{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "DB_PASSWORD=\"v8mFLmkF2fth_r\" timeout 360 .\/venv\/bin\/python -c \"\nimport json\nimport re\nimport ollama\nfrom db import db\n\ndb.connect()\n\n# Get entities without knowledge semantics\ncursor = db.execute('''\n SELECT e.id, e.name, e.type, e.description, ce.chunk_id, LEFT(c.content, 500) as context\n FROM entities e \n JOIN chunk_entities ce ON e.id = ce.entity_id \n JOIN chunks c ON ce.chunk_id = c.id \n LEFT JOIN entity_knowledge_semantics eks ON e.id = eks.entity_id \n WHERE c.document_id = 12 AND eks.id IS NULL\n LIMIT 20\n''')\nentities = cursor.fetchall()\ncursor.close()\n\nprint(f'Analysiere {len(entities)} Entitäten...')\nprint()\n\nPROMPT = '''Analysiere die Bedeutung dieser Entität. Antworte NUR mit JSON:\n{\\\"semantic_role\\\": \\\"agent\\\", \\\"functional_category\\\": \\\"concept\\\", \\\"context_meaning\\\": \\\"Bedeutung\\\"}\n\nsemantic_role: agent (handelt), patient (wird behandelt), instrument (Werkzeug), location (Ort), cause (Ursache), effect (Wirkung)\nfunctional_category: method, tool, concept, actor, outcome, process\n\nEntität: {name} (Typ: {entity_type})\nKontext: {context}'''\n\nsuccess = 0\nerrors = 0\n\nfor entity in entities:\n try:\n prompt = PROMPT.format(\n name=entity['name'],\n entity_type=entity['type'],\n context=(entity['context'] or '')[:600]\n )\n \n response = ollama.generate(model='mistral', prompt=prompt, options={'num_predict': 200})\n resp_text = response['response'].strip()\n \n match = re.search(r'\\{[^{}]+\\}', resp_text)\n if match:\n sem = json.loads(match.group())\n \n # Map and validate values\n valid_roles = ['agent', 'patient', 'instrument', 'location', 'cause', 'effect']\n valid_cats = ['method', 'tool', 'concept', 'actor', 'outcome', 'process']\n \n role = sem.get('semantic_role', 'instrument')\n if role not in valid_roles:\n # Map common mistakes\n role_map = {'actor': 'agent', 'object': 'patient', 'means': 'instrument'}\n role = role_map.get(role, 'instrument')\n \n cat = sem.get('functional_category', 'concept')\n if cat not in valid_cats:\n cat = 'concept'\n \n meaning = str(sem.get('context_meaning', ''))[:500]\n \n cursor = db.execute('''INSERT INTO entity_knowledge_semantics \n (entity_id, chunk_id, semantic_role, functional_category, context_meaning, model_used)\n VALUES (%s, %s, %s, %s, %s, %s)''',\n (entity['id'], entity['chunk_id'], role, cat, meaning, 'mistral'))\n db.commit()\n cursor.close()\n \n print(f'{entity[\\\"name\\\"][:22]:22} | {role:10} | {cat:8} | {meaning[:35]}')\n success += 1\n else:\n print(f'{entity[\\\"name\\\"][:22]:22} | No JSON in response')\n errors += 1\n except json.JSONDecodeError as e:\n print(f'{entity[\\\"name\\\"][:22]:22} | JSON error: {str(e)[:30]}')\n errors += 1\n except Exception as e:\n print(f'{entity[\\\"name\\\"][:22]:22} | Error: {str(e)[:40]}')\n errors += 1\n\nprint()\nprint(f'Fertig: {success} erfolgreich, {errors} Fehler')\n\ndb.disconnect()\n\"",
"timeout": 420000,
"description": "Analyze entities with fixed mapping"
}
}
{
"tool_response": {
"stdout": "Analysiere 20 Entitäten...\n\nCOACH PROFIL | Error: '\"semantic_role\"'\nCoach | Error: '\"semantic_role\"'\nnormalen Gespräch | Error: '\"semantic_role\"'\nBasisverhaltensweisen | Error: '\"semantic_role\"'\nAnforderungsprofil | Error: '\"semantic_role\"'\nCoaches | Error: '\"semantic_role\"'\nKoproduktion | Error: '\"semantic_role\"'\nJOBBESCHREIBUNG | Error: '\"semantic_role\"'\nBooklets | Error: '\"semantic_role\"'\nCoachingwelt | Error: '\"semantic_role\"'\nRedundanz | Error: '\"semantic_role\"'\nVollständigkeit | Error: '\"semantic_role\"'\nRichtigkeit | Error: '\"semantic_role\"'\nWichtigkeit | Error: '\"semantic_role\"'\nImpulsgeber | Error: '\"semantic_role\"'\nKompass | Error: '\"semantic_role\"'\nIdee | Error: '\"semantic_role\"'\nneue Worte | Error: '\"semantic_role\"'\nneue Gefühle | Error: '\"semantic_role\"'\nBlickwinkel | Error: '\"semantic_role\"'\n\nFertig: 0 erfolgreich, 20 Fehler",
"stderr": "",
"interrupted": false,
"isImage": false
}
}