{
"event": "PostToolUse",
"tool_name": "TaskOutput",
"tool_response": {
"retrieval_status": "success",
"task": {
"task_id": "ae51767",
"task_type": "local_agent",
"status": "completed",
"description": "Refactor pipeline.py modular",
"output": "[Tool: Read] {\"file_path\":\"\/var\/www\/CLAUDE.md\"}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/pipeline.py\"}\n[Tool: mcp__mcp-tasks__tasks_get] {\"id\":359}\n[Tool: mcp__mcp-tasks__tasks_get] {\"id\":359}\n[Tool: Glob] {\"pattern\":\"*.py\",\"path\":\"\/var\/www\/scripts\/pipeline\"}\n[Tool: Bash] {\"command\":\"ls -la \/var\/www\/scripts\/pipeline\/\",\"description\":\"List pipeline directory contents\"}\n[Tool: Bash] {\"command\":\"ls -la \/var\/www\/scripts\/pipeline\/\",\"description\":\"List pipeline directory contents\"}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/extract.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/chunk.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/embed.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/db.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/db.py\",\"limit\":50}\n[Tool: TodoWrite] {\"todos\":[{\"content\":\"Create step_extract.py module (extraction step)\",\"status\":\"in_progress\",\"activeForm\":\"Creating step_extract.py module\"},{\"content\":\"Create step_transform.py module (chunking + analysis)\",\"status\":\"pending\",\"activeForm\":\"Creating step_transform.py module\"},{\"content\":\"Create step_load.py module (database operations)\",\"status\":\"pending\",\"activeForm\":\"Creating step_load.py module\"},{\"content\":\"Create step_embed.py module (embedding generation)\",\"status\":\"pending\",\"activeForm\":\"Creating step_embed.py module\"},{\"content\":\"Refactor pipeline.py to use new modules\",\"status\":\"pending\",\"activeForm\":\"Refactoring pipeline.py orchestrator\"},{\"content\":\"Run \/var\/www\/scripts\/python-check.sh for quality validation\",\"status\":\"pending\",\"activeForm\":\"Running python-check.sh\"}]}\n[Tool: TodoWrite] {\"todos\":[{\"content\":\"Create step_extract.py module (extraction step)\",\"status\":\"in_progress\",\"activeForm\":\"Creating step_extract.py module\"},{\"content\":\"Create step_transform.py module (chunking + analysis)\",\"status\":\"pending\",\"activeForm\":\"Creating step_transform.py module\"},{\"content\":\"Create step_load.py module (database operations)\",\"status\":\"pending\",\"activeForm\":\"Creating step_load.py module\"},{\"content\":\"Create step_embed.py module (embedding generation)\",\"status\":\"pending\",\"activeForm\":\"Creating step_embed.py module\"},{\"content\":\"Refactor pipeline.py to use new modules\",\"status\":\"pending\",\"activeForm\":\"Refactoring pipeline.py orchestrator\"},{\"content\":\"Run \/var\/www\/scripts\/python-check.sh for quality validation\",\"status\":\"pending\",\"activeForm\":\"Running python-check.sh\"}]}\n[Tool: Write] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/step_extract.py\",\"content\":\"\\\"\\\"\\\"\\nExtraction Step Module\\nHandles document text extraction for all supported formats.\\n\\nPart of modularized pipeline architecture.\\n\\\"\\\"\\\"\\n\\nimport hashlib\\nimport os\\nfrom pathlib import Path\\n\\nfrom extract import extract, get_full_text\\n\\n\\nclass ExtractionStep:\\n \\\"\\\"\\\"Step: Extract text from documents.\\\"\\\"\\\"\\n\\n def __init__(self, db, progress=None):\\n \\\"\\\"\\\"\\n Initialize extraction step.\\n\\n Args:\\n db: Database instance\\n progress: Optional PipelineProgress instance\\n \\\"\\\"\\\"\\n self.db = db\\n self.progress = progress\\n\\n def execute(self, file_path):\\n \\\"\\\"\\\"\\n Extract text from document.\\n\\n Args:\\n file_path: Path to document file\\n\\n Returns:\\n dict: {\\n 'success': bool,\\n 'extraction': dict with content\/metadata,\\n 'file_info': dict with hash\/size\/type,\\n 'error': str (if failed)\\n }\\n \\\"\\\"\\\"\\n file_name = Path(file_path).name\\n\\n if self.progress:\\n self.progress.update_step(\\\"extract\\\")\\n self.progress.add_log(f\\\"Extrahiere Text: {file_name}\\\")\\n\\n self.db.log(\\\"INFO\\\", f\\\"Extracting: {file_path}\\\")\\n\\n # Check cancellation\\n if self.progress and self.progress.is_cancelled():\\n return {\\\"success\\\": False, \\\"error\\\": \\\"cancelled\\\"}\\n\\n # Extract content\\n extraction = extract(file_path)\\n if not extraction[\\\"success\\\"]:\\n error = extraction.get(\\\"error\\\", \\\"Unknown extraction error\\\")\\n self.db.log(\\\"ERROR\\\", f\\\"Extraction failed: {error}\\\")\\n if self.progress:\\n self.progress.add_log(\\\"FEHLER: Extraktion fehlgeschlagen\\\")\\n return {\\\"success\\\": False, \\\"error\\\": error}\\n\\n # Get file metadata\\n file_stat = os.stat(file_path)\\n with open(file_path, \\\"rb\\\") as f:\\n file_hash = hashlib.sha256(f.read()).hexdigest()\\n\\n file_info = {\\n \\\"path\\\": file_path,\\n \\\"name\\\": file_name,\\n \\\"type\\\": extraction[\\\"file_type\\\"],\\n \\\"size\\\": file_stat.st_size,\\n \\\"hash\\\": file_hash,\\n }\\n\\n # Count pages for PDFs\\n total_pages = 0\\n if extraction[\\\"file_type\\\"] == \\\".pdf\\\" and isinstance(extraction.get(\\\"content\\\"), list):\\n total_pages = len(extraction[\\\"content\\\"])\\n if self.progress:\\n self.progress.add_log(f\\\"{total_pages} Seiten extrahiert\\\")\\n\\n self.db.log(\\\"INFO\\\", f\\\"Extraction complete: {total_pages} pages\\\" if total_pages else \\\"Extraction complete\\\")\\n\\n return {\\n \\\"success\\\": True,\\n \\\"extraction\\\": extraction,\\n \\\"file_info\\\": file_info,\\n \\\"total_pages\\\": total_pages,\\n }\\n\\n def get_full_text_from_extraction(self, extraction):\\n \\\"\\\"\\\"\\n Get full text from extraction result.\\n\\n Args:\\n extraction: Extraction result dict\\n\\n Returns:\\n str: Full document text\\n \\\"\\\"\\\"\\n return get_full_text(extraction)\\n\"}\n[Tool: TodoWrite] {\"todos\":[{\"content\":\"Create step_extract.py module (extraction step)\",\"status\":\"completed\",\"activeForm\":\"Creating step_extract.py module\"},{\"content\":\"Create step_transform.py module (chunking + analysis)\",\"status\":\"in_progress\",\"activeForm\":\"Creating step_transform.py module\"},{\"content\":\"Create step_load.py module (database operations)\",\"status\":\"pending\",\"activeForm\":\"Creating step_load.py module\"},{\"content\":\"Create step_embed.py module (embedding generation)\",\"status\":\"pending\",\"activeForm\":\"Creating step_embed.py module\"},{\"content\":\"Refactor pipeline.py to use new modules\",\"status\":\"pending\",\"activeForm\":\"Refactoring pipeline.py orchestrator\"},{\"content\":\"Run \/var\/www\/scripts\/python-check.sh for quality validation\",\"status\":\"pending\",\"activeForm\":\"Running python-check.sh\"}]}\n[Tool: Write] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/step_transform.py\",\"content\":\"\\\"\\\"\\\"\\nTransformation Step Module\\nHandles chunking and semantic analysis of extracted content.\\n\\nPart of modularized pipeline architecture.\\n\\\"\\\"\\\"\\n\\nimport json\\n\\nfrom analyze import analyze_document\\nfrom chunk import chunk_by_structure\\nfrom enrich import run_enrichment_step\\nfrom vision import run_vision_step\\n\\n\\nclass TransformationStep:\\n \\\"\\\"\\\"Step: Transform extracted content into chunks and analyze.\\\"\\\"\\\"\\n\\n def __init__(self, db, progress=None):\\n \\\"\\\"\\\"\\n Initialize transformation step.\\n\\n Args:\\n db: Database instance\\n progress: Optional PipelineProgress instance\\n \\\"\\\"\\\"\\n self.db = db\\n self.progress = progress\\n\\n def execute_vision(self, doc_id, file_path, file_type):\\n \\\"\\\"\\\"\\n Execute vision analysis for PDFs.\\n\\n Args:\\n doc_id: Document database ID\\n file_path: Path to file\\n file_type: File extension\\n\\n Returns:\\n dict: Vision analysis result\\n \\\"\\\"\\\"\\n if file_type != \\\".pdf\\\":\\n return {\\\"success\\\": False, \\\"error\\\": \\\"Not a PDF\\\"}\\n\\n if self.progress:\\n self.progress.update_step(\\\"vision\\\")\\n self.progress.add_log(\\\"Vision-Analyse gestartet...\\\")\\n\\n self.db.log(\\\"INFO\\\", f\\\"Running vision analysis for document {doc_id}\\\")\\n\\n vision_config = {\\n \\\"model\\\": \\\"llama3.2-vision:11b\\\",\\n \\\"store_images\\\": True,\\n \\\"detect_images\\\": True,\\n \\\"detect_charts\\\": True,\\n \\\"detect_tables\\\": True,\\n }\\n\\n vision_result = run_vision_step(doc_id, file_path, vision_config, progress=self.progress)\\n\\n if vision_result[\\\"success\\\"]:\\n self.db.log(\\\"INFO\\\", f\\\"Vision: {vision_result['pages_analyzed']}\/{vision_result['pages_total']} pages\\\")\\n if self.progress:\\n self.progress.add_log(f\\\"Vision: {vision_result['pages_analyzed']} Seiten analysiert\\\")\\n else:\\n self.db.log(\\\"WARNING\\\", f\\\"Vision analysis failed: {vision_result.get('error')}\\\")\\n\\n return vision_result\\n\\n def execute_chunking(self, extraction, total_pages=0):\\n \\\"\\\"\\\"\\n Chunk extracted content.\\n\\n Args:\\n extraction: Extraction result dict\\n total_pages: Number of pages (for logging)\\n\\n Returns:\\n list: Chunk dictionaries\\n \\\"\\\"\\\"\\n if self.progress:\\n self.progress.update_step(\\\"chunk\\\")\\n if total_pages > 0:\\n self.progress.add_log(f\\\"Erstelle Chunks aus {total_pages} Seiten...\\\")\\n else:\\n self.progress.add_log(\\\"Erstelle Chunks...\\\")\\n\\n chunks = chunk_by_structure(extraction)\\n self.db.log(\\\"INFO\\\", f\\\"Created {len(chunks)} chunks\\\")\\n\\n if self.progress:\\n msg = f\\\"{len(chunks)} Chunks erstellt\\\"\\n if total_pages > 0:\\n msg += f\\\" (aus {total_pages} Seiten)\\\"\\n self.progress.add_log(msg)\\n\\n return chunks\\n\\n def execute_enrichment(self, doc_id, file_type):\\n \\\"\\\"\\\"\\n Enrich chunks with vision context (PDFs only).\\n\\n Args:\\n doc_id: Document database ID\\n file_type: File exte... [TRUNCATED-6abf4ff25ac36552]",
"prompt": "Lese \/var\/www\/CLAUDE.md für Projektkontext.\n\n**Task #359: pipeline.py - Pipeline-Schritte modularisieren**\n\n1. Analysiere `\/var\/www\/scripts\/pipeline\/pipeline.py` (452 LOC)\n2. Erstelle separate Step-Module:\n - `step_extract.py` - Extraktion\n - `step_transform.py` - Transformation\n - `step_load.py` - Laden\n - `step_embed.py` - Embedding\n3. Halte Pipeline-Orchestrator schlank\n4. Führe `\/var\/www\/scripts\/python-check.sh` aus\n\nMax 150 LOC pro Step-Modul. Klare Step-Interface-Definition.",
"result": "[Tool: Read] {\"file_path\":\"\/var\/www\/CLAUDE.md\"}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/pipeline.py\"}\n[Tool: mcp__mcp-tasks__tasks_get] {\"id\":359}\n[Tool: mcp__mcp-tasks__tasks_get] {\"id\":359}\n[Tool: Glob] {\"pattern\":\"*.py\",\"path\":\"\/var\/www\/scripts\/pipeline\"}\n[Tool: Bash] {\"command\":\"ls -la \/var\/www\/scripts\/pipeline\/\",\"description\":\"List pipeline directory contents\"}\n[Tool: Bash] {\"command\":\"ls -la \/var\/www\/scripts\/pipeline\/\",\"description\":\"List pipeline directory contents\"}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/extract.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/chunk.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/embed.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/db.py\",\"limit\":50}\n[Tool: Read] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/db.py\",\"limit\":50}\n[Tool: TodoWrite] {\"todos\":[{\"content\":\"Create step_extract.py module (extraction step)\",\"status\":\"in_progress\",\"activeForm\":\"Creating step_extract.py module\"},{\"content\":\"Create step_transform.py module (chunking + analysis)\",\"status\":\"pending\",\"activeForm\":\"Creating step_transform.py module\"},{\"content\":\"Create step_load.py module (database operations)\",\"status\":\"pending\",\"activeForm\":\"Creating step_load.py module\"},{\"content\":\"Create step_embed.py module (embedding generation)\",\"status\":\"pending\",\"activeForm\":\"Creating step_embed.py module\"},{\"content\":\"Refactor pipeline.py to use new modules\",\"status\":\"pending\",\"activeForm\":\"Refactoring pipeline.py orchestrator\"},{\"content\":\"Run \/var\/www\/scripts\/python-check.sh for quality validation\",\"status\":\"pending\",\"activeForm\":\"Running python-check.sh\"}]}\n[Tool: TodoWrite] {\"todos\":[{\"content\":\"Create step_extract.py module (extraction step)\",\"status\":\"in_progress\",\"activeForm\":\"Creating step_extract.py module\"},{\"content\":\"Create step_transform.py module (chunking + analysis)\",\"status\":\"pending\",\"activeForm\":\"Creating step_transform.py module\"},{\"content\":\"Create step_load.py module (database operations)\",\"status\":\"pending\",\"activeForm\":\"Creating step_load.py module\"},{\"content\":\"Create step_embed.py module (embedding generation)\",\"status\":\"pending\",\"activeForm\":\"Creating step_embed.py module\"},{\"content\":\"Refactor pipeline.py to use new modules\",\"status\":\"pending\",\"activeForm\":\"Refactoring pipeline.py orchestrator\"},{\"content\":\"Run \/var\/www\/scripts\/python-check.sh for quality validation\",\"status\":\"pending\",\"activeForm\":\"Running python-check.sh\"}]}\n[Tool: Write] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/step_extract.py\",\"content\":\"\\\"\\\"\\\"\\nExtraction Step Module\\nHandles document text extraction for all supported formats.\\n\\nPart of modularized pipeline architecture.\\n\\\"\\\"\\\"\\n\\nimport hashlib\\nimport os\\nfrom pathlib import Path\\n\\nfrom extract import extract, get_full_text\\n\\n\\nclass ExtractionStep:\\n \\\"\\\"\\\"Step: Extract text from documents.\\\"\\\"\\\"\\n\\n def __init__(self, db, progress=None):\\n \\\"\\\"\\\"\\n Initialize extraction step.\\n\\n Args:\\n db: Database instance\\n progress: Optional PipelineProgress instance\\n \\\"\\\"\\\"\\n self.db = db\\n self.progress = progress\\n\\n def execute(self, file_path):\\n \\\"\\\"\\\"\\n Extract text from document.\\n\\n Args:\\n file_path: Path to document file\\n\\n Returns:\\n dict: {\\n 'success': bool,\\n 'extraction': dict with content\/metadata,\\n 'file_info': dict with hash\/size\/type,\\n 'error': str (if failed)\\n }\\n \\\"\\\"\\\"\\n file_name = Path(file_path).name\\n\\n if self.progress:\\n self.progress.update_step(\\\"extract\\\")\\n self.progress.add_log(f\\\"Extrahiere Text: {file_name}\\\")\\n\\n self.db.log(\\\"INFO\\\", f\\\"Extracting: {file_path}\\\")\\n\\n # Check cancellation\\n if self.progress and self.progress.is_cancelled():\\n return {\\\"success\\\": False, \\\"error\\\": \\\"cancelled\\\"}\\n\\n # Extract content\\n extraction = extract(file_path)\\n if not extraction[\\\"success\\\"]:\\n error = extraction.get(\\\"error\\\", \\\"Unknown extraction error\\\")\\n self.db.log(\\\"ERROR\\\", f\\\"Extraction failed: {error}\\\")\\n if self.progress:\\n self.progress.add_log(\\\"FEHLER: Extraktion fehlgeschlagen\\\")\\n return {\\\"success\\\": False, \\\"error\\\": error}\\n\\n # Get file metadata\\n file_stat = os.stat(file_path)\\n with open(file_path, \\\"rb\\\") as f:\\n file_hash = hashlib.sha256(f.read()).hexdigest()\\n\\n file_info = {\\n \\\"path\\\": file_path,\\n \\\"name\\\": file_name,\\n \\\"type\\\": extraction[\\\"file_type\\\"],\\n \\\"size\\\": file_stat.st_size,\\n \\\"hash\\\": file_hash,\\n }\\n\\n # Count pages for PDFs\\n total_pages = 0\\n if extraction[\\\"file_type\\\"] == \\\".pdf\\\" and isinstance(extraction.get(\\\"content\\\"), list):\\n total_pages = len(extraction[\\\"content\\\"])\\n if self.progress:\\n self.progress.add_log(f\\\"{total_pages} Seiten extrahiert\\\")\\n\\n self.db.log(\\\"INFO\\\", f\\\"Extraction complete: {total_pages} pages\\\" if total_pages else \\\"Extraction complete\\\")\\n\\n return {\\n \\\"success\\\": True,\\n \\\"extraction\\\": extraction,\\n \\\"file_info\\\": file_info,\\n \\\"total_pages\\\": total_pages,\\n }\\n\\n def get_full_text_from_extraction(self, extraction):\\n \\\"\\\"\\\"\\n Get full text from extraction result.\\n\\n Args:\\n extraction: Extraction result dict\\n\\n Returns:\\n str: Full document text\\n \\\"\\\"\\\"\\n return get_full_text(extraction)\\n\"}\n[Tool: TodoWrite] {\"todos\":[{\"content\":\"Create step_extract.py module (extraction step)\",\"status\":\"completed\",\"activeForm\":\"Creating step_extract.py module\"},{\"content\":\"Create step_transform.py module (chunking + analysis)\",\"status\":\"in_progress\",\"activeForm\":\"Creating step_transform.py module\"},{\"content\":\"Create step_load.py module (database operations)\",\"status\":\"pending\",\"activeForm\":\"Creating step_load.py module\"},{\"content\":\"Create step_embed.py module (embedding generation)\",\"status\":\"pending\",\"activeForm\":\"Creating step_embed.py module\"},{\"content\":\"Refactor pipeline.py to use new modules\",\"status\":\"pending\",\"activeForm\":\"Refactoring pipeline.py orchestrator\"},{\"content\":\"Run \/var\/www\/scripts\/python-check.sh for quality validation\",\"status\":\"pending\",\"activeForm\":\"Running python-check.sh\"}]}\n[Tool: Write] {\"file_path\":\"\/var\/www\/scripts\/pipeline\/step_transform.py\",\"content\":\"\\\"\\\"\\\"\\nTransformation Step Module\\nHandles chunking and semantic analysis of extracted content.\\n\\nPart of modularized pipeline architecture.\\n\\\"\\\"\\\"\\n\\nimport json\\n\\nfrom analyze import analyze_document\\nfrom chunk import chunk_by_structure\\nfrom enrich import run_enrichment_step\\nfrom vision import run_vision_step\\n\\n\\nclass TransformationStep:\\n \\\"\\\"\\\"Step: Transform extracted content into chunks and analyze.\\\"\\\"\\\"\\n\\n def __init__(self, db, progress=None):\\n \\\"\\\"\\\"\\n Initialize transformation step.\\n\\n Args:\\n db: Database instance\\n progress: Optional PipelineProgress instance\\n \\\"\\\"\\\"\\n self.db = db\\n self.progress = progress\\n\\n def execute_vision(self, doc_id, file_path, file_type):\\n \\\"\\\"\\\"\\n Execute vision analysis for PDFs.\\n\\n Args:\\n doc_id: Document database ID\\n file_path: Path to file\\n file_type: File extension\\n\\n Returns:\\n dict: Vision analysis result\\n \\\"\\\"\\\"\\n if file_type != \\\".pdf\\\":\\n return {\\\"success\\\": False, \\\"error\\\": \\\"Not a PDF\\\"}\\n\\n if self.progress:\\n self.progress.update_step(\\\"vision\\\")\\n self.progress.add_log(\\\"Vision-Analyse gestartet...\\\")\\n\\n self.db.log(\\\"INFO\\\", f\\\"Running vision analysis for document {doc_id}\\\")\\n\\n vision_config = {\\n \\\"model\\\": \\\"llama3.2-vision:11b\\\",\\n \\\"store_images\\\": True,\\n \\\"detect_images\\\": True,\\n \\\"detect_charts\\\": True,\\n \\\"detect_tables\\\": True,\\n }\\n\\n vision_result = run_vision_step(doc_id, file_path, vision_config, progress=self.progress)\\n\\n if vision_result[\\\"success\\\"]:\\n self.db.log(\\\"INFO\\\", f\\\"Vision: {vision_result['pages_analyzed']}\/{vision_result['pages_total']} pages\\\")\\n if self.progress:\\n self.progress.add_log(f\\\"Vision: {vision_result['pages_analyzed']} Seiten analysiert\\\")\\n else:\\n self.db.log(\\\"WARNING\\\", f\\\"Vision analysis failed: {vision_result.get('error')}\\\")\\n\\n return vision_result\\n\\n def execute_chunking(self, extraction, total_pages=0):\\n \\\"\\\"\\\"\\n Chunk extracted content.\\n\\n Args:\\n extraction: Extraction result dict\\n total_pages: Number of pages (for logging)\\n\\n Returns:\\n list: Chunk dictionaries\\n \\\"\\\"\\\"\\n if self.progress:\\n self.progress.update_step(\\\"chunk\\\")\\n if total_pages > 0:\\n self.progress.add_log(f\\\"Erstelle Chunks aus {total_pages} Seiten...\\\")\\n else:\\n self.progress.add_log(\\\"Erstelle Chunks...\\\")\\n\\n chunks = chunk_by_structure(extraction)\\n self.db.log(\\\"INFO\\\", f\\\"Created {len(chunks)} chunks\\\")\\n\\n if self.progress:\\n msg = f\\\"{len(chunks)} Chunks erstellt\\\"\\n if total_pages > 0:\\n msg += f\\\" (aus {total_pages} Seiten)\\\"\\n self.progress.add_log(msg)\\n\\n return chunks\\n\\n def execute_enrichment(self, doc_id, file_type):\\n \\\"\\\"\\\"\\n Enrich chunks with vision context (PDFs only).\\n\\n Args:\\n doc_id: Document database ID\\n file_type: File exte... [TRUNCATED-6abf4ff25ac36552]"
}
}
}