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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llms-with-langchain-beginner-friendly/
课程评论：没有评论
**Coursera课程摘要：LangChain入门：构建AI PDF文档搜索引擎** 本课程专为希望零基础构建大型语言模型（LLM）应用程序的初学者设计。通过动手实践的编码示例，您将学习LangChain的基础概念，并最终构建一个功能强大的AI PDF文档搜索应用。 **为何学习LangChain？** LangChain是LLM应用开发的核心库，其重要性堪比Pandas在数据科学中的地位。对于数据科学家和机器学习专业人士而言，掌握LangChain将极大提升开发LLM应用的能力。 **课程亮点：** * **LangChain基础：** 深入理解Prompt、Chain和Agent，并通过易懂的代码示例进行实践。 * **Prompt：** 学习Prompt的概念，以及如何创建Prompt模板来自动化输入。 * **Chain：** 探索Prompt如何融入Chain，理解Simple Chain和Sequential Chain。 * **Agent：** 掌握Agent的三大组成部分——Tools（工具）、LLMs（大型语言模型）和Agent类型。学习使用Wikipedia、SerpAPI和LLMmath等工具来增强Agent的功能。 * **PDF文档处理：** 学习如何加载和处理PDF文档，利用Embeddings（嵌入）和Vector Stores（向量存储）与大型语言模型进行交互。 * **提升模型性能：** 通过优化Prompt来提高GPT-3.5的查询能力。 * **部署AI应用：** 使用Streamlit部署您的AI应用，使其能够广泛应用于实际场景。 **目标学员：** * 对构建LLM应用感兴趣的初学者。 * Python基础有限但希望通过分步指导学习的学员。 **课程承诺：** 本课程提供构建PDF文档搜索AI应用的详细步骤指导，涵盖LangChain的核心组件，并教授如何使用Streamlit进行应用部署。所有必要的代码文件和数据均已提供。
Course Overview:This beginner-friendly LangChain course is designed to help you start using LangChain to develop LLM (Large Language Model) applications with NO prior experience! Through hands-on coding examples, you'll learn the foundational concepts and build up to creating a functional AI app for PDF document search.Why LangChain?LangChain is poised to become as essential to LLM applications as Pandas is to Data Science. This core library will be invaluable for Data Scientists and Machine Learning professionals building applications with Large Language Models.What You Will Learn:LangChain Basics: Gain an understanding of Prompts, Chains, and Agents with easy-to-follow code examples.Prompts: Learn what a Prompt is and how to create Prompt templates to automate inputs.Chains: Discover how Prompts integrate into Chains, exploring both Simple and Sequential Chains.Agents: Master the three components of Agents-Tools, LLMs, and Agent types. Explore Tools like Wikipedia, SerpAPI, and LLMmath to leverage the power of Agents.Processing PDF Documents: Learn to load and process PDF documents with Large Language Models, utilizing embeddings and vector stores.Enhancing Model Performance: Work with prompts to improve querying capabilities of GPT-3.5.Deploying Your AI App: Serve your AI app using Streamlit, making it accessible for real-world applications.Who This Course Is For:Beginners interested in building applications with Large Language Models.Anyone with a limited understanding of Python-this course breaks down the code step-by-step.Course Highlights:Step-by-step guidance on building a Large Language Model (LLM) based AI app for PDF document search.Comprehensive understanding of LangChain's components.Practical knowledge for deploying AI apps with Streamlit.All necessary code files and data are provided.
