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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fundamentals-of-ai-agents-using-rag-and-langchain
课程评论：没有评论
课程名称：使用 RAG 和 LangChain 的 AI 代理基础 课程概述：随着对技术生成 AI 技能的需求飙升，能够使用大型语言模型（LLM）的 AI 工程师备受欢迎。本课程旨在培养适应市场的技能，为您的 AI 职业生涯奠定基础。 在本课程中，您将深入探讨增检生成（RAG）、提示工程和 LangChain 概念。您将学习 RAG 过程、其应用、编码器和分词器以及用于高维相似性搜索的 FAISS 库。 课程大纲： 1. **RAG 框架** - 本模块将探讨增检生成（RAG）的基础知识及其在聊天机器人和智能 AI 代理等应用中生成更准确和上下文感知的响应的应用。您将了解完整的 RAG 过程及其如何与 LangChain 集成，以构建模块化和可扩展的 AI 解决方案。模块涵盖密集段检索（DPR）的关键组件，使用上下文编码器和问题编码器，并配合分词器将文本转换为机器可读格式。同时介绍了由 Facebook AI 研究开发的 Facebook AI 相似性搜索（FAISS）库，用于在高维向量空间中执行高效的相似性搜索。 - 通过实验室实践，您将获得基于 RAG 系统的实际经验，使用两个主要的机器学习框架：Hugging Face 用于从数据集中检索信息，PyTorch 用于评估内容相关性和生成有意义的响应。 2. **提示工程与 LangChain** - 在本模块中，您将学习上下文学习和高级提示工程技术，以设计和优化生成相关和准确 AI 响应的提示。随后，您将深入探讨 LangChain 框架，这是一个简化使用大型语言模型（LLM）进行 AI 应用开发的开源接口。涵盖的关键概念包括 LangChain 的工具、组件和聊天模型，以及提示模板、示例选择器和输出解析器。您还将审视 LangChain 的文档加载器和检索器、链和代理，以构建智能应用。 - 通过实践实验室，您将应用这些概念以增强 LLM 应用，并开发一个集成 LLM、LangChain 和 RAG 的 AI 代理，以实现交互式和高效的文档检索。此外，课程还提供全面的备忘单和词汇表，以巩固您的学习。
Name:RAG Framework
Description:In this module, you will explore the fundamentals of retrieval-augmented generation (RAG) and how it is applied to generate more accurate and context-aware responses in applications such as chatbots and intelligent AI agents. You will learn about the complete RAG process, including its integration with LangChain for building modular and scalable AI solutions. The module covers key components such as dense passage retrieval (DPR), which uses a context encoder and a question encoder, each paired with tokenizers to convert text into a machine-readable format. It also introduces the Facebook AI similarity search (FAISS) library, developed by Facebook AI Research, for performing efficient similarity searches in high-dimensional vector spaces. Additionally, you will gain hands-on experience through labs that focus on implementing RAG-based systems using two major machine learning frameworks: Hugging Face, for retrieving information from datasets, and PyTorch, for evaluating content relevance and generating meaningful responses.
Name:Prompt Engineering and LangChain
Description:In this module, you will learn about in-context learning and advanced prompt engineering techniques to design and refine prompts for generating relevant and accurate AI responses. You’ll then explore the LangChain framework, an open-source interface that simplifies AI application development using large language models (LLMs). The key concepts covered include LangChain’s tools, components, and chat models, as well as prompt templates, example selectors, and output parsers. You’ll also examine LangChain’s document loader and retriever, chains, and agents to build intelligent applications. Through hands-on labs, you’ll apply these concepts to enhance LLM applications and develop an AI agent that integrates LLM, LangChain, and RAG for interactive and efficient document retrieval. Additionally, a comprehensive cheat sheet and glossary are available to reinforce your learning.
Business demand for technical gen AI skills is exploding, and AI engineers who can work with large language models (LLMs) are in high demand. This Fundamentals of Building AI Agents using RAG and LangChain course builds job-ready skills that will fuel your AI career. In this course, you’ll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. You’ll learn about the RAG process, its applications, encoders and tokenizers, and the FAISS library for high-dimensio
