🤖 RAG Chatbot – Cloud Deployed Generative AI Application 📌 Project Overview

This project is a Retrieval-Augmented Generation (RAG) based Chatbot that allows users to ask questions from their own documents. It combines document retrieval with LLM-based answer generation to provide accurate, context-aware responses. The application is built using Python, LangChain, Vector Databases, and deployed as a web application using Streamlit Cloud.

🧠 Key Features

Upload and query documents using RAG architecture Uses vector embeddings for semantic search Context-aware answers generated using an LLM Displays source documents for transparency Simple and interactive Streamlit UI Fully cloud deployed and publicly accessible

🏗️ Tech Stack

Language: Python Framework: Streamlit LLM Orchestration: LangChain Vector Store: FAISS / Chroma (as used in project) Embeddings: OpenAI / HuggingFace (based on your implementation)

Deployment Platform: Streamlit Cloud ☁️ Cloud Deployment Details 🔹 Cloud Platform Used

Streamlit Cloud 🔗 https://streamlit.io/cloud

🔹 Why I Chose Streamlit Cloud

I chose Streamlit Cloud because it is: Specifically optimized for ML & GenAI applications Extremely easy to deploy Python-based apps Free tier available for student & learning projects Direct integration with GitHub repositories No DevOps or server management required This makes it ideal for deploying RAG, LLM, and AI demo applications quickly.

🚀 Deployment Process (Step-by-Step)

Prepare the Project Ensure app.py is the entry file Add requirements.txt Keep secrets (API keys) outside the code

Push Code to GitHub

git add . git commit -m "Deploy RAG Chatbot" git push origin main

Create Streamlit Cloud App Go to: https://share.streamlit.io Click New App Select GitHub repository Choose: Branch: main File path: frontend/app.py Add Secrets (if required) In Streamlit Cloud → App Settings → Secrets Add API keys securely

Deploy Click Deploy

App builds automatically and becomes live 🎉

🌐 Live Application Link

🔗 RAG Chatbot (Streamlit Cloud): https://ragchatbotagent-fazxidgsxcv5rxzxnkcp7q.streamlit.app/

👍 Pros of Streamlit Cloud

✅ Free and fast deployment ✅ Perfect for AI / ML / GenAI demos ✅ GitHub integration ✅ Automatic rebuilds on code changes ✅ Secure secrets management ✅ No infrastructure setup needed

⚠️ Cons of Streamlit Cloud

❌ Limited resources on free tier ❌ Not ideal for high-traffic production apps ❌ Cold start delays sometimes ❌ Less customization compared to AWS/GCP/Azure

🎯 Use Cases

Generative AI demos RAG-based document assistants AI interview/showcase projects Learning and experimentation with LLMs

🔮 Future Enhancements

Multi-document upload support Chat history persistence Authentication Deployment on AWS / GCP Model switching support

🙌 Conclusion

This project demonstrates end-to-end development and cloud deployment of a Generative AI RAG system, showcasing practical skills in LLMs, retrieval systems, and cloud deployment.

👩‍💻 Author

M.Sai Sushma B.Tech CSE (AI & ML) AI | Machine Learning | Cloud Deployment 🔗 LinkedIn: https://www.linkedin.com/in/sai-sushma-maruboyina-382b34334?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app