Overview

Mastering Retrieval-Augmented Generation with LangChain and LlamaIndex: Advanced Python Frameworks, Custom Pipelines, and Production-Ready RAG Applications What if your AI systems could stop hallucinating and start delivering fact-grounded, reliable answers every time? As businesses and developers push large language models into critical use cases, Retrieval-Augmented Generation (RAG) has emerged as the proven approach to making AI more accurate, explainable, and adaptable. This book is a complete guide for developers, data engineers, and AI practitioners who want to build advanced RAG pipelines with Python using the industry's most powerful frameworks: LangChain and LlamaIndex. Whether you're creating a domain-specific assistant, scaling enterprise workflows, or deploying production-ready AI services, this book equips you with the skills to design, implement, and maintain RAG systems that work in the real world. Unlike high-level introductions, this book takes you deeper into practical development with structured chapters that balance theory and implementation. You'll explore the foundations of RAG and the limitations of LLMs, learn how to set up robust Python environments, and build your first retrieval chains with LangChain. You'll then move into advanced use cases such as hybrid retrieval, reranking strategies, memory management, and multi-agent integrations. Entire chapters are dedicated to embeddings and vector databases, orchestration of multi-step pipelines, evaluation techniques, and deployment strategies using FastAPI, Docker, and Kubernetes. The final section looks ahead to RAG 2.0, graph-augmented retrieval, and multi-modal systems, giving you a forward-facing view of where the field is heading. Inside, you'll find: Hands-on code templates for embeddings, retrieval, and reranking functions. Blueprints for production pipelines, including hybrid retrieval and orchestration designs. Evaluation and monitoring frameworks to keep your system accurate and reliable. Deployment-ready examples for APIs and containerized services. Insights into scaling RAG systems, building domain-specific assistants, and adopting responsible AI practices. If you've been asking yourself how to move from prototype to production with RAG, how to integrate LangChain and LlamaIndex effectively, or how to choose the right database and deployment strategy for your use case, this book has the answers. Take your AI systems beyond experimentation and into production with confidence. Get your copy today and start mastering Retrieval-Augmented Generation with LangChain and LlamaIndex.

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