- 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.
- Adjusted `max_tokens` in `config.yaml` for LLM models to optimize performance, setting `gpt-4o-mini` to 2048 and `gpt-4o` to 5120.
- Enhanced `assemble.py` to include a new `_weather_fields` function for improved metric handling, ensuring consistent data formatting across weather forecasts.
- Updated SQL queries in `weather.py` to incorporate additional wind speed metrics, enhancing the data model for weather analysis.
- Expanded query synthesis documentation in `_default.md` to include conditions for wind speed variations, improving advisory generation logic.
- Added unit tests in `test_assemble.py` to validate wind speed handling in weather forecasts and ensure robustness of the advisory generation process.
- Moved the assignment of `phenology_phase` in assemble.py to ensure it is included only for valid days.
- Updated the docstring in disease.py to clarify the rounding behavior of decimal values, enhancing the understanding of the formatting logic.
- Removed unnecessary checks in the decimal formatting function to streamline the code.
