{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "cd \/var\/www\/scripts\/pipeline && .\/venv\/bin\/python -c \"\nimport requests\nimport json\n\n# Embedding von Ollama\nresp = requests.post('http:\/\/localhost:11434\/api\/embeddings', json={\n 'model': 'mxbai-embed-large',\n 'prompt': 'Was ist systemisches Coaching?'\n})\nvector = resp.json()['embedding']\nprint(f'Embedding: {len(vector)} dims')\n\n# Suche in Qdrant via REST\nsearch_resp = requests.post('http:\/\/localhost:6333\/collections\/documents\/points\/search', json={\n 'vector': vector,\n 'limit': 5,\n 'with_payload': True\n})\ndata = search_resp.json()\n\nif 'result' in data:\n results = data['result']\n print(f'\\\\n=== RAG Ergebnisse ({len(results)}) ===')\n for i, r in enumerate(results):\n title = r.get('payload', {}).get('document_title', 'N\/A')[:45]\n score = r.get('score', 0)\n print(f'{i+1}. Score: {score:.4f} | {title}')\nelse:\n print('Error:', data)\n\" 2>&1",
"timeout": 60000,
"description": "Test RAG search"
}
}
{
"tool_response": {
"stdout": "Embedding: 1024 dims\n\n=== RAG Ergebnisse (5) ===\n1. Score: 0.8739 | Booklet_ProblemeLoesungen_RELAUNCH2026.pdf\n2. Score: 0.8466 | Booklet_ProzessAuftrag_RELAUNCH2026.pdf\n3. Score: 0.8462 | Booklet_AufstellungEntscheidung__RELAUNCH2026\n4. Score: 0.8462 | Booklet_AufstellungEntscheidung__RELAUNCH2026\n5. Score: 0.8462 | Booklet_AufstellungEntscheidung__RELAUNCH2026",
"stderr": "",
"interrupted": false,
"isImage": false
}
}