The problem UroCare solves

In India, people generally fear to discuss about problems related to their reproductive organs out of fear and shame.
At UroCare, our mission is to empower individuals with comprehensive and accessible urological health resources. We are dedicated to providing a trusted platform that fosters a proactive and informed approach to urological well-being.
By delivering personalized guidance, promoting preventive care, and ensuring utmost privacy, we strive to revolutionize the way individuals navigate their urological health journeys.
UroCare is committed to making a positive impact on lives, fostering a community of informed individuals, and contributing to a world where urological wellness is a priority for all.

Challenges we ran into

Developing Urocure, a specialized urology chatbot using RAG (Retrieval-Augmented Generation) with a Chinese dataset presents several challenges in the translation, embedding, and prompt engineering processes.

Firstly, translation introduces complexities, as nuances and medical terminology may not have direct equivalents between English and Chinese. Medical terms, especially in urology, require precise translations to maintain accuracy and relevance. Ambiguities in translation could lead to incorrect responses or misunderstandings, impacting the quality of user interactions.

Secondly, using a Chinese dataset for training introduces potential bias and cultural differences. Urological practices and patient concerns may vary between regions, and relying solely on a Chinese dataset may limit the chatbot's ability to address diverse global urological issues. Ensuring a comprehensive understanding of international urological practices becomes crucial to offer relevant and accurate information.

The interaction between RAG and MongoDB Atlas also poses challenges. Handling a vector database in MongoDB Atlas requires efficient indexing and retrieval mechanisms to quickly fetch relevant information. Maintaining the integrity of vector representations during translation and subsequent conversion adds complexity to the retrieval process.

Prompt engineering to convert Chinese embeddings back to English demands careful consideration. The process needs to accurately capture the user's intent and convey it effectively to the model. Misalignment in prompt engineering may lead to misinterpretations, resulting in inaccurate responses or failure to retrieve pertinent information.