{
"event": "PreToolUse",
"tool_name": "Read",
"tool_input": {
"file_path": "\/var\/www\/scripts\/pipeline\/generate.py",
"offset": 470,
"limit": 50
}
}
{
"tool_response": {
"type": "text",
"file": {
"filePath": "\/var\/www\/scripts\/pipeline\/generate.py",
"content": " )\n response_text = message.content[0].text\n\n # Extract token usage from Anthropic response\n if hasattr(message, \"usage\"):\n tokens_input = getattr(message.usage, \"input_tokens\", 0)\n tokens_output = getattr(message.usage, \"output_tokens\", 0)\n else:\n # Fallback to Ollama\n import requests\n\n model_name = OLLAMA_CHAT_MODEL\n\n response = requests.post(\n f\"{OLLAMA_HOST}\/api\/generate\",\n json={\"model\": OLLAMA_CHAT_MODEL, \"prompt\": prompt, \"stream\": False},\n timeout=300,\n )\n response.raise_for_status()\n result = response.json()\n response_text = result.get(\"response\", \"\")\n\n # Extract token counts from Ollama response\n tokens_input = result.get(\"prompt_eval_count\", 0)\n tokens_output = result.get(\"eval_count\", 0)\n\n # Clean up model artifacts (Gemma, Llama, etc.)\n artifacts = [\n \"<start_of_turn>\", \"<\/start_of_turn>\",\n \"<end_of_turn>\", \"<\/end_of_turn>\",\n \"<\/s>\", \"<|eot_id|>\", \"<|im_end|>\",\n ]\n for artifact in artifacts:\n response_text = response_text.replace(artifact, \"\").strip()\n\n except Exception as e:\n status = \"error\"\n error_message = str(e)\n raise\n\n finally:\n # Calculate duration\n duration_ms = int((time.time() - start_time) * 1000)\n\n # Log to protokoll\n db.log_to_protokoll(\n client_name=client_name,\n request=prompt,\n response=response_text if status == \"completed\" else None,\n model_name=model_name,",
"numLines": 50,
"startLine": 470,
"totalLines": 987
}
}
}