"Retrieval-Augmented Generation with RAGFlow: Building Document-Aware, Deep-Search RAG Engines for Production"
Modern RAG systems fail in subtle ways: they retrieve the wrong evidence, flatten rich documents into brittle chunks, and produce answers that sound plausible while losing traceability. This book is written for experienced engineers, architects, and technical leads who need more than a demo-grade chatbot. It offers a rigorous, production-focused guide to building document-aware, deeply grounded RAG systems with RAGFlow for enterprise search, research workflows, and knowledge-intensive applications.
Across the book, readers move from RAGFlow's core mental model to knowledge-base design, deep document parsing, structure-aware chunking, embeddings, indexing, hybrid retrieval, deep search, grounded generation, citations, agents, evaluation, and production operations. The emphasis is on engineering decisions and trade-offs: how dataset boundaries affect relevance and governance, how parsing quality shapes retrieval, how to diagnose recall-versus-precision failures, and how to turn retrieved context into auditable, trustworthy outputs.
Rather than treating RAG as prompt engineering with a vector store attached, this book treats it as a full-stack retrieval system whose quality depends on disciplined architecture and measurement. Readers should already be comfortable with modern LLM concepts, APIs, and deployment fundamentals. In return, they gain a cohesive blueprint for designing, tuning, and operating robust RAGFlow systems under real production constraints.
