- Renamed and refactored the AI pipeline auto run functionality to a daily run, enhancing clarity and purpose.
- Introduced a new `AIPipelineDailyWorker` to manage the daily execution of the AI pipeline, replacing the previous auto run implementation.
- Updated configuration fields and logging messages to reflect the change from auto to daily run, ensuring consistent terminology throughout the codebase.
- Removed the obsolete `ai_pipeline_auto.go` file to streamline the worker structure.
This update improves the maintainability and readability of the AI pipeline management by clearly distinguishing between auto and daily run functionalities.
- Introduced AIPipelineAutoWorker to manage the execution of the AI pipeline auto run, including startup catch-up and scheduled tasks.
- Enhanced WorkerConfig to include AIPipelineAutoEnabled and AIPipelineAutoInterval settings for better control over AI pipeline execution.
- Added logging for AI pipeline auto run status, including success and error handling, to improve observability.
- Updated daily job tracker to include AIPipelineAutoJobName for tracking AI pipeline job completions.
This update enhances the system's capability to automate AI pipeline executions, improving efficiency and reliability in processing AI tasks.
- Introduced a mutex to ensure only one TED scraper run executes at a time, preventing concurrent executions during startup and scheduled runs.
- Implemented a mechanism to check if today's TED scrape has already been completed during startup, logging appropriate messages for both completed and new runs.
- Added startup catch-up logic for tender translations and unprocessed notices, ensuring that any missed tasks are executed without blocking the application startup.
This update improves the reliability and efficiency of the TED scraper and worker processes, ensuring that all necessary tasks are completed after a server restart.
- Replaced the tender repository with a new notice repository, encapsulating notice-related data access methods.
- Introduced the Notice entity to represent tender/contract notices, including relevant fields and methods for managing notice data.
- Updated the TED scraper to utilize the new notice repository for creating and managing notices, enhancing the integration with the tender management system.
- Implemented Ollama SDK initialization in the web bootstrap process, allowing for improved AI interactions.
- Enhanced the tender service to include Ollama SDK for additional functionality, ensuring a more robust service layer.
- Introduced a new configuration file for the TED scraper, defining database connection settings, logging preferences, and scraping parameters.
- Implemented a CronScheduler to manage scheduled tasks for TED operations, utilizing the robfig/cron library for scheduling.
- Added new entities and structures for handling TED XML data, including eForms and tender-related information, enhancing the scraper's functionality.
- Updated the main application to initialize the TED scraper with the new configuration and set up graceful shutdown handling.
- Removed the deprecated scraping implementation to streamline the codebase and focus on the new architecture.
Mazyar