1075 lines
50 KiB
Python
1075 lines
50 KiB
Python
# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""
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Structure Filling Module
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Handles filling document structure with content, including:
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- Filling sections with content parts
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- Building section generation prompts
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- Aggregation logic
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"""
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import json
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import logging
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import copy
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from typing import Dict, Any, List, Optional
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from modules.datamodels.datamodelExtraction import ContentPart
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum
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logger = logging.getLogger(__name__)
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class StructureFiller:
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"""Handles filling document structure with content."""
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def __init__(self, services, aiService):
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"""Initialize StructureFiller with service center and AI service access."""
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self.services = services
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self.aiService = aiService
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async def fillStructure(
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self,
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structure: Dict[str, Any],
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contentParts: List[ContentPart],
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userPrompt: str,
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parentOperationId: str
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) -> Dict[str, Any]:
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"""
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Phase 5D: Chapter-Content-Generierung (Zwei-Phasen-Ansatz).
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Phase 5D.1: Generiert Sections-Struktur für jedes Chapter
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Phase 5D.2: Füllt Sections mit ContentParts
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Args:
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structure: Struktur-Dict mit documents und chapters (nicht sections!)
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contentParts: Alle vorbereiteten ContentParts
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userPrompt: User-Anfrage
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parentOperationId: Parent Operation-ID für ChatLog-Hierarchie
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Returns:
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Gefüllte Struktur mit elements in jeder Section (nach Flattening)
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"""
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# Erstelle Operation-ID für Struktur-Abfüllen
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fillOperationId = f"{parentOperationId}_structure_filling"
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# Prüfe ob Struktur Chapters oder Sections hat
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hasChapters = False
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for doc in structure.get("documents", []):
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if "chapters" in doc:
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hasChapters = True
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break
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if not hasChapters:
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# Fallback: Alte Struktur mit Sections direkt - verwende alte Logik
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logger.warning("Structure has no chapters, using legacy section-based filling")
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return await self._fillStructureLegacy(structure, contentParts, userPrompt, fillOperationId)
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# Starte ChatLog mit Parent-Referenz
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chapterCount = sum(len(doc.get("chapters", [])) for doc in structure.get("documents", []))
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self.services.chat.progressLogStart(
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fillOperationId,
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"Chapter Content Generation",
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"Filling",
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f"Processing {chapterCount} chapters",
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parentOperationId=parentOperationId
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)
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try:
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filledStructure = copy.deepcopy(structure)
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# Phase 5D.1: Sections-Struktur für jedes Chapter generieren
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filledStructure = await self._generateChapterSectionsStructure(
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filledStructure, contentParts, userPrompt, fillOperationId
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)
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# Phase 5D.2: Sections mit ContentParts füllen
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filledStructure = await self._fillChapterSections(
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filledStructure, contentParts, userPrompt, fillOperationId
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)
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# Flattening: Chapters zu Sections konvertieren
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flattenedStructure = self._flattenChaptersToSections(filledStructure)
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# Füge ContentParts-Metadaten zur Struktur hinzu (für Validierung)
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flattenedStructure = self._addContentPartsMetadata(flattenedStructure, contentParts)
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# ChatLog abschließen
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self.services.chat.progressLogFinish(fillOperationId, True)
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return flattenedStructure
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except Exception as e:
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self.services.chat.progressLogFinish(fillOperationId, False)
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logger.error(f"Error in fillStructure: {str(e)}")
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raise
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async def _generateChapterSectionsStructure(
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self,
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chapterStructure: Dict[str, Any],
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contentParts: List[ContentPart],
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userPrompt: str,
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parentOperationId: str
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) -> Dict[str, Any]:
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"""
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Phase 5D.1: Generiert Sections-Struktur für jedes Chapter (ohne Content).
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Sections enthalten: content_type, contentPartIds, generationHint, useAiCall
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"""
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for doc in chapterStructure.get("documents", []):
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for chapter in doc.get("chapters", []):
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chapterId = chapter.get("id", "unknown")
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chapterLevel = chapter.get("level", 1)
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chapterTitle = chapter.get("title", "")
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generationHint = chapter.get("generationHint", "")
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contentPartIds = chapter.get("contentPartIds", [])
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contentPartInstructions = chapter.get("contentPartInstructions", {})
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chapterPrompt = self._buildChapterSectionsStructurePrompt(
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chapterId=chapterId,
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chapterLevel=chapterLevel,
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chapterTitle=chapterTitle,
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generationHint=generationHint,
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contentPartIds=contentPartIds,
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contentPartInstructions=contentPartInstructions,
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contentParts=contentParts,
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userPrompt=userPrompt
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)
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# Debug: Log Prompt
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self.services.utils.writeDebugFile(
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chapterPrompt,
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f"chapter_structure_{chapterId}_prompt"
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)
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aiResponse = await self.aiService.callAiPlanning(
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prompt=chapterPrompt,
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debugType=f"chapter_structure_{chapterId}"
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)
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# Debug: Log Response
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self.services.utils.writeDebugFile(
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aiResponse,
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f"chapter_structure_{chapterId}_response"
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)
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sectionsStructure = json.loads(
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self.services.utils.jsonExtractString(aiResponse)
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)
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chapter["sections"] = sectionsStructure.get("sections", [])
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# Setze useAiCall Flag (falls nicht von AI gesetzt)
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for section in chapter["sections"]:
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if "useAiCall" not in section:
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contentType = section.get("content_type", "paragraph")
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useAiCall = contentType != "paragraph"
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# Prüfe contentPartInstructions
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if not useAiCall:
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for partId in section.get("contentPartIds", []):
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instruction = contentPartInstructions.get(partId, {}).get("instruction", "")
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if instruction and instruction.lower() not in ["include full text", "include all content", "use full extracted text"]:
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useAiCall = True
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break
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section["useAiCall"] = useAiCall
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return chapterStructure
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async def _fillChapterSections(
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self,
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chapterStructure: Dict[str, Any],
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contentParts: List[ContentPart],
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userPrompt: str,
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parentOperationId: str
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) -> Dict[str, Any]:
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"""
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Phase 5D.2: Füllt Sections mit ContentParts.
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"""
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# Sammle alle Sections für sequenzielle Verarbeitung
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sections_to_process = []
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all_sections_list = [] # Für Kontext-Informationen
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for doc in chapterStructure.get("documents", []):
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for chapter in doc.get("chapters", []):
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for section in chapter.get("sections", []):
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all_sections_list.append(section)
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sections_to_process.append((doc, chapter, section))
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# Sequenzielle Section-Generierung
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fillOperationId = parentOperationId
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for sectionIndex, (doc, chapter, section) in enumerate(sections_to_process):
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sectionId = section.get("id")
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contentPartIds = section.get("contentPartIds", [])
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contentFormats = section.get("contentFormats", {})
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generationHint = section.get("generation_hint")
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contentType = section.get("content_type", "paragraph")
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useAiCall = section.get("useAiCall", False)
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elements = []
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# Prüfe ob Aggregation nötig ist
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needsAggregation = self._needsAggregation(
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contentType=contentType,
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contentPartCount=len(contentPartIds)
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)
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if needsAggregation and useAiCall:
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# Aggregation: Alle Parts zusammen verarbeiten
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sectionParts = [
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self._findContentPartById(pid, contentParts)
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for pid in contentPartIds
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]
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sectionParts = [p for p in sectionParts if p is not None]
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if sectionParts:
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# Filtere nur extracted Parts für Aggregation (reference/object werden separat behandelt)
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extractedParts = [
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p for p in sectionParts
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if contentFormats.get(p.id, p.metadata.get("contentFormat")) == "extracted"
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]
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nonExtractedParts = [
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p for p in sectionParts
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if contentFormats.get(p.id, p.metadata.get("contentFormat")) != "extracted"
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]
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# Verarbeite non-extracted Parts separat (reference, object)
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for part in nonExtractedParts:
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contentFormat = contentFormats.get(part.id, part.metadata.get("contentFormat"))
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if contentFormat == "reference":
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elements.append({
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"type": "reference",
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"documentReference": part.metadata.get("documentReference"),
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"label": part.metadata.get("usageHint", part.label)
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})
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elif contentFormat == "object":
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elements.append({
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"type": part.typeGroup,
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"base64Data": part.data,
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"mimeType": part.mimeType,
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"altText": part.metadata.get("usageHint", part.label)
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})
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# Aggregiere extracted Parts mit AI
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if extractedParts:
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generationPrompt = self._buildSectionGenerationPrompt(
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section=section,
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contentParts=extractedParts, # ALLE PARTS für Aggregation!
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userPrompt=userPrompt,
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generationHint=generationHint,
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allSections=all_sections_list,
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sectionIndex=sectionIndex,
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isAggregation=True
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)
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# Erstelle Operation-ID für Section-Generierung
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sectionOperationId = f"{fillOperationId}_section_{sectionId}"
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# Starte ChatLog mit Parent-Referenz
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self.services.chat.progressLogStart(
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sectionOperationId,
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"Section Generation (Aggregation)",
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"Section",
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f"Generating section {sectionId} with {len(extractedParts)} parts",
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parentOperationId=fillOperationId
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)
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try:
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# Debug: Log Prompt
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self.services.utils.writeDebugFile(
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generationPrompt,
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f"section_content_{sectionId}_prompt"
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)
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# Verwende callAi für ContentParts-Unterstützung (nicht callAiPlanning!)
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request = AiCallRequest(
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prompt=generationPrompt,
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contentParts=extractedParts, # ALLE PARTS!
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options=AiCallOptions(
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operationType=OperationTypeEnum.DATA_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.DETAILED
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)
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)
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aiResponse = await self.aiService.callAi(request)
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# Debug: Log Response
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self.services.utils.writeDebugFile(
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aiResponse.content,
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f"section_content_{sectionId}_response"
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)
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# Parse und füge zu elements hinzu
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generatedElements = json.loads(
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self.services.utils.jsonExtractString(aiResponse.content)
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)
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if isinstance(generatedElements, list):
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elements.extend(generatedElements)
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elif isinstance(generatedElements, dict) and "elements" in generatedElements:
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elements.extend(generatedElements["elements"])
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# ChatLog abschließen
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self.services.chat.progressLogFinish(sectionOperationId, True)
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except Exception as e:
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# Fehlerhafte Section mit Fehlermeldung rendern (kein Abbruch!)
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self.services.chat.progressLogFinish(sectionOperationId, False)
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elements.append({
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"type": "error",
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"message": f"Error generating section {sectionId}: {str(e)}",
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"sectionId": sectionId
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})
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logger.error(f"Error generating section {sectionId}: {str(e)}")
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# NICHT raise - Section wird mit Fehlermeldung gerendert
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else:
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# Einzelverarbeitung: Jeder Part einzeln
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for partId in contentPartIds:
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part = self._findContentPartById(partId, contentParts)
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if not part:
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continue
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contentFormat = contentFormats.get(partId, part.metadata.get("contentFormat"))
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if contentFormat == "reference":
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# Füge Dokument-Referenz hinzu
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elements.append({
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"type": "reference",
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"documentReference": part.metadata.get("documentReference"),
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"label": part.metadata.get("usageHint", part.label)
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})
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elif contentFormat == "object":
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# Füge base64 Object hinzu
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elements.append({
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"type": part.typeGroup, # "image", "binary", etc.
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"base64Data": part.data,
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"mimeType": part.mimeType,
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"altText": part.metadata.get("usageHint", part.label)
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})
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elif contentFormat == "extracted":
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if generationHint:
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# AI-Call mit einzelnen ContentPart
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generationPrompt = self._buildSectionGenerationPrompt(
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section=section,
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contentParts=[part], # EIN PART
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userPrompt=userPrompt,
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generationHint=generationHint,
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allSections=all_sections_list,
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sectionIndex=sectionIndex,
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isAggregation=False
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)
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# Erstelle Operation-ID für Section-Generierung
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sectionOperationId = f"{fillOperationId}_section_{sectionId}"
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# Starte ChatLog mit Parent-Referenz
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self.services.chat.progressLogStart(
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sectionOperationId,
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"Section Generation",
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"Section",
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f"Generating section {sectionId}",
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parentOperationId=fillOperationId
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)
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try:
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# Debug: Log Prompt
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self.services.utils.writeDebugFile(
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generationPrompt,
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f"section_content_{sectionId}_prompt"
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)
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# Verwende callAi für ContentParts-Unterstützung
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request = AiCallRequest(
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prompt=generationPrompt,
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contentParts=[part],
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options=AiCallOptions(
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operationType=OperationTypeEnum.DATA_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.DETAILED
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)
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)
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aiResponse = await self.aiService.callAi(request)
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# Debug: Log Response
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self.services.utils.writeDebugFile(
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aiResponse.content,
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f"section_content_{sectionId}_response"
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)
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# Parse und füge zu elements hinzu
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generatedElements = json.loads(
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self.services.utils.jsonExtractString(aiResponse.content)
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)
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if isinstance(generatedElements, list):
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elements.extend(generatedElements)
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elif isinstance(generatedElements, dict) and "elements" in generatedElements:
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elements.extend(generatedElements["elements"])
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# ChatLog abschließen
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self.services.chat.progressLogFinish(sectionOperationId, True)
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except Exception as e:
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# Fehlerhafte Section mit Fehlermeldung rendern (kein Abbruch!)
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self.services.chat.progressLogFinish(sectionOperationId, False)
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elements.append({
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"type": "error",
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"message": f"Error generating section {sectionId}: {str(e)}",
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"sectionId": sectionId
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})
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logger.error(f"Error generating section {sectionId}: {str(e)}")
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# NICHT raise - Section wird mit Fehlermeldung gerendert
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else:
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# Füge extrahierten Text direkt hinzu (kein AI-Call)
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elements.append({
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"type": "extracted_text",
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"content": part.data,
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"source": part.metadata.get("documentId"),
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"extractionPrompt": part.metadata.get("extractionPrompt")
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})
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section["elements"] = elements
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return chapterStructure
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def _addContentPartsMetadata(
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self,
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structure: Dict[str, Any],
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contentParts: List[ContentPart]
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) -> Dict[str, Any]:
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"""
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Fügt ContentParts-Metadaten zur Struktur hinzu, wenn contentPartIds vorhanden sind.
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Dies hilft der Validierung, den Kontext der ContentParts zu verstehen.
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"""
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# Erstelle Mapping von ContentPart-ID zu Metadaten
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contentPartsMap = {}
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for part in contentParts:
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contentPartsMap[part.id] = {
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"id": part.id,
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"format": part.metadata.get("contentFormat", "unknown"),
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"type": part.typeGroup,
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"mimeType": part.mimeType,
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"originalFileName": part.metadata.get("originalFileName"),
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"usageHint": part.metadata.get("usageHint"),
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"documentId": part.metadata.get("documentId"),
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"dataSize": len(str(part.data)) if part.data else 0
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}
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# Füge Metadaten zu Sections hinzu, die contentPartIds haben
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for doc in structure.get("documents", []):
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# Prüfe ob Chapters vorhanden sind (neue Struktur)
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if "chapters" in doc:
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for chapter in doc.get("chapters", []):
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# Füge Metadaten zu Chapter-Level contentPartIds hinzu
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chapterContentPartIds = chapter.get("contentPartIds", [])
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if chapterContentPartIds:
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chapter["contentPartsMetadata"] = []
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for partId in chapterContentPartIds:
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if partId in contentPartsMap:
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chapter["contentPartsMetadata"].append(contentPartsMap[partId])
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|
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# Füge Metadaten zu Sections hinzu
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for section in chapter.get("sections", []):
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contentPartIds = section.get("contentPartIds", [])
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if contentPartIds:
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section["contentPartsMetadata"] = []
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for partId in contentPartIds:
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if partId in contentPartsMap:
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section["contentPartsMetadata"].append(contentPartsMap[partId])
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# Prüfe ob Sections direkt vorhanden sind (Legacy-Struktur)
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elif "sections" in doc:
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for section in doc.get("sections", []):
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contentPartIds = section.get("contentPartIds", [])
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if contentPartIds:
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section["contentPartsMetadata"] = []
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for partId in contentPartIds:
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if partId in contentPartsMap:
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section["contentPartsMetadata"].append(contentPartsMap[partId])
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return structure
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|
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def _flattenChaptersToSections(
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self,
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chapterStructure: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
|
|
Flattening: Konvertiert Chapters zu finaler Section-Struktur.
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Jedes Chapter wird zu einer Heading-Section + dessen Sections.
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"""
|
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result = {
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"metadata": chapterStructure.get("metadata", {}),
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"documents": []
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}
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|
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for doc in chapterStructure.get("documents", []):
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flattened_doc = {
|
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"id": doc.get("id"),
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"title": doc.get("title"),
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"filename": doc.get("filename"),
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"sections": []
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}
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|
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for chapter in doc.get("chapters", []):
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# 1. Vordefinierte Heading-Section für Chapter-Title
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heading_section = {
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"id": f"{chapter['id']}_heading",
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"content_type": "heading",
|
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"elements": [{
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"type": "heading",
|
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"content": chapter.get("title"),
|
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"level": chapter.get("level", 1)
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}]
|
|
}
|
|
flattened_doc["sections"].append(heading_section)
|
|
|
|
# 2. Generierte Sections
|
|
flattened_doc["sections"].extend(chapter.get("sections", []))
|
|
|
|
result["documents"].append(flattened_doc)
|
|
|
|
return result
|
|
|
|
async def _fillStructureLegacy(
|
|
self,
|
|
structure: Dict[str, Any],
|
|
contentParts: List[ContentPart],
|
|
userPrompt: str,
|
|
fillOperationId: str
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Legacy: Füllt Struktur mit Sections direkt (für Rückwärtskompatibilität).
|
|
"""
|
|
# Starte ChatLog
|
|
self.services.chat.progressLogStart(
|
|
fillOperationId,
|
|
"Structure Filling (Legacy)",
|
|
"Filling",
|
|
f"Filling {len(structure.get('documents', [{}])[0].get('sections', []))} sections",
|
|
parentOperationId=fillOperationId
|
|
)
|
|
|
|
try:
|
|
filledStructure = copy.deepcopy(structure)
|
|
|
|
# Sammle alle Sections
|
|
sections_to_process = []
|
|
all_sections_list = []
|
|
for doc in filledStructure.get("documents", []):
|
|
doc_sections = doc.get("sections", [])
|
|
all_sections_list.extend(doc_sections)
|
|
for section in doc_sections:
|
|
sections_to_process.append((doc, section))
|
|
|
|
# Verarbeite Sections (bestehende Logik)
|
|
for sectionIndex, (doc, section) in enumerate(sections_to_process):
|
|
sectionId = section.get("id")
|
|
contentPartIds = section.get("contentPartIds", [])
|
|
contentFormats = section.get("contentFormats", {})
|
|
generationHint = section.get("generation_hint")
|
|
contentType = section.get("content_type", "paragraph")
|
|
|
|
elements = []
|
|
|
|
# Prüfe ob Aggregation nötig ist
|
|
needsAggregation = self._needsAggregation(
|
|
contentType=contentType,
|
|
contentPartCount=len(contentPartIds)
|
|
)
|
|
|
|
if needsAggregation and generationHint:
|
|
# 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
|
|
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
|
|
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":
|
|
elements.append({
|
|
"type": part.typeGroup,
|
|
"base64Data": part.data,
|
|
"mimeType": part.mimeType,
|
|
"altText": part.metadata.get("usageHint", part.label)
|
|
})
|
|
|
|
# Aggregiere extracted Parts mit AI
|
|
if extractedParts:
|
|
generationPrompt = self._buildSectionGenerationPrompt(
|
|
section=section,
|
|
contentParts=extractedParts,
|
|
userPrompt=userPrompt,
|
|
generationHint=generationHint,
|
|
allSections=all_sections_list,
|
|
sectionIndex=sectionIndex,
|
|
isAggregation=True
|
|
)
|
|
|
|
sectionOperationId = f"{fillOperationId}_section_{sectionId}"
|
|
|
|
self.services.chat.progressLogStart(
|
|
sectionOperationId,
|
|
"Section Generation (Aggregation)",
|
|
"Section",
|
|
f"Generating section {sectionId} with {len(extractedParts)} parts",
|
|
parentOperationId=fillOperationId
|
|
)
|
|
|
|
try:
|
|
self.services.utils.writeDebugFile(
|
|
generationPrompt,
|
|
f"section_content_{sectionId}_prompt"
|
|
)
|
|
|
|
request = AiCallRequest(
|
|
prompt=generationPrompt,
|
|
contentParts=extractedParts,
|
|
options=AiCallOptions(
|
|
operationType=OperationTypeEnum.DATA_ANALYSE,
|
|
priority=PriorityEnum.BALANCED,
|
|
processingMode=ProcessingModeEnum.DETAILED
|
|
)
|
|
)
|
|
aiResponse = await self.aiService.callAi(request)
|
|
|
|
self.services.utils.writeDebugFile(
|
|
aiResponse.content,
|
|
f"section_content_{sectionId}_response"
|
|
)
|
|
|
|
generatedElements = json.loads(
|
|
self.services.utils.jsonExtractString(aiResponse.content)
|
|
)
|
|
if isinstance(generatedElements, list):
|
|
elements.extend(generatedElements)
|
|
elif isinstance(generatedElements, dict) and "elements" in generatedElements:
|
|
elements.extend(generatedElements["elements"])
|
|
|
|
self.services.chat.progressLogFinish(sectionOperationId, True)
|
|
|
|
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)}")
|
|
|
|
else:
|
|
# 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":
|
|
elements.append({
|
|
"type": part.typeGroup,
|
|
"base64Data": part.data,
|
|
"mimeType": part.mimeType,
|
|
"altText": part.metadata.get("usageHint", part.label)
|
|
})
|
|
|
|
elif contentFormat == "extracted":
|
|
if generationHint:
|
|
# AI-Call mit einzelnen ContentPart
|
|
generationPrompt = self._buildSectionGenerationPrompt(
|
|
section=section,
|
|
contentParts=[part],
|
|
userPrompt=userPrompt,
|
|
generationHint=generationHint,
|
|
allSections=all_sections_list,
|
|
sectionIndex=sectionIndex,
|
|
isAggregation=False
|
|
)
|
|
|
|
sectionOperationId = f"{fillOperationId}_section_{sectionId}"
|
|
|
|
self.services.chat.progressLogStart(
|
|
sectionOperationId,
|
|
"Section Generation",
|
|
"Section",
|
|
f"Generating section {sectionId}",
|
|
parentOperationId=fillOperationId
|
|
)
|
|
|
|
try:
|
|
self.services.utils.writeDebugFile(
|
|
generationPrompt,
|
|
f"section_content_{sectionId}_prompt"
|
|
)
|
|
|
|
request = AiCallRequest(
|
|
prompt=generationPrompt,
|
|
contentParts=[part],
|
|
options=AiCallOptions(
|
|
operationType=OperationTypeEnum.DATA_ANALYSE,
|
|
priority=PriorityEnum.BALANCED,
|
|
processingMode=ProcessingModeEnum.DETAILED
|
|
)
|
|
)
|
|
aiResponse = await self.aiService.callAi(request)
|
|
|
|
self.services.utils.writeDebugFile(
|
|
aiResponse.content,
|
|
f"section_content_{sectionId}_response"
|
|
)
|
|
|
|
generatedElements = json.loads(
|
|
self.services.utils.jsonExtractString(aiResponse.content)
|
|
)
|
|
if isinstance(generatedElements, list):
|
|
elements.extend(generatedElements)
|
|
elif isinstance(generatedElements, dict) and "elements" in generatedElements:
|
|
elements.extend(generatedElements["elements"])
|
|
|
|
self.services.chat.progressLogFinish(sectionOperationId, True)
|
|
|
|
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)}")
|
|
else:
|
|
elements.append({
|
|
"type": "extracted_text",
|
|
"content": part.data,
|
|
"source": part.metadata.get("documentId"),
|
|
"extractionPrompt": part.metadata.get("extractionPrompt")
|
|
})
|
|
|
|
section["elements"] = elements
|
|
|
|
# Füge ContentParts-Metadaten zur Struktur hinzu (für Validierung)
|
|
filledStructure = self._addContentPartsMetadata(filledStructure, contentParts)
|
|
|
|
self.services.chat.progressLogFinish(fillOperationId, True)
|
|
return filledStructure
|
|
|
|
except Exception as e:
|
|
self.services.chat.progressLogFinish(fillOperationId, False)
|
|
logger.error(f"Error in _fillStructureLegacy: {str(e)}")
|
|
raise
|
|
|
|
def _buildChapterSectionsStructurePrompt(
|
|
self,
|
|
chapterId: str,
|
|
chapterLevel: int,
|
|
chapterTitle: str,
|
|
generationHint: str,
|
|
contentPartIds: List[str],
|
|
contentPartInstructions: Dict[str, Any],
|
|
contentParts: List[ContentPart],
|
|
userPrompt: str
|
|
) -> str:
|
|
"""Baue Prompt für Chapter-Sections-Struktur-Generierung."""
|
|
# Baue ContentParts-Index (nur IDs, keine Previews!)
|
|
contentPartsIndex = ""
|
|
for partId in contentPartIds:
|
|
part = self._findContentPartById(partId, contentParts)
|
|
if not part:
|
|
continue
|
|
|
|
contentFormat = part.metadata.get("contentFormat", "unknown")
|
|
instruction = contentPartInstructions.get(partId, {}).get("instruction", "Use content as needed")
|
|
|
|
contentPartsIndex += f"\n- ContentPart ID: {partId}\n"
|
|
contentPartsIndex += f" Format: {contentFormat}\n"
|
|
contentPartsIndex += f" Type: {part.typeGroup}\n"
|
|
contentPartsIndex += f" Instruction: {instruction}\n"
|
|
|
|
if not contentPartsIndex:
|
|
contentPartsIndex = "\n(No content parts specified for this chapter)"
|
|
|
|
prompt = f"""TASK: Generate Chapter Sections Structure
|
|
|
|
CHAPTER METADATA:
|
|
- Chapter ID: {chapterId}
|
|
- Chapter Level: {chapterLevel}
|
|
- Chapter Title: {chapterTitle}
|
|
- Generation Hint: {generationHint}
|
|
|
|
WICHTIG: Chapter hat bereits vordefinierte Heading-Section.
|
|
Generiere NICHT eine Heading-Section für Chapter-Title!
|
|
|
|
AVAILABLE CONTENT PARTS:
|
|
{contentPartsIndex}
|
|
|
|
STANDARD JSON SCHEMA FOR SECTIONS:
|
|
Supported content_types: table, bullet_list, heading, paragraph, code_block, image
|
|
|
|
Return JSON:
|
|
{{
|
|
"sections": [
|
|
{{
|
|
"id": "section_1",
|
|
"content_type": "paragraph",
|
|
"contentPartIds": ["part_ext_1"],
|
|
"generationHint": "...",
|
|
"useAiCall": false,
|
|
"elements": []
|
|
}}
|
|
]
|
|
}}
|
|
|
|
CRITICAL: Return ONLY valid JSON. Do not include any explanatory text outside the JSON.
|
|
"""
|
|
return prompt
|
|
|
|
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
|
|
) -> 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: Zeige nur Metadaten, nicht Previews
|
|
contentPartsText += f"\n## CONTENT PARTS (Aggregation)\n"
|
|
contentPartsText += f"- Anzahl: {len(validParts)} ContentParts\n"
|
|
contentPartsText += f"- Alle ContentParts werden als Parameter übergeben (nicht im Prompt!)\n"
|
|
contentPartsText += f"- Jeder Part kann sehr groß sein → Chunking automatisch\n"
|
|
contentPartsText += f"- WICHTIG: Aggregiere ALLE Parts zu einem Element (z.B. eine Tabelle)\n\n"
|
|
contentPartsText += f"ContentPart IDs:\n"
|
|
for part in validParts:
|
|
contentFormat = part.metadata.get("contentFormat", "unknown")
|
|
contentPartsText += f" - {part.id} (Format: {contentFormat}, Type: {part.typeGroup}"
|
|
if part.metadata.get("originalFileName"):
|
|
contentPartsText += f", Source: {part.metadata.get('originalFileName')}"
|
|
contentPartsText += ")\n"
|
|
else:
|
|
# Einzelverarbeitung: Zeige Previews
|
|
for part in validParts:
|
|
contentFormat = part.metadata.get("contentFormat", "unknown")
|
|
contentPartsText += f"\n- ContentPart {part.id}:\n"
|
|
contentPartsText += f" Format: {contentFormat}\n"
|
|
contentPartsText += f" Type: {part.typeGroup}\n"
|
|
if part.metadata.get("originalFileName"):
|
|
contentPartsText += f" Source file: {part.metadata.get('originalFileName')}\n"
|
|
|
|
if contentFormat == "extracted":
|
|
# Zeige Preview von extrahiertem Text (länger für besseren Kontext)
|
|
previewLength = 1000
|
|
if part.data:
|
|
preview = part.data[:previewLength] + "..." if len(part.data) > previewLength else part.data
|
|
contentPartsText += f" Content preview:\n```\n{preview}\n```\n"
|
|
else:
|
|
contentPartsText += f" Content: (empty)\n"
|
|
elif contentFormat == "reference":
|
|
contentPartsText += f" Reference: {part.metadata.get('documentReference')}\n"
|
|
if part.metadata.get("usageHint"):
|
|
contentPartsText += f" Usage hint: {part.metadata.get('usageHint')}\n"
|
|
elif contentFormat == "object":
|
|
dataLength = len(part.data) if part.data else 0
|
|
contentPartsText += f" Object type: {part.typeGroup}\n"
|
|
contentPartsText += f" MIME type: {part.mimeType}\n"
|
|
contentPartsText += f" Data size: {dataLength} chars (base64 encoded)\n"
|
|
if part.metadata.get("usageHint"):
|
|
contentPartsText += f" Usage hint: {part.metadata.get('usageHint')}\n"
|
|
|
|
# Baue Section-Kontext (vorherige und nachfolgende Sections)
|
|
contextText = ""
|
|
if allSections and sectionIndex is not None:
|
|
prevSections = []
|
|
nextSections = []
|
|
|
|
if sectionIndex > 0:
|
|
for i in range(max(0, sectionIndex - 2), sectionIndex):
|
|
prevSection = allSections[i]
|
|
prevSections.append({
|
|
"id": prevSection.get("id"),
|
|
"content_type": prevSection.get("content_type"),
|
|
"generation_hint": prevSection.get("generation_hint", "")[:100]
|
|
})
|
|
|
|
if sectionIndex < len(allSections) - 1:
|
|
for i in range(sectionIndex + 1, min(len(allSections), sectionIndex + 3)):
|
|
nextSection = allSections[i]
|
|
nextSections.append({
|
|
"id": nextSection.get("id"),
|
|
"content_type": nextSection.get("content_type"),
|
|
"generation_hint": nextSection.get("generation_hint", "")[:100]
|
|
})
|
|
|
|
if prevSections or nextSections:
|
|
contextText = "\n## DOCUMENT CONTEXT\n"
|
|
if prevSections:
|
|
contextText += "\nPrevious sections:\n"
|
|
for prev in prevSections:
|
|
contextText += f"- {prev['id']} ({prev['content_type']}): {prev['generation_hint']}\n"
|
|
if nextSections:
|
|
contextText += "\nFollowing sections:\n"
|
|
for next in nextSections:
|
|
contextText += f"- {next['id']} ({next['content_type']}): {next['generation_hint']}\n"
|
|
|
|
if isAggregation:
|
|
prompt = f"""# TASK: Generate Section Content (Aggregation)
|
|
|
|
## SECTION METADATA
|
|
- Section ID: {sectionId}
|
|
- Content Type: {contentType}
|
|
- Generation Hint: {generationHint}
|
|
{contextText}
|
|
|
|
## USER REQUEST (for context)
|
|
```
|
|
{userPrompt}
|
|
```
|
|
|
|
## AVAILABLE CONTENT FOR THIS SECTION
|
|
{contentPartsText if contentPartsText else "(No content parts specified for this section)"}
|
|
|
|
## INSTRUCTIONS
|
|
1. Generate content for section "{sectionId}" based on the generation hint above
|
|
2. **AGGREGATION**: Combine ALL provided ContentParts into ONE element (e.g., one table with all data)
|
|
3. For table content_type: Create a single table with headers and rows from all ContentParts
|
|
4. For bullet_list content_type: Create a single list with items from all ContentParts
|
|
5. Format appropriately based on content_type ({contentType})
|
|
6. Ensure the generated content fits logically between previous and following sections
|
|
7. Return ONLY a JSON object with an "elements" array
|
|
8. Each element should match the content_type: {contentType}
|
|
|
|
## OUTPUT FORMAT
|
|
Return a JSON object with this structure:
|
|
```json
|
|
{{
|
|
"elements": [
|
|
{{
|
|
"type": "{contentType}",
|
|
"headers": [...], // if table
|
|
"rows": [...], // if table
|
|
"items": [...], // if bullet_list
|
|
"content": "..." // if paragraph
|
|
}}
|
|
]
|
|
}}
|
|
```
|
|
|
|
CRITICAL: Return ONLY valid JSON. Do not include any explanatory text outside the JSON.
|
|
"""
|
|
else:
|
|
prompt = f"""# TASK: Generate Section Content
|
|
|
|
## SECTION METADATA
|
|
- Section ID: {sectionId}
|
|
- Content Type: {contentType}
|
|
- Generation Hint: {generationHint}
|
|
{contextText}
|
|
|
|
## USER REQUEST (for context)
|
|
```
|
|
{userPrompt}
|
|
```
|
|
|
|
## AVAILABLE CONTENT FOR THIS SECTION
|
|
{contentPartsText if contentPartsText else "(No content parts specified for this section)"}
|
|
|
|
## INSTRUCTIONS
|
|
1. Generate content for section "{sectionId}" based on the generation hint above
|
|
2. Use the available content parts to populate this section
|
|
3. For images: Use data URI format (data:image/[type];base64,[data]) when embedding base64 image data
|
|
4. For extracted text: Format appropriately based on content_type ({contentType})
|
|
5. Ensure the generated content fits logically between previous and following sections
|
|
6. Return ONLY a JSON object with an "elements" array
|
|
7. Each element should match the content_type: {contentType}
|
|
|
|
## OUTPUT FORMAT
|
|
Return a JSON object with this structure:
|
|
```json
|
|
{{
|
|
"elements": [
|
|
{{
|
|
"type": "{contentType}",
|
|
"content": "..."
|
|
}}
|
|
]
|
|
}}
|
|
```
|
|
|
|
CRITICAL: Return ONLY valid JSON. Do not include any explanatory text outside the JSON.
|
|
"""
|
|
return prompt
|
|
|
|
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
|
|
|