voice route updated
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ValueOn AG
parent
259ccabbe3
commit
37aad732f5
7 changed files with 51 additions and 59 deletions
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@ -723,15 +723,16 @@ class ConnectorGoogleSpeech:
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logger.info("🌐 Getting available languages from Google Cloud TTS")
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# List voices from Google Cloud TTS
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voices = self.tts_client.list_voices()
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response = self.tts_client.list_voices()
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# Extract unique language codes
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# Note: Google TTS API doesn't provide language descriptions, only codes
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language_codes = set()
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for voice in voices:
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for voice in response.voices:
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if voice.language_codes:
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language_codes.update(voice.language_codes)
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# Convert to sorted list
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# Convert to sorted list of language codes
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available_languages = sorted(list(language_codes))
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logger.info(f"✅ Found {len(available_languages)} available languages")
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@ -763,11 +764,11 @@ class ConnectorGoogleSpeech:
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logger.info(f"🎤 Getting available voices from Google Cloud TTS, language filter: {languageCode}")
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# List voices from Google Cloud TTS
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voices = self.tts_client.list_voices()
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response = self.tts_client.list_voices()
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availableVoices = []
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for voice in voices:
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for voice in response.voices:
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# Extract language code from voice name (e.g., 'de-DE-Wavenet-A' -> 'de-DE')
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voiceLanguage = voice.language_codes[0] if voice.language_codes else None
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@ -783,15 +784,24 @@ class ConnectorGoogleSpeech:
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elif voice.name.endswith(('-B', '-D')):
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gender = "Female"
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# Create voice info
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# Create voice info with all available fields from Google API
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voiceInfo = {
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"name": voice.name,
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"language_code": voiceLanguage,
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"language_codes": list(voice.language_codes) if voice.language_codes else [],
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"gender": gender,
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"ssml_gender": voice.ssml_gender.name if voice.ssml_gender else "NEUTRAL",
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"natural_sample_rate_hertz": voice.natural_sample_rate_hertz
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}
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# Include any additional fields if available from Google API
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# Check for common fields that might exist
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for field_name in ['description', 'display_name', 'labels']:
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if hasattr(voice, field_name):
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field_value = getattr(voice, field_name, None)
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if field_value:
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voiceInfo[field_name] = field_value
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availableVoices.append(voiceInfo)
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# Sort by language code, then by gender, then by name
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@ -9,7 +9,7 @@ import uuid
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class VoiceSettings(BaseModel):
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id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", frontend_type="text", frontend_readonly=True, frontend_required=False)
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userId: str = Field(description="ID of the user these settings belong to", frontend_type="text", frontend_readonly=True, frontend_required=True)
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mandateId: str = Field(description="ID of the mandate these settings belong to", frontend_type="text", frontend_readonly=True, frontend_required=False)
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mandateId: str = Field(description="ID of the mandate these settings belong to", frontend_type="text", frontend_readonly=True, frontend_required=True)
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sttLanguage: str = Field(default="de-DE", description="Speech-to-Text language", frontend_type="select", frontend_readonly=False, frontend_required=True)
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ttsLanguage: str = Field(default="de-DE", description="Text-to-Speech language", frontend_type="select", frontend_readonly=False, frontend_required=True)
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ttsVoice: str = Field(default="de-DE-KatjaNeural", description="Text-to-Speech voice", frontend_type="select", frontend_readonly=False, frontend_required=True)
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@ -268,6 +268,12 @@ class VoiceObjects:
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try:
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logger.info(f"Creating voice settings: {settingsData}")
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# Ensure mandateId is set from user context if not provided
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if "mandateId" not in settingsData or not settingsData["mandateId"]:
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if not self.currentUser or not self.currentUser.mandateId:
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raise ValueError("mandateId is required but not provided and user context has no mandateId")
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settingsData["mandateId"] = self.currentUser.mandateId
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# Add timestamps
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currentTime = getUtcTimestamp()
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settingsData["creationDate"] = currentTime
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@ -330,6 +336,7 @@ class VoiceObjects:
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# Create default settings if none exist
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defaultSettings = {
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"userId": userId,
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"mandateId": self.currentUser.mandateId,
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"sttLanguage": "de-DE",
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"ttsLanguage": "de-DE",
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"ttsVoice": "de-DE-Wavenet-A",
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@ -87,7 +87,7 @@ class RendererDocx(BaseRenderer):
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# Process each section in order
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sections = json_content.get("sections", [])
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for section in sections:
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self._render_json_section(doc, section, styles)
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self._renderJsonSection(doc, section, styles)
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# Save to buffer
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buffer = io.BytesIO()
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@ -96,7 +96,7 @@ class RendererPdf(BaseRenderer):
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title_style = self._createTitleStyle(styles)
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story.append(Paragraph(document_title, title_style))
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story.append(Spacer(1, 50)) # Increased spacing to prevent overlap
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story.append(Paragraph(f"Generated: {self._format_timestamp()}", self._createNormalStyle(styles)))
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story.append(Paragraph(f"Generated: {self._formatTimestamp()}", self._createNormalStyle(styles)))
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story.append(Spacer(1, 30)) # Add spacing before page break
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story.append(PageBreak())
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@ -95,9 +95,12 @@ Instructions:
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- Continue from where it stopped — add NEW items only; do not repeat existing items.
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- Generate remaining content to complete the user request.
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- Fill with actual content (no placeholders or instructional text such as "Add more...").
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- IMPORTANT: Ensure "filename" in each document has meaningful name with appropriate extension matching the content.
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- When the request is fully satisfied, add "complete_response": true at root level.
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- Output JSON only; no markdown fences or extra text.
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IMPORTANT: Before responding, analyse the remaining data to fully satisfy user request.
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Continue generating:
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"""
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else:
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@ -114,6 +117,7 @@ Instructions:
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- Return ONLY valid JSON (strict). No comments. No trailing commas. Use double quotes.
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- Do NOT reuse example section IDs; create your own.
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- Generate complete content based on the user request.
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- IMPORTANT: Set a meaningful "filename" in each document with appropriate file extension (e.g., "prime_numbers.txt", "report.docx", "data.json"). The filename should reflect the content and task objective.
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- When the request is fully satisfied, add "complete_response": true at root level.
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- Output JSON only; no markdown fences or extra text.
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@ -83,10 +83,12 @@ class ContentValidator:
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availableModels = modelRegistry.getAvailableModels()
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# Create options for PLAN operation (what validation uses)
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# Use default values for priority and processingMode (will use defaults from AiCallOptions)
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from modules.datamodels.datamodelAi import PriorityEnum, ProcessingModeEnum
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options = AiCallOptions(
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operationType=OperationTypeEnum.PLAN,
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priority=None,
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processingMode=None
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC
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)
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# Get failover model list to find the model that will be used
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@ -118,60 +120,28 @@ class ContentValidator:
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return 8 * 1024
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def _analyzeDocumentsWithSizeLimit(self, documents: List[Any], maxTotalBytes: int) -> List[Dict[str, Any]]:
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"""Analyze documents with size limit, dividing available space evenly among documents."""
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"""
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Analyze documents for validation - METADATA ONLY (no document content/previews).
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For planning/validation, we only need metadata to assess format, type, and size compatibility.
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"""
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if not documents:
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return []
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# Reserve space for JSON structure overhead (approximately 200 bytes per document)
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jsonOverheadPerDoc = 200
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reservedOverhead = len(documents) * jsonOverheadPerDoc
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availableForContent = max(0, maxTotalBytes - reservedOverhead)
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# Divide available space evenly among documents
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bytesPerDoc = availableForContent // len(documents) if documents else 0
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# Ensure minimum space per document (at least 100 bytes)
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bytesPerDoc = max(bytesPerDoc, 100)
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logger.debug(f"Document summary space: total={maxTotalBytes} bytes, available={availableForContent} bytes, perDoc={bytesPerDoc} bytes")
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summaries = []
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for doc in documents:
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try:
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data = getattr(doc, 'documentData', None)
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name = getattr(doc, 'documentName', 'Unknown')
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mimeType = getattr(doc, 'mimeType', 'unknown')
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formatExt = self._detectFormat(doc)
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sizeInfo = self._calculateSize(doc)
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# Create preview with size limit
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preview = None
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if data is not None:
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if isinstance(data, (dict, list)):
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preview = json.dumps(data, indent=2, ensure_ascii=False)
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else:
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preview = str(data)
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# Truncate preview to fit within bytesPerDoc (accounting for JSON structure)
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# Estimate: preview takes ~70% of document summary space
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maxPreviewBytes = int(bytesPerDoc * 0.7)
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previewBytes = len(preview.encode('utf-8'))
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if previewBytes > maxPreviewBytes:
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# Truncate to fit
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truncated = preview.encode('utf-8')[:maxPreviewBytes]
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# Try to decode safely
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try:
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preview = truncated.decode('utf-8', errors='ignore')
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except:
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preview = truncated[:maxPreviewBytes-50].decode('utf-8', errors='ignore')
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preview += f"\n\n[Truncated - {self._formatBytes(sizeInfo['bytes'])} total]"
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# Only include metadata - NO document content/previews
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# This keeps prompts small and focused on validation criteria
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summary = {
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"name": name,
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"mimeType": mimeType,
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"format": formatExt,
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"size": sizeInfo["readable"],
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"preview": preview
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"size": sizeInfo["readable"]
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}
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summaries.append(summary)
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except Exception as e:
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@ -181,7 +151,6 @@ class ContentValidator:
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"mimeType": getattr(doc, 'mimeType', 'unknown'),
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"format": "unknown",
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"size": "0 B",
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"preview": None,
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"error": str(e)
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})
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@ -291,12 +260,14 @@ EXPECTED FORMAT: {intent.get('expectedFormat', 'unknown')}
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SUCCESS CRITERIA ({criteriaCount} items): {successCriteria}
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VALIDATION RULES:
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1. Check if delivered documents match expected data type
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IMPORTANT: You only have document METADATA (filename, format, size, mimeType) - NOT document content.
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Validate based on metadata only:
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1. Check if filenames are meaningful and match approximately the task objective
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2. Check if delivered formats are compatible with expected format
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3. Verify each success criterion is met based on document content/metadata
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4. Check document sizes are reasonable for the task
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5. Rate overall quality (0.0-1.0)
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6. Identify specific gaps based on what the user requested
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3. Check if document sizes are reasonable for the task objective
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4. Assess if filename and size combination suggests correct data type
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5. Rate overall quality (0.0-1.0) based on metadata indicators
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6. Identify specific gaps based on what the user requested (infer from filename, size, format - NOT content)
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OUTPUT FORMAT - JSON ONLY (no prose):
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{{
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@ -306,13 +277,13 @@ OUTPUT FORMAT - JSON ONLY (no prose):
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"formatMatch": false,
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"documentCount": {len(documents)},
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"successCriteriaMet": {[False] * criteriaCount},
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"gapAnalysis": "Describe what is missing or incorrect",
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"gapAnalysis": "Describe what is missing or incorrect based on filename, size, format metadata",
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"improvementSuggestions": ["General action to improve overall result"],
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"validationDetails": [
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{{
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"documentName": "document.ext",
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"issues": ["Specific problem with this document"],
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"suggestions": ["Specific fix for this document's issues"]
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"issues": ["Issue inferred from metadata (e.g., filename doesn't match task, size too small for objective)"],
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"suggestions": ["Specific fix based on metadata analysis"]
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}}
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]
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}}
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