Advanced RAG with AI Agents: A Practical Guide to Building Retrieval-Augmented Agents with LangChain

Advanced RAG with AI Agents: A Practical Guide to Building Retrieval-Augmented Agents with LangChain, Python, and ReAct for Context-Aware AI Workflows
Unlock the Future of AI Development with Retrieval-Augmented Generation and Agentic Workflows
Are you ready to build next-generation AI agents that think, reason, and retrieve with precision? Whether you're an ML engineer, backend developer, or AI enthusiast, this hands-on guide will equip you with everything you need to build production-ready, context-aware AI agents using cutting-edge tools like LangChain, ReAct, FAISS, and Python.
Inside this groundbreaking guide, you'll learn how to:
- Master Retrieval-Augmented Generation (RAG) from first principles
- Implement intelligent agents using LangChain and the ReAct framework
- Build scalable vector stores with Chroma and FAISS
- Create multi-agent systems that collaborate and reason across documents
- Add memory, personalization, and multimodal inputs using CLIP and Hugging Face
- Deploy real-world applications including code search agents, chatbots, and research assistants
- Containerize and scale your AI agents using FastAPI, Docker, AWS, and GCP
This book doesn't just teach you how RAG works-it walks you through dozens of real-world projects, including:
- Building a document Q&A chatbot with retrieval + reasoning
- Automating literature reviews with arXiv and LangChain
- Deploying scalable RAG APIs using cloud platforms
Why This Book Is Different:
- 100% project-based with runnable Python examples
- SEO-aligned for 2025 AI trends: Agentic RAG, Multimodal Retrieval, LangChain, and ReAct
- No fluff, no theory dumps-just real tools, real agents, real results
- Text-based diagrams, flowcharts, and code walkthroughs
Whether you're building your first AI workflow or scaling your own intelligent assistant, Advanced RAG with AI Agents gives you the tools, mindset, and code to lead in the agentic AI revolution.