Task 1.3: Create odf_data_quality_dashboard Module #4
Description
Task 1.3: Create odf_data_quality_dashboard Module
Goal: Implement the non-blocking data validation workflow and UI.
Prompt for AI Coding Agent:
Context:
Based on section 4.2 of the Project Constitution, you are to create an Odoo module named odf_data_quality_dashboard. This module's purpose is to identify data quality issues after data has been imported, providing a clean work queue for users instead of blocking the import process.
Use the j-base branch
The newly generated modules should be placed in the modules folder.
Confirm that you have read the project constitution.
Requirements:
- Model:
- Create a new model odf.data.quality.issue. It must have the following fields:
- name (e.g., "Invalid VAT on Partner XYZ").
- issue_type (e.g., 'Invalid VAT', 'VIES Check Failed').
- related_record: A Reference field to dynamically link to any problematic record in the database (e.g., a res.partner or product.product).
- status: A Selection field with states ('To Do', 'In Progress', 'Done').
- notes: A Text field for comments.
- Create a new model odf.data.quality.issue. It must have the following fields:
- Post-Import Validation Logic:
- Create a new Scheduled Action (ir.cron) that runs nightly.
- This action will call a method that performs data validations (e.g., checks for valid VAT numbers on res.partner records created in the last 24 hours).
- Crucially, if a validation fails, the method must NOT raise an error. Instead, it must create a new odf.data.quality.issue record with the relevant details.
- User Interface:
- Create a menu item for the "Data Quality Dashboard".
- Create a Kanban view for the odf.data.quality.issue model, grouped by status. Each card should clearly display the issue type and a link to the related record.
- Create a form view for the model to show all details.
Deliverable:
Provide the complete Python and XML code for the odf_data_quality_dashboard Odoo module.