# 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 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 _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 (only entries with "instruction" field) contentPartInstructions = {} for partId, partInfo in contentParts.items(): if isinstance(partInfo, dict) and "instruction" in partInfo: contentPartInstructions[partId] = {"instruction": partInfo["instruction"]} 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) # 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, 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 ) # AI-Call für Chapter-Struktur-Generierung # Note: Debug logging is handled by callAiPlanning 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", []): 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): 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=language, parentOperationId=parentOperationId, totalChapters=totalChapters ) task = processChapterWithSemaphore( chapter, chapterIndex, chapterId, chapterLevel, chapterTitle, generationHint, contentPartIds, contentPartInstructions ) 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]] ) -> 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: elements.append({ "type": "image", "content": { "base64Data": base64Data, "altText": generationHint or "Generated image", "caption": "" } }) 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, calculateOverallProgress: callable ) -> 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": elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": part.metadata.get("caption", "") } }) 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] ) 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 = self._buildSectionGenerationPrompt( section=section, contentParts=processedExtractedParts, userPrompt=userPrompt, generationHint=generationHint, allSections=all_sections_list, sectionIndex=sectionIndex, isAggregation=isAggregation, language=language ) 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 ) ) 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: async def buildSectionPromptWithContinuation( section: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, generationHint: str, allSections: List[Dict[str, Any]], sectionIndex: int, isAggregation: bool, continuationContext: Dict[str, Any], services: Any ) -> str: basePrompt = self._buildSectionGenerationPrompt( section=section, contentParts=contentParts, userPrompt=userPrompt, generationHint=generationHint, allSections=allSections, sectionIndex=sectionIndex, isAggregation=isAggregation, language=language ) continuationInfo = continuationContext.get("delivered_summary", "") cutOffElement = continuationContext.get("cut_off_element", "") continuationPrompt = f"""{basePrompt} --- CONTINUATION REQUEST --- The previous JSON response was incomplete. Please continue from where it stopped. PREVIOUSLY DELIVERED SUMMARY: {continuationInfo} LAST INCOMPLETE ELEMENT: {cutOffElement} TASK: Continue generating the JSON elements array from where it was cut off. Complete the incomplete element and continue with remaining elements. Return ONLY the continuation JSON (starting from the incomplete element). The JSON should be a fragment that can be merged with the previous response.""" return continuationPrompt 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=buildSectionPromptWithContinuation, promptArgs={ "section": section, "contentParts": extractedParts, "userPrompt": userPrompt, "generationHint": generationHint, "allSections": all_sections_list, "sectionIndex": sectionIndex, "isAggregation": isAggregation, "services": self.services }, 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 ) 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 = self._buildSectionGenerationPrompt( section=section, contentParts=[], userPrompt=userPrompt, generationHint=generationHint, allSections=all_sections_list, sectionIndex=sectionIndex, isAggregation=False, language=language ) 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 async def buildSectionPromptWithContinuation( section: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, generationHint: str, allSections: List[Dict[str, Any]], sectionIndex: int, isAggregation: bool, continuationContext: Dict[str, Any], services: Any ) -> str: basePrompt = self._buildSectionGenerationPrompt( section=section, contentParts=contentParts, userPrompt=userPrompt, generationHint=generationHint, allSections=allSections, sectionIndex=sectionIndex, isAggregation=isAggregation, language=language ) continuationInfo = continuationContext.get("delivered_summary", "") cutOffElement = continuationContext.get("cut_off_element", "") continuationPrompt = f"""{basePrompt} --- CONTINUATION REQUEST --- The previous JSON response was incomplete. Please continue from where it stopped. PREVIOUSLY DELIVERED SUMMARY: {continuationInfo} LAST INCOMPLETE ELEMENT: {cutOffElement} TASK: Continue generating the JSON elements array from where it was cut off. Complete the incomplete element and continue with remaining elements. Return ONLY the continuation JSON (starting from the incomplete element). The JSON should be a fragment that can be merged with the previous response.""" return continuationPrompt 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=buildSectionPromptWithContinuation, promptArgs={ "section": section, "contentParts": [], "userPrompt": userPrompt, "generationHint": generationHint, "allSections": all_sections_list, "sectionIndex": sectionIndex, "isAggregation": isAggregation, "services": self.services }, 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 ) 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": elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": part.metadata.get("caption", "") } }) else: elements.append({ "type": part.typeGroup, "content": { "data": part.data, "mimeType": part.mimeType, "label": part.metadata.get("usageHint", part.label) } }) elif contentFormat == "extracted": # 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] ) 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 = self._buildSectionGenerationPrompt( section=section, contentParts=[part], userPrompt=userPrompt, generationHint=generationHint, allSections=all_sections_list, sectionIndex=sectionIndex, isAggregation=False, language=language ) 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 async def buildSectionPromptWithContinuation( section: Dict[str, Any], contentParts: List[ContentPart], userPrompt: str, generationHint: str, allSections: List[Dict[str, Any]], sectionIndex: int, isAggregation: bool, continuationContext: Dict[str, Any], services: Any ) -> str: basePrompt = self._buildSectionGenerationPrompt( section=section, contentParts=contentParts, userPrompt=userPrompt, generationHint=generationHint, allSections=allSections, sectionIndex=sectionIndex, isAggregation=isAggregation, language=language ) continuationInfo = continuationContext.get("delivered_summary", "") cutOffElement = continuationContext.get("cut_off_element", "") continuationPrompt = f"""{basePrompt} --- CONTINUATION REQUEST --- The previous JSON response was incomplete. Please continue from where it stopped. PREVIOUSLY DELIVERED SUMMARY: {continuationInfo} LAST INCOMPLETE ELEMENT: {cutOffElement} TASK: Continue generating the JSON elements array from where it was cut off. Complete the incomplete element and continue with remaining elements. Return ONLY the continuation JSON (starting from the incomplete element). The JSON should be a fragment that can be merged with the previous response.""" return continuationPrompt 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=buildSectionPromptWithContinuation, promptArgs={ "section": section, "contentParts": [part], "userPrompt": userPrompt, "generationHint": generationHint, "allSections": all_sections_list, "sectionIndex": sectionIndex, "isAggregation": isAggregation, "services": self.services }, 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 ) 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) if part.typeGroup == "image": logger.debug(f"Processing section {sectionId}: Single extracted IMAGE part WITHOUT AI call") elements.append({ "type": "image", "content": { "base64Data": part.data, "altText": part.metadata.get("usageHint", part.label), "caption": part.metadata.get("caption", "") } }) 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) # Helper function to calculate overall progress def calculateOverallProgress(chapterIndex, totalChapters, sectionIndex, totalSections): """Calculate overall progress: 0.0 to 1.0""" if totalChapters == 0: return 1.0 # Progress from completed chapters (0 to chapterIndex-1) completedChaptersProgress = chapterIndex / totalChapters # Progress from current chapter (sectionIndex / totalSections) currentChapterProgress = (sectionIndex / totalSections) / totalChapters if totalSections > 0 else 0 return min(1.0, completedChaptersProgress + currentChapterProgress) # Process chapters sequentially with chapter-level progress chapterIndex = 0 for doc in chapterStructure.get("documents", []): for chapter in doc.get("chapters", []): chapterIndex += 1 chapterId = chapter.get("id", "unknown") chapterTitle = chapter.get("title", "Untitled Chapter") sections = chapter.get("sections", []) totalSections = len(sections) # Start chapter operation chapterOperationId = f"{fillOperationId}_chapter_{chapterId}" self.services.chat.progressLogStart( chapterOperationId, "Chapter Generation", f"Chapter {chapterIndex}/{totalChapters}", chapterTitle, parentOperationId=fillOperationId ) # Process sections within chapter in parallel with concurrency control sectionTasks = [] for sectionIndex, section in enumerate(sections): # Create task wrapper with semaphore for parallel processing async def processSectionWithSemaphore(section, sectionIndex, totalSections, chapterIndex, totalChapters, chapterId, chapterOperationId, fillOperationId, contentParts, userPrompt, all_sections_list, language, calculateOverallProgress): async with sectionSemaphore: return await self._processSingleSection( section=section, sectionIndex=sectionIndex, totalSections=totalSections, chapterIndex=chapterIndex, totalChapters=totalChapters, chapterId=chapterId, chapterOperationId=chapterOperationId, fillOperationId=fillOperationId, contentParts=contentParts, userPrompt=userPrompt, all_sections_list=all_sections_list, language=language, calculateOverallProgress=calculateOverallProgress ) task = processSectionWithSemaphore( section, sectionIndex, totalSections, chapterIndex, totalChapters, chapterId, chapterOperationId, fillOperationId, contentParts, userPrompt, all_sections_list, language, calculateOverallProgress ) sectionTasks.append((sectionIndex, section, task)) # Execute all section tasks in parallel with concurrency control if sectionTasks: # Create list of tasks (without indices for gather) tasks = [task for _, _, task in sectionTasks] # Execute in parallel with error handling results = await asyncio.gather(*tasks, return_exceptions=True) # Process results in order and assign elements to sections for (originalIndex, originalSection, _), result in zip(sectionTasks, results): if isinstance(result, Exception): logger.error(f"Error processing section {originalSection.get('id')}: {str(result)}") # Set error element originalSection["elements"] = [{ "type": "error", "message": f"Error processing section: {str(result)}", "sectionId": originalSection.get("id") }] else: # Assign elements to section in correct order originalSection["elements"] = result # Finish chapter operation after all sections processed self.services.chat.progressLogFinish(chapterOperationId, True) # Update overall progress after chapter completion overallProgress = chapterIndex / totalChapters if totalChapters > 0 else 1.0 self.services.chat.progressLogUpdate( fillOperationId, overallProgress, f"Chapter {chapterIndex}/{totalChapters} completed: {chapterTitle}" ) 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"), "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", []): adjusted_section = self._adjustSectionHeadingLevels(section) 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" ) -> str: """Baue Prompt für Chapter-Sections-Struktur-Generierung.""" # Baue ContentParts-Index (nur IDs, keine Previews!) contentPartsIndex = "" for partId in contentPartIds: part = self._findContentPartById(partId, contentParts) if not part: continue contentFormat = part.metadata.get("contentFormat", "unknown") instruction = contentPartInstructions.get(partId, {}).get("instruction", "Use content as needed") contentPartsIndex += f"\n- ContentPart ID: {partId}\n" contentPartsIndex += f" Format: {contentFormat}\n" contentPartsIndex += f" Type: {part.typeGroup}\n" contentPartsIndex += f" Instruction: {instruction}\n" if not contentPartsIndex: contentPartsIndex = "\n(No content parts specified for this chapter)" 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} 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 TYPES: table, bullet_list, heading, paragraph, code_block, image useAiCall RULES: - useAiCall: true ONLY if ContentPart Format is "extracted" AND transformation needed - useAiCall: false if Format is "object" or "reference" (direct insertion) - useAiCall: false if Format is "extracted" AND simple "include full text" instruction - useAiCall: true if no ContentPartIds provided (content must be generated from scratch); Sections without ContentParts must have a clear, detailed generationHint explaining what content to generate RETURN JSON: {{ "sections": [ {{ "id": "section_1", "content_type": "paragraph", "contentPartIds": ["extracted_part_1"], "generationHint": "Include full text", "useAiCall": false, "caption": "optional, only for image sections", "elements": [] }} ] }} EXAMPLES (all content types): - paragraph: {{"id": "s1", "content_type": "paragraph", "contentPartIds": ["extracted_1"], "generationHint": "Include full text", "useAiCall": false, "elements": []}} - bullet_list: {{"id": "s2", "content_type": "bullet_list", "contentPartIds": ["extracted_1"], "generationHint": "Create bullet list", "useAiCall": true, "elements": []}} - table: {{"id": "s3", "content_type": "table", "contentPartIds": ["extracted_1", "extracted_2"], "generationHint": "Create table", "useAiCall": true, "elements": []}} - heading: {{"id": "s4", "content_type": "heading", "contentPartIds": ["extracted_1"], "generationHint": "Extract heading", "useAiCall": true, "elements": []}} - code_block: {{"id": "s5", "content_type": "code_block", "contentPartIds": ["extracted_1"], "generationHint": "Format code", "useAiCall": true, "elements": []}} - image: {{"id": "s6", "content_type": "image", "contentPartIds": ["obj_1"], "generationHint": "Display image", "caption": "Figure 1: Description of the image", "useAiCall": false, "elements": []}} - reference: {{"id": "s7", "content_type": "paragraph", "contentPartIds": ["ref_1"], "generationHint": "Reference", "useAiCall": false, "elements": []}} - NO CONTENT PARTS (generate from scratch): {{"id": "s8", "content_type": "paragraph", "contentPartIds": [], "generationHint": "Write a detailed professional paragraph explaining [specific topic or purpose]. Include [key points to cover]. Address [important aspects]. Conclude with [summary or recommendations].", "useAiCall": true, "elements": []}} 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" ) -> 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": # Zeige Preview von extrahiertem Text (länger für besseren Kontext) 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" contentStructureExample = self._getContentStructureExample(contentType) # Special handling for image content type with IMAGE_GENERATE isImageGeneration = contentType == "image" and len(validParts) == 0 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": "{contentType}", "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: {contentType} - Generation Hint: {generationHint} ## 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 ({contentType}). 7. No HTML/styling: Plain text only, no markup. ## OUTPUT FORMAT Return a JSON object with this structure: {{ "elements": [ {{ "type": "{contentType}", "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: 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: {contentType} - Generation Hint: {generationHint} ## AVAILABLE CONTENT FOR THIS SECTION {contentPartsText if contentPartsText else "(No content parts specified for this section)"} ## 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 ({contentType}). 4. Return only valid JSON with "elements" array. 5. No HTML/styling: Plain text only, no markup. ## OUTPUT FORMAT Return a JSON object with this structure: {{ "elements": [ {{ "type": "{contentType}", "content": {contentStructureExample} }} ] }} Output requirements: - "content" must be an object (never a string) - Return only valid JSON, no explanatory text - No invented data: Return empty structures if ContentParts have no data ## USER REQUEST ``` {userPrompt} ``` ## CONTEXT {contextText if contextText else ""} """ return prompt 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 _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