{
"tool_response": {
"filePath": "\/var\/www\/scripts\/pipeline\/analyze.py",
"oldString": " # Parse JSON\n import re\n\n json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)\n if json_match:\n result = json.loads(json_match.group())\n return result.get(\"statements\", [])\n return []\n\n except Exception as e:\n db.log(\"ERROR\", f\"Statement extraction failed for chunk {chunk_id}: {e}\")\n protokoll.log_llm_call(\n request=f\"[statement_extraction] chunk_id={chunk_id}\",\n model_name=ANTHROPIC_MODEL if client else f\"ollama:{OLLAMA_CHAT_MODEL}\",\n status=\"error\",\n error_message=str(e),\n )\n return []",
"newString": " # Parse JSON\n import re\n\n json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)\n if json_match:\n result = json.loads(json_match.group())\n return {\n \"statements\": result.get(\"statements\", []),\n \"prompt_id\": prompt_id,\n \"prompt_version\": prompt_version,\n \"model_used\": model_name,\n }\n return {\"statements\": [], \"prompt_id\": prompt_id, \"prompt_version\": prompt_version}\n\n except Exception as e:\n db.log(\"ERROR\", f\"Statement extraction failed for chunk {chunk_id}: {e}\")\n protokoll.log_llm_call(\n request=f\"[statement_extraction] chunk_id={chunk_id}\",\n model_name=ANTHROPIC_MODEL if client else f\"ollama:{OLLAMA_CHAT_MODEL}\",\n status=\"error\",\n error_message=str(e),\n )\n return {\"statements\": [], \"prompt_id\": prompt_id, \"prompt_version\": prompt_version}",
"originalFile": "\"\"\"\nSemantic analysis for KI-System Pipeline\nEntity extraction, taxonomy classification, ontology mapping.\n\"\"\"\n\nimport json\nimport os\nimport time\n\nimport requests\n\nfrom config import ANTHROPIC_API_KEY, ANTHROPIC_MODEL, OLLAMA_CHAT_MODEL, OLLAMA_HOST\nfrom db import db\nfrom protokoll import protokoll\n\n\ndef get_anthropic_client():\n \"\"\"Get Anthropic API client.\"\"\"\n try:\n import anthropic\n\n if ANTHROPIC_API_KEY:\n db.log(\"INFO\", \"Using Anthropic API (Claude)\")\n return anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)\n else:\n db.log(\"WARNING\", \"No Anthropic API key found, falling back to Ollama\")\n except ImportError:\n db.log(\"WARNING\", \"anthropic package not installed, falling back to Ollama\")\n return None\n\n\ndef extract_entities_ollama(text, model=\"gemma3:27b-it-qat\"):\n \"\"\"Extract entities using Ollama.\"\"\"\n # Load prompt from database\n prompt_template = db.get_prompt(\"entity_extraction\")\n\n if not prompt_template:\n db.log(\"WARNING\", \"entity_extraction prompt not found in DB, using fallback\")\n prompt_template = \"\"\"Analysiere den Text und extrahiere wichtige Entitäten.\nKategorisiere als: PERSON, ORGANIZATION, CONCEPT, LOCATION\nAntworte NUR im JSON-Format:\n{\"entities\": [{\"name\": \"...\", \"type\": \"...\", \"description\": \"...\"}]}\n\nText:\n{{TEXT}}\"\"\"\n\n prompt = prompt_template.replace(\"{{TEXT}}\", text[:3000])\n\n try:\n start_time = time.time()\n response = requests.post(\n f\"{OLLAMA_HOST}\/api\/generate\",\n json={\"model\": model, \"prompt\": prompt, \"stream\": False, \"format\": \"json\"},\n timeout=120,\n )\n response.raise_for_status()\n data = response.json()\n duration_ms = int((time.time() - start_time) * 1000)\n\n # Parse JSON from response\n response_text = data.get(\"response\", \"{}\")\n\n # Log to ki-protokoll\n protokoll.log_llm_call(\n request=f\"[entity_extraction] {prompt[:500]}...\",\n response=response_text[:2000],\n model_name=f\"ollama:{model}\",\n tokens_input=data.get(\"prompt_eval_count\", 0),\n tokens_output=data.get(\"eval_count\", 0),\n duration_ms=duration_ms,\n status=\"completed\",\n )\n\n try:\n entities = json.loads(response_text)\n return entities.get(\"entities\", [])\n except json.JSONDecodeError:\n db.log(\"WARNING\", \"Failed to parse entity JSON from Ollama\")\n return []\n except Exception as e:\n db.log(\"ERROR\", f\"Ollama entity extraction failed: {e}\")\n protokoll.log_llm_call(\n request=f\"[entity_extraction] {prompt[:500]}...\",\n model_name=f\"ollama:{model}\",\n status=\"error\",\n error_message=str(e),\n )\n return []\n\n\ndef extract_entities_anthropic(text, client):\n \"\"\"Extract entities using Anthropic Claude.\"\"\"\n # Get prompt from database\n prompt_template = db.get_prompt(\"entity_extraction\")\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 # Log to ki-protokoll\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 # Extract JSON from response\n import re\n\n json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)\n if json_match:\n entities = json.loads(json_match.group())\n return entities.get(\"entities\", [])\n return []\n except Exception as e:\n db.log(\"ERROR\", f\"Anthropic entity extraction failed: {e}\")\n protokoll.log_llm_call(\n request=f\"[entity_extraction] {prompt[:500]}...\",\n model_name=ANTHROPIC_MODEL,\n status=\"error\",\n error_message=str(e),\n )\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 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=1000, 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=120,\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 # Log to ki-protokoll\n protokoll.log_llm_call(\n request=f\"[relation_extraction] {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 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 protokoll.log_llm_call(\n request=f\"[relation_extraction] {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 []\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 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 # Log to ki-protokoll\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 import re\n\n json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)\n if json_m... [TRUNCATED-2919096c1d538f48]",
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" json_match = re.search(r\"\\{[\\s\\S]*\\}\", response_text)",
" if json_match:",
" result = json.loads(json_match.group())",
"- return result.get(\"statements\", [])",
"- return []",
"+ return {",
"+ \"statements\": result.get(\"statements\", []),",
"+ \"prompt_id\": prompt_id,",
"+ \"prompt_version\": prompt_version,",
"+ \"model_used\": model_name,",
"+ }",
"+ return {\"statements\": [], \"prompt_id\": prompt_id, \"prompt_version\": prompt_version}",
" ",
" except Exception as e:",
" db.log(\"ERROR\", f\"Statement extraction failed for chunk {chunk_id}: {e}\")"
]
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" error_message=str(e),",
" )",
"- return []",
"+ return {\"statements\": [], \"prompt_id\": prompt_id, \"prompt_version\": prompt_version}",
" ",
" ",
" def find_entity_by_name(name: str) -> dict | None:"
]
}
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