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: - ruclips.net/video/KoWjy5PZdX0/видео.htmlsi=DN1Gt6sA8W-E4C2l - ruclips.net/video/55bztmEzAYU/видео.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!
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.
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
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Great. gonna implement it myself!
Amazing. This is superior to so much other content out there!
Thanks @smehra! That means alot. I am glad that you liked the content
You are amazing Farzad! Many thanks to you for this valuable tutorial :)
Thanks Saeid! It's a pleasure to see that the tutorials are helpful
Wow you are too good.
Thanks! I am glad to see that you liked the content!
So far the best video on this topic.
Very beginner friendly RAG implementation 🙂👍
Thanks! I am glad you liked it
this is very awesome. I need this type of series. now waiting next video for function calling?
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:
- ruclips.net/video/KoWjy5PZdX0/видео.htmlsi=DN1Gt6sA8W-E4C2l
- ruclips.net/video/55bztmEzAYU/видео.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!
you're a beast
:))) thanks @Mar10001! I am glad the content was helpful to you
Tnank you for the amazing tutorial. Quick question, what laptop or pc you are using ?
For this tutorial I used a PC with a 3090 GPU and 32 GB of RAM.
👏👏👏
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.
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
@@airoundtable thank you for the explanation.
And yes I am going to watch the entire playlist 🙂
@@sumitsp01 You're welcome! I hope the series is informative for you then. Enjoy 😉
Kudos to you. Great content and explanation
Thanks Mehrdad!