Bio-RAG Assistant - POC Demo

Description

Project: Bio-RAG Assistant Duration: 10 minutes Course: Ironhack Data Science & Machine Learning Bootcamp This demo showcases my mini-project on Retrieval-Augmented Generation (RAG) — a system that combines document retrieval and generative AI to answer scientific questions. I designed a practical assistant for biology students and researchers to explore topics such as gene expression activation via microRNA. 🔹 Technologies used: • LangChain 0.3 framework • ChromaDB vector store • OpenAI GPT-5 API • Streamlit web UI • pikepdf + PyPDF for document processing • Python 3.13 environment 🔹 Features: • Local document ingestion with caching and metadata tracking • Knowledge-base management tab (add or update PDFs easily) • Real-time response timer and reference links to Google Scholar • Clean, modular architecture – ready for domain adaptation 💡 Why this project matters: Retrieval-Augmented Generation bridges the gap between static knowledge and generative models. My goal was to create an AI that supports scientific learning with verified context and proper citation formatting. 📺 Channel: Data Ravers — exploring data, AI & creative technology. 🌐 Connect on LinkedIn: linkedin.com/in/faniia-prazdnikova-607a6a3b #DataScience #AI #LangChain #RAG #OpenAI #Ironhack #MachineLearning #Streamlit #Bioinformatics #GenerativeAI #DataRavers