Learn how to implement RAG from scratch from a LangChain software engineer. This course teaches you how to use RAG to combine custom data with LLMs. 💻 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/ ✏️ Course created by Lance Martin, PhD.
Lance on X: https://twitter.com/rlancemartin ⭐️ Course Contents ⭐️
⌨️ (0:00:00) Overview
⌨️ (0:05:53) Indexing
⌨️ (0:10:40) Retrieval
⌨️ (0:15:52) Generation
⌨️ (0:22:14) Query Translation (Multi-Query)
⌨️ (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) Is RAG Really Dead? 🎉 Thanks to our Champion and Sponsor supporters:
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👾 Agustín Kussrow
👾 Nattira Maneerat
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👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan 👾 Oscar Rahnama — Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news