Explore RAG with Google Gemini. Learn its architecture, APIs, and capabilities. Build hands-on applications, integrate LangChain, and create a customer service assistant with multimodal AI prompts.

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Unlock the power of RAG with Google Gemini in this hands-on course. Learn about Google Gemini, a family of multimodal large language models (LLMs), and its cutting-edge applications developed by Google.

Explore Gemini’s evolution, architecture, and APIs to understand its unimodal and multimodal AI content generation capabilities. Dive into retrieval-augmented generation (RAG) techniques using Gemini and LangChain. Implement RAG applications to generate text and image responses from external knowledge sources and provide prompts.

In the final project, create a customer service assistant application with a Streamlit interface, integrating Gemini’s multimodal AI capabilities for image-to-text and text-to-text prompts. After completing this course, you’ll have the expertise to build real-world RAG applications with Google Gemini.

Building Multimodal RAG Applications with Google Gemini

Get an overview of the streamlit customer service assistant application for text prompts with RAG.

Customer Service Assistant Application

Getting Started

Content Generation Using Gemini Models

Building RAG Applications with Google Gemini

Customer Service Assistant—Multimodal RAG Interface

Wrapping Up

Customer Service Assistant Application

Streamlit framework