{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "grep -A 30 \"def run_vision_step\" \/opt\/scripts\/pipeline\/vision.py | head -40",
"description": "Check vision step implementation"
}
}
{
"tool_response": {
"stdout": "def run_vision_step(document_id, file_path, config=None):\n \"\"\"\n Run vision analysis step for pipeline.\n\n Args:\n document_id: Document ID in database\n file_path: Path to PDF file\n config: Step configuration dict\n\n Returns:\n dict with success status and statistics\n \"\"\"\n if config is None:\n config = {}\n\n model = config.get(\"model\", DEFAULT_VISION_MODEL)\n store_images = config.get(\"store_images\", False)\n detect_images = config.get(\"detect_images\", True)\n detect_charts = config.get(\"detect_charts\", True)\n detect_tables = config.get(\"detect_tables\", True)\n\n # Build custom prompt based on config\n prompt_parts = [\"Analysiere diese Seite aus einem Schulungsdokument.\\n\\nBeschreibe strukturiert:\"]\n prompt_parts.append(\"1. **Überschriften\/Titel**: Welche Überschriften gibt es?\")\n prompt_parts.append(\"2. **Hauptinhalt**: Worum geht es auf dieser Seite?\")\n\n visual_parts = []\n if detect_images:\n visual_parts.append(\"Gibt es Bilder\/Fotos? Was zeigen sie?\")\n if detect_charts:\n visual_parts.append(\"Gibt es Diagramme\/Charts? Was stellen sie dar?\")",
"stderr": "",
"interrupted": false,
"isImage": false
}
}