- 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.
- Updated the tender service to include a new dependency for listing rejected tenders by company, allowing for more refined tender recommendations.
- Introduced a new field in the SearchForm to specify whether to exclude rejected tenders from the recommendation results.
- Enhanced the Recommend method to filter out rejected tenders based on the new exclusion logic, improving the relevance of AI-ranked tender recommendations.
- Added unit tests to verify the exclusion logic for rejected tenders, ensuring robust functionality.
This update improves the tender recommendation process by ensuring that company-rejected tenders are not included in the results, enhancing user experience and satisfaction.
- Implemented the IsRecommendable method in the Tender entity to determine if a tender should be included in AI recommendations based on its status and deadline.
- Added unit tests for the IsRecommendable method to cover various scenarios, ensuring accurate recommendation logic.
- Updated the Recommend method in the tender service to utilize the new IsRecommendable method for improved clarity and functionality.
This update enhances the recommendation logic for tenders, ensuring only appropriate tenders are considered for AI recommendations based on their status and deadlines.
- Introduced `ProcedureReference` struct to encapsulate AI procedure coordinates for better data management.
- Added `GetByProcedureReferences` method in the `TenderRepository` to retrieve multiple tenders based on AI procedure references in a single query.
- Updated the `resolveRecommendedTenders` method in the service layer to utilize the new repository method, improving efficiency in fetching recommended tenders.
- Enhanced error handling and logging for the new repository method to ensure robust operation.
This update improves the handling of AI procedure references, streamlining tender retrieval processes and enhancing overall system performance.
- Introduced `FormatAIProcedureRef` and `ParseAIProcedureRef` functions in the `tender` domain for handling AI service tender references.
- Added unit tests for these functions in `ai_reference_test.go` to ensure correct parsing and formatting behavior.
- Updated the `TenderResponse` struct to include a new `ProcedureRef` field for improved data representation.
- Enhanced the `GetByProcedureReference` method in the repository to retrieve tenders based on the new procedure reference format.
- Modified the `Recommend` method in the service layer to utilize the new procedure reference handling, improving the recommendation process.
This update enhances the handling of AI procedure references, ensuring better data integrity and usability in the tender management system.
- Added new fields `Rank` and `Analysis` to the `TenderResponse` struct for improved data representation.
- Updated the `RecommendTenders` handler to reflect changes in API documentation, including a new summary and description for AI-ranked tender recommendations.
- Improved error handling in the recommendation process, ensuring appropriate responses for missing company IDs and unavailable AI services.
This update enhances the tender response capabilities and improves the clarity of the recommendation API, aligning with the overall goal of providing better insights for users.
- 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.
- Updated the AI pipeline service to include a new `ScrapedDocumentMetadataSyncer` interface for persisting scraped document metadata onto tender records.
- Modified the `NewService` function to accept the new metadata syncer dependency.
- Implemented synchronization of scraped document metadata in the `ScrapeDocuments` and `Run` methods.
- Enhanced the tender service to enrich search filters based on scraped documents and added a new method for syncing scraped documents from storage.
- Updated the `SearchForm` to include `ContractFolderIDsWithDocuments` for better handling of scraped documents in queries.
This update improves the integration of scraped document handling within the AI pipeline, enhancing data consistency and operational efficiency in the tender management system.
- Added new routes and handlers for AI pipeline operations, including scraping documents, batch summarization, translation, and syncing with the Opplens AI service.
- Introduced request forms for handling tender references and batch operations.
- Enhanced the AI service with methods for triggering batch operations and managing pipeline runs.
- Updated Swagger documentation to reflect the new AI pipeline endpoints and their functionalities.
This update integrates comprehensive AI pipeline capabilities into the tender management system, improving operational efficiency and user experience.
Paginate un-summarized tenders and mark rows missing notice/folder IDs as handled so the worker cannot spin forever. Return 400/404 for validation and not-found cases on AI summarize/analyze triggers instead of leaking internal errors as 500.
- 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 feedback repository and service methods to use a consistent naming convention, changing `NewTenderRepository` and similar methods to `NewRepository`.
- Refactored feedback handler methods to improve clarity by renaming methods such as `ListFeedback` to `Search` and `GetFeedback` to `Get`.
- Enhanced feedback response structures to include detailed company and tender information, improving the clarity of feedback data returned to API consumers.
- Updated Swagger documentation to reflect changes in feedback response structures and endpoint paths, ensuring accurate representation of the API for consumers.
- Renamed and restructured tender-related API endpoints for improved clarity and consistency, including changing the route from `/admin/tenders` to `/admin/v1/companies` and updating the associated methods.
- Introduced a new SearchForm structure for listing tenders with advanced filtering capabilities, enhancing the API's search functionality.
- Updated the service and repository layers to align with the new endpoint structure, ensuring proper handling of tender data.
- Enhanced Swagger and YAML documentation to reflect the changes in endpoint structure, including detailed descriptions and examples for the new search functionality.
- Improved error handling and response structures to provide clearer feedback to API consumers, ensuring a more robust and user-friendly experience.
- Introduced a new search functionality for companies, allowing advanced filtering capabilities including tags, business criteria, and location.
- Updated the API documentation to reflect changes in the search endpoint, enhancing clarity for API consumers.
- Refactored existing company-related API endpoints for consistency, including renaming and restructuring routes.
- Enhanced response structures to return company entities directly, simplifying the response handling in API endpoints.
- Removed unused query parameters and handlers, streamlining the company management functionality.
- Changed references in API documentation from `main.HealthResponse` to `bootstrap.HealthResponse` for consistency.
- Added new query parameters `publication_from` and `publication_to` to the tender listing and recommendation endpoints to allow filtering by publication date.
- Updated the `ListTendersRequest` struct to include optional fields for `from` and `to` timestamps.
- Modified the `TenderRepository` and `TenderService` interfaces to support the new filtering parameters in the tender listing functionality.
- Enhanced the service and handler layers to process the new query parameters, improving the flexibility of tender retrieval.
- Updated the tender API to include a new endpoint for recommending public tenders based on company profiles, enhancing user experience for mobile application users.
- Modified existing routes to reflect the new structure, changing the tender details route and adding a dedicated recommendation route.
- Improved Swagger documentation to accurately represent the new endpoint, including detailed descriptions, parameters, and response formats.
- Ensured adherence to Clean Architecture principles by maintaining clear separation of concerns in the service and handler layers.
- Removed all scraping-related functionality, including routes, handlers, services, and repository methods, to streamline the tender management system.
- Updated the tender API to focus solely on tender management, enhancing endpoints for listing, retrieving, and updating tenders.
- Improved Swagger documentation to reflect the removal of scraping endpoints and the addition of new tender-related features.
- Ensured adherence to Clean Architecture principles throughout the refactoring process, maintaining a clear separation of concerns.
- Updated the tender service to include company-based matching for tenders, allowing for more relevant results based on company CPV codes.
- Modified the ListTenders endpoint to accept a company ID parameter for calculating match percentages and sorting tenders accordingly.
- Refactored the tender response structure to include match percentage and days until deadline for better client-side handling.
- Updated API documentation to reflect changes in the tender listing functionality and added new endpoints for public tender access.
- Removed deprecated customer-related methods and streamlined customer listing functionality for improved clarity and performance.
- Introduced a new TED scraper in `cmd/scraper` to handle downloading and parsing TED XML files.
- Added configuration management for the scraper, including a new `Config` struct and YAML configuration file.
- Created necessary handler, service, and repository layers for tender management, adhering to Clean Architecture principles.
- Implemented comprehensive logging and error handling throughout the scraper functionality.
- Updated API routes to include tender management operations, enhancing the overall system capabilities.
Mazyar