- 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.
- Removed the QueueConfig structure and related queue management files as they are no longer in use.
- Updated the worker initialization to reflect the removal of queue-related configurations.
- Cleaned up the bootstrap process by eliminating deprecated logging related to the queue system.
This update streamlines the worker's configuration and prepares the codebase for future enhancements without the legacy queue management components.
- 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.
- Improved the `IsDuplicateKeyError` function to utilize structured error messages for better accuracy in identifying duplicate key errors.
- Introduced `duplicateKeyMessages` and `hasStructuredWriteException` functions to parse and extract relevant information from MongoDB error messages.
- Updated `DuplicateKeyMatchesField` to match duplicate key errors against specific fields by analyzing error messages rather than relying on substring matching.
This refactor enhances the clarity and reliability of duplicate key error handling, ensuring more precise identification of conflicts in MongoDB operations.
- 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.
Map E11000 from the per-collection phone index to friendly conflict
errors and skip redundant GetByPhone when the phone is unchanged.
Not blocking: IAT test DB may need a one-off dedupe for index build;
phone uniqueness is per collection (users vs customers), not global.
Frontend sends nested address field names for sorting; accept them alongside the existing country and state aliases.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Introduced a comprehensive MinIO package for managing object storage, including support for hierarchical document storage and general file management.
- Implemented core components such as Config, ConnectionManager, and Service, following Clean Architecture principles.
- Added functionality for file uploads, downloads, bucket management, and structured logging, enhancing usability and maintainability.
- Included detailed error handling and validation for configuration and operations, ensuring robustness.
- Updated documentation with usage examples and configuration guidelines to facilitate integration and understanding of the package's functionality.
Mazyar