Ever stared at a cryptic medicine bottle, wondering if it interacts with your morning coffee or that other pill you're taking? For the elderly or those with visual impairments, reading tiny labels on medication packaging is more than a nuisance—it’s a safety hazard.
In this tutorial, we are building a Medication Safety Assistant. This isn't just a simple OCR tool; we are implementing a Multimodal Retrieval-Augmented Generation (RAG) pipeline. We'll use LLaVA (Large Language-and-Vision Assistant) to "see" the medicine box, ChromaDB to store and retrieve detailed medical instructions, and Ollama to run everything locally and privately.
By the end of this guide, you'll understand how to bridge the gap between computer vision and structured knowledge retrieval to build life-saving AI applications. 🚀
The Architecture: How Vision Meets Knowledge
Traditional RAG handles text. Multimodal RAG allows our system to process an image, convert the visual features into a query, and then fetch the relevant "truth" from a local vector database.
