feat: Notebook for RAG Engine with Vertex AI Search

[galexdor]

Description

https://cloud.google.com/vertex-ai/generative-ai/docs/use-vertexai-search

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB

[code-review-assist]

Hi @galexdor and reviewers,

I'm currently reviewing this pull request and will post my detailed review in a few minutes. In the meantime, here's a quick summary to help everyone get up to speed:

This pull request adds a new Jupyter notebook demonstrating how to use the Vertex AI RAG Engine with Vertex AI Search as the retrieval backend. The notebook covers setting up a Vertex AI Search datastore and engine (optional, if you already have one), creating a RAG corpus using the Vertex AI Search engine, and then using the Gemini GenerateContent API and other generation APIs with the RAG retrieval tool. The intent is to provide a practical example of integrating Vertex AI Search into a RAG application for improved scalability and performance. The main changes include the addition of a new notebook (gemini/rag-engine/rag_engine_vertex_ai_search.ipynb), and an update to the rag-grounding/README.md file to include a link to this new notebook.

The notebook is well-structured and clearly explains each step of the process. It also includes helpful comments and explanations throughout the code. The addition of this notebook significantly enhances the documentation and provides a valuable resource for developers looking to build RAG applications with Vertex AI Search.

To celebrate this addition to the generative-ai repository, here's a little haiku:

Search lights up the way,
Knowledge flows, a bright new path,
RAG finds its true form.

Thanks!

[code-review-assist]

This pull request introduces a new notebook demonstrating how to use the Vertex AI RAG Engine with Vertex AI Search. The notebook is well-structured, easy to follow, and the code appears correct. The explanations and helper functions are helpful for users. A few minor suggestions are included below.

[holtskinner]

LGTM, I can add this to the Docs page