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gateway/modules/services/serviceAi/subStructureFilling.py

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# 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,
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":
# 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
)
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
)
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
)
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)
# 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", []):
docId = doc.get("id", "unknown")
# Get language for this specific document
docLanguage = self._getDocumentLanguage(chapterStructure, docId)
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, docLanguage, calculateOverallProgress):
checkWorkflowStopped(self.services)
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=docLanguage, # Use document-specific language
calculateOverallProgress=calculateOverallProgress
)
task = processSectionWithSemaphore(
section, sectionIndex, totalSections, chapterIndex, totalChapters, chapterId, chapterOperationId, fillOperationId, contentParts, userPrompt, all_sections_list, docLanguage, 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
# CRITICAL: Always assign elements, even if empty list
# This ensures sections always have an elements field for validation
originalSection["elements"] = result if result is not None else []
# 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"),
"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}
**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
## CONTENT TYPES
Available content types for sections: table, bullet_list, heading, paragraph, code_block, image
## ACCEPTED SECTION TYPES FOR THIS FORMAT
The document output format ({outputFormat}) accepts only the following section types:
{', '.join(acceptedSectionTypes) if acceptedSectionTypes else 'All section types'}
**IMPORTANT**: Only create sections with content types from the accepted list above. Do not create sections with types that are not accepted by this format.
## FORMAT-APPROPRIATE SECTION STRUCTURE
When determining which sections to create for this chapter, consider the document's output format ({outputFormat}) and ensure sections are structured appropriately for that format:
- Different formats have different capabilities and constraints
- Structure sections to match what the format can effectively represent
- Consider what content types work best for each format
- Ensure the section structure aligns with the format's strengths and limitations
- Select content types that are well-suited for the target format
- **CRITICAL**: Only use section types from the ACCEPTED SECTION TYPES list above
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": []
}}
]
}}
**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": "<base64_encoded_image_data>", "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"
) -> 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"
contentStructureExample = self._getContentStructureExample(contentType)
# Create template structure explicitly (not extracted from prompt)
# This ensures exact identity between initial and continuation prompts
templateStructure = f"""{{
"elements": [
{{
"type": "{contentType}",
"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": "{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.
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": "{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:
# 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: {contentType}
- Generation Hint: {generationHint}
## 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 ({contentType}).
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": "{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 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: {contentType}
- Generation Hint: {generationHint}
## INSTRUCTIONS
1. Generate content based on the Generation Hint above.
2. Create appropriate content that matches the content_type ({contentType}).
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. 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": "{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 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
# Build overlap context: extract cut part and full part before (same level) for overlap
overlapContext = ""
if lastRawJson:
# Find break position in raw JSON
lastCompletePart = continuationContext.last_complete_part
breakPos = len(lastRawJson.rstrip())
if lastCompletePart:
from modules.shared.jsonUtils import stripCodeFences, normalizeJsonText
normalizedRaw = stripCodeFences(normalizeJsonText(lastRawJson)).strip()
normalizedComplete = stripCodeFences(normalizeJsonText(lastCompletePart)).strip()
# Find where normalizedComplete ends in normalizedRaw
pos = normalizedRaw.find(normalizedComplete)
if pos >= 0:
breakPos = pos + len(normalizedComplete)
else:
pos = lastRawJson.find(lastCompletePart)
if pos >= 0:
breakPos = pos + len(lastCompletePart)
elif incompletePart:
pos = lastRawJson.find(incompletePart)
if pos >= 0:
breakPos = pos
# Extract cut part and full part before (same level)
overlapContext = self._extractOverlapContext(lastRawJson, breakPos)
# Build unified context showing structure hierarchy with cut point
unifiedContext = ""
if lastRawJson:
# Find break position in raw JSON
# Use last_complete_part length to find where complete part ends
lastCompletePart = continuationContext.last_complete_part
if lastCompletePart:
# Break position is where the complete part ends
# Normalize lastRawJson to match the normalized lastCompletePart
from modules.shared.jsonUtils import stripCodeFences, normalizeJsonText
normalizedRaw = stripCodeFences(normalizeJsonText(lastRawJson)).strip()
normalizedComplete = stripCodeFences(normalizeJsonText(lastCompletePart)).strip()
# Find where normalizedComplete ends in normalizedRaw
breakPos = normalizedRaw.find(normalizedComplete)
if breakPos >= 0:
breakPos = breakPos + len(normalizedComplete)
else:
# Fallback: use length of lastCompletePart in original string
breakPos = lastRawJson.find(lastCompletePart)
if breakPos >= 0:
breakPos = breakPos + len(lastCompletePart)
else:
# Last resort: use incompletePart position
if incompletePart:
breakPos = lastRawJson.find(incompletePart)
if breakPos == -1:
breakPos = len(lastRawJson.rstrip())
else:
breakPos = len(lastRawJson.rstrip())
elif incompletePart:
# If no complete part, find where incomplete part starts
breakPos = lastRawJson.find(incompletePart)
if breakPos == -1:
breakPos = len(lastRawJson.rstrip())
else:
breakPos = len(lastRawJson.rstrip())
# Build intelligent context showing hierarchy
from modules.shared.jsonUtils import buildIncompleteContext
unifiedContext = buildIncompleteContext(lastRawJson, breakPos)
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.
JSON Structure Template:
```json
{templateStructure}
```
Context showing structure hierarchy with cut point:
```
{unifiedContext}
```
Overlap Requirement:
To ensure proper merging, your response MUST start by repeating the cut part and the full part before (same level) shown below, then continue with new content.
Overlap context (cut part and full part before at same level):
```json
{overlapContext if overlapContext else "No overlap context available"}
```
TASK:
1. Start your response by repeating the overlap context shown above (cut part and full part before at same level)
2. Complete the incomplete element shown in the context above (marked with CUT POINT)
3. Continue generating 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 be valid JSON matching the structure template above
- Start with overlap context (cut part and full part before at same level) then continue seamlessly
- Complete the incomplete element and continue with remaining elements"""
return continuationPrompt
def _extractOverlapContext(self, jsonContent: str, breakPosition: int) -> str:
"""
Extract overlap context: cut part and full part before (same level).
Delegates to shared function in jsonUtils for consistency.
"""
from modules.shared.jsonUtils import extractOverlapContext
return extractOverlapContext(jsonContent, breakPosition)
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"])
"""
try:
from modules.services.serviceGeneration.renderers.registry import getRenderer
# Get renderer for this format
renderer = getRenderer(outputFormat, self.services)
if renderer and hasattr(renderer, 'getAcceptedSectionTypes'):
# Query renderer for accepted types
acceptedTypes = renderer.getAcceptedSectionTypes(outputFormat)
if acceptedTypes:
logger.debug(f"Renderer for format '{outputFormat}' accepts section types: {acceptedTypes}")
return acceptedTypes
# Fallback: if no renderer or method not found, return all types
from modules.datamodels.datamodelJson import supportedSectionTypes
logger.debug(f"No renderer found for format '{outputFormat}' or method not available, using all section types")
return list(supportedSectionTypes)
except Exception as e:
logger.warning(f"Error querying renderer for accepted section types for format '{outputFormat}': {str(e)}")
# Fallback: return all types
from modules.datamodels.datamodelJson import supportedSectionTypes
return list(supportedSectionTypes)
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