RAG + Langchain Python Project: Easy AI/Chat For Your Docs
131,546
Published 2023-11-20
You can use this to create chat-bots for your documents, books or files. You can also use it to build rich, interactive AI applications that use your data as a source.
π Links
π Code: github.com/pixegami/langchain-rag-tutorial
π (Sample Data) AWS Docs: github.com/awsdocs/aws-lambda-developer-guide
π (Sample Data) Alice in Wonderland: www.gutenberg.org/ebooks/11
π Chapters
00:00 What is RAG?
01:36 Preparing the Data
05:05 Creating Chroma Database
06:36 What are Vector Embeddings?
09:38 Querying for Relevant Data
12:47 Crafting a Great Response
16:18 Wrapping Up
#pixegami #python
All Comments (21)
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Easily one of the best explained walk-throughs of LangChain RAG Iβve watched. Keep up the great content!
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I refreshed RAG completely for a presentation. This was unbelievable good and concise
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I never comment on videos, but this was such an in-depth and easy to understand walkthrough! Keep it up!
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Thanks so much for this. Your teaching style is incredible and the subject is well explained.
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Absolutely epic video. I was able to follow along with no problems by watching the video and following the code. Really tremendous job, thank you so much! Definitely subscribing!
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This is what I look for! Thanks for the simplest explanation. There are some adjustments on the codebase during the updates but it doesn't matter. Keep it up!
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I am brazilian software engineering studant, and ive so much to thank you for all the time you had invest on this amazing content that helped me so much!!!!!
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Your channel is one of the best of YouTube. Thank you. Now I'll go watch the video.
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Clean, strucktured, good to follow, tutorial. Thank you for that
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Fantastic, clear, concise and to the point. thanks so much for your efforts to share your knowledge with others.
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Thank you, that was a great walk through very easy to understand with a great pace. Please make a video on LangGraph as well.
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Great video! This was my first exposure to ChromaDB (worked flawlessly on a fairly large corpus of material). Looking forward to experimenting with other language models as well. This is a great stepping stone towards knowledge based expansions for LLMs. Nice work!
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this is the best tutorial i have ever seen on this topic, thank you so much, Keep up the good work. Immediately subscribed.
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Straight to the point. Awesome!
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This is really much easier than what I have imagined. Thank you so much for the explanation!!! I'll try to make my own specialized LLM this weekend :P
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Thank you so much for this video. I can't explain how much it helped me out. I have been struggling with a lot of AI concepts at work because I recently transitioned to a company that does that. Your video just solved a huge blocker for him, and also explained some basic things for me. Thank you so much, and God bless you
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Awesome walkthrough, thanks for making this π
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This was so informative and well presented. Exactly what I was looking for. Thank you!
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This was excellent. easy to follow, has codes and very useful! Thank you.
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Thanks for this good beginner video, telling the basics, easy to follow (finally someone) :))
