Protokoll #19144

{
    "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 41 entity types"
    }
}

{
    "tool_response": {
        "stdout": "[23:48:19] === START ===\n[23:48:19] 1. DB verbinden...\n[23:48:19]    OK (0.0s)\n[23:48:19] 2. DB Reset (Tabellen leeren)...\n[23:48:19]    entity_relations: OK\n[23:48:19]    chunk_entities: OK\n[23:48:19]    document_entities: OK\n[23:48:19]    chunk_semantics: OK\n[23:48:19]    chunk_taxonomy: OK\n[23:48:19]    document_taxonomy: OK\n[23:48:19]    document_pages: OK\n[23:48:19]    entities: OK\n[23:48:19]    chunks: OK\n[23:48:19]    documents: OK\n[23:48:19]    DB Reset done (0.0s)\n[23:48:19] 3. Qdrant Reset...\n[23:48:19]    Qdrant: 200 (0.0s)\n[23:48:19] 4. PDF laden...\n[23:48:19]    OK: 5561 chars, 3 pages (0.1s)\n[23:48:19] 5. Document in DB erstellen...\n[23:48:19]    OK: doc_id=11 (0.0s)\n[23:48:19] 6. Text chunken...\n[23:48:19]    OK: 4 chunks (0.0s)\n[23:48:19] 7. Chunks in DB speichern...\n[23:48:19]    Chunk 1: 1899 chars -> id=37\n[23:48:19]    Chunk 2: 1858 chars -> id=38\n[23:48:19]    Chunk 3: 535 chars -> id=39\n[23:48:19]    Chunk 4: 1521 chars -> id=40\n[23:48:19]    OK: 4 chunks gespeichert (0.0s)\n[23:48:19] 8. YAML Prompt aus DB laden...\n[23:48:19]    OK: Prompt geladen (0.0s)\n[23:48:19]    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[23:48:19] 9. Entity Extraction (Ollama)...\n[23:48:19]    Chunk 1\/4: 1899 chars...\n[23:49:30]       -> 25 entities (70.6s)\n[23:49:30]          - COACH PROFIL (PUBLICATION_WORK)\n[23:49:30]          - Coaching (METHOD)\n[23:49:30]          - Coach (ROLE)\n[23:49:30]          - Rahmens (FRAME_CONDITION_RESOURCE)\n[23:49:30]          - Basisverhaltensweisen (TRAIT_ATTRIBUTE)\n[23:49:30]          ... und 20 weitere\n[23:49:30]    Chunk 2\/4: 1858 chars...\n[23:50:55]       -> 31 entities (85.0s)\n[23:50:55]          - Koproduktion (METHOD)\n[23:50:55]          - individuelle Coach-Persönlichkeit (TRAIT_ATTRIBUTE)\n[23:50:55]          - Coach-System (SYSTEM_CONTEXT)\n[23:50:55]          - Coach-Rolle (ROLE)\n[23:50:55]          - Wertschätzung (VALUE_NORM_RIGHT_DUTY)\n[23:50:55]          ... und 26 weitere\n[23:50:55]    Chunk 3\/4: 535 chars...\n[23:51:10]       -> 3 entities (14.8s)\n[23:51:10]          - Klienten (DEMOGRAPHIC_GROUP)\n[23:51:10]          - Kollegen (DEMOGRAPHIC_GROUP)\n[23:51:10]          - COACH PROFIL (ASSESSMENT_INSTRUMENT)\n[23:51:10]    Chunk 4\/4: 1521 chars...\n[23:52:09]       -> 21 entities (59.2s)\n[23:52:09]          - Klienten (DEMOGRAPHIC_GROUP)\n[23:52:09]          - Kompetenz-DNA (CONCEPT)\n[23:52:09]          - allparteilich (TRAIT_ATTRIBUTE)\n[23:52:09]          - Coachees (DEMOGRAPHIC_GROUP)\n[23:52:09]          - Neutralität (VALUE_NORM_RIGHT_DUTY)\n[23:52:09]          ... und 16 weitere\n[23:52:09] 10. Entities in DB speichern...\n[23:52:09]    OK: 47 entities gespeichert (0.4s)\n[23:52:09] === ERGEBNIS ===\n[23:52:09]    CONCEPT: 14\n[23:52:09]    DEMOGRAPHIC_GROUP: 6\n[23:52:09]    ROLE: 5\n[23:52:09]    METHOD: 3\n[23:52:09]    ARTIFACT: 3\n[23:52:09]    PUBLICATION_WORK: 3\n[23:52:09]    SYMPTOM_SIGN: 2\n[23:52:09]    MODEL: 1\n[23:52:09]    METAPHOR: 1\n[23:52:09]    ASSESSMENT_INSTRUMENT: 1\n[23:52:09]    TOTAL: 39 entities\n[23:52:09] === DONE ===",
        "stderr": "",
        "interrupted": false,
        "isImage": false
    }
}