This Hugging Face tutorial walks through building a Retrieval-Augmented Generation system from scratch using Python and Ollama, demonstrating how RAG enhances language models by combining information retrieval with text generation to access knowledge beyond training data. The system comprises three components: an embedding model converting text to vectors, a vector database storing those embeddings, and an LLM generating responses from retrieved chunks. The pipeline operates in two phases — indexing documents via cosine-similarity chunking, then retrieving relevant context at query time.
