HHEM Victorious Medical Data Query Analyzer

Incorporating the HHEM Vectara RAG, our project sheds light on the impact of query structuring on sensitivity, with the goal of minimizing medical inaccuracies and enhancing patient care safety. This endeavor has led to the development of four pivotal components: Synthetic Data Custom GPT: This element is tasked with generating artificial medical data, thereby expediting the testing procedures. Data Query Custom GPT: Through the use of a RAG system, this component retrieves synthetic data and applies various transformations. These alterations enable us to assess the data's vulnerability to inaccuracies. HHEM-Vectara Query Tuner: This tool is designed to evaluate the transformed data, determining how adjustments to query structure influence the likelihood of errors. Agent Model Evaluation: This phase involves the scrutiny of mixed normal and specific models, including mixtral normal, mixtral crazy, gemini, phi2, and zephyr, to gauge the impact of query modifications on the precision of results. Our software serves as a crucial experimental platform, providing invaluable insights into how even minor modifications and model changes can significantly affect the retrieval of medical data.