I'm very puar men

Plz help me

Brazil loves India!

Such a wonderful presentation. Great people always explains a complex topic as if it is very simple. You seems to be one of them.

Actually pretty good content.. great job my man!

amazing man!

Hi, I had a question, is it possible to feed a pdf to the AI agent as a part of its content ? For example can i feed a science text book pdf since i want to create an ai agent which can teach students science.

@venkatathota633 4 months ago could you please provide git repo for the above code?

Really helpful

While invoking bedrok, I am getting error AttributeError: 'Bedrock' object has no attribute 'invoke_model', how to solve this

In your video you say "sentence_file_path". However shouldn't it be "embed_file_path" ? create_tree_ah_index function should have the GCS bucket of the embedded data and not the text with teh ids right ?

Can you provide GitHub repo for this app for me to tryout. Also please provide step by step instructions to build and deploy the rag in docker/kubenetes

really helpful I have question i have multiple pdf files how i handel with them?

Is there an inherent benefit to using Vertex Ai rather than AI Studio?

Very excellent Learning session Janakiram!

Very helpful. Thank you so much good sir.

really helpful for understanding the concept of embedding and retrieval. Thanks.

Great video & playlist ! Can you please share the source code ?

i want same thing in nodej can some one please help on which library to use

Sir, I have a long text which contains five bidder details and their financial values such as turnover, revenue etc. and that is stored in a variable, bidders_data. could you pls let me know how can I create a tool or function to draw a bar chart for the bidder names and turnover or any other financial parameters, when I give a prompt such as ''draw a bar chart for bidders (bidder names) and their turnover", so it should draw a bar chart as response. Awaiting for you reply sir!

great work sir , this was very useful but throughout the whole video the audio was coming only from left side of headphones i hope you will fix this issue next time

❤🙏

No voice

the code link u have shared is incomplete, load_file is missing and other few stuffs,

Good Content and explanation for all the beginners

Why is Chroma such hot garbage?

Hi Janakiram. Can you explain Self attention and Feed forwarder layer in more detail plz ? Example what are the fundamental purpose / responsibility of each of them...

Thank you for the useful video. I'm having a problem using the chroma and google embedding (new versions): >>> vectordb=Chroma.from_documents(pages,embeddings) ValueError: Expected each embedding in the embeddings to be a list, got ['Repeated'] Do you have any ideas? Thanks in advance!

Janakiram, This is one of the best Fine Tuning Video I have came across. I need to thank you for getting this concept clear in such a simple manner. Keeping making such videos, it helps community to grow. Thank you !!

Can you make an update video onnx?

Thanks, this is tremendously helpful One point to note - you need to upload the embed file, not the sentence file -> upload_file(bucket_name,embed_file_path)

Super helpful! Thank you so much.

Awesome work as always! Do you have a link for the collab? Thanks

can we make our own "online" models with the Brave-API ?

Nice Explanation Anna :) expecting Enterprise Gen AI on AWS/AZURE/GCP

Do the online models also give back the source of the websites or papers it uses, when asked?

What a pleasure to see Priyanka on your show! Awesum❤❤

Teaser really teased😊

Great job! Thanks a lot. What’s the difference between this approach and using langchain?

perplexity now supports mixtral-8x22b-instruct so if we uses can we get more accurate answers ?

Excellent video - can u please do same with Langchain with retrieval

Sir Can we use this gemini 1.5 pro with Langchian ..?

you didn't shared the pdf

Can you please do a video on "How to use the same in Langchain with retrieval"

how can we deploy this to the web?

Thanks for sharing. Do you have any insight on how to incorporate the feature where the response includes citation URL's?

impressive slide show

why always python is there any way to use js?

could you please provide git repo for the above code?

Any reason why you chose BAAI and not any other Embedding model? What are the impacts of mix and matching the Embedding model and the LLM? My current app works just fine with GPT4ALL Embeddings, and Gemma 1.1 7B. Another note: Deploying a quantized LLM will significantly reduce VRAM usage, Gemma 7B Q8_0 quantized takes up 12 gigs of VRAM for me. Implementing KEDA and using Quantization in tandem will be a game-changer.