RAG + Langchain Python Project: Easy AI/Chat For Your Docs

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Learn how to build a "retrieval augmented generation" (RAG) app with Langchain and OpenAI in Python.

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.

👉 Links

📚 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

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I refreshed RAG completely for a presentation. This was unbelievable good and concise

johannanderson

Easily one of the best explained walk-throughs of LangChain RAG I’ve watched. Keep up the great content!

colegoddin

I never comment on videos, but this was such an in-depth and easy to understand walkthrough! Keep it up!

elijahparis

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

mariannedutra

Must-watch for any data scientist looking to use AI to help others as well as themselves get ahead of the pack: thanks! 👽

uquantum

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

ygrkxit

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

nikihu

How to use model well is much practical and important recently, there is always have model waiting for you to use, you need to learn how to transform this into your company needs, nice tutorial 🎉

wolfmib

As others have asked: "Could you show how to do it with an open source LLM?" Also, instead of Markdown (.md) can you show how to use PDFs ? Thanks.

slipthetrap

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!

insan

Simple explained and kept an engaging tone.

I would also look for a use case where the source of vector data is a combination of files (PDF, DOCX, EXCEL etc.) along with some database (RDBMS or File based database)

rikhavthakkar

Fantastic, clear, concise and to the point. thanks so much for your efforts to share your knowledge with others.

StringOfMusic

Thank you for updating the repo and the details on the c++ install as well!!

IdPreferNot

Great explanation. Perhaps one criticism would be using open ai’s embedding library: would rather not be locked into their ecosystem and i believe that free alternatives exist that are perfectly good! But would have loved a quick overview there.

PoGGiE

Finally a good langchain video to understand better. Do you have a video in mind to use local llm using Ollama and local embeddings to port the code ?

basicvisual

Your channel is one of the best of YouTube. Thank you. Now I'll go watch the video.

gustavojuantorena

thanks for the video, this looks great, but I tried to implement it and seems like the langchain packages needed are no longer available has anyone had any luck getting this to work?

Thanks

SantiYounger

Thank you, that was a great walk through very easy to understand with a great pace. Please make a video on LangGraph as well.

jimm

Clean, strucktured, good to follow, tutorial. Thank you for that

lalalala

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!

MattSimmonsSysAdmin

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