- 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.
- Adjusted `thinking_budget` in `config.yaml` from 9216 to 8192 to optimize resource allocation.
- Increased `workers` from 8 to 12 in both `config.yaml` and `README.md` to enhance concurrency.
- Updated `gemini` limits from 4 to 12 in both files to improve request handling capacity.
- 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.
- 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.
- 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.
