RAG explained: A Step-by-Step Guide to Vector Search and Content Retrieval

This is the first video covering the fundamentals that we need for designing the Chatbots. We start by breaking down text embedding and its role in Retrieval-Augmented Generation (RAG) systems. Then, we put theory into practice by building a simple RAG system from scratch.
📘 Get the tutorial notebooks here: github.com/Far....
📚 Explore the full GitHub repo: github.com/Far...
🔗 Connect on LinkedIn: / farzad-roozitalab
🌐 Visit my website: farzad-r.githu...
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#RAG #llm #ChatBot #GPT #Python #AI #OpenAI #Langchain #Gradio #chroma #embedding

[@smehra]

Amazing. This is superior to so much other content out there!

[@airoundtable]

Thanks @smehra! That means alot. I am glad that you liked the content

[@saeidrezatalaeikhozani9466]

You are amazing Farzad! Many thanks to you for this valuable tutorial :)

[@airoundtable]

Thanks Saeid! It's a pleasure to see that the tutorials are helpful

[@sumitsp01]

So far the best video on this topic.
Very beginner friendly RAG implementation 🙂👍

[@airoundtable]

Thanks! I am glad you liked it

[@MuhammadAdnan-tq3fx]

this is very awesome. I need this type of series. now waiting next video for function calling?

[@airoundtable]

Thanks. I am glad you liked the video. I removed the function calling video because OpenAI updated the functions and they changed the way the models were called. But you can have access to the code here:
- github.com/Farzad-R/LLM-Zero-to-Hundred/tree/master/tutorials/LLM-function-calling-tutorial
plus, I am using function calling in the following two projects:
- th-cam.com/video/KoWjy5PZdX0/w-d-xo.htmlsi=DN1Gt6sA8W-E4C2l
- th-cam.com/video/55bztmEzAYU/w-d-xo.htmlsi=kMV5ZtPaGugVckP6
And in the next video I will explain the new ways of function calling and how to design LLM agents from scratch. That project is almost ready!

[@sumitsp01]

I have a question.
How do we search similar vectors in real applications when the vector db is huge?
Isn’t iterating over each vector is db inefficient?
I know there may be other efficient search mechanisms, just want to know what are they.

[@airoundtable]

Great question. On a normal database that would be a serious concern. But thanks to the architecture and computation behind vector databases this is not a serious concern unless you have millions of documents (which again that challenge can be solved by parallelization for example). But in general searching over vector databases does not occur by a simple for loop in the background and it is much faster than that. If you are curious to implement and test it, you can check my RAG-GPT video where I implement a full RAG chatbot using Chroma vectorDB. There you can see the speed of inferencing with GPT 3.5

[@sumitsp01]

@@airoundtable thank you for the explanation.
And yes I am going to watch the entire playlist 🙂

[@airoundtable]

@@sumitsp01 You're welcome! I hope the series is informative for you then. Enjoy 😉

[@user-em8sl6tj3e]

Wow you are too good.

[@airoundtable]

Thanks! I am glad to see that you liked the content!

[@Mar10001]

you're a beast

[@airoundtable]

:))) thanks @Mar10001! I am glad the content was helpful to you

[@hadi-yeg]

👏👏👏

[@mehrdadzarghami1599]

Kudos to you. Great content and explanation

[@airoundtable]

Thanks Mehrdad!