- Introduced a new function `dedupeRecommendationResponsesByTenderID` to remove duplicate tender recommendations based on their IDs, ensuring unique entries in the recommendation list.
- Updated the `mergeRecommendedTenders` function to utilize the new deduplication logic, streamlining the merging process of recommendation lists.
- Enhanced the `fetchAIRecommendations` method to apply deduplication on the fetched recommendations, improving data integrity.
- Added unit tests for the new deduplication function to validate its behavior and ensure correct handling of various input scenarios.
This update enhances the recommendation handling by ensuring that duplicate tender entries are effectively managed, improving the overall quality of the recommendations provided to users.
- Added functionality to retrieve merged AI recommendations for multiple companies, improving the relevance of tender suggestions based on company-specific data.
- Introduced normalization functions to clean and deduplicate company IDs, ensuring accurate processing of recommendations.
- Enhanced the company context resolution in customer middleware to support multiple assigned companies, improving the handling of company-specific requests.
- Updated the tender recommendation logic to utilize the new merged recommendations and handle exclusions for rejected tenders accordingly.
- Added unit tests to verify the new recommendation merging logic and company ID normalization, ensuring robust functionality.
This update significantly enhances the tender recommendation process by allowing for more comprehensive and relevant suggestions based on multiple company contexts, improving user experience and satisfaction.
Mazyar