- Updated `InitAISummarizerClient` to accept `mongoManager` for tracking translation success.
- Introduced new `Statistics` endpoint in the dashboard to fetch scraping and translation statistics.
- Enhanced `TranslationWorker` to utilize the new success counter for tracking successful translations.
- Added necessary data structures and query forms for statistics reporting.
This refactor improves the tracking of AI translation success and provides new insights through the dashboard statistics.
Map E11000 from the per-collection phone index to friendly conflict
errors and skip redundant GetByPhone when the phone is unchanged.
Not blocking: IAT test DB may need a one-off dedupe for index build;
phone uniqueness is per collection (users vs customers), not global.
Frontend sends nested address field names for sorting; accept them alongside the existing country and state aliases.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Introduced a comprehensive MinIO package for managing object storage, including support for hierarchical document storage and general file management.
- Implemented core components such as Config, ConnectionManager, and Service, following Clean Architecture principles.
- Added functionality for file uploads, downloads, bucket management, and structured logging, enhancing usability and maintainability.
- Included detailed error handling and validation for configuration and operations, ensuring robustness.
- Updated documentation with usage examples and configuration guidelines to facilitate integration and understanding of the package's functionality.
- Updated the worker initialization process to include the new GLM SDK, enhancing the worker's capabilities for translation tasks.
- Modified the InitWorker function and NewNoticeWorker constructor to accept the GLM service, ensuring a cohesive integration.
- Implemented the GLM service initialization and logging for successful setup, improving maintainability and usability.
- Updated the NoticeWorker to utilize the GLM SDK for translating notice titles and descriptions, enhancing functionality and user experience.
- Introduced a new GLM SDK for interacting with the GLM (Zhipu AI) API, designed for chat completions and text generation tasks.
- Implemented core components including Client, Service, and SDK layers following Clean Architecture principles.
- Added configuration management, error handling, and structured logging for improved usability and maintainability.
- Included comprehensive documentation and usage examples to facilitate integration and understanding of the SDK's functionality.
- Enhanced the API with features such as chat completion, streaming responses, and model management, ensuring robust interaction with the GLM service.
- Added 'image' and 'link' fields to the NotificationRequest and NotificationResponse structures, improving the flexibility of notification content.
- Updated the 'created_at', 'updated_at', and 'schedule_at' fields to use integer types for Unix timestamps, ensuring consistency in time handling.
- Expanded the tender status enumeration to include 'closed', 'modified', 'suspended', and 'published', enhancing the representation of tender states.
- Reflected these changes in the Swagger documentation, ensuring accurate API specifications for clients.
- Replaced the tender repository with a new notice repository, encapsulating notice-related data access methods.
- Introduced the Notice entity to represent tender/contract notices, including relevant fields and methods for managing notice data.
- Updated the TED scraper to utilize the new notice repository for creating and managing notices, enhancing the integration with the tender management system.
- Implemented Ollama SDK initialization in the web bootstrap process, allowing for improved AI interactions.
- Enhanced the tender service to include Ollama SDK for additional functionality, ensuring a more robust service layer.
m.nazemi