What do you think?
Rate this book
Building RAG Agents with Python: A Comprehensive Guide to Developing Scalable Retrieval-Augmented Generation Systems
Building RAG Agents with Python is your hands-on, professional guide to developing powerful Retrieval-Augmented Generation (RAG) systems that deliver accurate, grounded, and scalable AI solutions. Whether you're an AI engineer, data scientist, backend developer, or technical product leader, this book takes you from theory to full-scale implementation—equipping you with everything you need to build and deploy intelligent agents that answer real-world questions using real-world data.
Inside, you’ll master the key components of a modern RAG system, including dense and sparse retrievers, prompt engineering strategies, document chunking, vector indexing, LLM integration, caching, token management, and reranking. You’ll build reusable modules, optimize for performance and cost, evaluate output quality with precision, and deploy your RAG agents as production-ready APIs and apps using FastAPI, Streamlit, and Docker.
But this book goes far beyond implementation. You’ll explore fine-tuning techniques, scaling strategies, context window control, and multi-component orchestration—all while working through rich, practical case studies like a customer support assistant, an internal document QA bot, and a scientific research engine built on arXiv papers.
Whether you’re building enterprise AI tools, chat assistants, internal knowledge bots, or LLM-integrated research platforms, Building RAG Agents with Python gives you the real-world patterns, reusable code structures, and deployment workflows needed to ship reliable and intelligent AI products.
What You’ll
Design and implement scalable RAG pipelines with clean modularity
Integrate FAISS, HuggingFace, OpenAI, and LangChain with your own data
Optimize token usage, context relevance, and retrieval performance
Build and containerize robust systems for real-world use cases
Evaluate, monitor, and scale RAG agents with precision and confidence
If you're serious about building intelligent systems that combine the best of search and generation, this is the book you’ve been waiting for.
Inside, you’ll master the key components of a modern RAG system, including dense and sparse retrievers, prompt engineering strategies, document chunking, vector indexing, LLM integration, caching, token management, and reranking. You’ll build reusable modules, optimize for performance and cost, evaluate output quality with precision, and deploy your RAG agents as production-ready APIs and apps using FastAPI, Streamlit, and Docker.
But this book goes far beyond implementation. You’ll explore fine-tuning techniques, scaling strategies, context window control, and multi-component orchestration—all while working through rich, practical case studies like a customer support assistant, an internal document QA bot, and a scientific research engine built on arXiv papers.
Whether you’re building enterprise AI tools, chat assistants, internal knowledge bots, or LLM-integrated research platforms, Building RAG Agents with Python gives you the real-world patterns, reusable code structures, and deployment workflows needed to ship reliable and intelligent AI products.
What You’ll
Design and implement scalable RAG pipelines with clean modularity
Integrate FAISS, HuggingFace, OpenAI, and LangChain with your own data
Optimize token usage, context relevance, and retrieval performance
Build and containerize robust systems for real-world use cases
Evaluate, monitor, and scale RAG agents with precision and confidence
If you're serious about building intelligent systems that combine the best of search and generation, this is the book you’ve been waiting for.
Kindle Edition
Published April 4, 2025
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.