- 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.
- 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 handling of historical/test dates and the conditions for generating advice.
- Modified job.py to incorporate observation data into risk assessment and product prefiltering.
- Introduced a new function in disease.py to select the appropriate prefilter rule based on observation and FASE data.
- Enhanced product filtering logic in products.py to accommodate new prefilter rules.
- Updated assemble.py to include observation data in JSON assembly for advice generation.
- Improved documentation in the grapevine downy mildew system prompt to explain the significance of INCUBAZPRIMARIA and observation data.
- Updated .env.example and config.yaml to include observability settings.
- Added a new Phoenix service in docker-compose.yml for self-hosted tracing.
- Enhanced README.md with instructions on enabling and using observability features.
- Implemented tracing in the pipeline, including job spans and LLM stage spans.
- Introduced ObservabilitySettings class in config.py for better configuration management.
- Updated job.py and resources.py to support tracing without affecting fault isolation.
- Minor adjustments to other files for compatibility with the new observability features.
