Retrieval-Augmented Generation (RAG) represents the future of AI-powered knowledge systems, combining the best of large language models with domain-specific knowledge retrieval. In this technical deep-dive, we'll build a complete RAG system using local LLMs through Ollama, vector embeddings with Chroma database, and a Streamlit interface for document interaction. The system allows users to upload documents, automatically creates embeddings using nomic-embed-text, and provides accurate, context-aware answers to queries. We'll cover chunking strategies for different document types, similarity search optimization, prompt engineering for better responses, and implementing conversation memory. I'll also discuss privacy considerations, cost comparisons with cloud APIs, and performance optimization techniques. This approach has enabled me to build enterprise RAG solutions that process confidential documents while maintaining complete data privacy.