RAG Chatbot Tutorial - Integrating PDFs, TXT Files & Multiple Sources

🚀 In this video, I demonstrate how I enhanced my PetPals AI Chatbot by evolving from a single-source knowledge base to a multi-source Retrieval-Augmented Generation (RAG) architecture. Initially, the chatbot used a Vector Database with only a TXT file as its knowledge source. To make the solution more realistic and production-ready, I integrated multiple data sources including PDF documents, FAQs, product catalogs, and other business content. 🔹 What you'll learn: • How Vector Databases power semantic search • Converting documents into embeddings • Integrating multiple knowledge sources • Retrieval-Augmented Generation (RAG) architecture • Improving chatbot accuracy and context awareness • Building scalable AI assistants using LLMs 🛠️ Tech Stack: • Python • FastAPI • LangChain • Vector Database • OpenAI LLM • RAG Architecture This project showcases how modern AI applications can retrieve relevant information from multiple sources and generate accurate, context-aware responses in real time. #AI #RAG #LLM #VectorDatabase #Chatbot #GenerativeAI