- Introduced the `query_forecast_end` function in `incubations.py` to determine the end date for query-synthesis weather based on incubation status and treatment intervals.
- Updated the `_run_job_inner` function in `job.py` to utilize `query_forecast_end`, enhancing the job's ability to handle future disease forecasting.
- Added unit tests in `test_incubations.py` to validate the behavior of `query_forecast_end` under various scenarios, ensuring accurate forecasting logic.
- Improved overall code clarity and maintainability with the new function integration.
- Enhanced `.env.example` to include `SQL_APP_DATABASE` for managing additional advisory-related tables.
- Refactored `config.yaml` to remove the `crops` block, shifting to a database-driven approach for job discovery.
- Updated `README.md` to clarify the new database structure and job discovery process, emphasizing the role of the `AI_agrosupport_an_pathogen_models` and `AI_agrosupport_agro_models` tables.
- Modified SQL queries across multiple files to accommodate the new database schema, ensuring correct table references and improving overall data handling.
- Adjusted `pipeline` logic to utilize the new configuration, enhancing the efficiency of the advisory generation process.
- Updated `config.yaml` to change the advice storage table from `advice` to `advisories` and adjusted the `thinking_budget` from 11264 to 9216.
- Modified `README.md` to reflect the new `advisories` table in the advisory generation process.
- Refactored SQL queries in `batch.py`, `job.py`, and `advice_context.py` to align with the new table structure.
- Enhanced the advice generation logic in `advice.py` to accommodate changes in the data model, including updates to the JSON structure for advice output.
- Updated relevant functions and documentation to ensure consistency across the codebase regarding the new advisory schema.
