- Changed terminology in `config.yaml` from "SLA deadline" to "readiness target" to clarify that jobs still run after the specified time.
- Updated `README.md` to reflect the new readiness target concept, emphasizing that jobs started after the target are still executed and reported.
- Modified `pipeline` logic to incorporate the new readiness target, ensuring accurate tracking of jobs that begin after the target time.
- Enhanced reporting in the run report to include counts of jobs that started after the readiness target, improving transparency in job execution outcomes.
- 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.
- 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.
