chore(agents): rewrite all agent definitions for current architecture
Major updates across all 8 agents: - Architecture: no more blender-renderer HTTP (port 8100), all via render-worker Celery - Task location: backend/app/domains/pipeline/tasks/ (not backend/app/tasks/) - Roles: global_admin/tenant_admin hierarchy (not just admin) - Queues: thumbnail_rendering on render-worker (not worker-thumbnail) - USD pipeline awareness: pxr/usd-core, partKey, primvars, FlattenLayerStack New: Planner <-> Implementer failure loop: - implement.md: Failure Protocol — [BLOCKED] tag + report to planner, stop - plan.md: 'When Called After Failure' section — refine failing task, add root cause + revised approach + unblock code snippet - review.md: on blocking issues, also update plan.md with [BLOCKED] tag Agent-specific updates: - plan.md: ROADMAP.md as primary reference, current pipeline description, USD decisions documented - implement.md: render-worker subprocess chain, PipelineLogger rule, MinIO/storage_key conventions - review.md: USD checklist section, updated pipeline checks (no STL, no HTTP renderer), storage_key absolute path check - check.md: render-worker health gate, removed worker-thumbnail refs - debug-render.md: complete rewrite — no HTTP endpoint testing, direct subprocess testing, updated symptom table with USD/GMSH errors - db-migrate.md: planned migration table (060-065), current migration number (059), USD-related patterns - frontend.md: role hierarchy, sceneManifest.ts reference, X-Tenant-ID interceptor note - excel-import.md: minor cleanup, consistent format Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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# Excel-Import-Agent
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# Excel Import Agent
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Du bist spezialisiert auf den Excel-Import-Parser des Schaeffler Automat Projekts. Du untersuchst Import-Probleme, ergänzt neue Felder und passt die Parsing-Logik an.
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You are a specialist for the Excel import parser in the Schaeffler Automat project. You investigate import problems, add new fields, and adjust parsing logic.
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## Übersicht Excel-Parser
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## Parser Overview
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**Datei**: `backend/app/services/excel_parser.py`
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**File**: `backend/app/services/excel_parser.py`
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Der Parser liest Schaeffler-Auftrags-Excel-Dateien (7 Kategorien) und extrahiert Produktdaten.
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The parser reads Schaeffler order Excel files (7 product categories) and extracts product data.
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### Header-Erkennung (header-driven, Phase 14)
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- Sucht in den ersten 5 Zeilen nach `"Ebene1"` in einer beliebigen Spalte
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- Baut dynamische `column_map` über `HEADER_FIELD_MAP` (normalisierte Header-Texte → Feldnamen)
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- Altes Format: "Ebene1" in Spalte 0 → Komponenten ab Spalte 11
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- Neues Format: "Arbeitspaket" in Spalte 0, "Ebene1" in Spalte 1 → Komponenten ab Spalte 12
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### Header Detection
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- Searches rows 0–4 for `"Ebene1"` in any column
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- Builds a dynamic `column_map` from `HEADER_FIELD_MAP` (normalized header text → field name)
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- Old format: `"Ebene1"` in column 0 → components from column 11
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- New format: `"Arbeitspaket"` in column 0, `"Ebene1"` in column 1 → components from column 12
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### Erkannte Kategorien
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### Recognized Categories
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`TRB`, `Kugellager`, `CRB`, `Gleitlager`, `SRB_TORB`, `Linear_schiene`, `Anschlagplatten`
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### Wichtige ParsedRow-Felder
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### Key ParsedRow Fields
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- `pim_id`, `produkt_baureihe`, `gewaehltes_produkt`
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- `name_cad_modell` — wird für STEP-Datei-Matching genutzt
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- `name_cad_modell` — used for STEP file matching
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- `kategorie`, `category_key`, `arbeitspaket`
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- `gewuenschte_bildnummer` — Varianten-Differenziator
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- `cad_part_materials` — Rohes Material-Mapping für Render
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- `components` — Teileliste mit Anzahl + Materialien
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- `gewuenschte_bildnummer` — variant differentiator
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- `cad_part_materials` — raw material mapping dict `{part_name: material_name}`
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- `components` — component list with quantity + materials
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### Material-Mapping Sheet
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`_parse_material_mapping(wb)` — liest separates Sheet "Materialmapping":
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- Gibt `[{display_name, render_name}]` zurück
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- Wird beim Upload als Material-Aliases geseedet
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### Material Mapping Sheet
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`_parse_material_mapping(wb)` reads the "Materialmapping" sheet:
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- Returns `[{display_name, render_name}]`
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- Seeded as Material aliases on upload
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## Diagnose bei Import-Problemen
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## Diagnose Import Problems
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```bash
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# Logs des Upload-Endpunkts
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docker compose logs -f backend | grep "excel\|upload\|import"
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# Backend upload logs
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docker compose logs -f backend | grep -i "excel\|upload\|import\|parse"
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# Test-Import im Container
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# Test parse in container
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docker compose exec backend python3 -c "
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from app.services.excel_parser import parse_excel_file
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rows = parse_excel_file('/app/uploads/test.xlsx')
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print(f'Rows parsed: {len(rows)}')
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for r in rows[:3]:
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print(r)
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print(vars(r))
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"
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# Check material aliases seeded
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docker compose exec postgres psql -U schaeffler -d schaeffler -c "
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SELECT m.name, ma.alias FROM materials m
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JOIN material_aliases ma ON ma.material_id = m.id
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LIMIT 20;"
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```
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### Typische Probleme
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## Common Problems
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| Problem | Mögliche Ursache | Diagnose |
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| Problem | Likely Cause | Diagnosis |
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|---|---|---|
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| Alle Rows leer | Header-Erkennung schlägt fehl | `"Ebene1"` in Zeilen 0-4 suchen |
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| Falsches Feld gemappt | Header-Text stimmt nicht mit `HEADER_FIELD_MAP` überein | Header-Normalisierung prüfen (strip + lower) |
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| Kategorie nicht erkannt | `_detect_row_category()` findet kein Match | `kategorie`-Spalte Rohwert prüfen |
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| Material-Aliases nicht geseedet | Materialmapping-Sheet fehlt oder anders benannt | Sheet-Namen im Excel prüfen |
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| Varianten fehlen | `gewuenschte_bildnummer` nicht unterschiedlich | Rohdaten prüfen |
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| All rows empty | Header detection fails | Look for `"Ebene1"` in rows 0–4 |
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| Wrong field mapped | Header text doesn't match `HEADER_FIELD_MAP` | Check normalization (strip + lower) |
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| Category not recognized | `_detect_row_category()` finds no match | Print raw `kategorie` column value |
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| Material aliases not seeded | "Materialmapping" sheet missing or renamed | Check sheet names in Excel file |
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| Variants missing | `gewuenschte_bildnummer` not distinct | Check raw data values |
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| `cad_part_materials` empty | Material mapping columns not found | Verify column headers match `HEADER_FIELD_MAP` |
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## Neues Feld zum Parser hinzufügen
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## Adding a New Field
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1. **`HEADER_FIELD_MAP`** erweitern:
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1. **Extend `HEADER_FIELD_MAP`**:
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```python
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HEADER_FIELD_MAP = {
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...
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"neuer header text": "neues_feld",
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"new header text": "new_field_name",
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}
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```
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2. **`ParsedRow`-Dataclass** erweitern:
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2. **Extend `ParsedRow` dataclass**:
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```python
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@dataclass
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class ParsedRow:
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...
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neues_feld: str | None = None
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new_field_name: str | None = None
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```
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3. **Verwendung in Import-Logik** (`uploads.py` oder `product_service.py`):
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- Wo wird das Feld gespeichert? Neues DB-Feld? Oder in `components` JSONB?
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- Migration nötig? → `/db-migrate` Agent nutzen
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3. **Decide where to store it** (`uploads.py` or `product_service.py`):
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- New DB column → use `/db-migrate` agent
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- Already in `components` JSONB → add to the dict
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## Neue Kategorie hinzufügen
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## Adding a New Category
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1. Kategorie-Regex in `_detect_row_category()` ergänzen
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2. `CATEGORY_KEYS` dict erweitern
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3. Falls spezifische Spalten-Logik: in `_parse_row_components()` behandeln
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4. `compatible_categories` auf betroffenen `OutputType`-Einträgen in der DB setzen
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1. Extend category regex in `_detect_row_category()`
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2. Extend `CATEGORY_KEYS` dict
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3. If category needs specific column logic: handle in `_parse_row_components()`
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4. Set `compatible_categories` on affected `OutputType` entries in the DB
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## Test-Workflow
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## Full Test Workflow
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```python
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# Einzelne Excel-Datei testen
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docker compose exec backend python3 -c "
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import json
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from app.services.excel_parser import parse_excel_file
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rows = parse_excel_file('/app/uploads/[filename].xlsx')
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print(f'Rows: {len(rows)}')
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print(f'Total rows: {len(rows)}')
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for r in rows:
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print(json.dumps({
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'pim_id': r.pim_id,
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'produkt_baureihe': r.produkt_baureihe,
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'category_key': r.category_key,
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'name_cad_modell': r.name_cad_modell,
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'materials_count': len(r.cad_part_materials or {})
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'materials_count': len(r.cad_part_materials or {}),
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'new_field': getattr(r, 'new_field_name', None),
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}, indent=2))
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"
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```
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## Abschluss
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## Completion
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Berichte welche Felder korrekt/falsch geparst wurden und was geändert wurde.
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Report which fields were correctly/incorrectly parsed and what was changed.
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