokenJson(aiResponse.content) if repairedJson: fallbackElements = repairedJson parseError = None logger.info(f"Successfully repaired JSON for section {sectionId}") if parseError: raise parseError if isinstance(fallbackElements, list): elements.extend(fallbackElements) elif isinstance(fallbackElements, dict) and "elements" in fallbackElements: elements.extend(fallbackElements["elements"]) elif isinstance(fallbackElements, dict) and fallbackElements.get("type"): elements.append(fallbackElements) except (json.JSONDecodeError, ValueError) as json_error: logger.error(f"Error parsing JSON response for section {sectionId}: {str(json_error)}") elements.append({ "type": "error", "message": f"Failed to parse JSON response: {str(json_error)}", "sectionId": sectionId }) return elements async def _processSingleSection( self, section: Dict[str, Any], sectionIndex: int, totalSections: int, chapterIndex: int, totalChapters: int, chapterId: str, chapterOperationId: str, fillOperationId: str, contentParts: List[ContentPart], userPrompt: str, all_sections_list: List[Dict[str, Any]], language: str, outputFormat: str = "txt", calculateOverallProgress: callable = None ) -> List[Dict[str, Any]]: """ Process a single section and return its elements. Used for parallel processing of sections within a chapter. """ sectionId = section.get("id") sectionTitle = section.get("title", sectionId) contentPartIds = section.get("contentPartIds", []) contentFormats = section.get("contentFormats", {}) generationHint = section.get("generationHint") or section.get("generation_hint") contentType = section.get("content_type", "paragraph") useAiCall = section.get("useAiCall", False) # Update overall progress at start of section overallProgress = calculateOverallProgress(chapterIndex - 1, totalChapters, sectionIndex, totalSections) self.services.chat.progressLogUpdate( fillOperationId, overallProgress, f"Chapter {chapterIndex}/{totalChapters}, Section {sectionIndex + 1}/{totalSections}: {sectionTitle}" ) # WICHTIG: Wenn keine ContentParts vorhanden sind UND kein generationHint, kann kein AI-Call gemacht werden if len(contentPartIds) == 0 and not generationHint: useAiCall = False logger.debug(f"Section {sectionId}: No content parts and no generation hint, setting useAiCall=False") elif len(contentPartIds) == 0 and generationHint and not useAiCall: useAiCall = True logger.info(f"Section {sectionId}: Overriding useAiCall=True (has generationHint but no content parts)") elements = [] # Prüfe ob Aggregation nötig ist needsAggregation = self._needsAggregation( contentType=contentType, contentPartCount=len(contentPartIds) ) logger.info(f"Processing section {sectionId}: contentType={contentType}, contentPartCount={len(contentPartIds)}, useAiCall={useAiCall}, needsAggregation={needsAggregation}, hasGenerationHint={bool(generationHint)}") try: if needsAggregation and useAiCall: # Aggregation: Alle Parts zusammen verarbeiten sectionParts = [ self._findContentPartById(pid, contentParts) for pid in contentPartIds ] sectionParts = [p for p in sectionParts if p is not None] if sectionParts: # Filtere nur extracted Parts für Aggregation (reference/object werden separat behandelt) extractedParts = [ p for p in sectionParts if contentFormats.get(p.id, p.metadata.get("contentFormat")) == "extracted" ] nonExtractedParts = [ p for p in sectionParts if contentFormats.get(p.id, p.metadata.get("contentFormat")) != "extracted" ] # Verarbeite non-extracted Parts separat (reference, object) for part in nonExtractedParts: contentFormat = contentFormats.get(part.id, part.metadata.get("contentFormat")) if contentFormat == "reference": elements.append({ "type": "reference", "documentReference": part.metadata.get("documentReference"), "label": part.metadata.get("usageHint", part.label) }) elif contentFormat == "object": if part.typeGroup == "image": # Validate that image data exists if not part.data: logger.warning(f"Section {sectionId}: Image ContentPart {part.id} has no data (object format). Skipping image element.") elements.append({ "type": "error", "message": f"Image ContentPart {part.id} has no data", "sectionId": sectionId }) else: # Get caption from section (priority: section.caption > part.metadata.caption) caption = section.get("caption") or section.get("metadata", {}).get("caption") or part.metadata.get("caption", "") elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": caption # Use caption from section }, "caption": caption # Also at element level for compatibility }) else: elements.append({ "type": part.typeGroup, "content": { "data": part.data, "mimeType": part.mimeType, "label": part.metadata.get("usageHint", part.label) } }) # Extract images with Vision AI if needed (before aggregation) processedExtractedParts = [] for part in extractedParts: # Check if this is an image that needs Vision AI extraction if (part.typeGroup == "image" and part.metadata.get("needsVisionExtraction") == True and part.metadata.get("intent") == "extract"): logger.info(f"Section {sectionId}: Extracting text from image {part.id} using Vision AI") try: extractionPrompt = part.metadata.get("extractionPrompt") or "Extract all text content from this image. Return only the extracted text, no additional formatting." # Write debug file for image extraction prompt if self.services and hasattr(self.services, 'utils') and hasattr(self.services.utils, 'writeDebugFile'): try: partId = part.id[:8] if part.id else "unknown" partLabelSafe = (part.label or "image").replace(" ", "_").replace("/", "_").replace("\\", "_")[:30] debugPrefix = f"extraction_image_{partId}_{partLabelSafe}" self.services.utils.writeDebugFile(extractionPrompt, f"{debugPrefix}_prompt") logger.debug(f"Wrote image extraction prompt debug file: {debugPrefix}_prompt") except Exception as debugError: logger.warning(f"Failed to write image extraction debug file: {str(debugError)}") # Call Vision AI to extract text from image visionRequest = AiCallRequest( prompt=extractionPrompt, context="", options=AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE), contentParts=[part] ) checkWorkflowStopped(self.services) visionResponse = await self.aiService.callAi(visionRequest) # Write debug file for image extraction response if self.services and hasattr(self.services, 'utils') and hasattr(self.services.utils, 'writeDebugFile'): try: partId = part.id[:8] if part.id else "unknown" partLabelSafe = (part.label or "image").replace(" ", "_").replace("/", "_").replace("\\", "_")[:30] debugPrefix = f"extraction_image_{partId}_{partLabelSafe}" responseContent = visionResponse.content if visionResponse and visionResponse.content else "" self.services.utils.writeDebugFile(responseContent, f"{debugPrefix}_response") logger.debug(f"Wrote image extraction response debug file: {debugPrefix}_response") except Exception as debugError: logger.warning(f"Failed to write image extraction response debug file: {str(debugError)}") if visionResponse and visionResponse.content: # Create text part with extracted content textPart = ContentPart( id=f"vision_extracted_{part.id}", label=f"Extracted text from {part.label or 'Image'}", typeGroup="text", mimeType="text/plain", data=visionResponse.content.strip(), metadata={ **part.metadata, "contentFormat": "extracted", "extractionMethod": "vision", "sourceImagePartId": part.id, "needsVisionExtraction": False # Already extracted } ) processedExtractedParts.append(textPart) logger.info(f"✅ Extracted text from image {part.id}: {len(visionResponse.content)} chars") else: logger.warning(f"⚠️ Vision AI extraction returned no content for image {part.id}") # Keep original image part, but mark extraction as attempted part.metadata["needsVisionExtraction"] = False part.metadata["visionExtractionFailed"] = True processedExtractedParts.append(part) except Exception as e: logger.error(f"❌ Vision AI extraction failed for image {part.id}: {str(e)}") # Keep original image part, but mark extraction as attempted part.metadata["needsVisionExtraction"] = False part.metadata["visionExtractionFailed"] = True processedExtractedParts.append(part) else: # Not an image needing extraction, or already processed processedExtractedParts.append(part) # Aggregiere extracted Parts mit AI (now with Vision-extracted text parts) if processedExtractedParts: logger.debug(f"Section {sectionId}: Aggregating {len(processedExtractedParts)} extracted parts with AI") isAggregation = True generationPrompt, templateStructure = self._buildSectionGenerationPrompt( section=section, contentParts=processedExtractedParts, userPrompt=userPrompt, generationHint=generationHint, allSections=all_sections_list, sectionIndex=sectionIndex, isAggregation=isAggregation, language=language, outputFormat=outputFormat ) sectionOperationId = f"{fillOperationId}_section_{sectionId}" self.services.chat.progressLogStart( sectionOperationId, "Section Generation (Aggregation)", f"Section {sectionIndex + 1}/{totalSections}", f"{sectionTitle} ({len(extractedParts)} parts)", parentOperationId=chapterOperationId ) try: self.services.chat.progressLogUpdate(sectionOperationId, 0.2, "Building generation prompt") self.services.chat.progressLogUpdate(sectionOperationId, 0.4, "Calling AI for content generation") operationType = OperationTypeEnum.IMAGE_GENERATE if contentType == "image" else OperationTypeEnum.DATA_ANALYSE if operationType == OperationTypeEnum.IMAGE_GENERATE: maxPromptLength = 4000 if len(generationPrompt) > maxPromptLength: logger.warning(f"Truncating DALL-E prompt from {len(generationPrompt)} to {maxPromptLength} characters") generationPrompt = generationPrompt[:maxPromptLength].rsplit('\n', 1)[0] # Write debug file for IMAGE_GENERATE (direct callAi, no _callAiWithLooping) self.services.utils.writeDebugFile( generationPrompt, f"{chapterId}_section_{sectionId}_prompt" ) request = AiCallRequest( prompt=generationPrompt, contentParts=[], options=AiCallOptions( operationType=operationType, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.DETAILED ) ) checkWorkflowStopped(self.services) aiResponse = await self.aiService.callAi(request) generatedElements = [] # Write debug file for IMAGE_GENERATE response (direct callAi, no _callAiWithLooping) self.services.utils.writeDebugFile( aiResponse.content if hasattr(aiResponse, 'content') else str(aiResponse), f"{chapterId}_section_{sectionId}_response" ) else: # Use consolidated class method buildSectionPromptWithContinuation = self.buildSectionPromptWithContinuation options = AiCallOptions( operationType=operationType, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.DETAILED ) checkWorkflowStopped(self.services) aiResponseJson = await self.aiService.callAiWithLooping( prompt=generationPrompt, options=options, debugPrefix=f"{chapterId}_section_{sectionId}", promptBuilder=buildSectionPromptWithContinuation, promptArgs={ "section": section, "contentParts": extractedParts, "userPrompt": userPrompt, "generationHint": generationHint, "allSections": all_sections_list, "sectionIndex": sectionIndex, "isAggregation": isAggregation, "templateStructure": templateStructure, "basePrompt": generationPrompt }, operationId=sectionOperationId, userPrompt=userPrompt, contentParts=extractedParts, useCaseId="section_content" # REQUIRED: Explicit use case ID ) try: # Use tryParseJson which handles extraction and basic parsing from modules.shared.jsonUtils import tryParseJson, repairBrokenJson # Check if response contains multiple JSON blocks (separated by --- or multiple ```json blocks) # This can happen when AI returns multiple complete responses if isinstance(aiResponseJson, str) and ("---" in aiResponseJson or aiResponseJson.count("```json") > 1): logger.info(f"Section {sectionId}: Detected multiple JSON blocks in response, attempting to merge") generatedElements = self._extractAndMergeMultipleJsonBlocks(aiResponseJson, contentType, sectionId) else: parsedResponse, parseError, cleanedStr = tryParseJson(aiResponseJson) # If parsing failed, try repair if parseError and isinstance(aiResponseJson, str): logger.warning(f"Initial JSON parse failed for section {sectionId}, attempting repair: {str(parseError)}") repairedJson = repairBrokenJson(aiResponseJson) if repairedJson: parsedResponse = repairedJson parseError = None logger.info(f"Successfully repaired JSON for section {sectionId}") if parseError: raise parseError if isinstance(parsedResponse, list): generatedElements = parsedResponse elif isinstance(parsedResponse, dict): if "elements" in parsedResponse: generatedElements = parsedResponse["elements"] elif "sections" in parsedResponse and len(parsedResponse["sections"]) > 0: firstSection = parsedResponse["sections"][0] generatedElements = firstSection.get("elements", []) elif parsedResponse.get("type"): generatedElements = [parsedResponse] else: generatedElements = [] else: generatedElements = [] class AiResponse: def __init__(self, content): self.content = content aiResponse = AiResponse(aiResponseJson) except Exception as parseError: logger.error(f"Error parsing response from _callAiWithLooping for section {sectionId}: {str(parseError)}") class AiResponse: def __init__(self, content): self.content = content aiResponse = AiResponse(aiResponseJson) generatedElements = [] self.services.chat.progressLogUpdate(sectionOperationId, 0.6, "Processing AI response") # Note: Debug files are written by _callAiWithLooping using debugPrefix self.services.chat.progressLogUpdate(sectionOperationId, 0.8, "Validating generated content") # Process AI response responseElements = await self._processAiResponseForSection( aiResponse=aiResponse, contentType=contentType, operationType=operationType, sectionId=sectionId, generationHint=generationHint, generatedElements=generatedElements, section=section ) elements.extend(responseElements) self.services.chat.progressLogFinish(sectionOperationId, True) chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed" ) except Exception as e: self.services.chat.progressLogFinish(sectionOperationId, False) elements.append({ "type": "error", "message": f"Error generating section {sectionId}: {str(e)}", "sectionId": sectionId }) logger.error(f"Error generating section {sectionId}: {str(e)}") chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed (with errors)" ) else: # Einzelverarbeitung: Jeder Part einzeln ODER Generation ohne ContentParts if len(contentPartIds) == 0 and useAiCall and generationHint: # Generate content from scratch using only generationHint logger.debug(f"Processing section {sectionId}: No content parts, generating from generationHint only") generationPrompt, templateStructure = self._buildSectionGenerationPrompt( section=section, contentParts=[], userPrompt=userPrompt, generationHint=generationHint, allSections=all_sections_list, sectionIndex=sectionIndex, isAggregation=False, language=language, outputFormat=outputFormat ) sectionOperationId = f"{fillOperationId}_section_{sectionId}" self.services.chat.progressLogStart( sectionOperationId, "Section Generation", f"Section {sectionIndex + 1}/{totalSections}", f"{sectionTitle} (from generationHint)", parentOperationId=chapterOperationId ) try: self.services.chat.progressLogUpdate(sectionOperationId, 0.2, "Building generation prompt") self.services.chat.progressLogUpdate(sectionOperationId, 0.4, "Calling AI for content generation") operationType = OperationTypeEnum.IMAGE_GENERATE if contentType == "image" else OperationTypeEnum.DATA_ANALYSE if operationType == OperationTypeEnum.IMAGE_GENERATE: maxPromptLength = 4000 if len(generationPrompt) > maxPromptLength: logger.warning(f"Truncating DALL-E prompt from {len(generationPrompt)} to {maxPromptLength} characters") generationPrompt = generationPrompt[:maxPromptLength].rsplit('\n', 1)[0] # Write debug file for IMAGE_GENERATE (direct callAi, no _callAiWithLooping) self.services.utils.writeDebugFile( generationPrompt, f"{chapterId}_section_{sectionId}_prompt" ) request = AiCallRequest( prompt=generationPrompt, contentParts=[], options=AiCallOptions( operationType=operationType, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.DETAILED ) ) aiResponse = await self.aiService.callAi(request) generatedElements = [] # Write debug file for IMAGE_GENERATE response (direct callAi, no _callAiWithLooping) self.services.utils.writeDebugFile( aiResponse.content if hasattr(aiResponse, 'content') else str(aiResponse), f"{chapterId}_section_{sectionId}_response" ) else: isAggregation = False # Use consolidated class method buildSectionPromptWithContinuation = self.buildSectionPromptWithContinuation options = AiCallOptions( operationType=operationType, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.DETAILED ) aiResponseJson = await self.aiService.callAiWithLooping( prompt=generationPrompt, options=options, debugPrefix=f"{chapterId}_section_{sectionId}", promptBuilder=self.buildSectionPromptWithContinuation, promptArgs={ "section": section, "contentParts": [], "userPrompt": userPrompt, "generationHint": generationHint, "allSections": all_sections_list, "sectionIndex": sectionIndex, "isAggregation": isAggregation, "templateStructure": templateStructure, "basePrompt": generationPrompt, "language": language }, operationId=sectionOperationId, userPrompt=userPrompt, contentParts=[], useCaseId="section_content" # REQUIRED: Explicit use case ID ) try: parsedResponse = json.loads(self.services.utils.jsonExtractString(aiResponseJson)) if isinstance(parsedResponse, list): generatedElements = parsedResponse elif isinstance(parsedResponse, dict): if "elements" in parsedResponse: generatedElements = parsedResponse["elements"] elif "sections" in parsedResponse and len(parsedResponse["sections"]) > 0: firstSection = parsedResponse["sections"][0] generatedElements = firstSection.get("elements", []) elif parsedResponse.get("type"): generatedElements = [parsedResponse] else: generatedElements = [] else: generatedElements = [] class AiResponse: def __init__(self, content): self.content = content aiResponse = AiResponse(aiResponseJson) except Exception as parseError: logger.error(f"Error parsing response from _callAiWithLooping for section {sectionId}: {str(parseError)}") class AiResponse: def __init__(self, content): self.content = content aiResponse = AiResponse(aiResponseJson) generatedElements = [] self.services.chat.progressLogUpdate(sectionOperationId, 0.6, "Processing AI response") # Note: Debug files are written by _callAiWithLooping using debugPrefix self.services.chat.progressLogUpdate(sectionOperationId, 0.8, "Validating generated content") responseElements = await self._processAiResponseForSection( aiResponse=aiResponse, contentType=contentType, operationType=operationType, sectionId=sectionId, generationHint=generationHint, generatedElements=generatedElements, section=section ) elements.extend(responseElements) self.services.chat.progressLogFinish(sectionOperationId, True) chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed" ) except Exception as e: self.services.chat.progressLogFinish(sectionOperationId, False) elements.append({ "type": "error", "message": f"Error generating section {sectionId}: {str(e)}", "sectionId": sectionId }) logger.error(f"Error generating section {sectionId}: {str(e)}") chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed (with errors)" ) # Einzelverarbeitung: Jeder Part einzeln for partId in contentPartIds: part = self._findContentPartById(partId, contentParts) if not part: continue contentFormat = contentFormats.get(partId, part.metadata.get("contentFormat")) if contentFormat == "reference": elements.append({ "type": "reference", "documentReference": part.metadata.get("documentReference"), "label": part.metadata.get("usageHint", part.label) }) elif contentFormat == "object": if part.typeGroup == "image": # Validate that image data exists if not part.data: logger.warning(f"Section {sectionId}: Image ContentPart {part.id} has no data (object format). Skipping image element.") elements.append({ "type": "error", "message": f"Image ContentPart {part.id} has no data", "sectionId": sectionId }) else: # Get caption from section (priority: section.caption > part.metadata.caption) caption = section.get("caption") or section.get("metadata", {}).get("caption") or part.metadata.get("caption", "") elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": caption # Use caption from section }, "caption": caption # Also at element level for compatibility }) else: elements.append({ "type": part.typeGroup, "content": { "data": part.data, "mimeType": part.mimeType, "label": part.metadata.get("usageHint", part.label) } }) elif contentFormat == "extracted": # CRITICAL: If useAiCall is true, extracted parts are used as input for AI generation # and should NOT be added as elements. Only add extracted text as element if useAiCall is false. if useAiCall: # Extracted part will be used as input for AI call - skip adding as element logger.debug(f"Section {sectionId}: Skipping extracted part {part.id} as element (useAiCall=true, will be used as AI input)") # Continue to process this part for AI call, but don't add as element yet # Check if this is an image that needs Vision AI extraction originalPartId = part.id if (part.typeGroup == "image" and part.metadata.get("needsVisionExtraction") == True and part.metadata.get("intent") == "extract"): logger.info(f"Section {sectionId}: Extracting text from single image {part.id} using Vision AI") try: extractionPrompt = part.metadata.get("extractionPrompt") or "Extract all text content from this image. Return only the extracted text, no additional formatting." # Call Vision AI to extract text from image visionRequest = AiCallRequest( prompt=extractionPrompt, context="", options=AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE), contentParts=[part] ) checkWorkflowStopped(self.services) visionResponse = await self.aiService.callAi(visionRequest) if visionResponse and visionResponse.content: # Replace image part with text part for further processing part = ContentPart( id=f"vision_extracted_{originalPartId}", label=f"Extracted text from {part.label or 'Image'}", typeGroup="text", mimeType="text/plain", data=visionResponse.content.strip(), metadata={ **part.metadata, "contentFormat": "extracted", "extractionMethod": "vision", "sourceImagePartId": originalPartId, "needsVisionExtraction": False # Already extracted } ) logger.info(f"✅ Extracted text from image {originalPartId}: {len(visionResponse.content)} chars") else: logger.warning(f"⚠️ Vision AI extraction returned no content for image {originalPartId}") part.metadata["needsVisionExtraction"] = False part.metadata["visionExtractionFailed"] = True except Exception as e: logger.error(f"❌ Vision AI extraction failed for image {originalPartId}: {str(e)}") part.metadata["needsVisionExtraction"] = False part.metadata["visionExtractionFailed"] = True if useAiCall and generationHint: # AI-Call mit einzelnen ContentPart (now may be text part after Vision extraction) logger.debug(f"Processing section {sectionId}: Single extracted part with AI call") generationPrompt, templateStructure = self._buildSectionGenerationPrompt( section=section, contentParts=[part], userPrompt=userPrompt, generationHint=generationHint, allSections=all_sections_list, sectionIndex=sectionIndex, isAggregation=False, language=language, outputFormat=outputFormat ) sectionOperationId = f"{fillOperationId}_section_{sectionId}" self.services.chat.progressLogStart( sectionOperationId, "Section Generation", f"Section {sectionIndex + 1}/{totalSections}", f"{sectionTitle} (single part)", parentOperationId=chapterOperationId ) try: self.services.chat.progressLogUpdate(sectionOperationId, 0.2, "Building generation prompt") self.services.chat.progressLogUpdate(sectionOperationId, 0.4, "Calling AI for content generation") operationType = OperationTypeEnum.IMAGE_GENERATE if contentType == "image" else OperationTypeEnum.DATA_ANALYSE if operationType == OperationTypeEnum.IMAGE_GENERATE: maxPromptLength = 4000 if len(generationPrompt) > maxPromptLength: logger.warning(f"Truncating DALL-E prompt from {len(generationPrompt)} to {maxPromptLength} characters") generationPrompt = generationPrompt[:maxPromptLength].rsplit('\n', 1)[0] # Write debug file for IMAGE_GENERATE (direct callAi, no _callAiWithLooping) self.services.utils.writeDebugFile( generationPrompt, f"{chapterId}_section_{sectionId}_prompt" ) request = AiCallRequest( prompt=generationPrompt, contentParts=[], options=AiCallOptions( operationType=operationType, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.DETAILED ) ) aiResponse = await self.aiService.callAi(request) generatedElements = [] # Write debug file for IMAGE_GENERATE response (direct callAi, no _callAiWithLooping) self.services.utils.writeDebugFile( aiResponse.content if hasattr(aiResponse, 'content') else str(aiResponse), f"{chapterId}_section_{sectionId}_response" ) else: isAggregation = False # Use consolidated class method buildSectionPromptWithContinuation = self.buildSectionPromptWithContinuation options = AiCallOptions( operationType=operationType, priority=PriorityEnum.BALANCED, processingMode=ProcessingModeEnum.DETAILED ) aiResponseJson = await self.aiService.callAiWithLooping( prompt=generationPrompt, options=options, debugPrefix=f"{chapterId}_section_{sectionId}", promptBuilder=self.buildSectionPromptWithContinuation, promptArgs={ "section": section, "contentParts": [part], "userPrompt": userPrompt, "generationHint": generationHint, "allSections": all_sections_list, "sectionIndex": sectionIndex, "isAggregation": isAggregation, "services": self.services, "templateStructure": templateStructure, "basePrompt": generationPrompt, "language": language }, operationId=sectionOperationId, userPrompt=userPrompt, contentParts=[part], useCaseId="section_content" # REQUIRED: Explicit use case ID ) try: parsedResponse = json.loads(self.services.utils.jsonExtractString(aiResponseJson)) if isinstance(parsedResponse, list): generatedElements = parsedResponse elif isinstance(parsedResponse, dict): if "elements" in parsedResponse: generatedElements = parsedResponse["elements"] elif "sections" in parsedResponse and len(parsedResponse["sections"]) > 0: firstSection = parsedResponse["sections"][0] generatedElements = firstSection.get("elements", []) elif parsedResponse.get("type"): generatedElements = [parsedResponse] else: generatedElements = [] else: generatedElements = [] class AiResponse: def __init__(self, content): self.content = content aiResponse = AiResponse(aiResponseJson) except Exception as parseError: logger.error(f"Error parsing response from _callAiWithLooping for section {sectionId}: {str(parseError)}") class AiResponse: def __init__(self, content): self.content = content aiResponse = AiResponse(aiResponseJson) generatedElements = [] self.services.chat.progressLogUpdate(sectionOperationId, 0.6, "Processing AI response") # Note: Debug files are written by _callAiWithLooping using debugPrefix self.services.chat.progressLogUpdate(sectionOperationId, 0.8, "Validating generated content") responseElements = await self._processAiResponseForSection( aiResponse=aiResponse, contentType=contentType, operationType=operationType, sectionId=sectionId, generationHint=generationHint, generatedElements=generatedElements, section=section ) elements.extend(responseElements) self.services.chat.progressLogFinish(sectionOperationId, True) chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed" ) except Exception as e: self.services.chat.progressLogFinish(sectionOperationId, False) elements.append({ "type": "error", "message": f"Error generating section {sectionId}: {str(e)}", "sectionId": sectionId }) logger.error(f"Error generating section {sectionId}: {str(e)}") chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed (with errors)" ) else: # Füge extrahierten Content direkt hinzu (kein AI-Call) # CRITICAL: If content_type is "image", we must render an image, not extracted text if contentType == "image": # Section wants to display an image - find the image part if part.typeGroup == "image": # Direct image part - use it logger.debug(f"Processing section {sectionId}: Single extracted IMAGE part WITHOUT AI call") # Validate that image data exists if not part.data: logger.warning(f"Section {sectionId}: Image ContentPart {part.id} has no data (extracted format without AI call). Skipping image element.") elements.append({ "type": "error", "message": f"Image ContentPart {part.id} has no data", "sectionId": sectionId }) else: # Get caption from section (priority: section.caption > part.metadata.caption) caption = section.get("caption") or section.get("metadata", {}).get("caption") or part.metadata.get("caption", "") elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": caption # Use caption from section }, "caption": caption # Also at element level for compatibility }) elif part.typeGroup == "text" and part.metadata.get("sourceImagePartId"): # This is a vision-extracted text part - find the original image object part sourceImagePartId = part.metadata.get("sourceImagePartId") logger.debug(f"Processing section {sectionId}: Found vision-extracted text part, looking for original image object part: {sourceImagePartId}") # Try to find the object part (format: "obj_...") objectPartId = part.metadata.get("relatedObjectPartId") objectPart = None if objectPartId: objectPart = self._findContentPartById(objectPartId, contentParts) # If not found via metadata, search through all contentParts for object part if not objectPart: # Search for object part that references the source image part ID for candidatePart in contentParts: if (candidatePart.metadata.get("contentFormat") == "object" and candidatePart.typeGroup == "image" and sourceImagePartId in candidatePart.id): objectPart = candidatePart objectPartId = candidatePart.id logger.debug(f"Section {sectionId}: Found object part {objectPartId} by searching all contentParts") break if objectPart and objectPart.typeGroup == "image" and objectPart.data: logger.info(f"Section {sectionId}: Found object part {objectPartId} for image rendering") caption = section.get("caption") or section.get("metadata", {}).get("caption") or objectPart.metadata.get("caption", "") elements.append({ "type": "image", "content": { "base64Data": objectPart.data, "altText": objectPart.metadata.get("usageHint", objectPart.label), "caption": caption }, "caption": caption }) else: logger.warning(f"Section {sectionId}: No object part found for vision-extracted text part {part.id} (sourceImagePartId={sourceImagePartId}), cannot render image") elements.append({ "type": "error", "message": f"Cannot render image: no object part found for extracted text part (sourceImagePartId={sourceImagePartId})", "sectionId": sectionId }) else: logger.warning(f"Section {sectionId}: ContentPart {part.id} is not an image (typeGroup={part.typeGroup}), but section content_type is 'image'. Cannot render image.") elements.append({ "type": "error", "message": f"Cannot render image: ContentPart is not an image type", "sectionId": sectionId }) else: # content_type is not "image" - add extracted text as normal if part.typeGroup == "image": logger.debug(f"Processing section {sectionId}: Single extracted IMAGE part WITHOUT AI call") # Validate that image data exists if not part.data: logger.warning(f"Section {sectionId}: Image ContentPart {part.id} has no data (extracted format without AI call). Skipping image element.") elements.append({ "type": "error", "message": f"Image ContentPart {part.id} has no data", "sectionId": sectionId }) else: # Get caption from section (priority: section.caption > part.metadata.caption) caption = section.get("caption") or section.get("metadata", {}).get("caption") or part.metadata.get("caption", "") elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": caption # Use caption from section }, "caption": caption # Also at element level for compatibility }) else: logger.debug(f"Processing section {sectionId}: Single extracted TEXT part WITHOUT AI call") elements.append({ "type": "extracted_text", "content": part.data, "source": part.metadata.get("documentId"), "extractionPrompt": part.metadata.get("extractionPrompt") }) # Update progress after section completion chapterProgress = (sectionIndex + 1) / totalSections if totalSections > 0 else 1.0 self.services.chat.progressLogUpdate( chapterOperationId, chapterProgress, f"Section {sectionIndex + 1}/{totalSections} completed" ) overallProgress = calculateOverallProgress(chapterIndex - 1, totalChapters, sectionIndex + 1, totalSections) self.services.chat.progressLogUpdate( fillOperationId, overallProgress, f"Chapter {chapterIndex}/{totalChapters}, Section {sectionIndex + 1}/{totalSections} completed" ) except Exception as e: logger.error(f"Unexpected error processing section {sectionId}: {str(e)}") elements.append({ "type": "error", "message": f"Unexpected error processing section {sectionId}: {str(e)}", "sectionId": sectionId }) return elements async def _fillChapterSections( self, chapterStructure: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, parentOperationId: str, language: str, options: Optional[AiCallOptions] = None ) -> Dict[str, Any]: """ Phase 5D.2: Füllt Sections mit ContentParts. """ # Sammle alle Sections für Kontext-Informationen (für alle Sections) all_sections_list = [] for doc in chapterStructure.get("documents", []): for chapter in doc.get("chapters", []): for section in chapter.get("sections", []): all_sections_list.append(section) # Berechne Gesamtanzahl Chapters für Progress-Tracking totalChapters = sum(len(doc.get("chapters", [])) for doc in chapterStructure.get("documents", [])) fillOperationId = parentOperationId # Get concurrency limit for sections maxConcurrent = self._getMaxConcurrentGeneration(options) sectionSemaphore = asyncio.Semaphore(maxConcurrent) # Collect ALL sections from ALL chapters for fully parallel processing # Each task carries: (docId, chapterId, chapterTitle, sectionIndex, section, docLanguage) allSectionTasks = [] totalSections = len(all_sections_list) completedSections = [0] # Mutable counter for progress tracking for doc in chapterStructure.get("documents", []): docId = doc.get("id", "unknown") docLanguage = self._getDocumentLanguage(chapterStructure, docId) docFormat = doc.get("outputFormat", "txt") # Get output format for this document for chapter in doc.get("chapters", []): chapterId = chapter.get("id", "unknown") chapterTitle = chapter.get("title", "Untitled Chapter") sections = chapter.get("sections", []) chapterSectionCount = len(sections) for sectionIndex, section in enumerate(sections): allSectionTasks.append({ "docId": docId, "chapterId": chapterId, "chapterTitle": chapterTitle, "sectionIndex": sectionIndex, "chapterSectionCount": chapterSectionCount, "section": section, "docLanguage": docLanguage, "docFormat": docFormat # Include output format }) logger.info(f"Starting FULLY PARALLEL section generation: {totalSections} sections across {totalChapters} chapters") # Create task wrapper for each section with progress tracking async def processSectionWithSemaphore(taskInfo): checkWorkflowStopped(self.services) async with sectionSemaphore: result = await self._processSingleSection( section=taskInfo["section"], sectionIndex=taskInfo["sectionIndex"], totalSections=taskInfo["chapterSectionCount"], chapterIndex=0, # Not used for sequential logic anymore totalChapters=totalChapters, chapterId=taskInfo["chapterId"], chapterOperationId=fillOperationId, # Use fillOperationId as parent (no chapter-level ops in parallel mode) fillOperationId=fillOperationId, contentParts=contentParts, userPrompt=userPrompt, all_sections_list=all_sections_list, language=taskInfo["docLanguage"], outputFormat=taskInfo.get("docFormat", "txt"), # Pass output format calculateOverallProgress=lambda *args: completedSections[0] / totalSections if totalSections > 0 else 1.0 ) # Update progress after each section completes completedSections[0] += 1 overallProgress = completedSections[0] / totalSections if totalSections > 0 else 1.0 sectionId = taskInfo["section"].get("id", "unknown") self.services.chat.progressLogUpdate( fillOperationId, overallProgress, f"Section {completedSections[0]}/{totalSections} completed: {sectionId}" ) return result # Create all tasks tasks = [processSectionWithSemaphore(taskInfo) for taskInfo in allSectionTasks] # Execute ALL sections in parallel with concurrency control if tasks: results = await asyncio.gather(*tasks, return_exceptions=True) # Assign results back to sections for taskInfo, result in zip(allSectionTasks, results): section = taskInfo["section"] if isinstance(result, Exception): logger.error(f"Error processing section {section.get('id')}: {str(result)}") section["elements"] = [{ "type": "error", "message": f"Error processing section: {str(result)}", "sectionId": section.get("id") }] else: section["elements"] = result if result is not None else [] logger.info(f"Completed FULLY PARALLEL section generation: {totalSections} sections") return chapterStructure def _addContentPartsMetadata( self, structure: Dict[str, Any], contentParts: List[ContentPart] ) -> Dict[str, Any]: """ Fügt ContentParts-Metadaten zur Struktur hinzu, wenn contentPartIds vorhanden sind. Dies hilft der Validierung, den Kontext der ContentParts zu verstehen. """ # Erstelle Mapping von ContentPart-ID zu Metadaten contentPartsMap = {} for part in contentParts: contentPartsMap[part.id] = { "id": part.id, "format": part.metadata.get("contentFormat", "unknown"), "type": part.typeGroup, "mimeType": part.mimeType, "originalFileName": part.metadata.get("originalFileName"), "usageHint": part.metadata.get("usageHint"), "documentId": part.metadata.get("documentId"), "dataSize": len(str(part.data)) if part.data else 0 } # Füge Metadaten zu Sections hinzu, die contentPartIds haben for doc in structure.get("documents", []): # Prüfe ob Chapters vorhanden sind (neue Struktur) if "chapters" in doc: for chapter in doc.get("chapters", []): # Füge Metadaten zu Chapter-Level contentPartIds hinzu chapterContentPartIds, _ = self._extractContentPartInfo(chapter) if chapterContentPartIds: chapter["contentPartsMetadata"] = [] for partId in chapterContentPartIds: if partId in contentPartsMap: chapter["contentPartsMetadata"].append(contentPartsMap[partId]) # Füge Metadaten zu Sections hinzu for section in chapter.get("sections", []): contentPartIds = section.get("contentPartIds", []) if contentPartIds: section["contentPartsMetadata"] = [] for partId in contentPartIds: if partId in contentPartsMap: section["contentPartsMetadata"].append(contentPartsMap[partId]) return structure def _flattenChaptersToSections( self, chapterStructure: Dict[str, Any] ) -> Dict[str, Any]: """ Flattening: Konvertiert Chapters zu finaler Section-Struktur. Jedes Chapter wird zu einer Heading-Section (Level 1) + dessen Sections. Chapters are the main structure elements (heading level 1). All section headings with level < 2 are adjusted to level 2. """ result = { "metadata": chapterStructure.get("metadata", {}), "documents": [] } for doc in chapterStructure.get("documents", []): flattened_doc = { "id": doc.get("id"), "title": doc.get("title"), "filename": doc.get("filename"), "outputFormat": doc.get("outputFormat"), # Preserve from Phase 3 "language": doc.get("language"), # Preserve from Phase 3 "sections": [] } for chapter in doc.get("chapters", []): # 1. Vordefinierte Heading-Section für Chapter-Title (ALWAYS Level 1) heading_section = { "id": f"{chapter['id']}_heading", "content_type": "heading", "elements": [{ "type": "heading", "content": { "text": chapter.get("title", ""), "level": 1 # Chapters are always level 1 } }] } flattened_doc["sections"].append(heading_section) # 2. Generierte Sections - adjust heading levels for section in chapter.get("sections", []): # CRITICAL: Ensure elements are preserved when flattening # _adjustSectionHeadingLevels uses deepcopy which should preserve elements, # but verify that elements exist in the source section adjusted_section = self._adjustSectionHeadingLevels(section) # Ensure elements are preserved (deepcopy should handle this, but double-check) if "elements" in section and "elements" not in adjusted_section: adjusted_section["elements"] = section["elements"] flattened_doc["sections"].append(adjusted_section) result["documents"].append(flattened_doc) return result def _adjustSectionHeadingLevels(self, section: Dict[str, Any]) -> Dict[str, Any]: """ Adjust heading levels in sections: sections with type heading and level < 2 are changed to level 2. Only chapter headings have level 1. """ adjusted_section = copy.deepcopy(section) # Check if this is a heading section if adjusted_section.get("content_type") == "heading": elements = adjusted_section.get("elements", []) for element in elements: if isinstance(element, dict) and element.get("type") == "heading": content = element.get("content", {}) if isinstance(content, dict): level = content.get("level", 1) # If level < 2, change to level 2 (only chapters have level 1) if level < 2: content["level"] = 2 return adjusted_section def _buildChapterSectionsStructurePrompt( self, chapterId: str, chapterLevel: int, chapterTitle: str, generationHint: str, contentPartIds: List[str], contentPartInstructions: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, language: str = "en", outputFormat: str = "txt" ) -> str: """Baue Prompt für Chapter-Sections-Struktur-Generierung, querying renderer for accepted section types.""" # Baue ContentParts-Index (nur IDs, keine Previews!) contentPartsIndex = "" for partId in contentPartIds: part = self._findContentPartById(partId, contentParts) if not part: # Part not found - try to show info from chapter structure partInfo = contentPartInstructions.get(partId, {}) if partInfo: logger.warning(f"Chapter {chapterId}: ContentPart {partId} not found in contentParts list, but has chapter structure info.") contentPartsIndex += f"\n- ContentPart ID: {partId}\n" if "instruction" in partInfo: contentPartsIndex += f" Instruction: {partInfo['instruction']}\n" if "caption" in partInfo: contentPartsIndex += f" Caption: {partInfo['caption']}\n" contentPartsIndex += f" Note: ContentPart not found in contentParts list (ID may be from nested structure)\n" continue contentFormat = part.metadata.get("contentFormat", "unknown") partInfo = contentPartInstructions.get(partId, {}) instruction = partInfo.get("instruction", "Use content as needed") caption = partInfo.get("caption") contentPartsIndex += f"\n- ContentPart ID: {partId}\n" contentPartsIndex += f" Format: {contentFormat}\n" contentPartsIndex += f" Type: {part.typeGroup}\n" if instruction and instruction != "Use content as needed": contentPartsIndex += f" Instruction: {instruction}\n" if caption: contentPartsIndex += f" Caption: {caption}\n" if not contentPartsIndex: contentPartsIndex = "\n(No content parts specified for this chapter)" # Query renderer for accepted section types acceptedSectionTypes = self._getAcceptedSectionTypesForFormat(outputFormat) prompt = f"""TASK: Generate Chapter Sections Structure LANGUAGE: Generate all content in {language.upper()} language. All text, titles, headings, paragraphs, and content must be written in {language.upper()}. CHAPTER: {chapterTitle} (Level {chapterLevel}, ID: {chapterId}) GENERATION HINT: {generationHint} ## CONTENT EFFICIENCY PRINCIPLES - Generate COMPACT sections: Focus on essential information only - AVOID creating too many sections - combine related content where possible - Each section should serve a clear purpose with meaningful data - If no relevant data exists for a topic, do NOT create a section for it - Prefer ONE comprehensive section over multiple sparse sections **CRITICAL**: The chapter's generationHint above describes what content this chapter should generate. If the generationHint references documents/images/data, then EACH section that generates content for this chapter MUST assign the relevant ContentParts from AVAILABLE CONTENT PARTS below. NOTE: Chapter already has a heading section. Do NOT generate a heading for the chapter title. ## SECTION INDEPENDENCE - Each section is independent and self-contained - One section does NOT have information about another section - Each section must provide its own context and be understandable alone AVAILABLE CONTENT PARTS: {contentPartsIndex} ## CONTENT ASSIGNMENT RULE - CRITICAL If AVAILABLE CONTENT PARTS are listed above, then EVERY section that generates content related to those ContentParts MUST assign them explicitly. **Assignment logic:** - If section generates text content ABOUT a ContentPart → assign "extracted" format ContentPart with appropriate instruction - If section DISPLAYS a ContentPart → assign "object" format ContentPart - If section's generationHint or purpose relates to a ContentPart listed above → it MUST have contentPartIds assigned - If chapter's generationHint references documents/images/data AND section generates content for that chapter → section MUST assign relevant ContentParts - Empty contentPartIds [] are only allowed if section generates content WITHOUT referencing any available ContentParts AND WITHOUT relating to chapter's generationHint ## ACCEPTED CONTENT TYPES FOR THIS FORMAT The document output format ({outputFormat}) accepts only the following content types: {', '.join(acceptedSectionTypes)} **CRITICAL**: Only create sections with content types from this list. Other types will fail. useAiCall RULE (simple): - useAiCall: true → Content needs AI processing (extract, transform, generate, filter, summarize) - useAiCall: false → Content can be inserted directly without changes (Format is "object" or "reference") RETURN JSON: {{ "sections": [ {{ "id": "section_1", "content_type": "{acceptedSectionTypes[0]}", "contentPartIds": ["extracted_part_id"], "generationHint": "Description of what to extract or generate", "useAiCall": true, "elements": [] }} ] }} **MANDATORY CONTENT ASSIGNMENT CHECK:** For each section, verify: 1. Are ContentParts listed in AVAILABLE CONTENT PARTS above? 2. Does this section's generationHint or purpose relate to those ContentParts? 3. If YES to both → section MUST have contentPartIds assigned (cannot be empty []) 4. Assign ContentPart IDs exactly as listed in AVAILABLE CONTENT PARTS above IMAGE SECTIONS: - For image sections, always provide a "caption" field with a descriptive caption for the image. Return only valid JSON. Do not include any explanatory text outside the JSON. """ return prompt def _getContentStructureExample(self, contentType: str) -> str: """Get the JSON structure example for a specific content type.""" structures = { "table": '{{"headers": ["Column1", "Column2"], "rows": [["Value1", "Value2"], ["Value3", "Value4"]]}}', "bullet_list": '{{"items": ["Item 1", "Item 2", "Item 3"]}}', "heading": '{{"text": "Section Title", "level": 2}}', "paragraph": '{{"text": "This is paragraph text."}}', "code_block": '{{"code": "function example() {{ return true; }}", "language": "javascript"}}', "image": '{{"base64Data": "", "altText": "Description", "caption": "Optional caption"}}' } return structures.get(contentType, '{{"text": ""}}') def _buildSectionGenerationPrompt( self, section: Dict[str, Any], contentParts: List[Optional[ContentPart]], userPrompt: str, generationHint: str, allSections: Optional[List[Dict[str, Any]]] = None, sectionIndex: Optional[int] = None, isAggregation: bool = False, language: str = "en", outputFormat: str = "txt" ) -> tuple[str, str]: """Baue Prompt für Section-Generierung mit vollständigem Kontext.""" # Filtere None-Werte validParts = [p for p in contentParts if p is not None] # Section-Metadaten sectionId = section.get("id", "unknown") contentType = section.get("content_type", "paragraph") # Baue ContentParts-Beschreibung contentPartsText = "" if isAggregation: # Aggregation: ContentParts werden als Parameter übergeben, keine IDs im Prompt nötig # Keine ContentPart-Beschreibung nötig - Daten sind bereits im Context verfügbar contentPartsText = "" else: # Einzelverarbeitung: Zeige Previews for part in validParts: contentFormat = part.metadata.get("contentFormat", "unknown") contentPartsText += f"\n- ContentPart {part.id}:\n" contentPartsText += f" Format: {contentFormat}\n" contentPartsText += f" Type: {part.typeGroup}\n" if part.metadata.get("originalFileName"): contentPartsText += f" Source file: {part.metadata.get('originalFileName')}\n" if contentFormat == "extracted": # CRITICAL: Check if this is binary/image data - NEVER include in text prompt! isBinaryOrImage = ( part.typeGroup == "image" or part.typeGroup == "binary" or (part.mimeType and ( part.mimeType.startswith("image/") or part.mimeType.startswith("video/") or part.mimeType.startswith("audio/") or self._isBinaryMimeType(part.mimeType) )) or # Heuristic check: if data looks like base64 (long string with base64 chars) (part.data and isinstance(part.data, str) and len(part.data) > 100 and self._looksLikeBase64(part.data)) ) if isBinaryOrImage: # NEVER include binary/base64 data in text prompt - security risk and token explosion! dataLength = len(part.data) if part.data else 0 contentPartsText += f" Type: {part.typeGroup}\n" contentPartsText += f" MIME type: {part.mimeType or 'unknown'}\n" contentPartsText += f" Data size: {dataLength} chars (binary/base64 - not shown in prompt)\n" if part.metadata.get("needsVisionExtraction"): contentPartsText += f" Note: Will be processed with Vision AI\n" if part.metadata.get("usageHint"): contentPartsText += f" Usage hint: {part.metadata.get('usageHint')}\n" else: # Only for text data: Show preview previewLength = 1000 if part.data: preview = part.data[:previewLength] + "..." if len(part.data) > previewLength else part.data contentPartsText += f" Content preview:\n```\n{preview}\n```\n" else: contentPartsText += f" Content: (empty)\n" elif contentFormat == "reference": contentPartsText += f" Reference: {part.metadata.get('documentReference')}\n" if part.metadata.get("usageHint"): contentPartsText += f" Usage hint: {part.metadata.get('usageHint')}\n" elif contentFormat == "object": dataLength = len(part.data) if part.data else 0 contentPartsText += f" Object type: {part.typeGroup}\n" contentPartsText += f" MIME type: {part.mimeType}\n" contentPartsText += f" Data size: {dataLength} chars (base64 encoded)\n" if part.metadata.get("usageHint"): contentPartsText += f" Usage hint: {part.metadata.get('usageHint')}\n" # Baue Section-Kontext (vorherige und nachfolgende Sections) contextText = "" if allSections and sectionIndex is not None: prevSections = [] nextSections = [] if sectionIndex > 0: for i in range(max(0, sectionIndex - 2), sectionIndex): prevSection = allSections[i] prevSections.append({ "id": prevSection.get("id"), "content_type": prevSection.get("content_type"), "generation_hint": prevSection.get("generation_hint", "")[:100] }) if sectionIndex < len(allSections) - 1: for i in range(sectionIndex + 1, min(len(allSections), sectionIndex + 3)): nextSection = allSections[i] nextSections.append({ "id": nextSection.get("id"), "content_type": nextSection.get("content_type"), "generation_hint": nextSection.get("generation_hint", "")[:100] }) if prevSections or nextSections: contextText = "\n## DOCUMENT CONTEXT\n" if prevSections: contextText += "\nPrevious sections:\n" for prev in prevSections: contextText += f"- {prev['id']} ({prev['content_type']}): {prev['generation_hint']}\n" if nextSections: contextText += "\nFollowing sections:\n" for next in nextSections: contextText += f"- {next['id']} ({next['content_type']}): {next['generation_hint']}\n" # Get accepted section types for the output format acceptedTypesAggr = self._getAcceptedSectionTypesForFormat(outputFormat) # CRITICAL: If the section's content_type is not supported by the output format, # use the first accepted type instead. E.g., CSV only supports 'table', so # even if section says 'code_block', we must output as 'table'. effectiveContentType = contentType if contentType not in acceptedTypesAggr and acceptedTypesAggr: effectiveContentType = acceptedTypesAggr[0] logger.debug(f"Section {sectionId}: Content type '{contentType}' not supported by format '{outputFormat}', using '{effectiveContentType}' instead") contentStructureExample = self._getContentStructureExample(effectiveContentType) # Build format note for the prompt - purely dynamic from renderer # Always show what types are accepted for this format formatNoteAggr = f"\n- Target Output Format: {outputFormat.upper()} (accepted content types: {', '.join(acceptedTypesAggr)})" # Create template structure explicitly (not extracted from prompt) # This ensures exact identity between initial and continuation prompts templateStructure = f"""{{ "elements": [ {{ "type": "{effectiveContentType}", "content": {contentStructureExample} }} ] }}""" if isAggregation: prompt = f"""# TASK: Generate Section Content (Aggregation) Return only valid JSON. No explanatory text, no comments, no markdown formatting outside JSON. If ContentParts have no data, return: {{"elements": [{{"type": "{effectiveContentType}", "content": {{"headers": [], "rows": []}}}}]}} LANGUAGE: Generate all content in {language.upper()} language. All text, titles, headings, paragraphs, and content must be written in {language.upper()}. ## SECTION METADATA - Section ID: {sectionId} - Content Type: {effectiveContentType} - Generation Hint: {generationHint}{formatNoteAggr} ## CONTENT EFFICIENCY PRINCIPLES - Generate COMPACT content: Focus on essential facts only - AVOID verbose text, filler phrases, or redundant explanations - Be CONCISE and direct - every word should add value - NO introductory phrases like "This section describes..." or "Here we present..." - Minimize output size for efficient processing ## INSTRUCTIONS 1. Extract all data from the context provided. Do not skip or omit any data. 2. Extract data only from the provided context. Never invent, create, or generate data that is not in the context. 3. If the context contains no data, return empty structures (empty rows array for tables). 4. Aggregate all data into one element (e.g., one table). 5. For table: Extract all rows from the context. Return {{"headers": [...], "rows": []}} only if no data exists. 6. Format based on content_type ({effectiveContentType}). 7. No HTML/styling: Plain text only, no markup. 8. CONTINUE UNTIL COMPLETE: Extract ALL data from the provided context. Do NOT stop early because you think the response might be too long. Do NOT truncate or abbreviate. Do not impose artificial limits on yourself. ## OUTPUT FORMAT Return a JSON object with this structure: {{ "elements": [ {{ "type": "{effectiveContentType}", "content": {contentStructureExample} }} ] }} Output requirements: - "content" must be an object (never a string) - Return only valid JSON - no text before, no text after, no comments, no explanations - No invented data: Return empty structures if ContentParts have no data - Extract all data: Process every ContentPart completely and include all extracted data ## USER REQUEST (for context) ``` {userPrompt} ``` ## CONTEXT {contextText if contextText else ""} """ else: # Determine if we have ContentParts or need to generate from scratch hasContentParts = len(validParts) > 0 if hasContentParts: # EXTRACT MODE: Extract data from provided ContentParts prompt = f"""# TASK: Extract Section Content from Provided Data LANGUAGE: Generate all content in {language.upper()} language. All text, titles, headings, paragraphs, and content must be written in {language.upper()}. ## SECTION METADATA - Section ID: {sectionId} - Content Type: {effectiveContentType} - Generation Hint: {generationHint}{formatNoteAggr} ## CONTENT EFFICIENCY PRINCIPLES - Generate COMPACT content: Focus on essential facts only - AVOID verbose text, filler phrases, or redundant explanations - Be CONCISE and direct - every word should add value - NO introductory phrases like "This section describes..." or "Here we present..." - Minimize output size for efficient processing ## AVAILABLE CONTENT FOR THIS SECTION {contentPartsText} ## INSTRUCTIONS 1. Extract data only from provided ContentParts. Never invent or generate data. 2. If ContentParts contain no data, return empty structures (empty rows array for tables). 3. Format based on content_type ({effectiveContentType}). 4. Return only valid JSON with "elements" array. 5. No HTML/styling: Plain text only, no markup. 6. CONTINUE UNTIL COMPLETE: Extract ALL data from the provided context. Do NOT stop early because you think the response might be too long. Do NOT truncate or abbreviate. Do not impose artificial limits on yourself. ## OUTPUT FORMAT Return a JSON object with this structure: {{ "elements": [ {{ "type": "{effectiveContentType}", "content": {contentStructureExample} }} ] }} Output requirements: - "content" must be an object (never a string) - Return only valid JSON - no text before, no text after, no comments, no explanations, no markdown code fences - Start with {{ and end with }} - return ONLY the JSON object itself - No invented data: Return empty structures if ContentParts have no data ## USER REQUEST ``` {userPrompt} ``` ## CONTEXT {contextText if contextText else ""} """ else: # GENERATE MODE: Generate content from scratch based on generationHint prompt = f"""# TASK: Generate Section Content LANGUAGE: Generate all content in {language.upper()} language. All text, titles, headings, paragraphs, and content must be written in {language.upper()}. ## SECTION METADATA - Section ID: {sectionId} - Content Type: {effectiveContentType} - Generation Hint: {generationHint}{formatNoteAggr} ## CONTENT EFFICIENCY PRINCIPLES - Generate COMPACT content: Focus on essential facts only - AVOID verbose text, filler phrases, or redundant explanations - Be CONCISE and direct - every word should add value - NO introductory phrases like "This section describes..." or "Here we present..." - Minimize output size for efficient processing ## INSTRUCTIONS 1. Generate content based on the Generation Hint above. 2. Create appropriate content that matches the content_type ({effectiveContentType}). 3. The content should be relevant to the USER REQUEST and fit the context of surrounding sections. 4. Return only valid JSON with "elements" array. 5. No HTML/styling: Plain text only, no markup. 6. Keep content CONCISE - focus on substance, not length. ## OUTPUT FORMAT Return a JSON object with this structure: {{ "elements": [ {{ "type": "{effectiveContentType}", "content": {contentStructureExample} }} ] }} Output requirements: - "content" must be an object (never a string) - Return only valid JSON - no text before, no text after, no comments, no explanations, no markdown code fences - Start with {{ and end with }} - return ONLY the JSON object itself - Generate meaningful content based on the Generation Hint ## USER REQUEST ``` {userPrompt} ``` ## CONTEXT {contextText if contextText else ""} """ return prompt, templateStructure async def buildSectionPromptWithContinuation( self, continuationContext: Any, templateStructure: str, basePrompt: str ) -> str: """Build section prompt with continuation context. Uses unified signature. Single unified implementation for all section content generation contexts. Note: All initial context (section, contentParts, userPrompt, etc.) is already contained in basePrompt. This function only adds continuation-specific instructions. """ # Extract continuation context fields (only what's needed for continuation) incompletePart = continuationContext.incomplete_part lastRawJson = continuationContext.last_raw_json # Generate both overlap context and hierarchy context using jsonContinuation overlapContext = "" unifiedContext = "" if lastRawJson: # Get contexts directly from jsonContinuation from modules.shared.jsonContinuation import getContexts contexts = getContexts(lastRawJson) overlapContext = contexts.overlapContext unifiedContext = contexts.hierarchyContextForPrompt elif incompletePart: unifiedContext = incompletePart else: unifiedContext = "Unable to extract context - response was completely broken" # Build unified continuation prompt format continuationPrompt = f"""{basePrompt} --- CONTINUATION REQUEST --- The previous JSON response was incomplete. Continue from where it stopped. Context showing structure hierarchy with cut point: ``` {unifiedContext} ``` Overlap Requirement: To ensure proper merging, your response MUST start EXACTLY with the overlap context shown below, then continue with new content. Overlap context (start your response with this exact text): ```json {overlapContext if overlapContext else "No overlap context available"} ``` TASK: 1. Start your response EXACTLY with the overlap context shown above (character by character) 2. Continue seamlessly from where the overlap context ends 3. Complete the remaining content following the JSON structure template above 4. Return ONLY valid JSON following the structure template - no overlap/continuation wrapper objects CRITICAL: - Your response MUST begin with the exact overlap context text (this enables automatic merging) - Continue seamlessly after the overlap context with new content - Your response must be valid JSON matching the structure template above""" return continuationPrompt def _extractAndMergeMultipleJsonBlocks(self, responseText: str, contentType: str, sectionId: str) -> List[Dict[str, Any]]: """ Extract multiple JSON blocks from response and merge them appropriately. For tables: Merge all rows into a single table. For other types: Combine elements. """ from modules.shared.jsonUtils import tryParseJson, stripCodeFences, normalizeJsonText, extractFirstBalancedJson # Extract all JSON blocks, handling both --- separators and multiple ```json blocks blocks = [] # Strategy: Extract all ```json blocks first (most reliable), then fall back to other methods # This handles cases where --- separators and ```json blocks are mixed if "```json" in responseText: # Extract all ```json blocks regardless of --- separators jsonParts = responseText.split("```json") for jsonPart in jsonParts[1:]: # Skip first empty part jsonPart = "```json" + jsonPart # Extract just the JSON block (until closing ```) closingFence = jsonPart.find("```", 7) # Find closing ``` after "```json" if closingFence != -1: jsonPart = jsonPart[:closingFence + 3] jsonPart = jsonPart.strip() if jsonPart: blocks.append(jsonPart) # If no ```json blocks found, try splitting by --- and extracting JSON if not blocks and "---" in responseText: parts = responseText.split("---") for part in parts: part = part.strip() if not part: continue # Try to extract JSON directly from this part normalized = normalizeJsonText(part) normalized = stripCodeFences(normalized) jsonBlock = extractFirstBalancedJson(normalized) if jsonBlock: blocks.append(jsonBlock) elif responseText.count("```json") > 1: # Split by ```json markers (no --- separator) parts = responseText.split("```json") for part in parts[1:]: # Skip first empty part part = "```json" + part part = part.strip() if part: blocks.append(part) else: # Try to find multiple JSON objects/arrays directly normalized = normalizeJsonText(responseText) normalized = stripCodeFences(normalized) # Find all JSON blocks start = 0 while start < len(normalized): # Find next JSON start brace = normalized.find('{', start) bracket = normalized.find('[', start) jsonStart = -1 if brace != -1 and (bracket == -1 or brace < bracket): jsonStart = brace elif bracket != -1: jsonStart = bracket if jsonStart == -1: break # Extract balanced JSON jsonBlock = extractFirstBalancedJson(normalized[jsonStart:]) if jsonBlock: blocks.append(jsonBlock) start = jsonStart + len(jsonBlock) else: break if not blocks: logger.warning(f"Section {sectionId}: Could not extract multiple JSON blocks") return [] logger.info(f"Section {sectionId}: Extracted {len(blocks)} JSON blocks, merging for contentType={contentType}") # Parse all blocks allElements = [] for i, block in enumerate(blocks): parsed, parseError, _ = tryParseJson(block) if parseError: logger.warning(f"Section {sectionId}: Failed to parse JSON block {i+1}: {str(parseError)}") continue elementsFromBlock = [] if isinstance(parsed, dict): if "elements" in parsed: elementsFromBlock = parsed["elements"] allElements.extend(elementsFromBlock) elif parsed.get("type"): elementsFromBlock = [parsed] allElements.append(parsed) elif isinstance(parsed, list): elementsFromBlock = parsed allElements.extend(parsed) # Log row count for table elements if contentType == "table": tableCount = sum(1 for e in elementsFromBlock if isinstance(e, dict) and e.get("type") == "table") rowCount = sum( len(e.get("content", {}).get("rows", [])) for e in elementsFromBlock if isinstance(e, dict) and e.get("type") == "table" ) if tableCount > 0: logger.info(f"Section {sectionId}: JSON block {i+1}: {tableCount} table(s) with {rowCount} total rows") # Merge elements based on contentType if contentType == "table" and len(allElements) > 1: # Find all table elements tableElements = [e for e in allElements if isinstance(e, dict) and e.get("type") == "table"] if len(tableElements) > 1: # Check if tables can be merged (same column counts) canMerge = self._canMergeTables(tableElements) if canMerge: logger.info(f"Section {sectionId}: Merging {len(tableElements)} tables into one") mergedTable = self._mergeTableElements(tableElements) # Replace all table elements with merged one nonTableElements = [e for e in allElements if not (isinstance(e, dict) and e.get("type") == "table")] return [mergedTable] + nonTableElements else: logger.warning(f"Section {sectionId}: Cannot merge {len(tableElements)} tables (incompatible headers/columns). Keeping tables separate.") # Return all elements as-is (tables remain separate) return allElements return allElements def _canMergeTables(self, tableElements: List[Dict[str, Any]]) -> bool: """Check if tables can be safely merged (same column counts).""" if len(tableElements) <= 1: return True # Extract column counts from all tables columnCounts = [] for table in tableElements: headers = [] if isinstance(table.get("content"), dict): headers = table["content"].get("headers", []) elif isinstance(table.get("content"), list): # Old format: content is list of rows if table["content"] and isinstance(table["content"][0], list): headers = table["content"][0] columnCounts.append(len(headers)) # Check if all tables have the same column count firstCount = columnCounts[0] if columnCounts else 0 return all(count == firstCount for count in columnCounts) def _mergeTableElements(self, tableElements: List[Dict[str, Any]]) -> Dict[str, Any]: """Merge multiple table elements into a single table. Assumes tables have compatible column counts (checked by _canMergeTables). """ if not tableElements: return {"type": "table", "content": {"headers": [], "rows": []}} if len(tableElements) == 1: return tableElements[0] # Extract headers from all tables allHeaders = [] for table in tableElements: headers = [] if isinstance(table.get("content"), dict): headers = table["content"].get("headers", []) elif isinstance(table.get("content"), list): # Old format: content is list of rows if table["content"] and isinstance(table["content"][0], list): headers = table["content"][0] allHeaders.append(headers) # Check header compatibility (same headers or just same column count) firstHeaders = allHeaders[0] headersCompatible = all(headers == firstHeaders for headers in allHeaders) # If headers differ but column counts match, use first table's headers and log warning if not headersCompatible: logger.warning(f"Merging {len(tableElements)} tables with different headers but same column count. Using headers from first table.") # Use headers from first table headers = firstHeaders # Collect all rows from all tables, validating column count allRows = [] for tableIdx, table in enumerate(tableElements): rows = [] if isinstance(table.get("content"), dict): rows = table["content"].get("rows", []) elif isinstance(table.get("content"), list): # Old format: content is list of rows if table["content"] and isinstance(table["content"][0], list): rows = table["content"][1:] if len(table["content"]) > 1 else [] # Validate row column count matches header count expectedColCount = len(headers) validRows = [] for rowIdx, row in enumerate(rows): if isinstance(row, list): if len(row) == expectedColCount: validRows.append(row) else: logger.warning(f"Table {tableIdx+1}, row {rowIdx+1}: column count mismatch ({len(row)} vs {expectedColCount}), skipping row") elif isinstance(row, dict): # Convert dict row to list based on header order rowList = [row.get(h, "") for h in headers] validRows.append(rowList) else: logger.warning(f"Table {tableIdx+1}, row {rowIdx+1}: invalid row format, skipping") allRows.extend(validRows) # Keep all rows, including duplicates (duplicates may be intentional) logger.info(f"Merged {len(tableElements)} tables: {len(allRows)} total rows (duplicates preserved)") return { "type": "table", "content": { "headers": headers, "rows": allRows } } def _isBinaryMimeType(self, mimeType: str) -> bool: """Check if MIME type is binary.""" binaryTypes = [ "application/octet-stream", "application/pdf", "application/zip", "application/x-zip-compressed" ] return mimeType in binaryTypes def _looksLikeBase64(self, data: str) -> bool: """ Heuristic check if string looks like base64-encoded data. Base64 contains only: A-Z, a-z, 0-9, +, /, =, and whitespace. If >95% of characters are base64 chars and no normal text patterns, likely base64. """ if not data or len(data) < 100: return False base64Chars = set("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=\n\r\t ") sample = data[:500] # Check first 500 chars if not sample: return False base64Ratio = sum(1 for c in sample if c in base64Chars) / len(sample) # If >95% base64 chars and no normal text patterns (like spaces between words) → likely base64 # Base64 typically has very long strings without spaces or punctuation hasNormalTextPatterns = any( c in sample[:200] for c in ".,!?;:()[]{}\"'" ) or " " in sample[:200] # Double spaces suggest text return base64Ratio > 0.95 and not hasNormalTextPatterns def _findContentPartById(self, partId: str, contentParts: List[ContentPart]) -> Optional[ContentPart]: """Finde ContentPart nach ID.""" for part in contentParts: if part.id == partId: return part return None def _needsAggregation( self, contentType: str, contentPartCount: int ) -> bool: """ Bestimmt ob mehrere ContentParts aggregiert werden müssen. Aggregation nötig wenn: - content_type erfordert Aggregation (table, bullet_list) - UND mehrere ContentParts vorhanden sind (> 1) Args: contentType: Section content_type contentPartCount: Anzahl der ContentParts in dieser Section Returns: True wenn Aggregation nötig, False sonst """ aggregationTypes = ["table", "bullet_list"] if contentType in aggregationTypes and contentPartCount > 1: return True # Optional: Auch für paragraph wenn mehrere Parts vorhanden # (z.B. Vergleich mehrerer Dokumente) # Standard: Keine Aggregation für paragraph return False def _getAcceptedSectionTypesForFormat(self, outputFormat: str) -> List[str]: """ Get accepted section types for a given output format by querying the renderer. Args: outputFormat: Format name (e.g., 'csv', 'json', 'pdf') Returns: List of accepted section content types (e.g., ["table", "code_block"]) Raises: ValueError: If renderer not found or doesn't provide accepted types """ from modules.services.serviceGeneration.renderers.registry import getRenderer # Get document renderer for this format (structure filling is document generation path) renderer = getRenderer(outputFormat, self.services, outputStyle='document') if not renderer: raise ValueError(f"No renderer found for output format '{outputFormat}'. Check renderer registry.") if not hasattr(renderer, 'getAcceptedSectionTypes'): raise ValueError(f"Renderer for '{outputFormat}' does not implement getAcceptedSectionTypes(). Add this method to the renderer.") acceptedTypes = renderer.getAcceptedSectionTypes(outputFormat) if not acceptedTypes: raise ValueError(f"Renderer for '{outputFormat}' returned empty accepted types. Fix getAcceptedSectionTypes() in the renderer.") logger.debug(f"Renderer for '{outputFormat}' accepts: {acceptedTypes}") return acceptedTypes