LangChain.js is a framework for building AI apps. Click here to get to the course's interactive challenges: [ Ссылка ]

At the end of this course, you will be able use LangChain's Expression Language to build GPT-powered chatbots that have specific knowledge about an underlying dataset. You will use text embeddings and a vector database to perform retrieval-augmented generation (RAG).

The course requires an understanding of JavaScript. Here is a free JS course if you need a refresher:
[ Ссылка ]

✏️ If you have any feedback to the teacher Tom Chant, please reach out to him on X here:
[ Ссылка ]

Also, feel free to follow Scrimba on YouTube: [ Ссылка ]

⭐️ Code ⭐️
🔗 Download via Scrimba: [ Ссылка ]


⭐️ Chapters ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:02:01) What you'll learn and build
⌨️ (0:04:08) Introduction to LangChain from Jacob Lee (Lead Maintainer of LangChain.js)
⌨️ (0:05:36) App Flow Diagrams
⌨️ (0:09:04) What are embeddings
⌨️ (0:15:20) Supabase Setup
⌨️ (0:19:27) Split the text
⌨️ (0:21:28) Split the text II
⌨️ (0:27:47) Upload to supabase
⌨️ (0:31:53) Starter code
⌨️ (0:33:19) Explainer The Standalone Question
⌨️ (0:35:31) Aside Prompt Templates
⌨️ (0:38:59) Aside Prompt Templates II
⌨️ (0:42:08) Adding the first chain
⌨️ (0:46:11) Retrieval
⌨️ (0:52:45) Add StringOutputParser
⌨️ (0:55:15) Fetching the answer the template
⌨️ (1:00:39) Serialize the docs
⌨️ (1:02:53) Aside RunnableSequence
⌨️ (1:07:54) Aside RunnableSequence 2
⌨️ (1:10:57) Aside RunnableSequence 3 RunnablePassthrough
⌨️ (1:14:47) Super Challenge - add the RunnableSequence
⌨️ (1:17:10) Super Challenge - solution
⌨️ (1:21:47) Wire up the UI
⌨️ (1:24:34) Setting up the memory
⌨️ (1:27:27) Super Challenge Wire up the memory
⌨️ (1:33:51) Performance Issues Check-list
⌨️ (1:37:44) Outro

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

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