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所在平台: Udemy |
课程主页: https://www.udemy.com/course/build-chat-applications-with-openai-and-langchain/
课程评论：没有评论
课程名称：使用 OpenAI 和 LangChain 构建聊天应用 概述：如果你是一名有志的 AI 工程师，渴望将 AI 集成到自己的产品中，或者对 AI 领域的突破感到兴奋，甚至想要学习备受关注的 LangChain 框架，那么你来对地方了！在这个“使用 OpenAI 和 LangChain 构建聊天应用”的课程中，我们将探索越来越受欢迎的 LangChain Python 库，以开发引人入胜的聊天机器人应用。 在本课程中，您将获得详细的逐步指导，使用 OpenAI 的 API 密钥访问其强大的大型语言模型（LLMs）。一旦我们能够访问基础模型，就会利用 LangChain 及其集成功能来创建有吸引力的提示，添加记忆功能，输入外部数据并链接到第三方工具。LangChain 的独特之处在于能够连接多种语言模型，并支持多种格式的文档加载。它还允许选择合适的嵌入模型，将嵌入存储在向量存储中，并与搜索引擎、代码解释器以及维基百科、GitHub、Gmail 等工具连接。 掌握 LangChain 表达语言（LCEL）是实现状态感知的推理聊天机器人的关键。这些聊天机器人能够记住过去的对话，回答未见数据的问题，并解决更复杂的问题。此外，我们将花很多时间讨论最先进的检索增强生成（RAG）技术，理论与实践结合，使 LLM 驱动的应用能够分析及回答超出其训练数据的信息。 最终，我们将创建一个能够回答学生关于 365 内课程问题的聊天机器人。 您将获得的技能： - 将现有应用与强大的 LLM 集成。 - 使用 OpenAI API 密钥连接 OpenAI 的语言和嵌入模型。 - 开发提示工程技术，以提升 AI 响应的性能和相关性。 - 实施 RAG，以丰富您的 AI 驱动产品的知识库。 - 熟练掌握 LCEL 协议，这是使用 LangChain Python 库开发应用程序的关键。 - 将外部工具连接到您的 LLM 驱动应用。 - 理解代理和代理执行器的机制。 通过注册本课程，您可以提升职业前景，获得在 AI 工程领域中稀缺且极具市场需求的技能。点击“立即购买”，获取实际的 AI 工程师技能！
Are you an aspiring AI engineer excited to integrate AI into your product? Are you thrilled about the breakthroughs in the field of AI? Or maybe you're eager to learn this new and exciting LangChain framework everyone's talking about.If yes, then you've come to the right place!Why should you consider taking this LangChain course?In this Build Chat Applications with OpenAI and LangChain course, we'll explore the increasingly popular LangChain Python library to develop engaging chatbot applications.With detailed, step-by-step guidance, you will use OpenAI's API key to access their powerful large language models (LLMs). Once we have access to foundational models, we'll utilize LangChain and its integrations to create compelling prompts, add memory, input external data, and link it to third-party tools.LangChain's integration with third-party tools distinguishes it by enabling connections to various language models and loading documents in multiple formats. It also allows for selecting suitable embedding models, storing embeddings in a vector store, and linking to search engines, code interpreters, and tools like Wikipedia, GitHub, Gmail, and more.None of this would be possible without mastering the LangChain Expression Language (LCEL)-essential for developing stateful, context-aware reasoning chatbots. These chatbots remember past conversations, answer questions about unseen data, and tackle more complex problems.Additionally, we'll spend much of our time discussing the state-of-the-art Retrieval Augmented Generation (RAG), both theoretically and practically. This technique allows LLM-powered applications to analyze and answer questions about information outside their training data. Ultimately, we'll create a chatbot that answers students' questions on courses from the 365 library.What skills do you gain?- Integrate existing applications with powerful LLMs.- Connect to OpenAI's language and embedding models using an OpenAI API key.- Develop prompt engineering techniques to enhance AI response performance and relevance.- Implement RAG to enrich your AI-driven product with a knowledge base.- Master the LCEL protocol-essential for developing applications with the LangChain Python library.- Connect external tools to your LLM-powered application.- Understand the mechanics behind agents and agent executors.Enhance your career prospects with rare and highly sought-after AI Engineering skills by enrolling in this LangChain and OpenAI course.Click ‘Buy Now' and acquire real-world AI engineer skills today!
