RAG-based Medical Research Chatbot

The Problem

Medical researchers spend hours manually searching through PubMed articles to find relevant information. Traditional keyword search often misses semantically related content.

AI Approach

Implemented a Retrieval-Augmented Generation (RAG) pipeline with semantic embeddings stored in Pinecone vector database. Built comprehensive data ingestion pipeline to process medical literature and generate high-quality embeddings.

Key Outcomes

• Semantic search across 100,000+ medical articles
• Data ingestion pipeline with automated updates
• Backend validation logic for medical accuracy
• Output guardrails to ensure reliable information
• 85% reduction in research time for common queries