- Removed the `RecommendationPageCacheLanguages` configuration from `AISummarizerConfig` to streamline cache management.
- Updated the `companyService` and `tenderService` to utilize the new `InvalidateRecommendedTendersPageCache` method for cache invalidation, enhancing clarity and efficiency.
- Refactored the `invalidateRecommendedTendersPageCache` method to eliminate unnecessary context parameters, simplifying the function signature.
- Improved the handling of page cache refresh logic by consolidating language handling within the `tenderService`, ensuring consistent behavior across services.
- Cleaned up related tests and removed deprecated functions to maintain code quality and readability.
This update enhances the maintainability of the recommendation caching system by simplifying configuration and improving cache invalidation logic.
- Introduced a new `RecommendedTendersPageCacheRefresher` interface to manage the asynchronous refresh of recommendation pages in Redis, improving cache management.
- Updated the `companyService` to support setting page cache languages and refreshing the recommended tenders page cache based on company IDs.
- Enhanced the `tenderService` to build and invalidate recommended tenders page caches, ensuring timely updates and efficient retrieval of cached recommendations.
- Added configuration options for recommendation page cache languages in the `AISummarizerConfig`, allowing for flexible language support.
- Implemented unit tests for the new caching logic and page refresh functionality, ensuring robust validation of the recommendation caching process.
This update significantly improves the efficiency and responsiveness of the tender recommendation service by integrating enhanced caching mechanisms and page refresh capabilities.
- Updated the company service to include a new method for scheduling the refresh of cached AI recommendations after the AI pipeline execution.
- Introduced a new interface for managing cached recommendation refreshes, improving the separation of concerns within the service layer.
- Enhanced the worker initialization to include Redis client support, allowing for better management of recommendation caching.
- Added functionality to list company IDs with existing recommendation caches, ensuring efficient updates post-pipeline runs.
- Implemented unit tests to validate the new recommendation refresh logic and ensure proper handling of various scenarios.
This update significantly improves the handling of AI recommendations by integrating caching mechanisms with the AI pipeline, enhancing overall system performance and responsiveness.
- Renamed and refactored the AI pipeline auto run functionality to a daily run, enhancing clarity and purpose.
- Introduced a new `AIPipelineDailyWorker` to manage the daily execution of the AI pipeline, replacing the previous auto run implementation.
- Updated configuration fields and logging messages to reflect the change from auto to daily run, ensuring consistent terminology throughout the codebase.
- Removed the obsolete `ai_pipeline_auto.go` file to streamline the worker structure.
This update improves the maintainability and readability of the AI pipeline management by clearly distinguishing between auto and daily run functionalities.
- Introduced AIPipelineAutoWorker to manage the execution of the AI pipeline auto run, including startup catch-up and scheduled tasks.
- Enhanced WorkerConfig to include AIPipelineAutoEnabled and AIPipelineAutoInterval settings for better control over AI pipeline execution.
- Added logging for AI pipeline auto run status, including success and error handling, to improve observability.
- Updated daily job tracker to include AIPipelineAutoJobName for tracking AI pipeline job completions.
This update enhances the system's capability to automate AI pipeline executions, improving efficiency and reliability in processing AI tasks.
- Introduced a new `NotificationWorker` to promote due scheduled notifications from pending to sent, improving notification management.
- Added `NotificationInterval` configuration to schedule the notification delivery worker, with a default value for flexibility.
- Implemented `MarkDueScheduledAsSent` method in the notification repository to update the status of notifications based on their delivery time.
- Updated the notification service to process due scheduled notifications during relevant operations, ensuring timely delivery.
This update enhances the notification system by automating the delivery of scheduled notifications, improving user engagement and operational efficiency.
- Changed the default MinIO bucket name from "opplens-documents" to "opplens" across multiple configuration files.
- Introduced caching for dashboard statistics in the service layer to improve performance and reduce redundant data fetching.
- Implemented a mutex for thread-safe access to cached statistics, ensuring data integrity during concurrent requests.
This update streamlines the configuration for the AI summarizer and optimizes the dashboard service, enhancing overall system efficiency.
- Added `TranslationEnabled` and `TranslationInterval` fields to the worker configuration to manage automatic translation scheduling.
- Updated the worker initialization to log when the translation worker is disabled.
- Improved error handling in the AI summarizer client by introducing `APIStatusError` for better context on API failures, replacing direct error messages with structured error responses.
This update enhances the configurability of the worker and improves error reporting for AI service interactions, contributing to better maintainability and user experience.
- Removed the QueueConfig structure and related queue management files as they are no longer in use.
- Updated the worker initialization to reflect the removal of queue-related configurations.
- Cleaned up the bootstrap process by eliminating deprecated logging related to the queue system.
This update streamlines the worker's configuration and prepares the codebase for future enhancements without the legacy queue management components.
- 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 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