Building Open-Source AI Chatbots with LangChain and Streamlit

Modern AI allows us to build powerful conversational interfaces, but relying solely on proprietary APIs can be costly and restrictive. By leveraging open-source large language models (LLMs) and modern framework tooling, you can build secure, customizable, and cost-effective chatbot solutions. This text-based course guides you through the foundational concepts of natural language processing and transformer architectures before moving into hands-on development. You will learn how to connect open-source models to your own data sources, manage conversation history, and construct interactive web interfaces. What you'll learn: - Understand the fundamental architecture of transformer models and open-source LLMs. - Configure LangChain to manage prompts, chains, and conversational memory. - Build Retrieval-Augmented Generation (RAG) systems using vector databases to query custom documents. - Design interactive chatbot user interfaces using Streamlit. - Apply prompt engineering techniques to guide model behavior and maintain safety. - Evaluate chatbot performance and deploy your application effectively. The course begins with foundational AI terminology and transformer concepts before guiding you through data preparation, vector search integration, and interface design. You will progress from simple text generation to a fully functional conversational agent through step-by-step written explanations and practical code analyses. This course is designed for beginning developers, data enthusiasts, and software creators who want to build AI applications. No prior experience with LLMs or chatbot development is required, though a basic understanding of Python is helpful. Start reading today to build your own custom, private AI assistants.