Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer. This Python course teaches you how to use RAG to combine your own custom data with the power of Large Language Models (LLMs).
๐ป Code: https://github.com/langchain-ai/rag-from-scratch
If you’re completely new to LangChain and want to learn about some fundamentals, check out our guide for beginners: https://www.freecodecamp.org/news/beginners-guide-to-langchain/
โ๏ธ Course created by Lance Martin, PhD.
Lance on X: https://twitter.com/rlancemartin
โญ๏ธ Course Contents โญ๏ธ
โจ๏ธ (0:00:00) Overview
โจ๏ธ (0:05:53) Indexing
โจ๏ธ (0:10:40) Retrieval
โจ๏ธ (0:15:52) Generation
โจ๏ธ (0:22:14) Query Translation (Multi-Query)
โจ๏ธ (0:28:20) Query Translation (RAG Fusion)
โจ๏ธ (0:33:57) Query Translation (Decomposition)
โจ๏ธ (0:40:31) Query Translation (Step Back)
โจ๏ธ (0:47:24) Query Translation (HyDE)
โจ๏ธ (0:52:07) Routing
โจ๏ธ (0:59:08) Query Construction
โจ๏ธ (1:05:05) Indexing (Multi Representation)
โจ๏ธ (1:11:39) Indexing (RAPTOR)
โจ๏ธ (1:19:19) Indexing (ColBERT)
โจ๏ธ (1:26:32) CRAG
โจ๏ธ (1:44:09) Adaptive RAG
โจ๏ธ (2:12:02) The future of RAG
๐ Thanks to our Champion and Sponsor supporters:
๐พ davthecoder
๐พ jedi-or-sith
๐พ ๅๅฎฎๅๅฝฑ
๐พ Agustรญn Kussrow
๐พ Nattira Maneerat
๐พ Heather Wcislo
๐พ Serhiy Kalinets
๐พ Justin Hual
๐พ Otis Morgan
๐พ Oscar Rahnama
—
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