- 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.
- Changed `thinking_budget` to `thinking_level` in `config.yaml` for Gemini models, updating the logic to reflect the new parameter.
- Updated LLM models in `config.yaml` to `gemini-3.8-flash` and `gemini-3.1-pro-preview`, adjusting their respective `max_tokens` and `thinking_level`.
- Enhanced `docker-compose.yml` to include a new `reranker-transformers` service for improved product ranking.
- Modified vector search logic in `vector.py` to incorporate reranking with the local transformers model, improving the accuracy of product recommendations.
- Updated unit tests to validate the new reranking functionality and ensure correct behavior in product searches.
- Added optional `thinking_budget` parameter to `config.yaml` for Gemini model, allowing for reserved thinking tokens.
- Updated `LlmSettings` class in `config.py` to include `thinking_budget` and adjusted parsing logic accordingly.
- Modified LLM call logic in `llm.py` to utilize `thinking_budget` for calculating `max_output_tokens`, improving output management.
- 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.
