{
"event": "PreToolUse",
"tool_name": "Edit",
"tool_input": {
"file_path": "\/var\/www\/scripts\/pipeline\/analyzers\/taxonomy_classifier.py",
"old_string": "from config import ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST\nfrom db import db\nfrom protokoll import protokoll",
"new_string": "from config import ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST\nfrom constants import MS_PER_SECOND, OLLAMA_TIMEOUT\nfrom db import db\nfrom protokoll import protokoll"
}
}
{
"tool_response": {
"filePath": "\/var\/www\/scripts\/pipeline\/analyzers\/taxonomy_classifier.py",
"oldString": "from config import ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST\nfrom db import db\nfrom protokoll import protokoll",
"newString": "from config import ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST\nfrom constants import MS_PER_SECOND, OLLAMA_TIMEOUT\nfrom db import db\nfrom protokoll import protokoll",
"originalFile": "\"\"\"\nTaxonomy Classification - Classify text into taxonomy categories.\n\"\"\"\n\nimport json\nimport re\nimport sys\nimport time\n\nimport requests\n\nsys.path.insert(0, \"\/var\/www\/scripts\/pipeline\")\n\nfrom config import ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST\nfrom db import db\nfrom protokoll import protokoll\n\n\ndef classify_taxonomy(text: str, client=None) -> dict:\n \"\"\"Classify text into taxonomy categories.\"\"\"\n prompt_template = db.get_prompt(\"taxonomy_classification\")\n\n if not prompt_template:\n prompt_template = \"\"\"Klassifiziere den folgenden Text in passende Kategorien.\n\nWähle aus diesen Hauptkategorien:\n- Methoden (Therapiemethoden, Techniken)\n- Theorie (Konzepte, Modelle, Grundlagen)\n- Praxis (Anwendung, Fallbeispiele)\n- Organisation (Strukturen, Prozesse)\n- Kommunikation (Gesprächsführung, Interaktion)\n- Entwicklung (Persönliche Entwicklung, Veränderung)\n\nAntworte NUR im JSON-Format:\n{\"categories\": [\"...\", \"...\"], \"confidence\": 0.0-1.0}\n\nText:\n{{TEXT}}\"\"\"\n\n prompt = prompt_template.replace(\"{{TEXT}}\", text[:2000])\n\n try:\n start_time = time.time()\n tokens_in, tokens_out = 0, 0\n model_name = \"\"\n\n if client:\n message = client.messages.create(\n model=ANTHROPIC_MODEL, max_tokens=500, messages=[{\"role\": \"user\", \"content\": prompt}]\n )\n response_text = message.content[0].text\n tokens_in = message.usage.input_tokens\n tokens_out = message.usage.output_tokens\n model_name = ANTHROPIC_MODEL\n else:\n response = requests.post(\n f\"{OLLAMA_HOST}\/api\/generate\",\n json={\"model\": OLLAMA_CHAT_MODEL, \"prompt\": prompt, \"stream\": False, \"format\": \"json\"},\n timeout=60,\n )\n response.raise_for_status()\n data = response.json()\n response_text = data.get(\"response\", \"{}\")\n tokens_in = data.get(\"prompt_eval_count\", 0)\n tokens_out = data.get(\"eval_count\", 0)\n model_name = f\"ollama:{OLLAMA_CHAT_MODEL}\"\n\n duration_ms = int((time.time() - start_time) * 1000)\n\n protokoll.log_llm_call(\n request=f\"[taxonomy_classification] {prompt[:500]}...\",\n response=response_text[:2000],\n model_name=model_name,\n tokens_input=tokens_in,\n tokens_output=tokens_out,\n duration_ms=duration_ms,\n status=\"completed\",\n )\n\n json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)\n if json_match:\n return json.loads(json_match.group())\n return {\"categories\": [], \"confidence\": 0}\n except Exception as e:\n db.log(\"ERROR\", f\"Taxonomy classification failed: {e}\")\n protokoll.log_llm_call(\n request=f\"[taxonomy_classification] {prompt[:500]}...\",\n model_name=ANTHROPIC_MODEL if client else f\"ollama:{OLLAMA_CHAT_MODEL}\",\n status=\"error\",\n error_message=str(e),\n )\n return {\"categories\": [], \"confidence\": 0}\n",
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"lines": [
" sys.path.insert(0, \"\/var\/www\/scripts\/pipeline\")",
" ",
" from config import ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST",
"+from constants import MS_PER_SECOND, OLLAMA_TIMEOUT",
" from db import db",
" from protokoll import protokoll",
" "
]
}
],
"userModified": false,
"replaceAll": false
}
}