RAG Pipelines with Python
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Home > Computing and Information Technology > Computer programming / software engineering > RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation(The AI Architect's Guide: Graph-Based Retrieval, AI Agents, and Advanced Generative AI)
RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation(The AI Architect's Guide: Graph-Based Retrieval, AI Agents, and Advanced Generative AI)

RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation(The AI Architect's Guide: Graph-Based Retrieval, AI Agents, and Advanced Generative AI)


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About the Book

RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented GenerationHarness 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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Product Details
  • ISBN-13: 9798309238750
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 196
  • Returnable: N
  • Spine Width: 11 mm
  • Weight: 351 gr
  • ISBN-10: 8309238754
  • Publisher Date: 02 Feb 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Series Title: The AI Architect's Guide: Graph-Based Retrieval, AI Agents, and Advanced Generative AI
  • Sub Title: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation
  • Width: 178 mm


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RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation(The AI Architect's Guide: Graph-Based Retrieval, AI Agents, and Advanced Generative AI)
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RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation(The AI Architect's Guide: Graph-Based Retrieval, AI Agents, and Advanced Generative AI)
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