{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "DB_PASSWORD=\"v8mFLmkF2fth_r\" timeout 300 .\/venv\/bin\/python run_demo.py 2>&1",
"timeout": 320000,
"description": "Test pipeline with dynamic entity types"
}
}
{
"tool_response": {
"stdout": "[23:36:15] === START ===\n[23:36:15] 1. DB verbinden...\n[23:36:15] OK (0.0s)\n[23:36:15] 2. DB Reset (Tabellen leeren)...\n[23:36:15] entity_relations: OK\n[23:36:15] chunk_entities: OK\n[23:36:15] document_entities: OK\n[23:36:15] chunk_semantics: OK\n[23:36:15] chunk_taxonomy: OK\n[23:36:15] document_taxonomy: OK\n[23:36:15] document_pages: OK\n[23:36:15] entities: OK\n[23:36:15] chunks: OK\n[23:36:15] documents: OK\n[23:36:15] DB Reset done (0.0s)\n[23:36:15] 3. Qdrant Reset...\n[23:36:15] Qdrant: 200 (0.0s)\n[23:36:15] 4. PDF laden...\n[23:36:15] OK: 5561 chars, 3 pages (0.1s)\n[23:36:15] 5. Document in DB erstellen...\n[23:36:15] OK: doc_id=10 (0.0s)\n[23:36:15] 6. Text chunken...\n[23:36:15] OK: 4 chunks (0.0s)\n[23:36:15] 7. Chunks in DB speichern...\n[23:36:15] Chunk 1: 1899 chars -> id=33\n[23:36:15] Chunk 2: 1858 chars -> id=34\n[23:36:15] Chunk 3: 535 chars -> id=35\n[23:36:15] Chunk 4: 1521 chars -> id=36\n[23:36:15] OK: 4 chunks gespeichert (0.0s)\n[23:36:15] 8. YAML Prompt aus DB laden...\n[23:36:15] OK: Prompt geladen (0.0s)\n[23:36:15] Prompt-Preview:\nversion: \"1.0\"\nname: entity_extraction\n\ntask: |\n Extrahiere alle Fachbegriffe aus dem Text.\n Kategorisiere jeden Begriff nach den unten definierten Kriterien.\n\ncategories:\n PERSON: |\n Konkrete, namentlich genannte Einzelpersonen.\n Kriterium: Hat Vor- UND Nachname, ist eine historische\/reale Person.\n NICHT: Funktionsbezeichnungen oder Rollen.\n \n ORGANIZATION: |\n Institutionen, Grup...\n[23:36:15] 9. Entity Extraction (Ollama)...\n[23:36:15] Chunk 1\/4: 1899 chars...\n[23:37:30] -> 28 entities (74.9s)\n[23:37:30] - COACH PROFIL (ARTIFACT)\n[23:37:30] - Coaching (METHOD)\n[23:37:30] - Coach (ROLE)\n[23:37:30] - Rahmen (CONCEPT)\n[23:37:30] - Basisverhaltensweisen (CONCEPT)\n[23:37:30] ... und 23 weitere\n[23:37:30] Chunk 2\/4: 1858 chars...\n[23:38:54] -> 32 entities (84.6s)\n[23:38:54] - Koproduktion (METHOD)\n[23:38:54] - individuelle Coach-Persönlichkeit (PERSON)\n[23:38:54] - Coach-System (MODEL)\n[23:38:54] - Coach-Rolle (ROLE)\n[23:38:54] - Wertschätzung (VALUE_NORM)\n[23:38:54] ... und 27 weitere\n[23:38:54] Chunk 3\/4: 535 chars...\n[23:39:05] -> 2 entities (10.2s)\n[23:39:05] - Klienten (DEMOGRAPHIC_GROUP)\n[23:39:05] - COACH PROFIL (ROLE)\n[23:39:05] Chunk 4\/4: 1521 chars...\n[23:40:00] -> 20 entities (55.7s)\n[23:40:00] - Klienten (DEMOGRAPHIC_GROUP)\n[23:40:00] - Coachees (DEMOGRAPHIC_GROUP)\n[23:40:00] - Kompetenz-DNA (CONCEPT)\n[23:40:00] - Allparteilichkeit (VALUE_NORM)\n[23:40:00] - Neutralität (VALUE_NORM)\n[23:40:00] ... und 15 weitere\n[23:40:00] 10. Entities in DB speichern...\n[23:40:00] OK: 82 entities gespeichert (0.1s)\n[23:40:00] === ERGEBNIS ===\n[23:40:00] CONCEPT: 33\n[23:40:00] VALUE_NORM: 10\n[23:40:00] ROLE: 6\n[23:40:00] DEMOGRAPHIC_GROUP: 5\n[23:40:00] ARTIFACT: 4\n[23:40:00] MODEL: 3\n[23:40:00] SYMPTOM_SIGN: 3\n[23:40:00] METHOD: 3\n[23:40:00] LOCATION: 2\n[23:40:00] METAPHOR: 1\n[23:40:00] ASSESSMENT_INSTRUMENT: 1\n[23:40:00] PERSON: 1\n[23:40:00] TOTAL: 72 entities\n[23:40:00] === DONE ===",
"stderr": "",
"interrupted": false,
"isImage": false
}
}