- 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.
- Removed the DocumentSummarizationWorker and its related scheduling logic from the worker bootstrap.
- Updated the AI summarizer client initialization comment for clarity.
- Added a new error type for cases when tender documents have not been scraped yet, enhancing error handling in the tender service.
- Modified API documentation to reflect changes in AI summary retrieval logic, ensuring accurate descriptions of on-demand summarization behavior.
This update streamlines the AI summarization process by eliminating the document summarization worker, improving overall system efficiency and clarity in error handling.
- 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