Build Your Own Private ChatGPT—Without Relying on the Cloud. In an age of growing demand for privacy, control, and performance in AI applications, The RAG Handbook: Building a Private ChatGPT with PyTorch 2.x & LangChain equips you with everything you need to deploy a secure, high-performing Retrieval-Augmented Generation (RAG) system—fully under your control. Whether you're an AI engineer, backend developer, or enterprise architect, this concise and highly actionable guide walks you step-by-step through creating a private chatbot using open-source LLMs, your own documents, and modern inference stacks. You'll move beyond plug-and-play tools and instead build, fine-tune, deploy, and monitor your own AI assistant with production-grade practices. From vector databases and LangChain retrievers to LoRA fine-tuning, FastAPI backends, and TorchServe deployments, this book delivers hands-on techniques that reflect the latest advancements in open-source AI engineering. Inside, you’ll master: Retrieval-Augmented Generation (RAG) architecture with PyTorch 2.x - Local deployment of open-source large language models (LLMs) - Fine-tuning with LoRA, PEFT, and QLoRA for domain-specific use cases - Optimizing responses with prompt engineering and template routing - Building secure APIs with FastAPI, LangChain Agents, and streaming output - Production deployment using TorchServe, vLLM, Docker, and Kubernetes - Monitoring with Prometheus, Grafana, and live feedback loops - Planning for multimodal RAG, agentic workflows, and RAG 2.0 innovations Table of Contents : Chapter 1: Understanding RAG: From Transformer to Context-Aware Chatbots - Chapter 4: Loading and Running Open-Source LLMs with PyTorch - Chapter 5: Wiring the Retriever–Generator Pipeline - Chapter 7: Fine-Tuning with LoRA & PEFT for Domain-Specific Mastery - Chapter 8: Building the Chatbot Interface with FastAPI and Streamlit - Chapter 9: Deploying to Production with TorchServe and vLLM - Chapter 10: Monitoring, Testing, and Evolving Your RAG Stack - Appendices: Debugging tips, environment setup, project structure, and more This book is designed to be concise yet deeply practical , blending software engineering rigor with cutting-edge AI workflows. It assumes intermediate knowledge of Python and deep learning, making it ideal for machine learning engineers, backend developers, security-minded data scientists, and advanced hobbyists. About the Author: Caesar Daniel is a software engineer and AI infrastructure specialist with a track record of building secure, scalable machine learning systems. Known for his ability to distill complex AI workflows into actionable steps, Caesar brings both credibility and clarity to one of today’s fastest-evolving fields. Technology Focus: Built entirely on PyTorch 2.x , LangChain , LoRA , FastAPI , vLLM , and Docker/Kubernetes , this book reflects the current best practices in RAG-based chatbot development . All code is fully working and explained for real-world deployment. If you're ready to break free from closed platforms and take control of your AI stack, The RAG Handbook is your essential guide.