Building a RAG Chatbot with Python, LangChain, and pgvector

Want to build AI applications that actually understand your specific data? Retrieval-Augmented Generation (RAG) is the modern standard for creating chatbots that go beyond generic training data to deliver accurate, context-aware responses. This course teaches you how to design and implement a practical RAG pipeline using Python, LangChain, and pgvector. You will learn how to prepare text documents, generate semantic embeddings, store them in a vector database, and connect a Large Language Model (LLM) to retrieve highly relevant, domain-specific answers. What you will learn: • Understand the fundamental architecture and terminology of Retrieval-Augmented Generation. • Process and chunk custom text data for optimal embedding generation and retrieval. • Configure pgvector to store and search vector representations efficiently. • Build modular, scalable AI workflows using the LangChain framework. • Apply modern prompt engineering basics to reduce hallucinations and improve accuracy. • Implement retrieval patterns that connect your knowledge base directly to an LLM. The course begins with foundational concepts of vector search and embeddings before guiding you through practical, text-based coding exercises. Step-by-step, you will read through explanations and write code to construct a complete RAG system from initial data ingestion to the final conversational pipeline. Designed for backend developers, data enthusiasts, and Python programmers who are beginners to AI engineering and want to master custom chatbots without prior machine learning experience. Start building intelligent, data-driven AI assistants today.