RAG Pipelines with Python: Unlock Smarter AI Solutions and Supercharge Your Projects with Retrieval-Augmented Generation

By Ronald Taylor

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

• Format: Paperback
• Language: English
• ISBN: B0DVT4D1SR
• ISBN13: 9798309238750
• Release Date: February 2025
• Publisher: Independently Published
• Length: 196 Pages
• Weight: 0.77 lbs.
• Dimensions: 0.4" x 7.0" x 10.0"