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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-llms-with-langchain/
课程评论：没有评论
课程名称：掌握 LLM 与 LangChain 课程概述：在本课程中，您将深入探索生成性人工智能的世界，专注于大型语言模型（LLMs）的潜力，并研究如何将 LangChain 与 Python 结合使用。您将实现专有解决方案（如 ChatGPT）和现代开源模型，如 Llama 和 Phi。通过实践性项目，您将开发创新应用程序，包括自定义虚拟助手和能够与文档和视频交互的聊天机器人。课程还将探讨高级技术，如 RAG（检索增强生成）和代理，并使用工具如 Streamlit 创建直观的界面。您将学习如何在 Google Colab 上免费使用这些技术，以及如何在本地运行项目。 在课程介绍部分，您将了解大型语言模型（LLMs）的理论和基本概念。此外，我们将探索 Hugging Face 生态系统，该系统为自然语言处理（NLP）提供现代解决方案。您将学习如何使用 Hugging Face 流水线和 LangChain 库实现 LLM，理解每种方法的优缺点。 第二部分集中在精通 LangChain。您将学习如何访问开源模型，如 Meta 的 Llama 和微软的 Phi，以及专有 LLM，如 OpenAI 的 ChatGPT。我们将解释模型量化，以提高性能和可扩展性。将介绍 LangChain 的关键组件，如链、模板和工具，以及如何利用它们开发稳健的 NLP 解决方案。此外，我们将介绍提示工程技术，以帮助您获得更准确的结果。RAG（检索增强生成）的概念将被探索，包括信息存储和检索过程。您将学习如何实现向量存储，并理解嵌入的重要性及其有效使用方法。同时，我们将演示如何利用 RAG 与 PDF 文档和网页进行交互。此外，您将有机会探索集成代理和工具，如使用 LLM 进行网络搜索并检索最新信息的能力。解决方案将在本地实现，使您即使没有网络连接也能访问开源模型。 在项目开发阶段，您将学习创建一个具有界面和记忆功能的自定义聊天机器人，支持问答。您还将学习使用 Streamlit 开发互动应用程序，轻松构建直观的界面。其中一个项目涉及使用 RAG 开发先进应用程序，以多文档交互并通过聊天界面提取相关信息。另一个项目将专注于构建能够自动总结视频并回答相关问题的应用，成为一个强大的实时自动视频理解工具。
In this course, you will dive deep into the world of Generative AI with LLMs (Large Language Models), exploring the potential of combining LangChain with Python. You will implement proprietary solutions (like ChatGPT) and modern open-source models like Llama and Phi. Through practical, real-world projects, you'll develop innovative applications, including a custom virtual assistant and a chatbot that interacts with documents and videos. We'll explore advanced techniques such as RAG and agents, and use tools like Streamlit to create intuitive interfaces. You'll learn how to use these technologies for free in Google Colab and also how to run projects locally.In the introduction, you'll be introduced to the theory of Large Language Models (LLMs) and their fundamental concepts. Additionally, we'll explore the Hugging Face ecosystem, which offers modern solutions for Natural Language Processing (NLP). You'll learn to implement LLMs using both the Hugging Face pipeline and the LangChain library, understanding the advantages of each approach.The second part is focused on mastering LangChain. You'll learn to access open-source models, like Meta's Llama and Microsoft's Phi, as well as proprietary LLMs, like OpenAI's ChatGPT. We'll explain model quantization to enhance performance and scalability. Key LangChain components, such as chains, templates, and tools, will be presented, along with how to use them to develop robust NLP solutions. Prompt engineering techniques will be covered to help you achieve more accurate results. The concept of RAG (Retrieval-Augmented Generation) will be explored, including information storage and retrieval processes. You'll learn to implement vector stores and understand the importance of embeddings and how to use them effectively. We'll also demonstrate how to use RAG to interact with PDF documents and web pages. Additionally, you'll have the opportunity to explore integrating agents and tools, like using LLMs to perform web searches and retrieve recent information. Solutions will be implemented locally, enabling access to open-source models even without an internet connection.In the project development phase, you'll learn to create a custom chatbot with an interface and memory for Q & A. You'll also learn to develop interactive applications using Streamlit, making it easy to build intuitive interfaces. One project involves developing an advanced application using RAG to interact with multiple documents and extract relevant information through a chat interface. Another project will focus on building an application that automatically summarizes videos and answers related questions, resulting in a powerful tool for instant, automated video comprehension.
