RAG Pipelines with Python - Taylor, Ronald
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RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation Harness the power of Retrieval-Augmented Generation (RAG) and revolutionize your AI projects with cutting-edge Python techniques. Whether you're a machine learning engineer, data scientist, or AI enthusiast, this book provides a step-by-step guide to building scalable, high-performance RAG pipelines that integrate Large Language Models (LLMs), knowledge graphs, and graph-based retrieval systems. From understanding the foundations of RAG to deploying advanced multimodal and…mehr

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RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation Harness the power of Retrieval-Augmented Generation (RAG) and revolutionize your AI projects with cutting-edge Python techniques. Whether you're a machine learning engineer, data scientist, or AI enthusiast, this book provides a step-by-step guide to building scalable, high-performance RAG pipelines that integrate Large Language Models (LLMs), knowledge graphs, and graph-based retrieval systems. From understanding the foundations of RAG to deploying advanced multimodal and multi-agent AI architectures, this book covers everything you need to design, optimize, and debug intelligent AI systems. What You'll Learn: * Master Graph-Based RAG Pipelines: Implement knowledge graphs, graph neural networks, and LangGraph-powered AI agents for smarter retrieval and generation. * Build Scalable LLM Workflows: Learn how to integrate retrieval and fine-tuning techniques for efficient and optimized LLM performance. * Develop Advanced Retrieval Systems: Explore multimodal retrieval, integrating text, images, and structured data for next-level AI applications. * Enhance Model Performance with MLOps: Implement debugging strategies, monitoring techniques, and scalable architectures for real-world production pipelines. * Explore Multi-Agent AI Systems: Use CrewAI, LangGraph, and knowledge graphs to orchestrate intelligent, context-aware AI agents. Why This Book? * Practical & Hands-On: Includes real-world code examples and step-by-step implementations of state-of-the-art RAG techniques. * Scalable & Production-Ready: Learn MLOps best practices for deploying and maintaining high-performance RAG pipelines in real-world applications. * Future-Proof Your AI Skills: Stay ahead with the latest advancements in LLMs, prompt engineering, knowledge graph-based RAG, and AI-powered search. Who Is This For? * AI engineers and developers looking to build robust RAG pipelines with Python. * Data scientists and researchers exploring LLM-powered retrieval and knowledge integration. * ML engineers interested in debugging, optimizing, and scaling RAG architectures. * Entrepreneurs and AI enthusiasts who want to implement next-gen AI solutions with LangGraph, CrewAI, and multimodal AI. Take your RAG expertise to the next level and build smarter, faster AI solutions-all with hands-on Python examples and expert guidance. Get your copy today and start mastering RAG pipelines!
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