Protokoll #6505

{
    "event": "PreToolUse",
    "tool_name": "Read",
    "tool_input": {
        "file_path": "\/opt\/scripts\/pipeline\/analyze.py",
        "offset": 150,
        "limit": 80
    }
}

{
    "tool_response": {
        "type": "text",
        "file": {
            "filePath": "\/opt\/scripts\/pipeline\/analyze.py",
            "content": "        )\n        return []\n\n\ndef extract_relations(text, entities, client=None):\n    \"\"\"Extract relations between entities.\"\"\"\n    if not entities or len(entities) < 2:\n        return []\n\n    entity_names = [e[\"name\"] for e in entities[:20]]\n\n    # Load prompt from database\n    prompt_template = db.get_prompt(\"relation_extraction\")\n\n    if not prompt_template:\n        db.log(\"WARNING\", \"relation_extraction prompt not found in DB, using fallback\")\n        prompt_template = \"\"\"Identifiziere Beziehungen zwischen Entitäten.\nEntitäten: {{ENTITIES}}\nBeziehungstypen: DEVELOPED_BY, RELATED_TO, PART_OF, USED_IN, BASED_ON\nAntworte NUR im JSON-Format:\n{\"relations\": [{\"source\": \"...\", \"relation\": \"...\", \"target\": \"...\"}]}\n\nText:\n{{TEXT}}\"\"\"\n\n    prompt = prompt_template.replace(\"{{ENTITIES}}\", \", \".join(entity_names))\n    prompt = prompt.replace(\"{{TEXT}}\", text[:3000])\n\n    try:\n        if client:\n            message = client.messages.create(\n                model=ANTHROPIC_MODEL, max_tokens=1000, messages=[{\"role\": \"user\", \"content\": prompt}]\n            )\n            response_text = message.content[0].text\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=120,\n            )\n            response.raise_for_status()\n            response_text = response.json().get(\"response\", \"{}\")\n\n        import re\n\n        json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)\n        if json_match:\n            data = json.loads(json_match.group())\n            return data.get(\"relations\", [])\n        return []\n    except Exception as e:\n        db.log(\"ERROR\", f\"Relation extraction failed: {e}\")\n        return []\n\n\ndef classify_taxonomy(text, client=None):\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        if client:",
            "numLines": 80,
            "startLine": 150,
            "totalLines": 399
        }
    }
}