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"content": " return []\n except Exception as e:\n db.log(\"ERROR\", f\"Ollama entity extraction failed: {e}\")\n return []\n\n\ndef extract_entities_anthropic(text: str, client) -> list[dict]:\n \"\"\"Extract entities using Anthropic Claude.\"\"\"\n prompt_data = db.get_prompt_by_use_case(\"entity_extraction\")\n prompt_template = prompt_data[\"content\"] if prompt_data else None\n\n if not prompt_template:\n prompt_template = \"\"\"Analysiere den folgenden deutschen Text und extrahiere alle wichtigen Entitäten.\n\nKategorisiere jede Entität als:\n- PERSON (Namen von Personen)\n- ORGANIZATION (Firmen, Institutionen, Gruppen)\n- CONCEPT (Fachbegriffe, Methoden, Theorien)\n- LOCATION (Orte, Länder)\n- DATE (Zeitangaben)\n- OTHER (Sonstiges)\n\nAntworte NUR im JSON-Format:\n{\"entities\": [{\"name\": \"...\", \"type\": \"...\", \"context\": \"kurzer Kontext der Erwähnung\"}]}\n\nText:\n{{TEXT}}\"\"\"\n\n prompt = prompt_template.replace(\"{{TEXT}}\", text[:4000])\n\n try:\n start_time = time.time()\n message = client.messages.create(\n model=ANTHROPIC_MODEL, max_tokens=2000, messages=[{\"role\": \"user\", \"content\": prompt}]\n )\n duration_ms = int((time.time() - start_time) * 1000)\n\n response_text = message.content[0].text\n\n protokoll.log_llm_call(\n request=f\"[entity_extraction] {prompt[:500]}...\",\n response=response_text[:2000],\n model_name=ANTHROPIC_MODEL,\n tokens_input=message.usage.input_tokens,\n tokens_output=message.usage.output_tokens,\n duration_ms=duration_ms,\n status=\"completed\",\n )\n\n json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)",
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