Build a Local AI Assistant with LLMs

Build your own AI assistant that answers questions from your documents – entirely on your local machine. Assuming a basic acquaintance with Python, this course will teach you how to run a local LLM, turn PDFs into searchable chunks, generate embeddings, store them in a vector database, and connect retrieval and generation into a complete RAG (Retrieval-Augmented Generation) pipeline. You’ll create OpenAI-compatible and RAG endpoints with FastAPI, work with Ollama and Qdrant, and finish by building a browser-based interface for asking questions and reviewing sources. This course stands out because everything is built locally, from end to end. Instead of relying on black-box cloud services, you will master every step of the system you build – from document processing and vector search to prompt construction and answer generation. You’ll learn by actually building, adding one piece at a time. With each module, you’ll unlock a new feature in your project., By the end, you will have a production-ready AI project you can run, customize, and share.