📡Large Model Technology

MoE Architecture

Based on large model technology, the MoE (Mixture-of-Experts) architecture is adopted, drawing on practices from DeepSeek-v3 and Mistral to build a hybrid expert model system. This system is fine-tuned for different health management scenarios (e.g., chronic disease management, diabetes knowledge base, sports injury prevention for the Asian population) and supports dynamic plug-and-play invocation. It significantly reduces operational costs while ensuring low token consumption, offering a cost reduction of 1/20 to 1/50 compared to mainstream solutions like OpenAI and Anthropic.

Knowledge Base

Using RAG (Retrieval-Augmented Generation) technology to structure common medical databases, combined with intent recognition mechanisms to dynamically select content from the knowledge base. This design reduces context dependence while ensuring that the large model can provide precise and insightful health recommendations based on real-time user activity and basic physiological data.

Advanced Feature

Integrating a genetic recognition suite (e.g., wegene) to structure and store user genetic data (including hereditary disease history, target gene defect analysis, common disease risks, and individual traits) in a RAG knowledge base, significantly improving the accuracy of personalized treatment plan generation by the large model.