Better than most paid courses online! Thanks.

I've been using the function ChatOpenai() rather than Openai() to call the model "gpt-3.5-turbo" which costs $0.002 rather than $0.025. Cheaper and more powerful, can still be used for standard querying.

Would be nice to make the same video but for Llama-2. Llama-2 can run in our private cloud. Many companies dont want to use OpenAI because of data privacy concerns. Also Llama-2 is completely free and can run locally for free.

Thanks, Sreeni. Your content is always the best!

Thank you for your great videos. Just a quick note, you are not training anything here, you're building a RAG system. You could say "training" if you were optimizing the parameters of a model (e.g. neural nets) for minimizing a loss function.

I have an pdf with thousands of pages, is the gpt-4 able to undestand and memorize all of it ? My questions to this big pdf need to correlate all the information.

Hi Sreeni! Love the content, everything's always amazingly explained. I was wondering if you were planning on covering the YOLOv7 algorithm. It would be really interesting seeing a video of you covering it and your takes on it. Keep up the good content :)

GPT is general purpose and its been trained on millions pieces of text so that it can understand human language. Sure, it might be able to answer specific questions based on the information that it was trained on - for example, "Who is the CEO of Google?" - but as soon as you need to produce specific results based on your product, results will be unpredictable and often just wrong. GPT-3 is notorious for just like confidently making up answers that are just plain wrong. There are two approaches to address this: 1) Fine-tune the model - Need to retrain the model with your own custom data or every time new data is added 2) Context injection - Pre-process knowledge base (embedding), store it as object or in database, based on user's query, search your knowledge base for most relevant info, inject the top most relevant pieces into the actually prompt as context

Great material! Thanks for sharing, good job 🚀

Hi Sreeni, I enjoy your content every time I see it. Just a question why you jumped from 311 to 323?

this was awesome! I never do any coding, and was able to follow and do it

Great video. Thanks!👍

Great vid. thank you for your time and effort for these vids.

Thank you very much for this great video! Could you please let me know here we used ChatGPT or GPT4? And it’s not fine tuning here, it’s embedding, right? Which one do you think is better? Fine tuning or embedding? Thank you very much!

you ate amazing mate. thank you for awesome lectures

Always appreciate your work. Thanks sir...

thank you! It's exactly I was looking for.

Sir Can you please make a video on usage costs of api and other cost factors !

nice tutorial... how could I limit the topic only with the PDFs? for example in case that the chatbot must not answer.

The biggest problem is the API key. Try to make it using without all this Open AI company. What happen if you dont extend your API key subscription ? Will the pipeline just stop working ?

Does it work of csv filled with numeric data converted to pdf and then imported in the file?

May I ask if this tutorial example simply extracts the content from the PDF article as context and sends it along with the question to the OPENAI API? Or is there any training being done locally? I'm curious about this because the video mentioned the use of an API KEY. Thank you.

Thanks!

Is it better than chatwithpdf plugin model ?

Thanks

Where's the training?

But langchain is free ?

can you link the txt file you used

But IDK how to code😢😢😢😢😢😂😂

Thanks!