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"title": "RAG: document_sections für Abschnitts-Hierarchie aktivieren",
"description": "# Auftrag\n\nAktiviere die `document_sections` Tabelle für strukturierte Dokument-Hierarchie.\n\n## Voraussetzungen\n\n**ERSTE AKTION:** `docs_get(path=\"\/\")` → Lese CLAUDE.md vollständig\n\n**Relevante Contracts:**\n- `contracts_get(name=\"python-pipeline-contract_v1.0\")` - Python-Standards\n\n**Paradigmen (ZWINGEND):**\n- KISS: Einfache Heading-Hierarchie\n- DRY: Nutze bestehende extract.py Logik\n- YAGNI: Nur H1-H6 Hierarchie, keine komplexen Strukturen\n\n## Kontext\n\n**Referenz-Dokumentation:** `docs_get(id=100)` (RAG-Prozess)\n\n**IST-Zustand:**\n- Tabelle `document_sections` existiert mit 0 Rows\n- `chunks.heading_path` enthält bereits JSON-Array mit Headings\n\n## Aufgabe\n\n### 1. Schema prüfen\n\n```bash\ndb_describe(table=\"document_sections\", database=\"ki_content\")\n```\n\n### 2. Section-Extraktion in extract.py\n\nErweitere `\/var\/www\/scripts\/pipeline\/extract.py`:\n\n```python\ndef extract_sections(document_id: int, text: str) -> list[dict]:\n \"\"\"Extract document structure as section hierarchy.\n \n Returns list of:\n {\n 'document_id': int,\n 'title': str,\n 'level': int (1-6),\n 'parent_id': int or None,\n 'start_pos': int,\n 'end_pos': int,\n 'sort_order': int\n }\n \"\"\"\n sections = []\n # Parse Markdown\/HTML headings\n # Build parent-child relationships\n return sections\n\ndef save_sections(document_id: int, sections: list[dict]):\n \"\"\"Save sections to database.\"\"\"\n # Clear existing sections for document\n db.delete_sections(document_id)\n \n # Insert new sections\n for section in sections:\n db.insert_section(section)\n```\n\n### 3. Pipeline-Integration\n\nIn `pipeline.py:process_file()` nach extract:\n```python\n# Extract document structure\nsections = extract_sections(document_id, full_text)\nsave_sections(document_id, sections)\n```\n\n### 4. Chunk-Section Mapping\n\n```sql\ndb_execute(statement=\"\"\"\nALTER TABLE chunks\n ADD COLUMN IF NOT EXISTS section_id INT,\n ADD FOREIGN KEY (section_id) REFERENCES document_sections(id)\n\"\"\", database=\"ki_content\")\n```\n\n## Validierung\n\n```bash\n# Schema prüfen\ndb_describe(table=\"document_sections\", database=\"ki_content\")\n\n# Python Check\n\/opt\/scripts\/python-check.sh\n\n# Funktionstest\ndb_select(query=\"SELECT COUNT(*) FROM document_sections\", database=\"ki_content\")\n```\n\n## Qualitätskriterien\n\n- [ ] extract_sections() implementiert\n- [ ] save_sections() implementiert\n- [ ] Pipeline-Integration\n- [ ] Mindestens 1 Dokument mit Sections\n- [ ] Type Hints und Docstrings",
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"text": "{\n \"success\": true,\n \"task\": {\n \"id\": 307,\n \"uuid\": \"da1f3cca-08d1-4551-9f12-f74b961ba13c\",\n \"title\": \"RAG: document_sections für Abschnitts-Hierarchie aktivieren\",\n \"description\": \"# Auftrag\\n\\nAktiviere die `document_sections` Tabelle für strukturierte Dokument-Hierarchie.\\n\\n## Voraussetzungen\\n\\n**ERSTE AKTION:** `docs_get(path=\\\"\/\\\")` → Lese CLAUDE.md vollständig\\n\\n**Relevante Contracts:**\\n- `contracts_get(name=\\\"python-pipeline-contract_v1.0\\\")` - Python-Standards\\n\\n**Paradigmen (ZWINGEND):**\\n- KISS: Einfache Heading-Hierarchie\\n- DRY: Nutze bestehende extract.py Logik\\n- YAGNI: Nur H1-H6 Hierarchie, keine komplexen Strukturen\\n\\n## Kontext\\n\\n**Referenz-Dokumentation:** `docs_get(id=100)` (RAG-Prozess)\\n\\n**IST-Zustand:**\\n- Tabelle `document_sections` existiert mit 0 Rows\\n- `chunks.heading_path` enthält bereits JSON-Array mit Headings\\n\\n## Aufgabe\\n\\n### 1. Schema prüfen\\n\\n```bash\\ndb_describe(table=\\\"document_sections\\\", database=\\\"ki_content\\\")\\n```\\n\\n### 2. Section-Extraktion in extract.py\\n\\nErweitere `\/var\/www\/scripts\/pipeline\/extract.py`:\\n\\n```python\\ndef extract_sections(document_id: int, text: str) -> list[dict]:\\n \\\"\\\"\\\"Extract document structure as section hierarchy.\\n \\n Returns list of:\\n {\\n 'document_id': int,\\n 'title': str,\\n 'level': int (1-6),\\n 'parent_id': int or None,\\n 'start_pos': int,\\n 'end_pos': int,\\n 'sort_order': int\\n }\\n \\\"\\\"\\\"\\n sections = []\\n # Parse Markdown\/HTML headings\\n # Build parent-child relationships\\n return sections\\n\\ndef save_sections(document_id: int, sections: list[dict]):\\n \\\"\\\"\\\"Save sections to database.\\\"\\\"\\\"\\n # Clear existing sections for document\\n db.delete_sections(document_id)\\n \\n # Insert new sections\\n for section in sections:\\n db.insert_section(section)\\n```\\n\\n### 3. Pipeline-Integration\\n\\nIn `pipeline.py:process_file()` nach extract:\\n```python\\n# Extract document structure\\nsections = extract_sections(document_id, full_text)\\nsave_sections(document_id, sections)\\n```\\n\\n### 4. Chunk-Section Mapping\\n\\n```sql\\ndb_execute(statement=\\\"\\\"\\\"\\nALTER TABLE chunks\\n ADD COLUMN IF NOT EXISTS section_id INT,\\n ADD FOREIGN KEY (section_id) REFERENCES document_sections(id)\\n\\\"\\\"\\\", database=\\\"ki_content\\\")\\n```\\n\\n## Validierung\\n\\n```bash\\n# Schema prüfen\\ndb_describe(table=\\\"document_sections\\\", database=\\\"ki_content\\\")\\n\\n# Python Check\\n\/opt\/scripts\/python-check.sh\\n\\n# Funktionstest\\ndb_select(query=\\\"SELECT COUNT(*) FROM document_sections\\\", database=\\\"ki_content\\\")\\n```\\n\\n## Qualitätskriterien\\n\\n- [ ] extract_sections() implementiert\\n- [ ] save_sections() implementiert\\n- [ ] Pipeline-Integration\\n- [ ] Mindestens 1 Dokument mit Sections\\n- [ ] Type Hints und Docstrings\",\n \"type\": \"ai_task\",\n \"status\": \"pending\",\n \"created_by\": \"mcp-tasks\",\n \"created_by_type\": \"ai\",\n \"parent_task_id\": null,\n \"due_date\": null,\n \"created_at\": \"2025-12-24T15:02:55.490607\",\n \"updated_at\": \"2025-12-24T15:02:55.490609\",\n \"completed_at\": null,\n \"metadata\": {}\n },\n \"message\": \"Task #307 created\"\n}"
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