Build a Local AI Assistant with LLMs
About this course
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.
75/100
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What you'll learn
- run a local large language model (LLM)
- convert PDFs into searchable chunks
- generate and store embeddings in a vector database
- build a RAG pipeline using FastAPI
- develop a browser-based interface for querying the assistant
Course objectives
- to create a local AI assistant that can answer questions from documents
- to understand and implement document processing and vector search
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