Are you ready to take your question-answering systems to the next level? In this tutorial, we'll dive into integrating Retrieval Augmented Generation (RAG) with Google Cloud's Vertex AI Vector Search and the powerful Gemini language model.

You can access the complete code at https://gist.github.com/janakiramm/55... (Vector Search)
and
https://gist.github.com/janakiramm/7d... (RAG)

What you'll learn:
Understanding RAG: How RAG combines retrieval and generative techniques for superior question answering.

Setting up Vertex AI Vector Search: Create and configure your vector search index for efficient document storage and retrieval.

Harnessing Gemini: Leverage Gemini's language capabilities to enhance RAG's answer generation.

Step-by-step Implementation: Follow along as we build a RAG-powered question-answering system on Vertex AI.

Tips and Best Practices: Get insights for optimizing your RAG implementation.

Chapters:
00:00 Introduction
00:54 Overview of RAG
07:05 Configuring and Deploying Vector Search Index Endpoint
18:10 RAG with Gemini

LinkedIn:   / janakiramm  


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