- Introduced the `tender_submission` domain, including entity, repository, service, and handler implementations for managing tender submissions.
- Added new routes for both admin and public access to tender submissions, allowing for listing, ensuring, and retrieving submissions by ID and tender.
- Enhanced the tender approval service to synchronize submission workflows with approval changes, ensuring proper state management.
- Implemented validation and response structures for tender submission operations, improving API consistency and usability.
- Added unit tests for tender submission status transitions and workflow logic, ensuring robust functionality.
This update enhances the tender management system by providing comprehensive support for tender submissions, improving overall workflow and user experience.
- 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.
- 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.
- Updated the dashboard service to integrate Redis caching for improved performance in statistics retrieval.
- Modified the NewService function to accept a Redis client, enabling caching of dashboard statistics.
- Implemented logic to retrieve statistics from Redis, falling back to the database if necessary, and introduced a background process to warm the cache.
- Enhanced error handling and logging for Redis operations to ensure robust statistics management.
- Increased cache duration for scraped documents and adjusted timeout settings for MongoDB queries to optimize performance.
This update significantly improves the responsiveness and efficiency of the dashboard by leveraging Redis for caching statistics.
- Introduced a new `auditlog` package to handle audit logging for user actions, including creation, updates, deletions, and authentication events.
- Enhanced existing services (customer, user) to log relevant actions using the new audit logger, capturing details such as actor ID, action type, target type, and success status.
- Added middleware to enrich request context with metadata for audit logging, ensuring comprehensive tracking of user interactions.
- Integrated Elasticsearch for persistent storage of audit logs, with fallback to file-only logging if Elasticsearch is unavailable.
- Updated API documentation to include new audit log endpoints for administrative access.
This update significantly improves the system's ability to track and audit user actions, enhancing security and accountability within the application.
- Updated the `companyService` to include Redis caching for AI recommendations, improving performance and reducing redundant AI calls.
- Introduced asynchronous AI onboarding triggered after company profile updates, enhancing user experience by offloading processing.
- Added configuration for recommendation cache TTL in the `AISummarizerConfig`, allowing for flexible cache management.
- Implemented methods for caching, retrieving, and invalidating AI recommendations in the `companyService`, ensuring efficient data handling.
This update enhances the company's AI recommendation capabilities, providing faster responses and a more efficient onboarding process.
- Updated the `UploadDocuments` method to sanitize document file IDs before saving, ensuring only valid references are stored.
- Introduced `DetachDocumentFileID` method in the `companyService` to remove file IDs from all companies referencing a deleted file, improving data integrity.
- Enhanced the `companyRepository` with a new method to handle the removal of document file IDs from the database.
- Updated the `filestore` handler to utilize the new detachment functionality when files are deleted, ensuring consistent state across domain entities.
This update improves the management of document file IDs within the company domain, enhancing data integrity and reference handling.
- Updated the document scraper service to include a new ScrapePortalsProvider interface, allowing for dynamic retrieval of supported scraping portals.
- Modified the ListPendingTenders and GetTenderByNoticeID methods to filter tenders based on document URLs that match the configured portals.
- Introduced new error handling for cases when the scrape portals provider is not configured, returning appropriate service unavailable responses.
- Enhanced API documentation to reflect changes in tender retrieval logic and added error response details for unsupported portal scenarios.
This update improves the document scraping functionality by integrating AI portal support, enhancing the overall reliability and flexibility of the tender management system.
- Introduced the ProcedureDocumentsLister interface to list contract folders with scraped documents, enhancing the accuracy of document-scrape statistics.
- Updated the dashboard repository to accept ProcedureDocumentsLister as a dependency, allowing for improved data retrieval.
- Implemented tests for the new functionality, ensuring proper handling of scraped document folder IDs and error propagation.
This update enhances the dashboard's capability to manage and report on scraped documents, improving overall system efficiency and data integrity.
- 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.
- Added new AI onboarding and recommendation endpoints in the company handler for starting onboarding and retrieving ranked tender recommendations.
- Introduced `StartAIOnboarding` and `GetAIRecommendations` methods in the company service to handle AI interactions.
- Updated the company service constructor to include the AI recommendation client.
- Enhanced the AI summarizer client with methods for onboarding and fetching recommendations.
- Added response structures for onboarding and recommended tenders in the company form.
This update enhances the tender management system by integrating AI capabilities for onboarding and tender recommendations, improving user experience and operational efficiency.
- 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.
- Introduced a new CMS management feature, including the ability to create, retrieve, update, search, and delete CMS entries.
- Implemented the CMS entity, repository, service, and handler layers following Clean Architecture principles.
- Added API endpoints for CMS operations in Swagger documentation, ensuring comprehensive API specifications.
- Enhanced error handling and validation for CMS data, improving robustness and user experience.
- Updated the main application to initialize the CMS repository and service, integrating them into the existing system.
- Introduced a new contact management feature, including the ability to create, retrieve, search, update, and delete contact messages.
- Implemented the Contact entity, repository, service, and handler layers following Clean Architecture principles.
- Added API endpoints for contact operations in Swagger documentation, ensuring comprehensive API specifications.
- Enhanced error handling and validation for contact data, improving robustness and user experience.
- Updated the main application to initialize the contact repository and service, integrating them into the existing system.
- Changed API title and version in Swagger documentation from "Tender Management API" to "Opplens API" with version 1.0.0.
- Updated contact information and terms of service URLs to reflect the new branding for Opplens.
- Added a new "Notification" tag in the Swagger documentation for public notification management operations.
- Adjusted the Swagger endpoint path for documentation to improve routing consistency.
- Enhanced response structure in notification handlers to include metadata, improving clarity in API responses.
- Introduced a new ValidationService for customer operations to ensure valid username formats.
- Updated customer service and handler to utilize the new validation service, enhancing input validation during registration and updates.
- Modified CreateCustomerForm and UpdateCustomerForm to enforce username validation rules.
- Adjusted repository indexing to improve search efficiency by refining the text index for customer data.
- Ensured consistent handling of usernames across the customer module, improving overall data integrity and user experience.
- 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.
- Updated the InquiryService to include a notification SDK for sending notifications upon inquiry creation, enhancing user engagement.
- Modified the NewInquiryService constructor to accept the notification SDK as a dependency, adhering to dependency injection principles.
- Refactored the Create method to send notifications with inquiry details, improving communication and response to inquiries.
- Cleaned up commented-out code related to duplicate inquiry checks for better readability and maintainability.
- Updated the notification handling logic to utilize a new SDK for sending notifications, improving the flexibility and scalability of the notification system.
- Introduced new methods in the notification service for sending notifications to users and customers based on various target audience types, enhancing the notification delivery capabilities.
- Added a new endpoint to assign companies to a customer, improving customer management functionalities.
- Refactored the customer entity and forms to replace 'CompanyIDs' with 'Companies', ensuring consistency across the data model.
- Enhanced API documentation with Swagger comments for the new endpoint and updated notification structures, ensuring clarity for API consumers.
- Added a new notification domain, including entities, services, handlers, and repositories to manage notifications effectively.
- Implemented CRUD operations for notifications, allowing for creation, retrieval, updating, and deletion of notifications via the API.
- Enhanced API documentation with Swagger comments for new notification endpoints, ensuring clarity for API consumers.
- Updated routes to include notification management, improving administrative capabilities within the web panel.
- Introduced validation for notification requests and responses, ensuring data integrity and proper error handling.
- Updated the go.mod file to include the latest version of the go-redis library, ensuring compatibility with the notification service.
Mazyar