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).

Code: https://github.com/langchainai/ragf...

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/beg...

✏ Course created by Lance Martin, PhD.
Lance on X:   / rlancemartin  

⭐ Course Contents ⭐
⌨ (0:00:00) Overview
⌨ (0:05:53) Indexing
⌨ (0:10:40) Retrieval
⌨ (0:15:52) Generation
⌨ (0:22:14) Query Translation (MultiQuery)
⌨ (0:28:20) Query Translation (RAG Fusion)
⌨ (0:33:57) Query Translation (Decomposition)
⌨ (0:40:31) Query Translation (Step Back)
⌨ (0:47:24) Query Translation (HyDE)
⌨ (0:52:07) Routing
⌨ (0:59:08) Query Construction
⌨ (1:05:05) Indexing (Multi Representation)
⌨ (1:11:39) Indexing (RAPTOR)
⌨ (1:19:19) Indexing (ColBERT)
⌨ (1:26:32) CRAG
⌨ (1:44:09) Adaptive RAG
⌨ (2:12:02) The future of RAG

Thanks to our Champion and Sponsor supporters:
davthecoder
jediorsith
南宮千影
Agustín Kussrow
Nattira Maneerat
Heather Wcislo
Serhiy Kalinets
Justin Hual
Otis Morgan
Oscar Rahnama



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