- Increased `thinking_budget` in `config.yaml` from 9216 to 11264 to enhance LLM performance.
- Clarified conditions for including `last_applied_treatment` in the advice JSON structure, ensuring it is appended even when `allowed_products` is empty.
- Refactored product filtering logic in `products.py` to implement a new function for checking if the treatment interval is open, improving the accuracy of product recommendations.
- Updated unit tests to validate the new treatment interval logic and ensure correct behavior in various scenarios.
- Updated README.md to clarify the conditions for `last_applied_treatment` and its integration into the advice JSON structure.
- Refactored job.py to utilize a new `TreatmentHistory` class for managing treatment data, improving clarity and efficiency in treatment retrieval.
- Enhanced advice_context.py to conditionally include `last_applied_treatment` based on the treatment history, ensuring no redundant queries are made.
- Modified products.py to implement logic for excluding active ingredients based on the latest treatment's interval, refining the product prefiltering process.
- Updated vector.py to ensure the SQL allowlist is applied correctly, improving the efficiency of product searches.
- Added unit tests to validate the new treatment history functionality and product filtering logic.
- Updated README.md to clarify the conditions for including `last_applied_treatment` in the advice JSON structure.
- Modified job.py to pass `as_of` and `field_id` to the advice generation function.
- Enhanced advice_context.py to conditionally add `last_applied_treatment` based on recent treatment history and allowed products.
- Introduced new functions in treatments.py to group treatments by day and load the last treatment before the recent window.
- Updated _output_format.md to specify the conditions under which `last_applied_treatment` is included in the advisory.
