Struggling to build a simple RAG solution

[JasonC]

I'm trying to build a solution that accomplishes the following:

• Passes text files from a GCS bucket to the embeddings API (I think the files will need to be chunked first? Not sure.)
• Saves the returned embeddings into a .json file in the same GCS bucket
• Loads the .json file into Vector search
• Allows me to have multi-turn conversations with my data

So I guess the first question is, are the steps I've listed above the appropriate steps to build a RAG solution from data in a GCS bucket?

I've gone through several notebooks on the Google Gen AI Github repo. I can get those to work just fine, but I can't seem to get anywhere when I attempt to customize them to accomplish what I've listed above. Is anyone aware of any good step by step documentation or code samples that performs what I'm trying to do?