{
"event": "PreToolUse",
"tool_name": "Read",
"tool_input": {
"file_path": "\/opt\/scripts\/pipeline\/vision.py",
"offset": 265,
"limit": 30
}
}
{
"tool_response": {
"type": "text",
"file": {
"filePath": "\/opt\/scripts\/pipeline\/vision.py",
"content": " visual_parts.append(\"Gibt es Tabellen? Was enthalten sie?\")\n\n if visual_parts:\n prompt_parts.append(\"3. **Visuelle Elemente**:\\n - \" + \"\\n - \".join(visual_parts))\n\n prompt_parts.append(\"4. **Layout**: Wie ist die Seite aufgebaut?\")\n prompt_parts.append(\"5. **Schlüsselbegriffe**: Welche wichtigen Begriffe\/Konzepte werden genannt?\")\n prompt_parts.append(\"\\nAntworte auf Deutsch und sei präzise.\")\n\n custom_prompt = \"\\n\".join(prompt_parts)\n\n try:\n # Analyze document\n results = analyze_document(file_path, model=model, store_images=store_images)\n\n # Store results\n stored = store_page_analysis(document_id, results)\n\n # Calculate statistics\n successful = sum(1 for r in results if r[\"analysis\"])\n total_tokens = sum(r[\"eval_tokens\"] for r in results)\n total_time_ms = sum(r[\"eval_duration_ms\"] for r in results)\n\n return {\n \"success\": True,\n \"pages_total\": len(results),\n \"pages_analyzed\": successful,\n \"pages_stored\": stored,\n \"total_tokens\": total_tokens,\n \"total_time_ms\": total_time_ms,",
"numLines": 30,
"startLine": 265,
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}