Building Multimodal Generative AI and Agentic Applications

Shaping concept to code for the future of multimodal and advanced agentic GenAI applications (English Edition)

Indrajit Kar

• Publisher: BPB Publications
• Year: 2025
• eBook ISBN: 9789365898385
• Subtopic: Visione artificiale e riconoscimento di schemi

Description
Generative AI and agentic AI are reshaping how we interact with data, enabling intelligent systems that can reason, generate, and autonomously act across multiple modalities. From text and images to voice and structured data, these technologies are increasingly essential in enterprise and research applications today. This book offers a complete roadmap to mastering multimodal generative AI and agentic AI systems. It covers foundational concepts, vision-language models, retrieval-augmented generation, human-in-the-loop and multi-agent workflows, text-to-SQL, OCR, and hybrid AI integrations. Each chapter combines theory, practical guidance, code implementations, and real-world case studies, helping readers understand architectures, pipelines, and production-grade deployments. By the end of this book, readers will be capable of designing, implementing, and scaling robust multimodal and agentic AI systems. They will gain hands-on expertise in reasoning, generation, retrieval, agent orchestration, and Ops, equipping them to build production-ready AI applications and excel in their roles.

What you will learn
? Understand multimodal generative AI and agentic AI systems. ? Architecting RAG, vector DBs, embeddings, cross-encoders, and core agentic planning. ? Build retrieval-augmented generation workflows efficiently. ? Implement human-in-the-loop and multi-agent pipelines. ? Apply text-to-SQL for real-time data queries. ? Develop OCR solutions for images and documents. ? Integrate traditional ML models with GenAI workflows. ? Deploy production-grade AI with monitoring and observability.

Who this book is for
This book is for AI enthusiasts, data scientists, ML engineers, and software developers with foundational Python and ML knowledge. It is ideal for tech leads, researchers, and enterprise architects aiming to build and scale multimodal, production-grade agentic AI systems.

Table of Contents
1. Introducing New Age Generative AI 2. Deep Dive into Multimodal Systems 3. Implementing Unimodal Local GenAI System 4. Implementing Unimodal API-based GenAI Systems 5. Implementing Agentic GenAI Systems with Human-in-the-loop 6. Two and Multi-stage GenAI Systems 7. Building a Bidirectional Multimodal Retrieval System 8. Building a Multimodal RAG System 9. Building GenAI Systems with Reranking 10. Retrieval Optimization for Multimodal GenAI 11. Building Multimodal GenAI Systems with Voice as Input 12. Advanced Multimodal GenAI Systems 13. Advanced Multimodal GenAI Systems Implementation 14. Building Text-to-SQL Systems 15. Agentic Text-to-SQL Systems and Architecture Decision-Making 16. GenAI for Extracting Text from Images 17. Integrating Traditional AI/ML into GenAI Workflow 18. LLM Operations and GenAI Evaluation Techniques

Table of contents

• Cover
• Title Page
• Copyright Page
• Dedication Page
• About the Author
• About the Reviewers
• Acknowledgement
• Preface
• Table of Contents
• 1. Introducing New Age Generative AI
• 2. Deep Dive into Multimodal Systems
• 3. Implementing Unimodal Local GenAI System
• 4. Implementing Unimodal API-based GenAI Systems
• 5. Implementing Agentic GenAI Systems with Human-in-the-loop
• 6. Two and Multi-stage GenAI Systems
• 7. Building a Bidirectional Multimodal Retrieval System
• 8. Building a Multimodal RAG System
• 9. Building GenAI Systems with Reranking
• 10. Retrieval Optimization for Multimodal GenAI
• 11. Building Multimodal GenAI Systems with Voice as Input
• 12. Advanced Multimodal GenAI Systems
• 13. Advanced Multimodal GenAI Systems Implementation
• 14. Building Text-to-SQL Systems
• 15. Agentic Text-to-SQL Systems and Architecture Decision-Making
• 16. GenAI for Extracting Text from Images
• 17. Integrating Traditional AI/ML into GenAI Workflow
• 18. LLM Operations and GenAI Evaluation Techniques
• Index