Building an AI Pharmacist: Detecting Drug-Drug Interactions with RAG and OCR

Ever looked at a pile of medicine bottles and wondered, "Is it actually safe to take these together?" Polypharmacy—the simultaneous use of multiple drugs—is a significant challenge in modern healthcare. Misunderstanding Drug-Drug Interactions (DDI) can lead to severe side effects or reduced efficacy. In this tutorial, we are building an AI Pharmacist Assistant , an automated engine that uses Optical Character Recognition (OCR) to scan drug labels and Retrieval-Augmented Generation (RAG) to cross-reference a drug database. By leveraging AI healthcare automation and sophisticated LLM reasoning , we can create a safety net that identifies potential contraindications in seconds. The Architecture 🏗️ The system follows a linear pipeline: capturing raw image data, converting it to structured text, retrieving medical facts from a local SQLite-based knowledge base, and finally, using an LLM to reason about the interactions. graph TD A[Drug Packaging Image] -->|Tesseract OCR| B(Extract Drug Names) B --> C{Search SQLite DB} C -->|Found Interaction Data| D[Context Construction] D --> E[LLM Reasoning Engine] E --> F[Safety Report & Warnings] C -->|Not Found| G[Web Search/LLM General Knowledge] G --> E Prerequisites 🛠️ To follow along, you'll need the following tech stack: Python 3.10+ Tesseract OCR : For extracting text from images. SQLite : To store our curated DrugBank-style interaction data. RAG Pattern : To provide the LLM with ground-truth medical data. OpenAI SDK : For the final reasoning step. Step 1: Extracting Labels with OCR 📸 First, we need to turn those pixels into text. We use pytesseract to handle the OCR process. import pytesseract from PIL import Image def extract_drug_names ( image_path ): # Pre-processing could be added here (grayscale, thresholding) text = pytesseract . image_to_string ( Image . open ( image_path )) # In a real scenario, use an LLM or Regex to pull specific # active ingredients from the raw text print ( f " Detected Text: { text } " ) return t