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所在平台: Coursera |
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课程名称：LangChain大师班：通过Python构建15个大型语言模型应用 课程概述：在我们的LangChain大师班中，您将解锁人工智能和基于语言的应用程序的无限潜力。这一综合课程将带您通过LangChain、Pinecone、OpenAI和LLAMA 2大型语言模型的变革之旅，由行业专家指导。您将参与从动态问答应用程序到对话机器人、教育人工智能体验和引人入胜的营销活动等的实践项目。 课程大纲： 1. **LangChain介绍**：简介LangChain——一个开放源代码框架，旨在帮助创建使用大型语言模型的应用程序，讨论LangChain的目标和好处。 2. **OpenAI介绍**：探讨OpenAI的基础知识，包括其历史和强大的语言模型（如GPT-3和Codex），并讲解如何设置和使用OpenAI的API。 3. **演示与环境设置**：演示LangChain的实际应用，改善问答能力，并指导安装Anaconda，为后续使用Jupyter笔记本的项目做准备。 4. **LangChain模型模块概念**：概述LangChain中的不同模块，包括模型、提示、内存、索引、链、代理和回调，强调其功能和实际应用。 5. **项目#1：简单问答应用**：创建一个简单的问答应用，探索不同的模型并设置项目环境。 6. **项目#2：简单对话应用**：开发一个简单的对话应用，配置环境，设置聊天机器人，并实现自定义对话上下文。 7. **项目#3：儿童相似物品查找应用**：学习实现文本嵌入，帮助儿童理解相似的单词和物体。 8. **LangChain提示模块概念与Python实现**：学习设计有效的提示，使用提示模板，优化模型的响应。 9. **项目#4：营销活动应用**：构建一个营销活动应用，转换Jupyter笔记本为Python脚本，开发用户界面，并集成前后端组件。 10. **LangChain内存模块概念**：探讨内存模块，强调对话缓冲内存的重要性，以提升AI的交互能力。 11. **项目#5：带有摘要功能的ChatGPT克隆**：创建一个具有摘要功能的ChatGPT克隆，设置项目环境并实现对话视图。 12. **LangChain数据连接模块概念**：探讨数据连接模块的重要性，学习处理数据和创建向量嵌入。 13. **项目#6：多项选择题创作应用**：创建一个MCQ创作应用，加载文档，创建嵌入并格式化输出。 14. **LangChain链模块概念**：学习连接不同的LLM，实施通用和顺序链，简化复杂任务。 15. **LangChain代理模块概念**：探讨行动代理和计划与执行代理，了解如何决策和创建计划。 16. **项目#7：CSV数据分析工具**：创建一个基于用户查询处理CSV文件的数据分析工具。 17. **项目#8：YouTube脚本写作工具**：开发一个YouTube脚本创作工具，生成动态视频脚本。 18. **项目#9：网站支持聊天机器人**：创建一个网站聊天机器人，实现数据抓取并提供自然语言支持。 19. **项目#10：自动票据分类工具**：构建一个自动票据分类工具，处理文档上传，训练分类模型。 20. **项目#11：HR简历筛选助手**：创建HR简历筛选工具，分析简历并与职位描述匹配。 21. **LLAMA 2介绍**：了解LLAMA 2模型，学习其版本和潜在的生成AI应用。 22. **项目#12：使用LLAMA 2的邮件生成器**：开发一个邮件生成器，生成个性化邮件内容。 23. **项目#13：发票提取机器人**：创建一个发票提取机器人，处理PDF发票数据。 24. **项目#14：文本到SQL查询助手**：创建文本到SQL查询助手，生成基于用户输入的SQL查询。 25. **项目#15：客户服务通话摘要警报**：构建一个客户服务通话摘要警报工具，处理通话数据并自动生成摘要。 该课程将带您深入探索大型语言模型的强大应用，具有挑战性且富有成就感的实际项目将提升您的技能。
Name:LangChain Introduction
Description:In this module, we will introduce you to LangChain, an open-source framework designed to facilitate the creation of applications powered by large language models. We will discuss the objectives and benefits of LangChain and provide an overview of what you will gain from this course.
Name:OpenAI Introduction
Description:In this module, we will delve into the foundational aspects of OpenAI, exploring its history and powerful language models like GPT-3 and Codex. Additionally, we will cover the practical steps to set up and utilize OpenAI's API for various applications.
Name:Demo and Environment Setup
Description:In this module, we will demonstrate a practical implementation of LangChain to improve question-answering capabilities and guide you through the installation of Anaconda, which is essential for our upcoming projects using Jupyter Notebooks.
Name:LangChain - Models Module Concept
Description:In this module, we will provide an overview of the different modules within LangChain, including models, prompts, memory, indexes, chains, agents, and callbacks, highlighting their functionalities and practical applications.
Name:Project #1 - Simple Question and Answer App
Description:In this module, we will walk you through the creation of a simple question-answering app using LangChain. You will explore different models, set up the project environment, and build an application that utilizes LangChain for effective AI-driven interactions.
Name:Project #2 - Simple Conversational App
Description:In this module, we will guide you through the development of a simple conversational app using LangChain. You will learn to configure the environment, set up the chatbot, and implement custom conversational contexts for an interactive user experience.
Name:Project #3 - Find Similar Things App for Kids
Description:In this module, we will explore word embeddings and their applications in machine learning projects. You will learn to implement text embeddings using LangChain and build an application that helps children understand similar words and objects.
Name:LangChain - Prompt Module Concept and Implementation Using Python
Description:In this module, we will delve into the prompts module in LangChain, learning how to design effective prompts, use prompt templates, and explore features like example selectors and output parsers to optimize the model's responses.
Name:Project #4 - Marketing Campaign App
Description:In this module, we will build a marketing campaign app using LangChain. You will learn to convert Jupyter Notebooks to Python scripts, develop a user interface with Streamlit, and integrate frontend and backend components for a seamless application.
Name:LangChain - Memory Module Concept
Description:In this module, we will focus on the memory modules in LangChain, highlighting the importance of conversation buffer memory and exploring different types of memory to enhance AI interactions by retaining and recalling past conversations.
Name:Project #5 - ChatGPT Clone with Summarization Option
Description:In this module, we will guide you through the creation of a ChatGPT clone with summarization capabilities. You will learn to set up the project environment, implement a conversational view, and enhance the chatbot with features like dynamic inputs and user API keys.
Name:LangChain - Data Connection Module Concept
Description:In this module, we will delve into the Data Connection module in LangChain, exploring its importance and functionality. You will learn to process data, create vector embeddings, and use retrievers to fetch relevant information efficiently.
Name:Project #6 - Quiz MCQ Creator App
Description:In this module, we will build an MCQ creator app using LangChain. You will learn to load documents, create embeddings, retrieve answers, and format the output in an MCQ format for practical applications.
Name:LangChain - Chains Module Concept
Description:In this module, we will explore the concept of chains within LangChain, learning how to connect different LLMs, implement generic and sequential chains, and utilize utility chains for simplifying complex tasks and saving development time.
Name:LangChain - Agents Module Concept
Description:In this module, we will explore the concepts of action agents and plan and execute agents within LangChain. You will learn how these agents make decisions and create plans, combining their functionalities for smarter and more efficient outcomes.
Name:Project #7 - CSV Data Analysis Tool
Description:In this module, we will create a data analysis tool that processes CSV files based on user queries. You will learn to set up the application, implement the UI, and use LangChain and OpenAI to analyze and generate responses from CSV data.
Name:Project #8 - YouTube Script Writing Tool
Description:In this module, we will develop a YouTube script-writing tool using LangChain. You will learn to set up the project environment, create the UI, and integrate LangChain to generate dynamic and engaging video scripts based on user inputs.
Name:Project #9 - Support Chatbot for Your Website
Description:In this module, we will guide you through the creation of a support chatbot for websites using LangChain. You will learn to implement data scraping, set up a user interface, and integrate frontend and backend components to provide natural language assistance on your website.
Name:Project #10 - Automatic Ticket Classification Tool
Description:In this module, we will build an automatic ticket classification tool using LangChain. You will learn to implement the UI, handle document uploads, and train a classification model to categorize tickets into different departments based on user queries.
Name:Project #11 - HR - Resume Screening Assistance
Description:In this module, we will create a resume screening assistance tool for HR using LangChain. You will learn to set up the project, analyze resumes, and match them with job descriptions, simplifying the screening process and enhancing HR efficiency.
Name:LLAMA 2 Introduction
Description:In this module, we will introduce you to the LLAMA 2 model, a powerful open-source tool offered by Meta. You will learn about its versions, parameter sizes, and potential applications in generative AI, along with the steps to download and set up LLAMA 2 for local use.
Name:Project #12 - Email Generator Using LLAMA 2 || Streamlit App
Description:In this module, we will guide you through the development of an email generator using LangChain and LLAMA 2. You will learn to set up the project, design the UI, and implement the LLAMA 2 model to generate dynamic and personalized email content.
Name:Project #13 - Invoice Extraction Bot
Description:In this module, we will build an invoice extraction bot using LangChain and LLAMA 2. You will learn to implement data extraction from PDF invoices, process the extracted data, and create a user interface for viewing and downloading the results.
Name:Project #14 - Text to SQL Query - Helper Tool || Google Collab || LLAMA 2
Description:In this module, we will create a text-to-SQL query helper tool using LangChain, Google Colab, and LLAMA 2. You will learn to set up the environment, implement pipelines for text prediction, and utilize LangChain to generate SQL queries based on user inputs.
Name:Project #15 - Customer Care Call Summary Alert || OpenAI || Zapier NLA
Description:In this module, we will build a customer care call summary alert tool using LangChain, OpenAI, and Zapier. You will learn to implement the UI and backend logic for processing call data, automate the summarization, and set up an email alert system for efficient customer care management.
Unlock the limitless potential of AI and language-based applications with our LangChain Masterclass. This comprehensive course takes you on a transformative journey through LangChain, Pinecone, OpenAI, and LLAMA 2 LLM, guided by industry experts. You'll engage in hands-on projects ranging from dynamic question-answering applications to conversational bots, educational AI experiences, and captivating marketing campaigns. Additionally, you'll delve into summarization-enriched chatbots, multiple-
