Discover the evolution, architecture, and APIs of Google Gemini. Delve into hands-on exercises, mastering RAG applications, and develop a customer service assistant using multimodal prompting with image and text prompts.

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This course will introduce you to Google Gemini, a family of multimodal large language models developed by Google.

You’ll start with learning about LLMs, the evolution of Google Gemini, its architecture and APIs, and its diverse capabilities. Next, you’ll complete hands-on exercises using Gemini models for unimodal and multimodal text generation. You’ll understand the retrieval augmented-generation (RAG) process using Gemini and LangChain. You’ll implement an RAG application for generating textual responses based on the provided unimodal prompts and an external knowledge source. Finally, you’ll develop a customer service assistant application with a Streamlit interface that integrates RAG and Gemini for multimodal prompting using image and text prompts.

After completing this course, you will have an in-depth knowledge of using Google Gemini for unimodal and multimodal prompting in real-world AI-based applications.

Building Multimodal RAG Applications with Google Gemini

Get an overview of the LangChain framework, the components it offers, and how it works with the RAG.

Introduction to LangChain

Getting Started

Content Generation Using Gemini Models

Building RAG Applications with Google Gemini

Customer Service Assistant—Multimodal RAG Interface

Wrapping Up

Introduction to LangChain