- 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.
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.
- Introduced the Ollama SDK, providing a comprehensive interface for interacting with Ollama AI models, including text generation, chat conversations, embeddings, and model management.
- Implemented core components such as Client, Config, and various request/response entities to facilitate API interactions.
- Added structured error handling with specific error types for improved clarity and debugging.
- Included a README and usage guide to assist developers in integrating the SDK into their applications, ensuring a smooth onboarding experience.
- Established a fluent API for chat interactions and streaming responses, enhancing usability and flexibility for developers.
- Introduced a new configuration file for the TED scraper, defining database connection settings, logging preferences, and scraping parameters.
- Implemented a CronScheduler to manage scheduled tasks for TED operations, utilizing the robfig/cron library for scheduling.
- Added new entities and structures for handling TED XML data, including eForms and tender-related information, enhancing the scraper's functionality.
- Updated the main application to initialize the TED scraper with the new configuration and set up graceful shutdown handling.
- Removed the deprecated scraping implementation to streamline the codebase and focus on the new architecture.
- Modified the route for marking notifications as seen to ensure a consistent and clear API structure.
- Updated the notification client to reflect the new endpoint path for marking all notifications as seen, enhancing clarity for API consumers.
- These changes improve the overall usability and maintainability of the notification API.
- Introduced the AllMarkSeen method in the notification service to mark all notifications as seen for a user, enhancing user experience by allowing bulk actions on notifications.
- Implemented the PublicAllMarkSeen handler to handle HTTP requests for marking all notifications as seen, ensuring proper request validation and response handling.
- Updated the notification client and SDK to support the new AllMarkSeen functionality, improving the overall API capabilities.
- Enhanced API documentation with Swagger comments for the new endpoint, ensuring clarity for API consumers.
Mazyar