- Introduced a caching mechanism for translation responses in the tender recommendation service, improving efficiency by reducing redundant translation requests.
- Refactored the `enrichWithTranslation` method to support an optional translation cache, allowing for faster retrieval of stored translations.
- Implemented parallel processing for building recommended tender responses, utilizing goroutines to enhance performance and responsiveness.
- Added unit tests to validate the new caching behavior and ensure the correct application of translations in various scenarios.
This update significantly improves the performance and responsiveness of the tender recommendation service by integrating caching and parallel processing, enhancing the overall user experience.
- 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.
- 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.
- Introduced the ability to append external links to company profiles through a new API endpoint.
- Enhanced the `Company` entity to include a `Links` field for storing external resource links.
- Created `AddLinksForm` for validating incoming link data and implemented corresponding logic in the service layer.
- Added error handling for link validation, ensuring only valid URLs are accepted and limiting the number of links to 20.
- Implemented unit tests for link management functions, including sanitization and merging of links.
- Updated relevant API documentation to reflect the new functionality.
This update significantly enhances the company management capabilities by allowing the addition of external links, improving the overall user experience and data richness in company profiles.
- Introduced new functionality in `form_companies.go` to handle company selection from various input formats, including legacy `company_ids` and company summary objects.
- Implemented `UnmarshalJSON` methods for `CreateCustomerForm`, `UpdateCustomerForm`, and `AssignCompaniesForm` to support flexible company data parsing.
- Added unit tests in `form_companies_test.go` to validate the correct unmarshalling of company IDs and summaries, ensuring robust handling of different input scenarios.
- Enhanced the `GetByCompanies` method in the customer repository to directly use string company IDs, improving data retrieval efficiency.
This update significantly improves the handling of company data in customer forms, ensuring accurate processing and validation of company selections, while expanding test coverage for these functionalities.
- Introduced a new test file `recommendation_translation_test.go` to validate the behavior of the tender recommendation service regarding translation handling.
- Implemented tests to ensure that the service correctly applies stored translations and retains original text when no translation is available.
- Enhanced the `Recommend` method in the tender service to utilize a structured approach for managing recommended tenders, improving clarity and maintainability.
This update improves the testing coverage for translation handling in tender recommendations, ensuring accurate responses based on available translations and original content.
- Renamed `buildRejectedTenderIDSetForCompanies` to `buildRejectedTenderIDSetsForCompanies` to better reflect its functionality of returning a map of excluded tender IDs by company.
- Updated the implementation to return a nested map structure for better organization of excluded tender IDs per company.
- Added a new test function `TestIsRecommendedTenderExcludedForAllCompanies` to validate the visibility of tenders based on company-specific exclusions.
- Enhanced existing tests to ensure comprehensive coverage of the new logic for handling rejected tenders.
This update improves the clarity and functionality of the tender recommendation process by refining the handling of rejected tenders and expanding test coverage.
- Introduced a new test function `TestMapProcedureReferencesToTenders` to validate the mapping of procedure references to corresponding tenders.
- Enhanced the `GetByProcedureReferences` method in the tender repository to utilize a more efficient filtering mechanism based on unique contract folder IDs.
- Updated pagination logic to dynamically adjust limits based on the number of unique folders and references, ensuring optimal data retrieval.
This update improves the testing coverage for tender mapping functionality and optimizes the repository's data fetching strategy, enhancing overall performance and reliability.
- Introduced a new method to validate company IDs for AI recommendations, ensuring all requested companies exist before processing.
- Added caching logic to retrieve AI recommendations efficiently, reducing unnecessary calls and improving performance.
- Utilized errgroup for concurrent processing of AI recommendations across multiple companies, enhancing responsiveness.
- Updated the tender service to handle rejected tenders more effectively by incorporating concurrent fetching of rejected tender IDs.
This update significantly improves the AI recommendations process by ensuring accurate company validation and optimizing data retrieval through caching and concurrency, enhancing overall system performance and user experience.
- 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.
- Enhanced API documentation for `GetTenderApprovalByTenderAndCompany` to clarify the default behavior of the status filter, indicating it defaults to 'submitted' when omitted.
- Updated the `PublicGetTenderApprovals` method to set the default status to 'submitted' if no status is provided, ensuring consistent behavior in approval retrieval.
This update improves the clarity of the API documentation and enhances the functionality of the tender approval retrieval process by establishing a default status, thereby improving user experience.
- Added new field `Days` and `ScrapedTED` to `SummaryResponse` for tracking daily TED scrape counts.
- Updated `SummaryQuery` to include `Days` parameter for querying scraped TED data.
- Modified `Summary` handler to accept and process the new `Days` parameter.
- Refactored `Summary` method in the repository to support the new `Days` parameter and improved aggregation logic.
- Enhanced caching logic in the service layer to utilize a composite cache key based on `closingWindowSec` and `days`, improving cache management and retrieval efficiency.
This update improves the dashboard's functionality by providing more detailed insights into TED scraping activities and optimizing the caching strategy for better performance.
- Introduced InvalidStatusTransitionError to handle invalid status transitions for inquiries, providing clearer error messages.
- Updated UpdateInquiryStatusForm to make the Reason field optional and added logic to set a default reason if not provided.
- Enhanced form validation tests to cover new status transition error scenarios and validation messages.
- Refactored handler methods to utilize new error handling functions for improved response management.
This update improves the robustness of inquiry status management by ensuring proper error handling and validation, enhancing user experience during status updates.
- Modified CompanyForm fields (RegistrationNumber, TaxID, Industry) to be optional, allowing for more flexible company creation.
- Updated the Create method in company service to check for existing companies by registration number and tax ID only if provided, improving error handling and user experience during company creation.
This update enhances the company creation process by allowing optional fields while ensuring that existing company checks are performed only when necessary.
- Updated the ListPendingTenders method to filter tenders based on their active publication submission window instead of just deadlines, enhancing the accuracy of tender listings.
- Introduced a new HasActivePublicationWindow method in the Tender entity to determine if the publication-based submission window is still open.
- Modified the GetTenderByNoticeID method to reflect the new logic for filtering tenders based on their publication status.
- Enhanced the documentation for relevant API endpoints to clarify the changes in tender retrieval criteria.
This update improves the precision of tender scraping by ensuring only relevant tenders with active submission windows are processed, contributing to better data management and scraping efficiency.
- Added a new field, ScrapedTEDNotices, to the StatisticsLifetimeTotals struct to track the total number of TED notices scraped.
- Updated the Statistics method in the statistics repository to include a background process for retrieving total scraped TED notices, improving the accuracy of dashboard statistics.
- Introduced new methods in the Counter to increment and retrieve daily counts for scraped TED notices, ensuring reliable metrics for reporting.
- Modified the TEDScraper to increment the TED notice scraped counter upon successful import, enhancing the tracking of scraping activity.
This update improves the dashboard's statistics by providing detailed insights into TED notice scraping activities, contributing to better data visibility and reporting.
- Introduced CompanyContextMiddleware to resolve the active company context for customer requests, ensuring that tender recommendations and company-scoped APIs remain in sync with the database.
- Updated public routes to utilize the new CompanyContextMiddleware alongside the existing AuthMiddleware, improving the handling of company-specific requests.
- Added unit tests for the pickActiveCompanyID function to validate the logic for selecting the appropriate company context based on customer assignments and requested company IDs.
This update enhances the accuracy and reliability of company context management in the application, improving user experience and data consistency.
- Introduced a new constant, statisticsColdLoadWait, to define the wait time for cold-cache requests before serving a placeholder response.
- Updated the Statistics method to initiate a background load for statistics while allowing for a brief wait for real data, improving responsiveness during cache warming.
- Implemented a channel to handle the result of the background loading process, ensuring that users receive timely feedback while the cache is being populated.
This update optimizes the dashboard's performance by ensuring that users are served quickly, even when the statistics are being freshly loaded from the backend.
- Updated the Statistics method in the dashboard service to initiate a background refresh for statistics while serving a placeholder report, enhancing responsiveness during cache warming.
- Removed blocking calls to load statistics directly, allowing for faster response times.
- Improved logging to indicate when placeholder statistics are served, providing better visibility into the caching process.
This update optimizes the dashboard's performance by ensuring that users receive immediate feedback while the system prepares accurate statistics in the background.
- 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.
- Increased the cache duration for dashboard statistics from 60 seconds to 5 minutes, improving data freshness and reducing load on the backend.
- Introduced a stale cache mechanism that allows retrieval of stale statistics while refreshing them in the background, enhancing user experience by providing quicker access to data.
- Updated the statistics repository to handle the new caching logic, ensuring accurate and timely statistics reporting.
- Added tests to validate the new caching behavior and ensure the integrity of statistics retrieval.
This update optimizes the dashboard's performance and responsiveness by improving the caching strategy for statistics.
- Updated the ListPaginationOptions struct to include SkipCount and IncludeCount fields, allowing for more flexible pagination behavior.
- Modified the BuildListPagination function to handle cursor pagination with count options, improving performance by running count queries in parallel with data retrieval.
- Enhanced the FindAll method in the repository to support concurrent counting of documents, reducing overall latency for list operations.
- Added tests for pagination behavior, ensuring accurate handling of count scenarios in both offset and cursor pagination.
This update improves the efficiency and flexibility of pagination in the MongoDB repository, enhancing the overall performance of list operations.
- Added the PublicationSubmissionDeadline method to the Tender entity, which calculates the submission deadline based on the publication date and stored submission deadline.
- Updated the IsRecommendable method to utilize the new PublicationSubmissionDeadline logic for determining recommendation eligibility.
- Introduced unit tests for the PublicationSubmissionDeadline method, ensuring accurate calculation and validation of submission deadlines.
- Enhanced existing tests for the IsRecommendable method to cover new scenarios related to submission deadlines.
This update improves the accuracy of submission deadline handling and enhances the recommendation logic for tenders based on publication dates.
- 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 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.
- Added a new `UserID` field to the `SearchForm` to allow filtering notifications by user ID.
- Implemented the `ResolvedRecipients` method to return user IDs based on the `user_id` or `recipient` query parameters, improving the flexibility of notification searches.
- Updated the `GetNotifications` method in the service layer to utilize the new recipient resolution logic, ensuring accurate retrieval of notifications based on user ID.
This update enhances the notification management capabilities, providing more granular control over notification searches.
- Introduced the `scrapedDocumentsScanner` interface to facilitate scanning of scraped documents from MinIO, returning both procedure summaries and daily document counts.
- Updated the `ListProceduresWithDocuments` method to utilize the new scanning functionality, improving data retrieval efficiency.
- Enhanced the `scrapedDocumentsPerDay` method to filter daily counts based on a specified start date, ensuring accurate reporting of document statistics.
- Added unit tests for the new scanning logic and daily counts filtering, ensuring robust functionality and error handling.
This update enhances the dashboard's document management capabilities, providing better insights into scraped documents and their daily counts.
- Introduced retry logic for fetching AI recommendations after onboarding, enhancing reliability in recommendation retrieval.
- Updated logging levels for better observability, changing cache miss logs to Info level.
- Renamed methods for clarity, replacing `refreshAIRecommendationsCacheAsync` with `scheduleRecommendationRefreshAfterOnboarding` and `fetchAndCacheAIRecommendations` with `fetchAIRecommendations`.
- Implemented a mechanism to clear the cache if no recommendations are returned, improving cache management.
This update optimizes the AI recommendation process, ensuring more robust handling of recommendation fetching and caching during onboarding.
- Updated `AISummarizerConfig` to allow for a default `RecommendationCacheTTL` of 0, enabling persistent caching until company updates.
- Refactored `StartAIOnboarding` to include cache invalidation and asynchronous recommendation refresh, improving responsiveness during onboarding.
- Introduced `triggerAIOnboardingAsync` method for background processing of AI onboarding and cache refresh, enhancing user experience.
- Improved logging for AI onboarding and recommendation fetching processes, providing better observability and error tracking.
This update optimizes the AI recommendation caching mechanism and onboarding workflow, ensuring a smoother and more efficient experience for users.
- Introduced `refreshAIRecommendationsCacheAsync` method to refresh AI recommendations in the background, improving responsiveness by serving the previous cache until the refresh completes.
- Updated `StartAIOnboarding` to call the new asynchronous cache refresh method instead of invalidating the cache directly.
- Added logging for cache refresh operations, including success and error handling, to enhance observability.
This update enhances the AI recommendation caching mechanism, providing a smoother onboarding experience and reducing latency in recommendation retrieval.
- 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.
- Added a new endpoint in the `tender` handler for retrieving AI-ranked tender recommendations for a company, improving the functionality of the admin panel.
- Updated the `SearchForm` to include a query parameter for `only_active_deadlines`, allowing for more flexible search options.
- Enhanced API documentation for the new endpoint to provide clear usage instructions and expected parameters.
This update improves the tender management system by providing administrators with better tools for accessing relevant tender information.
- Changed the response message in the `Send` method of the `NotificationHandler` from "Notification sent successfully" to "Notification created successfully" to better reflect the action performed.
This update improves the clarity of the notification response, ensuring users receive accurate feedback on their actions.
- Modified the `DocumentFileIDs` field in the `Company` struct to ensure it is always persisted in BSON format, enhancing data consistency.
- Implemented logic in the `Update` method of the `companyRepository` to clear document file IDs when the slice is empty, ensuring accurate database updates.
- Added logging for errors encountered while clearing document file IDs, improving error tracking and debugging capabilities.
This update enhances the management of document file IDs within the company domain, ensuring proper handling and persistence in the database.
- Modified the `EmployeeCount` field validation in `SearchForm` to use a range of 1 to 100000, ensuring more accurate input constraints.
- Updated `EmployeeCountMin` and `EmployeeCountMax` fields to reflect the same range validation, enhancing the robustness of search queries.
This update improves the validation logic for employee count fields in the company search functionality, providing better input handling and user experience.
- 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 `SearchForm` to include optional filters for `name`, `email`, `phone`, and `employee_count`, allowing for more granular search capabilities.
- Modified the `Search` method in the `companyRepository` to handle the new filters, improving the search logic with regex support for `name`, `email`, and `phone`.
- Updated API documentation in the `company` handler to reflect the new query parameters for enhanced clarity.
This update improves the search functionality within the company domain, providing users with more flexible and precise search options.
- Updated the `Search` method in the `companyRepository` to improve search capabilities by allowing regex-based filtering on `name`, `email`, and `phone` fields.
- Added logic to handle numeric search queries for `employee_count`, enhancing the search flexibility.
- Implemented input sanitization to trim whitespace from search queries, ensuring cleaner search inputs.
This update improves the search functionality within the company repository, providing more robust and flexible search options for users.
- Introduced `form_test.go` to validate the behavior of the `UpdateUserForm` struct's JSON unmarshalling, specifically for the `profile_image` field.
- Added tests to ensure omitted `profile_image` is not treated as an update, null values clear the image, and valid URLs are preserved.
- Enhanced the `UpdateUserForm` and `UpdateProfileForm` structs to include logic for distinguishing between omitted and explicitly null profile images during JSON unmarshalling.
This update improves the robustness of user profile updates by ensuring correct handling of profile image data in JSON requests, enhancing overall data integrity in the user management system.
- 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.
- 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.
- Changed the `Rank` field type from `string` to `int` in the `RecommendedTenderResponse` struct within the `company` domain for better data representation.
- Updated the `Rank` field type from `string` to `int` in the `TenderResponse` struct within the `tender` domain to ensure consistency in ranking data.
- Modified the `Rank` field type from `string` to `int` in the `RecommendedTender` struct within the AI summarizer package to align with the updated data structure.
This update enhances the data integrity and consistency across the tender management system by standardizing the rank representation as an integer.
- Added `DeliveryChannelInApp` to support in-app notifications.
- Introduced `EventTypeInApp` constant for identifying in-app notifications.
- Updated `AdminNotifications` and `CustomerNotifications` handlers to set the event type to `EventTypeInApp`.
- Modified the `GetByUserID` repository method to handle filtering for in-app notifications, allowing for more flexible retrieval options.
- Updated the `persistNotification` method to include in-app as a delivery method.
This update improves the notification system by enabling in-app notifications, enhancing user engagement and notification management capabilities.
- 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 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.
- Updated the `AggregateProcurementLotEstimatedValue` function to reject mixed currencies and allow empty lot currencies, ensuring accurate aggregation of estimated values.
- Introduced new unit tests in `entity_test.go` to validate the behavior of the aggregation function under various scenarios, including mixed currencies and empty lot currencies.
- Refactored budget calculation in `budget_test.go` to utilize the new aggregation logic, improving consistency in budget retrieval.
This update improves the handling of estimated values in procurement lots, enhancing the reliability of the tender management system.
Mazyar