Retrieval-Augmented Generation in Action : Build Real-World Rag Pipelines with Langchain, Langgraph, and Agentic AI for Enterprise-Grade Knowledge Syst
Overview
Unlock the full potential of Retrieval-Augmented Generation (RAG) and master the creation of advanced AI knowledge systems. This comprehensive guide takes you step-by-step from the foundational theory behind RAG to building production-ready pipelines using LangChain, LangGraph, and agentic AI architectures.
Whether you are an AI engineer, developer, or data scientist, you will learn to:
Design scalable RAG pipelines for enterprise-grade knowledge systems.
Integrate vector databases for efficient information retrieval.
Build agentic AI systems capable of reasoning across multimodal data.
Apply best practices for production-ready, fault-tolerant AI applications.
Packed with detailed Python examples, full implementation walkthroughs, and expert commentary, this book is your ultimate roadmap to mastering RAG in real-world environments.
This item is Non-Returnable
Customers Also Bought
Details
- ISBN-13: 9798273684188
- ISBN-10: 9798273684188
- Publisher: Independently Published
- Publish Date: November 2025
- Dimensions: 8.5 x 5.5 x 0.54 inches
- Shipping Weight: 0.67 pounds
- Page Count: 258
Related Categories
