Feb 24
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In this video, we build a real Retrieval-Augmented Generation (RAG) application from scratch using LangChain, Streamlit, and SingleStore. Instead of a simple chatbot, you’ll learn how to design an AI system that can upload documents, break them into meaningful chunks, generate embeddings, store them in a persistent vector database, and answer questions using only the retrieved context. We start by understanding why LangChain exists and how it helps structure AI workflows beyond prompt engineering. You’ll see how LangChain documents, text splitters, embeddings, retrievers, and prompt templates come together to form a reliable RAG pipeline. On the frontend, we use Streamlit to build a ChatGPT-like interface with streaming responses, while SingleStore acts as the vector database that stores embeddings and enables fast semantic search. Along the way, we tackle real-world problems like hallucinations, incomplete retrieval, and mixed document context. You’ll learn how to debug retrieval results, tune chunking and top-K search, reset the knowledge base for clean demos, and enforce strict prompt rules so the model answers only from your data. By the end of this tutorial, you’ll have a production-style document chat application. The complete code repo to try: https://github.com/pavanbelagatti/LangChain-RAG-Application