- 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.
- 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.
- Adjusted the maximum number of recommended products from 6 to 4 in README.md to reflect changes in the product retrieval process.
- Modified vector.py to limit the number of products retrieved per query to 4 for single queries and 2 for multiple queries, ensuring better deduplication and efficiency in product searches.
