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所在平台: Udemy |
课程主页: https://www.udemy.com/course/genai_rag/
课程评论：没有评论
课程名称：高级RAG：向图形RAG的向量转移 Neo4j 自适应 AutoGen AWS 课程概述：本课程将教授您如何掌握检索增强生成（RAG）这一前沿AI技术，该技术将基于检索的方法与生成模型结合。课程面向开发者、数据科学家和AI爱好者，以及希望利用RAG构建实用应用的质量工程师和学生。您将探索RAG的理论基础、实际实现和现实世界的应用案例。从简单的向量RAG聊天机器人到高级的图形RAG和自反射RAG聊天机器人，您都能在本课程中找到相关内容。完成课程后，您将掌握创建基于RAG的AI应用程序的技能，并能够使用Streamlit、LangChain、LangGraph、Groq API等多种RAG技术创建聊天机器人。 课程目标： - 理解RAG和自然语言处理（NLP）的基本概念。 - 理解NLP的不同概念，例如分词、块化、TF-IDF和嵌入。 - 理解从规则基础到变换模型的NLP模型评估。 - 理解变换模型及其组件，并通过示例进行学习。 - 设置环境以进行动手实现。 - 使用Streamlit和Langchain构建第一个聊天机器人。 - 使用Groq API构建Streamlit聊天机器人的向量RAG。 - 理解图形RAG，并使用Neo4j实现图形RAG。 - 理解自反射或自适应RAG，并使用LangGraph实现。 - 探索RAG的现实世界应用案例。 - 理解重排序RAG技术。 - 理解智能体RAG或基于智能体的RAG AutoGen RAG。 - 使用AWS Bedrock知识库、BOTO3、Streamlit、Lambda和S3创建RAG应用程序。 - 通过测验验证您的理解。 让我们深入了解RAG的世界，加深对其的理解。
In this course, you will learn how to master Retrieval-Augmented Generation (RAG), a cutting-edge AI technique that combines retrieval-based methods with generative models. This course is designed for developers, data scientists, and AI enthusiasts, quality engineers, Students who want to build practical applications using RAG, ranging from simple vector RAG chatbot to advanced chatbot with Graph RAG and Self Reflective RAG. You'll explore the theoretical foundations, practical implementations, and real-world use cases of RAG. By the end of this course, you will have the skills to create RAG-based AI applications. After completing the course, you will be able to create chatbot with multiple RAG techniques using Streamlit, LangChain, LangGraph, Groq API and many more. Along with that you will also learn fundamentals and concepts.Course ObjectivesUnderstand the fundamental concepts of RAG and NLP.Understand concepts of NLP with examples like tokenization, chunking, TF-IDF, embedding.Understand evaluation of NLP models from rule based to transformer model.Understand transformer model and components with examples.Environment setup for hands on implementation.Build first chatbot with Streamlit and Langchain.Build a vector RAG with Streamlit chatbot with Groq API.Understand Graph RAG and implement Graph RAG with Neo4j.Understand Self Reflective or Adaptive RAG and implement with LangGraph.Real world use cases of RAG.Re-ranking RAG techniqueAgentic RAG or Agent based RAG. AutoGen RAG.Create RAG application with AWS Bedrock Knowledge Base , BOTO3, Streamlit, Lamda, S3Check your understanding with Quizzes.Lets deep dive into world of RAG to understand it.
