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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llm-retrieval-augmented-generation-masterclass/
课程评论：没有评论
课程名称：掌握RAG：终极检索增强生成课程 课程概述： 欢迎参加“掌握RAG：终极检索增强生成课程”！本课程深入探讨检索增强生成（RAG）系统的世界。无论您是希望构建强大的人工智能驱动应用程序，还是利用语言模型，这门课程将为您提供所需的技能，帮助您掌握开发智能检索应用的能力。该实践性课程将引导您了解RAG架构的核心概念、探索各种框架，并提供深入的理解和实践经验，帮助您构建先进的RAG系统。立即报名，迈出掌握RAG系统的第一步！ 您将学习的内容： 1. 基于LLM的应用开发：理解大语言模型（LLMs）的核心概念和能力，探索支持检索和生成任务的高层框架。 2. 优化和扩展RAG管道：学习使用LangChain优化和扩展RAG管道的最佳实践，包括索引、分块和检索优化技术。 3. 高级RAG技术：通过预检索和后检索优化技术增强RAG系统，学习查询变换和分解的检索优化。 4. 文档变换器和分块策略：理解智能文本分割策略，处理大数据集，改善文档索引和嵌入。 5. 调试、测试和监控LLM应用：使用LangSmith调试、测试和监控LLM应用，评估RAG管道的每个组件。 6. 构建多智能体LLM驱动的应用：使用LangGraph开发复杂的有状态应用，使多个智能体协作完成数据检索和生成任务。 7. 提升RAG质量：学习处理非结构化数据，从PDF文件中提取表格和图像元素，整合GPT-4视觉，识别和描述图像中的元素。 课程内容包括： - 入门和设置：Python开发环境设置，构建基本到高级的RAG管道，使用OpenAI API构建您的第一个LLM驱动应用。 - RAG基础及其优势与局限，实施基础RAG问答系统的动手项目。 - 高级RAG技术和策略，包括索引和分块优化技术。 - 文档变换器和分块策略的优化，处理大数据集。 - 使用LangSmith评估RAG管道的每个组件，并开发多智能体LLM应用的综合项目。 - 提升RAG质量的实践项目，包括处理PDF中的非结构化数据并整合GPT-4视觉。 这门课程适合： - Python开发者：希望利用高层库和API构建AI驱动应用的个人。 - 机器学习工程师：寻求提升RAG技术技能的专业人士。 - 学生和学习者：渴望深入RAG系统并获得动手实践经验的个人。 - 技术企业家和AI爱好者：希望创建智能检索应用并探索新的AI商业机会的任何人。 无论您是初学者还是高级从业者，这门课程都将提升您构建智能高效RAG管道的能力，提供案例研究和实际示例的全面指导。今天开始学习之旅，转变您开发检索应用的方式！
Welcome to "Master RAG: Ultimate Retrieval-Augmented Generation Course"!This course is a deep dive into the world of Retrieval-Augmented Generation (RAG) systems. If you aim to build powerful AI-driven applications and leverage language models, this course is for you! Perfect for anyone wanting to master the skills needed to develop intelligent retrieval-based applications.This hands-on course will guide you through the core concepts of RAG architecture, explore various frameworks, and provide a thorough understanding and practical experience in building advanced RAG systems.Enroll now and take the first step towards mastering RAG systems!# What You'll Learn:Development of LLM-based applications: Understand the core concepts and capabilities of Large Language Models (LLMs) and explore high-level frameworks that facilitate powered by retrieval and generation tasks,Optimizing and Scaling RAG Pipelines: Learn best practices for optimizing and scaling RAG pipelines using LangChain, including indexing, chunking, and retrieval optimization techniques,Advanced RAG Techniques: Enhance RAG systems with pre-retrieval and post-retrieval optimization techniques and learn retrieval optimization with query transformation and decomposition,Document Transformers and Chunking Strategies: Understand strategies for smart text division, handling large datasets, and improving document indexing and embeddings.Debugging, Testing, and Monitoring LLM Applications: Use LangSmith to debug, test, and monitor LLM applications, evaluating each component of the RAG pipeline.Building Multi-Agent LLM-Driven Applications: Develop complex stateful applications using LangGraph, making multiple agents collaborate on data retrieval and generation tasks.Enhanced RAG Quality: Learn to process unstructured data, extract elements like tables and images from PDF files, and integrate GPT-4 Vision to identify and describe elements within images.# What is Included?1. Getting Started: Introduction and SetupPython Development Environment SetupImplement basic to advanced RAG pipelinesQuickstart: Building Your First LLM-Powered Application using OpenAIStep-by-step OpenAI Guide to creating a basic application integrating the ChatOpenAI API for text and message generation2. RAG: From Native (101) to Advanced RAGKey benefits and limitations of using LLMsOverview and understanding of the RAG pipeline and multiple use casesHands-on project: Implement a basic RAG Q & A system using LLMs, LangChain, and the FAISS vector database[Project] - Build end-to-end RAG solutions using tools like FAISS and ChromaDB3. Advanced RAG Techniques & StrategiesEnhance RAG systems with pre-retrieval and post-retrieval optimization techniquesIndexing and chunking optimization techniquesRetrieval optimization with query transformation and decomposition4. Optimized RAG: Document Transformers & Chunking StrategiesStrategies for smart text division to handle large datasets and scaling applicationsImprove document indexing and embeddingsExperiment with commonly used text splitters:Split into chunks by characters with a fixed-size parameterSplit recursively by characterSemantic chunking with LangChain to split into sentences based on text similarity5. LangSmith: Debug, Test, and Monitor LLM ApplicationsEvaluate each component of the RAG pipelineDevelop a comprehensive project: A multi-agent LLM-driven application using LangGraph6. Enhanced RAG Quality: Conventional vs. Structured RAGLearn to process unstructured data to facilitate integration and preparation for LLMsPractice with a project aimed at extracting elements like tables and images from PDF files and integrating GPT-4 Vision to identify and describe elements within imagesBonus materials: Assessment questions, downloadable resources, interactive playgrounds (Google Colab)# Who is This Course For?Python Developers: Individuals who want to build AI-driven applications leveraging language models using high-level libraries and APIsML Engineers: Professionals looking to enhance their skills in RAG techniquesStudents and Learners: Individuals eager to dive into the world of RAG systems and gain hands-on experience with practical examplesTech Entrepreneurs and AI Enthusiasts: Anyone seeking to create intelligent, retrieval-based applications and explore new business opportunities in AIWhether you're a beginner or an advanced practitioner, this course will elevate your capabilities in constructing intelligent and efficient RAG pipelines with case studies and real-world examples.This course offers a comprehensive guide through the main concepts of RAG architecture, providing a structured learning path from basic to advanced techniques, ensuring a robust understanding to gain practical experience in building LLM-powered apps.Start your learning journey today and transform the way you develop retrieval-based applications!
