88 lines
3.4 KiB
Plaintext
88 lines
3.4 KiB
Plaintext
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"# 1. 简介\n",
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"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#1.1-LangChain的诞生和发展\" data-toc-modified-id=\"1.1-LangChain的诞生和发展-1\">1.1 LangChain的诞生和发展</a></span></li><li><span><a href=\"#1.2-课程基本内容\" data-toc-modified-id=\"1.2-课程基本内容-2\">1.2 课程基本内容</a></span></li><li><span><a href=\"#1.3-致谢课程重要贡献者\" data-toc-modified-id=\"1.3-致谢课程重要贡献者-3\">1.3 致谢课程重要贡献者</a></span></li></ul></div>\n",
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"\n",
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"欢迎来到LangChain大模型应用开发短期课程👏🏻👏🏻\n",
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"\n",
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"本课程由哈里森·蔡斯 (Harrison Chase，LangChain作者）与Deeplearning.ai合作开发，旨在教大家使用这个神奇工具。\n",
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"\n",
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"\n",
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"\n",
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"## 1.1 LangChain的诞生和发展\n",
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"\n",
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"通过对LLM或大型语言模型给出提示(prompt)，现在可以比以往更快地开发AI应用程序，但是一个应用程序可能需要进行多轮提示以及解析输出。\n",
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"\n",
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"在此过程有很多胶水代码需要编写，基于此需求，哈里森·蔡斯 (Harrison Chase) 创建了LangChain，使开发过程变得更加丝滑。\n",
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"\n",
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"LangChain开源社区快速发展，贡献者已达数百人，正以惊人的速度更新代码和功能。\n",
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"\n",
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"\n",
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"## 1.2 课程基本内容\n",
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"\n",
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"LangChain是用于构建大模型应用程序的开源框架，有Python和JavaScript两个不同版本的包。LangChain基于模块化组合，有许多单独的组件，可以一起使用或单独使用。此外LangChain还拥有很多应用案例，帮助我们了解如何将这些模块化组件以链式方式组合，以形成更多端到端的应用程序 。\n",
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"\n",
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"在本课程中，我们将介绍LandChain的常见组件。具体而言我们会讨论一下几个方面\n",
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"- 模型(Models)\n",
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"- 提示(Prompts): 使模型执行操作的方式\n",
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"- 索引(Indexes): 获取数据的方式，可以与模型结合使用\n",
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"- 链式(Chains): 端到端功能实现\n",
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"- 代理(Agents): 使用模型作为推理引擎\n",
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"\n",
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" \n",
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"\n",
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"## 1.3 致谢课程重要贡献者\n",
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"\n",
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"最后特别感谢Ankush Gholar（LandChain的联合作者）、Geoff Ladwig,、Eddy Shyu 以及 Diala Ezzedine，他们也为本课程内容贡献颇多~ "
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