540 lines
31 KiB
Python
540 lines
31 KiB
Python
# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""
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Content Extraction Module
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Handles content extraction and preparation, including:
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- Extracting content from documents based on intents
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- Processing pre-extracted documents
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- Vision AI for image text extraction
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- AI processing of text content
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"""
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import json
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import logging
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import base64
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from typing import Dict, Any, List, Optional
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from modules.datamodels.datamodelChat import ChatDocument
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from modules.datamodels.datamodelExtraction import ContentPart, DocumentIntent
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logger = logging.getLogger(__name__)
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class ContentExtractor:
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"""Handles content extraction and preparation."""
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def __init__(self, services, aiService, intentAnalyzer):
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"""Initialize ContentExtractor with service center, AI service, and intent analyzer access."""
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self.services = services
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self.aiService = aiService
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self.intentAnalyzer = intentAnalyzer
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async def extractAndPrepareContent(
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self,
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documents: List[ChatDocument],
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documentIntents: List[DocumentIntent],
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parentOperationId: str,
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getIntentForDocument: callable
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) -> List[ContentPart]:
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"""
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Phase 5B: Extrahiert Content basierend auf Intents und bereitet ContentParts mit Metadaten vor.
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Gibt Liste von ContentParts im passenden Format zurück.
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WICHTIG: Ein Dokument kann mehrere ContentParts erzeugen, wenn mehrere Intents vorhanden sind.
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Beispiel: Bild mit intents=["extract", "render"] erzeugt:
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- ContentPart(contentFormat="object", ...) für Rendering
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- ContentPart(contentFormat="extracted", ...) für Text-Analyse
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Args:
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documents: Liste der zu verarbeitenden Dokumente
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documentIntents: Liste von DocumentIntent-Objekten
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parentOperationId: Parent Operation-ID für ChatLog-Hierarchie
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getIntentForDocument: Callable to get intent for document ID
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Returns:
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Liste von ContentParts mit vollständigen Metadaten
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"""
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# Erstelle Operation-ID für Extraktion
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extractionOperationId = f"{parentOperationId}_content_extraction"
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# Starte ChatLog mit Parent-Referenz
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self.services.chat.progressLogStart(
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extractionOperationId,
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"Content Extraction",
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"Extraction",
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f"Extracting from {len(documents)} documents",
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parentOperationId=parentOperationId
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)
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try:
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allContentParts = []
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for document in documents:
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# Check if document is already a ContentExtracted document (pre-extracted JSON)
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logger.debug(f"Checking document {document.id} ({document.fileName}, mimeType={document.mimeType}) for pre-extracted content")
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preExtracted = self.intentAnalyzer.resolvePreExtractedDocument(document)
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if preExtracted:
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logger.info(f"✅ Found pre-extracted document: {document.fileName} -> Original: {preExtracted['originalDocument']['fileName']}")
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logger.info(f" Pre-extracted document ID: {document.id}, Original document ID: {preExtracted['originalDocument']['id']}")
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logger.info(f" ContentParts count: {len(preExtracted['contentExtracted'].parts) if preExtracted['contentExtracted'].parts else 0}")
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# Verwende bereits extrahierte ContentParts direkt
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contentExtracted = preExtracted["contentExtracted"]
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# WICHTIG: Intent muss für das JSON-Dokument gefunden werden, nicht für das Original
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# (Intent-Analyse mappt bereits zurück zu JSON-Dokument-ID)
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intent = getIntentForDocument(document.id, documentIntents)
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logger.info(f" Intent lookup for document {document.id}: found={intent is not None}")
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if intent:
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logger.info(f" Intent: {intent.intents}, extractionPrompt: {intent.extractionPrompt[:100] if intent.extractionPrompt else None}...")
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else:
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logger.warning(f" ⚠️ No intent found for pre-extracted document {document.id}! Available intent documentIds: {[i.documentId for i in documentIntents]}")
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if contentExtracted.parts:
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for part in contentExtracted.parts:
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# Überspringe leere Parts (Container ohne Daten)
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if not part.data or (isinstance(part.data, str) and len(part.data.strip()) == 0):
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if part.typeGroup == "container":
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continue # Überspringe leere Container
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if not part.metadata:
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part.metadata = {}
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# Ensure metadata is complete
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if "documentId" not in part.metadata:
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part.metadata["documentId"] = document.id
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# WICHTIG: Prüfe Intent für dieses Part
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partIntent = intent.intents if intent else ["extract"]
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# Debug-Logging für Intent-Verarbeitung
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logger.debug(f"Processing part {part.id}: typeGroup={part.typeGroup}, intents={partIntent}, hasData={bool(part.data)}, dataLength={len(str(part.data)) if part.data else 0}")
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# WICHTIG: Ein Part kann mehrere Intents haben - erstelle für jeden Intent einen ContentPart
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# Generische Intent-Verarbeitung für ALLE Content-Typen
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hasReferenceIntent = "reference" in partIntent
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hasRenderIntent = "render" in partIntent
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hasExtractIntent = "extract" in partIntent
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hasPartData = bool(part.data) and (not isinstance(part.data, str) or len(part.data.strip()) > 0)
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logger.debug(f"Part {part.id}: reference={hasReferenceIntent}, render={hasRenderIntent}, extract={hasExtractIntent}, hasData={hasPartData}")
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# Track ob der originale Part bereits hinzugefügt wurde
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originalPartAdded = False
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# 1. Reference Intent: Erstelle Reference ContentPart
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if hasReferenceIntent:
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referencePart = ContentPart(
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id=f"ref_{document.id}_{part.id}",
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label=f"Reference: {part.label or 'Content'}",
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typeGroup="reference",
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mimeType=part.mimeType or "application/octet-stream",
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data="", # Leer - nur Referenz
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metadata={
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"contentFormat": "reference",
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"documentId": document.id,
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"documentReference": f"docItem:{document.id}:{preExtracted['originalDocument']['fileName']}",
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"intent": "reference",
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"usageHint": f"Reference: {preExtracted['originalDocument']['fileName']}",
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"originalFileName": preExtracted["originalDocument"]["fileName"]
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}
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)
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allContentParts.append(referencePart)
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logger.debug(f"✅ Created reference ContentPart for {part.id}")
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# 2. Render Intent: Erstelle Object ContentPart (für Binary/Image Rendering)
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if hasRenderIntent and hasPartData:
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# Prüfe ob es ein Binary/Image ist (kann gerendert werden)
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isRenderable = (
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part.typeGroup == "image" or
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part.typeGroup == "binary" or
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(part.mimeType and (
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part.mimeType.startswith("image/") or
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part.mimeType.startswith("video/") or
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part.mimeType.startswith("audio/") or
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self._isBinary(part.mimeType)
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))
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)
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if isRenderable:
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objectPart = ContentPart(
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id=f"obj_{document.id}_{part.id}",
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label=f"Object: {part.label or 'Content'}",
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typeGroup=part.typeGroup,
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mimeType=part.mimeType or "application/octet-stream",
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data=part.data, # Base64/Binary data ist bereits vorhanden
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metadata={
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"contentFormat": "object",
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"documentId": document.id,
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"intent": "render",
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"usageHint": f"Render as visual element: {preExtracted['originalDocument']['fileName']}",
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"originalFileName": preExtracted["originalDocument"]["fileName"],
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"relatedExtractedPartId": f"extracted_{document.id}_{part.id}" if hasExtractIntent else None
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}
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)
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allContentParts.append(objectPart)
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logger.debug(f"✅ Created object ContentPart for {part.id} (render intent)")
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else:
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logger.warning(f"⚠️ Part {part.id} has render intent but is not renderable (typeGroup={part.typeGroup}, mimeType={part.mimeType})")
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elif hasRenderIntent and not hasPartData:
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logger.warning(f"⚠️ Part {part.id} has render intent but no data, skipping render part")
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# 3. Extract Intent: Erstelle Extracted ContentPart (NO AI processing here - happens during section generation)
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if hasExtractIntent:
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# For images: Keep as image part with extract intent - Vision AI extraction happens during section generation
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if part.typeGroup == "image" and hasPartData:
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logger.info(f"📷 Image {part.id} with extract intent - will be processed with Vision AI during section generation")
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# Keep image part as-is, mark with extract intent
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part.metadata.update({
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"contentFormat": "extracted", # Marked for extraction, but not yet extracted
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"intent": "extract",
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"originalFileName": preExtracted["originalDocument"]["fileName"],
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"relatedObjectPartId": f"obj_{document.id}_{part.id}" if hasRenderIntent else None,
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"extractionPrompt": intent.extractionPrompt if intent and intent.extractionPrompt else "Extract all text content from this image.",
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"needsVisionExtraction": True # Flag to indicate Vision AI extraction needed
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})
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allContentParts.append(part)
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originalPartAdded = True
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else:
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# For text/table content: Use directly as extracted (no AI processing here)
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# AI processing with extractionPrompt happens during section generation
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if not originalPartAdded:
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part.metadata.update({
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"contentFormat": "extracted",
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"intent": "extract",
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"fromExtractContent": True,
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"skipExtraction": True, # Already extracted (raw extraction)
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"originalFileName": preExtracted["originalDocument"]["fileName"],
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"relatedObjectPartId": f"obj_{document.id}_{part.id}" if hasRenderIntent else None,
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"extractionPrompt": intent.extractionPrompt if intent and intent.extractionPrompt else None
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})
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# Stelle sicher dass contentFormat gesetzt ist
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if "contentFormat" not in part.metadata:
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part.metadata["contentFormat"] = "extracted"
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allContentParts.append(part)
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originalPartAdded = True
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logger.debug(f"✅ Using pre-extracted ContentPart {part.id} as extracted (no AI processing needed)")
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# 4. Fallback: Wenn kein Intent vorhanden oder Part wurde noch nicht hinzugefügt
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# (sollte normalerweise nicht vorkommen, da default "extract" ist)
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if not hasReferenceIntent and not hasRenderIntent and not hasExtractIntent and not originalPartAdded:
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logger.warning(f"⚠️ Part {part.id} has no recognized intents, adding as extracted by default")
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part.metadata.update({
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"contentFormat": "extracted",
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"intent": "extract",
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"fromExtractContent": True,
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"skipExtraction": True,
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"originalFileName": preExtracted["originalDocument"]["fileName"]
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})
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allContentParts.append(part)
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originalPartAdded = True
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logger.info(f"✅ Using {len([p for p in contentExtracted.parts if p.data and len(str(p.data)) > 0])} pre-extracted ContentParts from ContentExtracted document {document.fileName}")
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logger.info(f" Original document: {preExtracted['originalDocument']['fileName']}")
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continue # Skip normal extraction for this document
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# Check if it's standardized JSON format (has "documents" or "sections")
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if document.mimeType == "application/json":
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try:
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docBytes = self.services.interfaceDbComponent.getFileData(document.fileId)
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if docBytes:
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docData = docBytes.decode('utf-8')
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jsonData = json.loads(docData)
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if isinstance(jsonData, dict) and ("documents" in jsonData or "sections" in jsonData):
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logger.info(f"Document is already in standardized JSON format, using as reference")
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# Create reference ContentPart for structured JSON
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contentPart = ContentPart(
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id=f"ref_{document.id}",
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label=f"Reference: {document.fileName}",
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typeGroup="structure",
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mimeType="application/json",
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data=docData,
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metadata={
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"contentFormat": "reference",
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"documentId": document.id,
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"documentReference": f"docItem:{document.id}:{document.fileName}",
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"skipExtraction": True,
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"intent": "reference"
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}
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)
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allContentParts.append(contentPart)
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logger.info(f"✅ Using JSON document directly without extraction")
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continue # Skip normal extraction for this document
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except Exception as e:
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logger.warning(f"Could not parse JSON document {document.fileName}, will extract normally: {str(e)}")
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# Continue with normal extraction
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# Normal extraction path
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intent = getIntentForDocument(document.id, documentIntents)
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if not intent:
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# Default: extract für alle Dokumente ohne Intent
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logger.warning(f"No intent found for document {document.id}, using default 'extract'")
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intent = DocumentIntent(
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documentId=document.id,
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intents=["extract"],
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extractionPrompt="Extract all content from the document",
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reasoning="Default intent: no specific intent found"
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)
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# WICHTIG: Prüfe alle Intents - ein Dokument kann mehrere ContentParts erzeugen
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if "reference" in intent.intents:
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# Erstelle Reference ContentPart
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contentPart = ContentPart(
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id=f"ref_{document.id}",
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label=f"Reference: {document.fileName}",
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typeGroup="reference",
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mimeType=document.mimeType,
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data="",
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metadata={
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"contentFormat": "reference",
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"documentId": document.id,
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"documentReference": f"docItem:{document.id}:{document.fileName}",
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"intent": "reference",
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"usageHint": f"Reference document: {document.fileName}"
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}
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)
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allContentParts.append(contentPart)
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# WICHTIG: "render" und "extract" können beide vorhanden sein!
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# In diesem Fall erzeugen wir BEIDE ContentParts
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if "render" in intent.intents:
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# Für Images/Binary: extrahiere als Object
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if document.mimeType.startswith("image/") or self._isBinary(document.mimeType):
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try:
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# Lade Binary-Daten (getFileData ist nicht async - keine await nötig)
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binaryData = self.services.interfaceDbComponent.getFileData(document.fileId)
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if not binaryData:
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logger.warning(f"No binary data found for document {document.id}")
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continue
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base64Data = base64.b64encode(binaryData).decode('utf-8')
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contentPart = ContentPart(
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id=f"obj_{document.id}",
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label=f"Object: {document.fileName}",
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typeGroup="image" if document.mimeType.startswith("image/") else "binary",
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mimeType=document.mimeType,
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data=base64Data,
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metadata={
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"contentFormat": "object",
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"documentId": document.id,
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"intent": "render",
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"usageHint": f"Render as visual element: {document.fileName}",
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"originalFileName": document.fileName,
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# Verknüpfung zu extracted Part (falls vorhanden)
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"relatedExtractedPartId": f"ext_{document.id}" if "extract" in intent.intents else None
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}
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)
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allContentParts.append(contentPart)
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except Exception as e:
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logger.error(f"Failed to load binary data for document {document.id}: {str(e)}")
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if "extract" in intent.intents:
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# Extrahiere Content mit Extraction Service
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extractionPrompt = intent.extractionPrompt or "Extract all content from the document"
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# Debug-Log (harmonisiert)
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self.services.utils.writeDebugFile(
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extractionPrompt,
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f"content_extraction_prompt_{document.id}"
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)
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# Führe Extraktion aus
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from modules.datamodels.datamodelExtraction import ExtractionOptions, MergeStrategy
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extractionOptions = ExtractionOptions(
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prompt=extractionPrompt,
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mergeStrategy=MergeStrategy()
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)
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# extractContent ist nicht async - keine await nötig
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extractedResults = self.services.extraction.extractContent(
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[document],
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extractionOptions,
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operationId=extractionOperationId,
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parentOperationId=extractionOperationId
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)
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# Konvertiere extrahierte Ergebnisse zu ContentParts mit Metadaten
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for extracted in extractedResults:
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for part in extracted.parts:
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# Markiere als extracted Format
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part.metadata.update({
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"contentFormat": "extracted",
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"documentId": document.id,
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"extractionPrompt": extractionPrompt,
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"intent": "extract",
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"usageHint": f"Use extracted content from {document.fileName}",
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# Verknüpfung zu object Part (falls vorhanden)
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"relatedObjectPartId": f"obj_{document.id}" if "render" in intent.intents else None
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})
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# For images: Mark that Vision AI extraction is needed during section generation
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if part.typeGroup == "image":
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part.metadata["needsVisionExtraction"] = True
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logger.info(f"📷 Image part {part.id} marked for Vision AI extraction during section generation")
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# Stelle sicher, dass ID eindeutig ist (falls object Part existiert)
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if "render" in intent.intents:
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part.id = f"ext_{document.id}_{part.id}"
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allContentParts.append(part)
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# Debug-Log (harmonisiert)
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self.services.utils.writeDebugFile(
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json.dumps([part.dict() for part in allContentParts], indent=2, default=str),
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"content_extraction_result"
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)
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# ChatLog abschließen
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self.services.chat.progressLogFinish(extractionOperationId, True)
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return allContentParts
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except Exception as e:
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self.services.chat.progressLogFinish(extractionOperationId, False)
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logger.error(f"Error in extractAndPrepareContent: {str(e)}")
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raise
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async def extractTextFromImage(self, imagePart: ContentPart, extractionPrompt: str) -> Optional[str]:
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"""
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Extrahiere Text aus einem Image-Part mit Vision AI.
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Args:
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imagePart: ContentPart mit typeGroup="image"
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extractionPrompt: Prompt für die Text-Extraktion
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Returns:
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Extrahierter Text oder None bei Fehler
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"""
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try:
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum
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# Final extraction prompt
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finalPrompt = extractionPrompt or "Extract all text content from this image. Return only the extracted text, no additional formatting."
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# Debug-Log (harmonisiert)
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self.services.utils.writeDebugFile(
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finalPrompt,
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f"content_extraction_prompt_image_{imagePart.id}"
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)
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# Erstelle AI-Call-Request mit Image-Part
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request = AiCallRequest(
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prompt=finalPrompt,
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context="",
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options=AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE),
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contentParts=[imagePart]
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)
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# Verwende AI-Service für Vision AI-Verarbeitung
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response = await self.aiService.callAi(request)
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# Debug-Log für Response (harmonisiert)
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if response and response.content:
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self.services.utils.writeDebugFile(
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response.content,
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f"content_extraction_response_image_{imagePart.id}"
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)
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if response and response.content:
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return response.content.strip()
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# Kein Content zurückgegeben - return error message für Debugging
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errorMsg = f"Vision AI extraction failed: No content returned for image {imagePart.id}"
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logger.warning(errorMsg)
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return f"[ERROR: {errorMsg}]"
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except Exception as e:
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errorMsg = f"Vision AI extraction failed for image {imagePart.id}: {str(e)}"
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logger.error(errorMsg)
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import traceback
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logger.debug(f"Traceback: {traceback.format_exc()}")
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# Return error message statt None für Debugging
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return f"[ERROR: {errorMsg}]"
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async def processTextContentWithAi(self, textPart: ContentPart, extractionPrompt: str) -> Optional[str]:
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"""
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Verarbeite Text-Content mit AI basierend auf extractionPrompt.
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WICHTIG: Pre-extracted ContentParts von context.extractContent enthalten RAW extrahierten Text
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(z.B. aus PDF-Text-Layer). Wenn "extract" Intent vorhanden ist, muss dieser Text mit AI
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verarbeitet werden (Transformation, Strukturierung, etc.) basierend auf extractionPrompt.
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Args:
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textPart: ContentPart mit typeGroup="text" (oder anderer Text-basierter Typ)
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extractionPrompt: Prompt für die AI-Verarbeitung des Textes
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Returns:
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AI-verarbeiteter Text oder None bei Fehler
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"""
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try:
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum
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# Final extraction prompt
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finalPrompt = extractionPrompt or "Process and extract the key information from the following text content."
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# Debug-Log (harmonisiert) - log prompt with text preview
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textPreview = textPart.data[:500] + "..." if textPart.data and len(textPart.data) > 500 else (textPart.data or "")
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promptWithContext = f"{finalPrompt}\n\n--- Text Content (preview) ---\n{textPreview}"
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self.services.utils.writeDebugFile(
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promptWithContext,
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f"content_extraction_prompt_text_{textPart.id}"
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)
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# Erstelle Text-ContentPart für AI-Verarbeitung
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# Verwende den vorhandenen Text als Input
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textContentPart = ContentPart(
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id=textPart.id,
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label=textPart.label,
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typeGroup="text",
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mimeType="text/plain",
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data=textPart.data if textPart.data else "",
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metadata=textPart.metadata.copy() if textPart.metadata else {}
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)
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# Erstelle AI-Call-Request mit Text-Part
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request = AiCallRequest(
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prompt=finalPrompt,
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context="",
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options=AiCallOptions(operationType=OperationTypeEnum.DATA_EXTRACT),
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contentParts=[textContentPart]
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)
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# Verwende AI-Service für Text-Verarbeitung
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response = await self.aiService.callAi(request)
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# Debug-Log für Response (harmonisiert)
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if response and response.content:
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self.services.utils.writeDebugFile(
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response.content,
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f"content_extraction_response_text_{textPart.id}"
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)
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if response and response.content:
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return response.content.strip()
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# Kein Content zurückgegeben - return error message für Debugging
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errorMsg = f"AI text processing failed: No content returned for text part {textPart.id}"
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logger.warning(errorMsg)
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return f"[ERROR: {errorMsg}]"
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except Exception as e:
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errorMsg = f"AI text processing failed for text part {textPart.id}: {str(e)}"
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logger.error(errorMsg)
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import traceback
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logger.debug(f"Traceback: {traceback.format_exc()}")
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# Return error message statt None für Debugging
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return f"[ERROR: {errorMsg}]"
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def _isBinary(self, mimeType: str) -> bool:
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"""Prüfe ob MIME-Type binary ist."""
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binaryTypes = [
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"application/octet-stream",
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"application/pdf",
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"application/zip",
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"application/x-zip-compressed"
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]
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return mimeType in binaryTypes or mimeType.startswith("image/") or mimeType.startswith("video/") or mimeType.startswith("audio/")
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