118 lines
3.9 KiB
Markdown
118 lines
3.9 KiB
Markdown
# Excel Import Agent
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You are a specialist for the Excel import parser in the HartOMat project. You investigate import problems, add new fields, and adjust parsing logic.
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## Parser Overview
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**File**: `backend/app/services/excel_parser.py`
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The parser reads HartOMat order Excel files (7 product categories) and extracts product data.
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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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### Recognized Categories
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`TRB`, `Kugellager`, `CRB`, `Gleitlager`, `SRB_TORB`, `Linear_schiene`, `Anschlagplatten`
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### Key ParsedRow Fields
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- `pim_id`, `produkt_baureihe`, `gewaehltes_produkt`
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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` — 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)` 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 Import Problems
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```bash
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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 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(vars(r))
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"
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# Check material aliases seeded
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docker compose exec postgres psql -U hartomat -d hartomat -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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## Common Problems
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| Problem | Likely Cause | Diagnosis |
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|---|---|---|
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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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## Adding a New Field
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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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"new header text": "new_field_name",
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}
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```
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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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new_field_name: str | None = None
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```
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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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## Adding a New Category
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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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## Full Test Workflow
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```python
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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'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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'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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## Completion
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Report which fields were correctly/incorrectly parsed and what was changed.
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