Building a RAG application in Python | Udemy [Update 06/2026]
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Genre: eLearning[/center]

Build a streaming web chat with hybrid retrieval, multi-turn memory, and image support - from scratch

What you'll learn:
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[*]Build a complete Retrieval-Augmented Generation pipeline in Python, from document ingestion to streaming chat output
[*]Run Postgres with the pgvector extension via Docker Compose, including HNSW indexing for fast approximate-nearest-neighbour vector search
[*]Chunk documents with paragraph-aware splitting and overlap, and explain why each chunking choice affects retrieval quality
[*]Implement idempotent, atomic document ingestion using SHA-256 content hashes and transactional upserts
[*]Use the OpenAI SDK to call local Ollama models and OpenAI's hosted API through the same code path
[*]Implement hybrid retrieval that combines dense vector search with Postgres full-text BM25, fused with Reciprocal Rank Fusion
[*]Build a query rewriter that turns follow-up questions like "what does it eat?" into standalone search queries that actually retrieve useful chunks
[*]Build a directory watcher with watchdog, including per-path debouncing so editor saves never trigger reads of half-written files
[*]Apply the Strategy/Adapter pattern to swap a Postgres backend for Weaviate via a single environment variable, with zero changes to the rest of the code
[*]Build a streaming chat web UI with FastAPI, Server-Sent Events, and vanilla JavaScript - no React, no build step
[*]Ingest images using a "describe-then-embed" vision-model pipeline, including format normalization for vision backends
[*]Render LLM markdown output safely in the browser with marked + DOMPurify, including inline images
[*]Apply standard software-engineering patterns - Dependency Injection, Factory, Strategy/Adapter, context managers, lazy imports, etc.
[*]Diagnose RAG failures empirically (cosine scores, full-text ranks, fused output) instead of guessing at prompts
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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:
[list]
[*]Runs entirely locally against Ollama, or transparently against the OpenAI API by changing one environment variable
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[list]
[*]Stores embeddings in Postgres + pgvector with HNSW indexing, or in Weaviate - backends swappable via a single config setting
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[list]
[*]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
[/list]
[list]
[*]A directory watcher that ingests new files automatically, with editor-save debouncing so it never reads a half-written file
[/list]
[list]
[*]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
[/list]
[list]
[*]Image ingestion through a vision model with a "describe-then-embed" pipeline - multimodal in the same chunks table, no schema change required
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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.

Who this course is for:
Python developers interested in integrating LLMs into their projects, and adding RAG functionality.

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