A collection of ten Jupyter notebooks teaching Retrieval-Augmented Generation (RAG) with LangGraph, progressing from core primitives through advanced agentic techniques like self-correction, human oversight, multi-agent routing, and parallel execution. The first four notebooks are free; the remaining six are paid and cover increasingly sophisticated ways to make RAG pipelines autonomous and reliable.
Four Jupyter notebooks demonstrate agentic Retrieval-Augmented Generation (RAG) patterns using LangGraph, enabling pipelines to self-correct, route queries, and incorporate human oversight. The notebooks progress from basic agentic retrieval through corrective and adaptive variants to human-in-the-loop approval workflows, with setup instructions using Python, uv, and free API keys from Groq and Tavily.