Local RAG Retrieval with Ollama and Vector Databases

Running Large Language Models locally gives you complete control over your data privacy and infrastructure costs. This text-based course introduces you to the fundamentals of Retrieval-Augmented Generation (RAG) using Ollama to run powerful models on your own hardware. You will transition from understanding basic AI concepts to designing a fully functional, local RAG pipeline. By learning how to prepare text documents, generate vector embeddings, and query a local database, you will be able to provide precise, private context to your language models. What you'll learn: Understand the foundational architecture of Retrieval-Augmented Generation and local LLM deployment; Configure Ollama on your machine to manage and run open-source language models; Generate high-quality vector embeddings from raw text data; Store and query embeddings inside a vector database with correct dimensional configurations; Apply text chunking and metadata strategies to optimize information retrieval; Construct clean prompts that combine retrieved context with user queries for accurate responses. The course begins with core definitions of vector space and RAG architecture, then guides you through setting up Ollama, managing embeddings, and integrating a local database. You will practice these concepts through clear, step-by-step written explanations and practical configuration examples. This course is designed for developers, data enthusiasts, and tech-savvy beginners who want to build private AI search tools without relying on expensive cloud APIs. No prior experience with vector databases or machine learning is required. Start reading today to build your first local, secure RAG system.