Medical Symptom Assistant
Medical Symptom Assistant is an educational health information tool that combines hybrid retrieval-augmented generation with safety-first urgency classification. It helps users explore possible conditions and follow-up questions using authoritative public health sources — not as a substitute for professional medical care.
FEATURES
Conversational Symptom Q&A
Users describe symptoms in natural language and receive structured responses with possible conditions, follow-up questions, cited sources, and clear medical disclaimers.
Hybrid RAG Retrieval
Combines pgvector cosine similarity and PostgreSQL full-text search in parallel, then merges results with Reciprocal Rank Fusion for more relevant medical context.
Urgency Classification
A rule-based pre-retrieval classifier detects red-flag symptoms and vague queries so the assistant can prioritize urgent guidance or ask clarifying questions before generation.
Cross-Encoder Reranking
Locally runs a cross-encoder model to rescore and reorder retrieved passages, improving the quality of context sent to the LLM.
Authoritative Data Sources
Ingests and chunks content from MedlinePlus, WHO fact sheets, outbreak news, and Kaggle symptom-disease datasets into a searchable vector store.
Chat Sessions & Feedback
Persists multi-turn conversations with session history and collects user feedback to support iterative improvements to safety and response quality.