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RETRIEVAL-AUGMENTED GENERATION IN ACTION
BUILD REAL-WORLD RAG PIPELINES WITH LANGCHAIN, LANGGRAPH, AND AGENTIC AI FOR ENTERPRISE-GRADE KNOWLEDGE SYSTEMS
Angličtina
38 b
Až 30 dní na vrátenie tovaru
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.
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