Updated 27 Aug
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Building Multimodal RAG Applications with Google Gemini
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.
14 lessons
1 project
3h
Created by Educative
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What You'll Learn
- An understanding of the basics of Google Gemini, its architecture, APIs, and multimodal capabilities
- The ability to build applications using text-to-text, image-to-text, and multimodal prompts
- Hands-on experience implementing retrieval-augmented generation (RAG) with Gemini for textual and image-based queries
- The ability to leverage LangChain for advanced RAG workflows with external knowledge sources
- Hands-on experience creating a customer service assistant integrating multimodal RAG and Google Gemini in Streamlit
Learning Roadmap
1.
Getting Started
Getting Started
Get familiar with Google Gemini's multimodal AI, APIs, and advanced capabilities.
2.
Content Generation Using Gemini Models
Content Generation Using Gemini Models
Grasp the fundamentals of using Gemini models for versatile content generation across text and images.
3.
Building RAG Applications with Google Gemini
Building RAG Applications with Google Gemini
5 Lessons
5 Lessons
Examine creating sophisticated customer service applications using Retrieval-Augmented Generation and multimodal capabilities with Google Gemini.
Certificate of Completion
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Anthony Walker
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E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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