Build a working Retrieval-Augmented Generation (RAG) application in Python — from an empty directory to a streaming web chat with multi-turn memory, hybrid retrieval, image ingestion, and two interchangeable vector-store backends. No LangChain, no LlamaIndex, no magic. You write every line yourself, and by the end you understand exactly what each one does.
Most RAG tutorials wrap everything in a single high-level library and stop at “it works.” This course goes the other way. You’ll build the pipeline from scratch — chunking, embeddings, idempotent ingestion, hybrid semantic-plus-lexical retrieval with Reciprocal Rank Fusion, a query rewriter for follow-up questions, server-sent token streaming, a vision-model branch for images — on top of plain Postgres (with pgvector) and a local Ollama server. No API bills while you learn. No black boxes. When you later reach for a framework like LangChain, you’ll actually understand what it’s doing under the hood.
