- Added a new endpoint to trigger on-demand agentic analysis for tenders via the AI service.
- Introduced `TriggerAIAnalyze` method in the `TenderHandler` to handle requests and responses for AI analysis.
- Updated the `tenderService` to include `TriggerAIAnalyze` method, which validates input and interacts with the AI summarizer client.
- Enhanced the `AIAnalyzeResponse` and `AIAnalyzeDocument` structures to support the new analysis feature.
- Refactored the `DocumentSummarizationWorker` and `TranslationWorker` to remove deprecated MinIO dependencies, streamlining the AI service interactions.
This update improves the functionality of the tender management system by allowing users to trigger AI analysis on-demand, enhancing the overall user experience and system capabilities.
- 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.
- 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.
- Removed the Ollama SDK from the worker initialization process, simplifying the worker's dependencies and enhancing maintainability.
- Updated the InitWorker function and NewNoticeWorker constructor to reflect the removal of the Ollama SDK, ensuring a cleaner and more focused implementation.
- Commented out the Ollama model listing logic for potential future use, maintaining clarity in the main function while reducing unnecessary complexity.
- Introduced AlertMail configuration in both scraper and worker bootstrap files, allowing for customizable email notifications.
- Updated the TED scraper to utilize the AlertMail configuration for sending completion notifications, improving flexibility in notification management.
- Enhanced error logging in the worker's main function to capture issues when listing Ollama models, ensuring better visibility into potential failures.
- Refactored notification sending logic to check for a valid AlertMail before dispatching emails, ensuring notifications are only sent when configured.
- Improved overall structure and readability of the bootstrap configuration files, aligning with best practices for maintainability.
- Introduced a new worker service with a dedicated main entry point for handling background tasks, including MongoDB connection management and notification service initialization.
- Added a bootstrap package to manage application configuration, logging, and service initialization for the worker.
- Implemented a NoticeWorker to process unprocessed notices and create corresponding tender entities, enhancing the integration of notice management within the tender system.
- Refactored the tender service to remove the Ollama SDK dependency, streamlining the service initialization in the main application.
- Enhanced the notice repository with a method to retrieve unprocessed notices, improving data handling capabilities.
Mazyar