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A hands-on guide to building a Retrieval-Augmented Generation (RAG) system from scratch using Python and Ollama. Covers the core components: an embedding model (bge-base-en-v1.5), an in-memory vector database with cosine similarity search, and a language model (Llama-3.2-1B) for response generation. Walks through the full pipeline—indexing documents into chunks, embedding them, retrieving top-N relevant chunks via cosine similarity, and injecting them into a prompt for the LLM. Also discusses limitations (multi-topic queries, scalability, chunking strategies) and briefly introduces advanced RAG variants like Graph RAG, Hybrid RAG, and Modular RAG.

Code a simple RAG from scratch

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