{
"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": "Run entity extraction pipeline test"
}
}
{
"tool_response": {
"stdout": "[23:11:54] === START ===\n[23:11:54] 1. DB verbinden...\n[23:11:54] OK (0.0s)\n[23:11:54] 2. DB Reset (Tabellen leeren)...\n[23:11:54] entity_relations: OK\n[23:11:54] chunk_entities: OK\n[23:11:54] document_entities: OK\n[23:11:54] chunk_semantics: OK\n[23:11:54] chunk_taxonomy: OK\n[23:11:54] document_taxonomy: OK\n[23:11:54] document_pages: OK\n[23:11:54] entities: OK\n[23:11:54] chunks: OK\n[23:11:54] documents: OK\n[23:11:54] DB Reset done (0.0s)\n[23:11:54] 3. Qdrant Reset...\n[23:11:54] Qdrant: 200 (0.0s)\n[23:11:54] 4. PDF laden...\n[23:11:54] OK: 5561 chars, 3 pages (0.1s)\n[23:11:54] 5. Document in DB erstellen...\n[23:11:54] OK: doc_id=8 (0.0s)\n[23:11:54] 6. Text chunken...\n[23:11:54] OK: 4 chunks (0.0s)\n[23:11:54] 7. Chunks in DB speichern...\n[23:11:54] Chunk 1: 1899 chars -> id=25\n[23:11:54] Chunk 2: 1858 chars -> id=26\n[23:11:54] Chunk 3: 535 chars -> id=27\n[23:11:54] Chunk 4: 1521 chars -> id=28\n[23:11:54] OK: 4 chunks gespeichert (0.0s)\n[23:11:54] 8. YAML Prompt aus DB laden...\n[23:11:54] OK: Prompt geladen (0.0s)\n[23:11:54] 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[23:11:54] 9. Entity Extraction (Ollama)...\n[23:11:54] Chunk 1\/4: 1899 chars...\n[23:12:10] -> 4 entities (15.8s)\n[23:12:10] - COACH (ROLE)\n[23:12:10] - Jobbeschreibung (ARTIFACT)\n[23:12:10] - Booklet (ARTIFACT)\n[23:12:10] - Kompetenz-DNA (CONCEPT)\n[23:12:10] Chunk 2\/4: 1858 chars...\n[23:13:03] -> 14 entities (53.1s)\n[23:13:03] - Koproduktion (CONCEPT)\n[23:13:03] - Persönlichkeit (CONCEPT)\n[23:13:03] - Humor (CONCEPT)\n[23:13:03] - Coach-Persönlichkeit (CONCEPT)\n[23:13:03] - Coach-System (CONCEPT)\n[23:13:03] ... und 9 weitere\n[23:13:03] Chunk 3\/4: 535 chars...\n[23:13:09] -> 2 entities (5.9s)\n[23:13:09] - Transparenz (CONCEPT)\n[23:13:09] - Prozess (MODEL)\n[23:13:09] Chunk 4\/4: 1521 chars...\n[23:13:33] -> 4 entities (24.1s)\n[23:13:33] - Allparteilichkeit (CONCEPT)\n[23:13:33] - Demut (PRINCIPLE)\n[23:13:33] - Coachee (ROLE)\n[23:13:33] - Coach (ROLE)\n[23:13:33] 10. Entities in DB speichern...\n[23:13:33] OK: 24 entities gespeichert (0.0s)\n[23:13:33] === ERGEBNIS ===\n[23:13:33] CONCEPT: 15\n[23:13:33] ROLE: 3\n[23:13:33] ARTIFACT: 2\n[23:13:33] PRINCIPLE: 2\n[23:13:33] MODEL: 1\n[23:13:33] TOTAL: 23 entities\n[23:13:33] === DONE ===",
"stderr": "",
"interrupted": false,
"isImage": false
}
}