GopediaPro/sabangnet_API

AI Summary: This GitHub issue reports a failure in uploading specific order management forms, including the "Basic Form (Other Sites, Zigzag, Ali)" and "Star Delivery Basic Form combined packaging files." The root cause has been identified as a mismatch in the file name order, which subsequently leads to incorrect 'site_type', 'usage_type', and 'sub_site' field values.

AI Summary: This issue describes two problems in the order management system: specific values (like delivery costs) are being stored as 'NaN' strings in `down_from_orders`, and null values (e.g., 'reg_data') are appearing when transferring data from `receive_orders` to `down_from_orders`. The latter is attributed to saving data based only on `template_config`, suggesting an incomplete data mapping or processing logic.

AI Summary: The task requires creating an API endpoint to upload product data from a local database (likely `product_registration`) to Sabangnet's API. This involves reading data, potentially transforming it to match Sabangnet's API requirements, and handling the API interaction, including error handling and response parsing. The current system uses an Excel import for initial data entry, but lacks a direct upload to Sabangnet. The solution should bridge this gap, likely using the `test_product_raw_data` table as an intermediary.

AI Summary: Debug and fix two issues in a Python CLI application that interacts with a Sabangnet API. The first issue involves incorrect data type access in the `process_orders_for_db` method, leading to a list index error. The second issue is a keyword argument mismatch between the processed order data and the `BaseDownFormOrder` model. The solution likely involves correcting data type handling and ensuring data consistency between the processed orders and the model.

AI Summary: Debug and resolve a data type mismatch error occurring when saving specific macro data to a database within a Python CLI application that interacts with the Sabangnet shopping mall API. This involves examining the application's database interaction code, potentially modifying data type handling (e.g., casting), and verifying data consistency between the application and the database schema.

AI Summary: Debug and fix pricing errors in a Python-based Sabangnet shopping mall code retrieval API client. The errors specifically relate to Gmarket and Auction orders showing zero shipping costs and potential pricing issues with Brandy and other sites. This involves examining the API interaction, XML parsing, and potentially the ERP integration to identify and correct the root cause of the discrepancies.

AI Summary: Debug and fix multiple issues in a Python CLI application that interacts with the Sabangnet shopping mall API. The issues include incorrect price calculations for various shopping malls (YES24, 11번가, SmartStore, Ablee, KakaoGift, CJONSTYLE, KakaoTalkStore), inconsistent order number parsing (Coupang, KakaoGift), complete data omission for one mall (Always), and misalignment of column data. The fixes require adjustments to parsing logic, price calculation algorithms, and potentially API interaction methods.

AI Summary: Implement a unique index constraint on the `idx` column of the `export_form_orders` table in a Python application interacting with a Sabangnet API to prevent duplicate order entries. This involves modifying the database schema and potentially updating the application's data insertion logic.

AI Summary: Modify the existing Sabangnet shopping mall code retrieval API client to handle variations in Excel column order for different templates. This involves storing all template data for each template ID instead of relying on `column_order` to retrieve data based on `template_id`. This will require changes to the data storage and retrieval logic within the Python application.

AI Summary: The task involves standardizing column names in Excel templates used for order management to match database field names. This requires reviewing existing templates, identifying inconsistencies (e.g., "배송메세지" vs. "배송메시지"), and updating them for consistency. A Google Sheet document provides a reference for the correct field names.

AI Summary: The task is to fix a type error in a Python CLI application that interacts with the Sabangnet API. The error occurs when processing 'service fee' and 'delivery fee' data during package consolidation, where these fees are sometimes represented as strings like '0/3000' instead of integers. The solution involves modifying the data handling to correctly parse and convert these potentially mixed-type fee values into a consistent numerical format.

AI Summary: Implement a duplicate check using the IDX field before inserting data into the `receive_orders` table to prevent duplicate entries in the database and subsequent Excel output. This is for a Python CLI application interacting with the Sabangnet shopping mall API.

AI Summary: Debug and fix the product registration functionality in a Python CLI application that interacts with the Sabangnet API. The current issue is that the application fails to correctly update the `product_id` field based on the received `company_goods_cd` after a product registration. This involves inspecting the code that handles the API response, parsing the XML data, and updating the database (or relevant data structure) with the correct `product_id`.

AI Summary: This task involves resolving discrepancies between the field names in a Python application's database and the specifications outlined in a product management table. The application interacts with a Sabangnet shopping mall API. The issue is that 24 field names related to product images are incorrectly mapped, requiring either a complete field name correction in the application's code and database or a workaround involving on-the-fly name transformations during API calls. Additionally, several other inconsistencies between the database, the application's data models, and the product management specifications need to be addressed, including missing fields, differing field names, and inconsistencies in model names and category information.

AI Summary: The task involves addressing data type and size issues in a Python CLI application that interacts with a Sabangnet shopping mall API. Specifically, it requires handling non-numeric values in the `mall_order_seq` field (either by data cleansing or changing the data type to string) and addressing string length limitations in database columns by either increasing column sizes or truncating strings before saving them to the database. This will involve modifying the application's data handling logic and potentially the database schema.

AI Summary: The task involves increasing the character limit of database fields to accommodate larger text entries. This is in response to an error encountered when the limit was previously increased to 100 characters, suggesting a need for a further increase. The project uses a Python CLI application to interact with a Sabangnet API for shopping mall data.

AI Summary: The task requires resolving character encoding issues in a Python client for the Sabangnet shopping mall code API. The API expects EUC-KR encoding, while the database uses UTF-8, leading to character corruption. The solution involves correctly handling encoding conversions between EUC-KR and UTF-8 at various points in the data pipeline, likely within the XML parsing and database interaction sections of the Python code.

AI Summary: The task requires debugging and fixing a bug in a Python CLI application that interacts with a Sabangnet shopping mall API. The application incorrectly maps category codes (CLASS_CD1-4) during product registration. The issue stems from a mismatch between the application's interpretation of category codes (e.g., A1, A2, A3, A4) and the actual structure (e.g., A01, B02, C03, D04) with three-digit codes. The fix involves correcting the code that handles category code mapping and potentially addressing the handling of null values in the API request.