Building a RAG application in Python

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.

What you'll build, in one project:

• Runs entirely locally against Ollama, or transparently against the OpenAI API by changing one environment variable

• Stores embeddings in Postgres + pgvector with HNSW indexing, or in Weaviate — backends swappable via a single config setting

• Hybrid retrieval: dense vector search and Postgres full-text BM25, fused with Reciprocal Rank Fusion — fixing the cases where pure semantic search silently fails on rare terms, names, and identifiers

• A directory watcher that ingests new files automatically, with editor-save debouncing so it never reads a half-written file

• A streaming web chat UI built on FastAPI + Server-Sent Events + vanilla JavaScript — no React, no build step — with multi-turn memory, query rewriting for follow-ups, source citations, and inline image rendering

• Image ingestion through a vision model with a "describe-then-embed" pipeline — multimodal in the same chunks table, no schema change required

Along the way you'll work through real software-design patterns in real code: Dependency Injection, Strategy/Adapter, Factory, lifespans, context managers, thread-safety boundaries, atomic transactions, defensive coding against external services that quietly don't work the way their docs claim. The course's recurring theme is the payoff of good abstractions: the vector-store interface designed early lets you bolt on a second backend in one file; the same retrieval pipeline serves both the CLI and the web app; the chunk-metadata field that seemed academic early in the course is what makes image support a simple change later on.

You'll finish with a codebase you can extend — add a reranker, try a different embedder, swap the chat model, point it at a corpus of your own docs — and the engineering vocabulary to talk about RAG as production software, not a notebook demo.