Master RAG on Vertex AI with Vector Search and Gemini Pro 

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 ?

Great video! What is the difference between Vertex Search service VS Vector Search for RAG application? which one is better in terms of handling better retrieval of relevant documents for RAG application where we deal with 100+ PDF documents? Can you share some insights?

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

Best tutorial. Big thanks for your shared.

Excelent video! Thanks for sharing the code too.

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

Great Video, thank you soo much........

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

Thanks for the tutorial! Instead of going through the ids in the json file to fetch the sentences, is it possible to integrate those directly as metadata in the index?

Nice. Are you ok to share the colab notebook?

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

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)

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

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

Thanks for the tutorial. I am bit confused which file to be uploaded to bucket. sentence file or embedding file

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

Very excellent Learning session Janakiram!

Great!

Great Video. One question, I noticed you used a different model (gecko) to Gemini Pro for the embeddings. Is this ok to do? I assumed the models needed to be the same for both training and inference? Thanks again

why always python is there any way to use js?

Thanks for sharing knowledge. Can you share the notebook

Possible to share the notebook?