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"content": "\n # Entity Types\n def get_entity_types(self, active_only: bool = True) -> list[dict]:\n \"\"\"Get all entity types from database.\n\n Returns:\n List of dicts with code, name, description, criteria, indicators, examples\n \"\"\"\n query = \"\"\"SELECT code, name, description, criteria, indicators, examples\n FROM entity_types\"\"\"\n if active_only:\n query += \" WHERE is_active = 1\"\n query += \" ORDER BY sort_order\"\n\n cursor = self.execute(query)\n results = cursor.fetchall()\n cursor.close()\n return list(results) if results else []\n\n def get_entity_type_codes(self) -> set[str]:\n \"\"\"Get set of valid entity type codes.\"\"\"\n cursor = self.execute(\"SELECT code FROM entity_types WHERE is_active = 1\")\n results = cursor.fetchall()\n cursor.close()\n return {r[\"code\"] for r in results} if results else set()\n\n def build_entity_prompt_categories(self) -> str:\n \"\"\"Build categories section for entity extraction prompt from DB.\"\"\"\n types = self.get_entity_types()\n lines = []\n for t in types:\n lines.append(f\" {t['code']}: {t['criteria']}\")\n return \"\\n\".join(lines)\n\n # Protokoll Logging (LLM calls)\n def log_to_protokoll(\n self,\n client_name,\n request,\n response=None,\n model_name=None,\n tokens_input=0,\n tokens_output=0,\n duration_ms=0,\n status=\"completed\",\n error_message=None,\n ):\n \"\"\"\n Log LLM call to ki_dev.protokoll table.\n\n Args:\n client_name: Caller identifier (e.g., 'content-studio', 'pipeline')\n request: The prompt sent to the LLM\n response: The LLM response\n model_name: Model used (e.g., 'claude-opus-4-5-20251101')\n tokens_input: Input token count\n tokens_output: Output token count\n duration_ms: Request duration in milliseconds\n status: 'pending', 'completed', or 'error'\n error_message: Error details if status is 'error'",
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