From ecc101930d602ac3b9f491ea68d4c1ae1a964927 Mon Sep 17 00:00:00 2001 From: joyenjoye Date: Tue, 11 Jul 2023 21:08:03 +0800 Subject: [PATCH] Update file name and update title --- .../1.简介 Introduction.md | 32 ++++++ .../1.简介.ipynb | 87 --------------- ...提示和解析器 Models, Prompts and Output Parsers.ipynb} | 105 ++++++++---------- .../{3.存储.ipynb => 3.存储 Memory.ipynb} | 64 ++++++----- .../{4.模型链.ipynb => 4.模型链 Chains.ipynb} | 92 +++++---------- ...nb => 5.基于文档的问答 Question and Answer.ipynb} | 21 ++-- .../{6.评估.ipynb => 6.评估 Evaluation.ipynb} | 16 +-- .../{7.代理.ipynb => 7.代理 Agent.ipynb} | 57 +--------- .../8.总结 Conclusion.md | 19 ++++ .../8.总结.ipynb | 64 ----------- 10 files changed, 177 insertions(+), 380 deletions(-) create mode 100644 content/LangChain for LLM Application Development/1.简介 Introduction.md delete mode 100644 content/LangChain for LLM Application Development/1.简介.ipynb rename content/LangChain for LLM Application Development/{2.模型、提示和解析器.ipynb => 2.模型、提示和解析器 Models, Prompts and Output Parsers.ipynb} (98%) rename content/LangChain for LLM Application Development/{3.存储.ipynb => 3.存储 Memory.ipynb} (96%) rename content/LangChain for LLM Application Development/{4.模型链.ipynb => 4.模型链 Chains.ipynb} (96%) rename content/LangChain for LLM Application Development/{5.基于文档的问答.ipynb => 5.基于文档的问答 Question and Answer.ipynb} (95%) rename content/LangChain for LLM Application Development/{6.评估.ipynb => 6.评估 Evaluation.ipynb} (99%) rename content/LangChain for LLM Application Development/{7.代理.ipynb => 7.代理 Agent.ipynb} (97%) create mode 100644 content/LangChain for LLM Application Development/8.总结 Conclusion.md delete mode 100644 content/LangChain for LLM Application Development/8.总结.ipynb diff --git a/content/LangChain for LLM Application Development/1.简介 Introduction.md b/content/LangChain for LLM Application Development/1.简介 Introduction.md new file mode 100644 index 0000000..fe143a4 --- /dev/null +++ b/content/LangChain for LLM Application Development/1.简介 Introduction.md @@ -0,0 +1,32 @@ +# 第一章 简介 + +欢迎来到LangChain大模型应用开发短期课程👏🏻👏🏻 + +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。 + + +## 一、LangChain的诞生和发展 + +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。 + +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。 + +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。 + + +## 二、课程基本内容 + +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 。 + +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 +- 模型(Models) +- 提示(Prompts): 使模型执行操作的方式 +- 索引(Indexes): 获取数据的方式，可以与模型结合使用 +- 链式(Chains): 端到端功能实现 +- 代理(Agents): 使用模型作为推理引擎 + + + +## 三、致谢课程重要贡献者 + +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~ \ No newline at end of file diff --git a/content/LangChain for LLM Application Development/1.简介.ipynb b/content/LangChain for LLM Application Development/1.简介.ipynb deleted file mode 100644 index 62cee6b..0000000 --- a/content/LangChain for LLM Application Development/1.简介.ipynb +++ /dev/null @@ -1,87 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cfab521b-77fa-41be-a964-1f50f2ef4689", - "metadata": {}, - "source": [ - "# 1. 简介\n", - "
\n", - "\n", - "欢迎来到LangChain大模型应用开发短期课程👏🏻👏🏻\n", - "\n", - "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", - "\n", - "\n", - "\n", - "## 1.1 LangChain的诞生和发展\n", - "\n", - "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", - "\n", - "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", - "\n", - "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", - "\n", - "\n", - "## 1.2 课程基本内容\n", - "\n", - "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", - "\n", - "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", - "- 模型(Models)\n", - "- 提示(Prompts): 使模型执行操作的方式\n", - "- 索引(Indexes): 获取数据的方式，可以与模型结合使用\n", - "- 链式(Chains): 端到端功能实现\n", - "- 代理(Agents): 使用模型作为推理引擎\n", - "\n", - " \n", - "\n", - "## 1.3 致谢课程重要贡献者\n", - "\n", - "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~ " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3618ca8", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": false, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/content/LangChain for LLM Application Development/2.模型、提示和解析器.ipynb b/content/LangChain for LLM Application Development/2.模型、提示和解析器 Models, Prompts and Output Parsers.ipynb similarity index 98% rename from content/LangChain for LLM Application Development/2.模型、提示和解析器.ipynb rename to content/LangChain for LLM Application Development/2.模型、提示和解析器 Models, Prompts and Output Parsers.ipynb index ee92c3e..eb47fd2 100644 --- a/content/LangChain for LLM Application Development/2.模型、提示和解析器.ipynb +++ b/content/LangChain for LLM Application Development/2.模型、提示和解析器 Models, Prompts and Output Parsers.ipynb @@ -4,16 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 模型，提示和输出解释器\n", - "\n", - "\n", - "**目录**\n", - "* 获取你的OpenAI API Key\n", - "* 直接调用OpenAI的API\n", - "* 通过LangChain进行的API调用：\n", - " * 提示（Prompts）\n", - " * [模型（Models)](#model)\n", - " * 输出解析器（Output parsers）\n", + "# 第二章 模型，提示和输出解释器\n", " " ] }, @@ -23,9 +14,9 @@ "tags": [] }, "source": [ - "## 获取你的OpenAI API Key\n", + "## 一、设置OpenAI API Key\n", "\n", - "登陆[OpenAI账户获取你的API Key](https://platform.openai.com/account/api-keys) " + "登陆[OpenAI账户](https://platform.openai.com/account/api-keys) 获取你的API Key" ] }, { @@ -72,10 +63,11 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ - "## Chat API：OpenAI\n", + "## 二、Chat API：OpenAI\n", "\n", "我们先从直接调用OpenAI的API开始。\n", "\n", @@ -113,7 +105,7 @@ "tags": [] }, "source": [ - "### 一个简单的例子\n", + "### 2.1 一个简单的例子\n", "\n", "我们来一个简单的例子 - 分别用中英文问问模型\n", "\n", @@ -166,9 +158,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### 复杂一点例子\n", + "### 2.2 复杂一点例子\n", "\n", "上面的简单例子，模型`gpt-3.5-turbo`对我们的关于1+1是什么的提问给出了回答。\n", "\n", @@ -228,6 +222,22 @@ "下一步需要做的是将`customer_email`和`style`结合起来构造我们的提示:`prompt`" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 非正式用语\n", + "customer_email = \"\"\" \n", + "阿，我很生气，\\\n", + "因为我的搅拌机盖掉了，\\\n", + "把奶昔溅到了厨房的墙上！\\\n", + "更糟糕的是，保修不包括打扫厨房的费用。\\\n", + "我现在需要你的帮助，伙计！\n", + "\"\"\"" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -313,9 +323,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### 中文" + "### 2.3 中文版本提示" ] }, { @@ -325,22 +337,6 @@ "假设我们是电商公司员工，我们的顾客是一名海盗A，他在我们的网站上买了一个榨汁机用来做奶昔，在制作奶昔的过程中，奶昔的盖子飞了出去，弄得厨房墙上到处都是。于是海盗A给我们的客服中心写来以下邮件：`customer_email`" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# 非正式用语\n", - "customer_email = \"\"\" \n", - "阿，我很生气，\\\n", - "因为我的搅拌机盖掉了，\\\n", - "把奶昔溅到了厨房的墙上！\\\n", - "更糟糕的是，保修不包括打扫厨房的费用。\\\n", - "我现在需要你的帮助，伙计！\n", - "\"\"\"" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -442,7 +438,7 @@ "tags": [] }, "source": [ - "## Chat API：LangChain\n", + "## 三、Chat API：LangChain\n", "\n", "在前面一部分，我们通过封装函数`get_completion`直接调用了OpenAI完成了对方言邮件进行了的翻译，得到用平和尊重的语气、正式的普通话表达的邮件。\n", "\n", @@ -451,19 +447,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "^C\n" - ] - } - ], + "outputs": [], "source": [ "# 如果你需要查看安装过程日志，可删除 -q \n", "# --upgrade 让我们可以安装到最新版本的 langchain\n", @@ -476,7 +464,7 @@ "tags": [] }, "source": [ - "### 模型\n", + "### 3.1 模型\n", "\n", "从`langchain.chat_models`导入`OpenAI`的对话模型`ChatOpenAI`。 除去OpenAI以外，`langchain.chat_models`还集成了其他对话模型，更多细节可以查看[Langchain官方文档](https://python.langchain.com/en/latest/modules/models/chat/integrations.html)。" ] @@ -528,7 +516,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 提示模板\n", + "### 3.2 提示模板\n", "\n", "在前面的例子中，我们通过[f字符串](https://docs.python.org/zh-cn/3/tutorial/inputoutput.html#tut-f-strings)把Python表达式的值`style`和`customer_email`添加到`prompt`字符串内。\n", "\n", @@ -546,7 +534,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 📚 使用LangChain提示模版\n", + "#### 3.2.1 使用LangChain提示模版\n", "##### 1️⃣ 构造提示模版字符串\n", "我们构造一个提示模版字符串：`template_string`" ] @@ -933,7 +921,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -1059,14 +1047,17 @@ "tags": [] }, "source": [ - "#### ❓为什么需要提示模版\n", + "#### 3.2.2 为什么需要提示模版\n", "\n", "\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "\n", "在应用于比较复杂的场景时，提示可能会非常长并且包含涉及许多细节。**使用提示模版，可以让我们更为方便地重复使用设计好的提示**。\n", @@ -1118,8 +1109,6 @@ " \"\"\"\n", "```\n", "\n", - "# 中文\n", - "\n", "```python\n", " prompt = \"\"\" 你的任务是判断学生的解决方案是正确的还是不正确的\n", "\n", @@ -1226,14 +1215,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 输出解析器" + "### 3.3 输出解析器" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 📚 如果没有输出解析器\n", + "#### 3.3.1 如果没有输出解析器\n", "\n", "对于给定的评价`customer_review`, 我们希望提取信息，并按以下格式输出：\n", "\n", @@ -1514,7 +1503,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 📚 LangChain输出解析器" + "#### 3.3.2 LangChain输出解析器" ] }, { @@ -1947,14 +1936,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 补充材料" + "## 四、补充材料" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 链式思考推理(ReAct) \n", + "### 4.1 链式思考推理(ReAct) \n", "参考资料：[ReAct (Reason+Act) prompting in OpenAI GPT and LangChain](https://tsmatz.wordpress.com/2023/03/07/react-with-openai-gpt-and-langchain/)" ] }, diff --git a/content/LangChain for LLM Application Development/3.存储.ipynb b/content/LangChain for LLM Application Development/3.存储 Memory.ipynb similarity index 96% rename from content/LangChain for LLM Application Development/3.存储.ipynb rename to content/LangChain for LLM Application Development/3.存储 Memory.ipynb index 305bc33..8ae5468 100644 --- a/content/LangChain for LLM Application Development/3.存储.ipynb +++ b/content/LangChain for LLM Application Development/3.存储 Memory.ipynb @@ -5,9 +5,7 @@ "id": "a786c77c", "metadata": {}, "source": [ - "# 3. 储存\n", - "\n", - "
\n", + "# 第三章 储存\n", "\n", "当你与那些语言模型进行交互的时候，他们不会记得你之前和他进行的交流内容，这在我们构建一些应用程序（如聊天机器人）的时候，是一个很大的问题---显得不够智能！\n", "\n", @@ -48,22 +46,12 @@ "为了延长LLM短期记忆的保留时间，则需要借助一些外部存储方式来进行记忆，以便在用户与LLM对话中，LLM能够尽可能的知道用户与它所进行的历史对话信息。 " ] }, - { - "cell_type": "markdown", - "id": "1297dcd5", - "metadata": {}, - "source": [ - "## 3.1 对话缓存储存 \n", - " \n", - "这种记忆允许存储消息，然后从变量中提取消息。" - ] - }, { "cell_type": "markdown", "id": "0768ca9b", "metadata": {}, "source": [ - "### 1.首先，让我们导入相关的包和 OpenAI API 秘钥" + "## 一、设置OpenAI API Key" ] }, { @@ -71,7 +59,7 @@ "id": "d76f6ba7", "metadata": {}, "source": [ - "### dotenv模块使用解析： \n", + "dotenv模块使用解析： \n", "- 安装方式：pip install python-dotenv\n", "- load_dotenv()函数用于加载环境变量，\n", "- find_dotenv()函数用于寻找并定位.env文件的路径 \n", @@ -97,6 +85,16 @@ "_ = load_dotenv(find_dotenv()) " ] }, + { + "cell_type": "markdown", + "id": "1297dcd5", + "metadata": {}, + "source": [ + "## 二、对话缓存储存 \n", + " \n", + "这种记忆允许存储消息，然后从变量中提取消息。" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -135,7 +133,7 @@ "id": "dea83837", "metadata": {}, "source": [ - "### 2.开始对话，第一轮" + "### 2.1 开始对话，第一轮" ] }, { @@ -232,7 +230,7 @@ "id": "e71564ad", "metadata": {}, "source": [ - "### 3.第二轮对话" + "### 2.2 第二轮对话" ] }, { @@ -343,7 +341,7 @@ "id": "33cb734b", "metadata": {}, "source": [ - "### 4.第三轮对话" + "### 2.3 第三轮对话" ] }, { @@ -458,7 +456,7 @@ "id": "5a96a8d9", "metadata": {}, "source": [ - "### 5.memory.buffer存储了当前为止所有的对话信息" + "### 2.4 .memory.buffer存储了当前为止所有的对话信息" ] }, { @@ -527,7 +525,7 @@ "id": "6bd222c3", "metadata": {}, "source": [ - "### 6.也可以通过memory.load_memory_variables({})打印历史消息" + "### 2.5 也可以通过memory.load_memory_variables({})打印历史消息" ] }, { @@ -588,7 +586,7 @@ "id": "07d2e892", "metadata": {}, "source": [ - "### 7.添加指定的输入输出内容到记忆缓存区" + "### 2.6 添加指定的输入输出内容到记忆缓存区" ] }, { @@ -768,7 +766,7 @@ "id": "cf98e9ff", "metadata": {}, "source": [ - "## 3.2 对话缓存窗口储存\n", + "## 三、对话缓存窗口储存\n", " \n", "随着对话变得越来越长，所需的内存量也变得非常长。将大量的tokens发送到LLM的成本，也会变得更加昂贵,这也就是为什么API的调用费用，通常是基于它需要处理的tokens数量而收费的。\n", " \n", @@ -793,7 +791,7 @@ "id": "641477a4", "metadata": {}, "source": [ - "### 1.向memory添加两轮对话，并查看记忆变量当前的记录" + "### 3.1 向memory添加两轮对话，并查看记忆变量当前的记录" ] }, { @@ -851,7 +849,7 @@ "id": "9b401f0b", "metadata": {}, "source": [ - "### 2.在看一个例子，发现和上面的结果一样，只保留了一轮对话记忆" + "### 3.2 在看一个例子，发现和上面的结果一样，只保留了一轮对话记忆" ] }, { @@ -886,7 +884,7 @@ "id": "63bda148", "metadata": {}, "source": [ - "### 3.将对话缓存窗口记忆应用到对话链中" + "### 3.3 将对话缓存窗口记忆应用到对话链中" ] }, { @@ -1022,7 +1020,7 @@ "id": "d2931b92", "metadata": {}, "source": [ - "## 3.3 对话token缓存储存" + "## 四、对话token缓存储存" ] }, { @@ -1051,7 +1049,7 @@ "id": "2187cfe6", "metadata": {}, "source": [ - "### 1.导入相关包和API密钥" + "### 4.1 导入相关包和API密钥" ] }, { @@ -1075,7 +1073,7 @@ "id": "f3a84112", "metadata": {}, "source": [ - "### 2.限制token数量，进行测试" + "### 4.2 限制token数量，进行测试" ] }, { @@ -1132,7 +1130,7 @@ "id": "f7f6be43", "metadata": {}, "source": [ - "### 3.在看一个中文例子" + "### 4.3 中文例子" ] }, { @@ -1181,7 +1179,7 @@ "id": "5ff55d5d", "metadata": {}, "source": [ - "## 3.4 对话摘要缓存储存" + "## 四、对话摘要缓存储存" ] }, { @@ -1222,7 +1220,7 @@ "id": "6572ef39", "metadata": {}, "source": [ - "### 创建一个长字符串，其中包含某人的日程安排" + "### 5.1 创建一个长字符串，其中包含某人的日程安排" ] }, { @@ -1279,7 +1277,7 @@ "id": "7ccb97b6", "metadata": {}, "source": [ - "### 基于上面的memory，新建一个对话链" + "### 5.2 基于上面的memory，新建一个对话链" ] }, { @@ -1371,7 +1369,7 @@ "height": 31 }, "source": [ - "### 中文" + "### 5.3 中文例子" ] }, { diff --git a/content/LangChain for LLM Application Development/4.模型链.ipynb b/content/LangChain for LLM Application Development/4.模型链 Chains.ipynb similarity index 96% rename from content/LangChain for LLM Application Development/4.模型链.ipynb rename to content/LangChain for LLM Application Development/4.模型链 Chains.ipynb index 7477286..4418366 100644 --- a/content/LangChain for LLM Application Development/4.模型链.ipynb +++ b/content/LangChain for LLM Application Development/4.模型链 Chains.ipynb @@ -1,32 +1,31 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "id": "52824b89-532a-4e54-87e9-1410813cd39e", "metadata": {}, "source": [ - "# Chains in LangChain（LangChain中的链）\n", - "\n", - "## Outline\n", - "\n", - "* LLMChain（大语言模型链）\n", - "* Sequential Chains（顺序链）\n", - " * SimpleSequentialChain\n", - " * SequentialChain\n", - "* Router Chain（路由链）" + "# 第四章 模型链" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "54810ef7", "metadata": {}, "source": [ - "### 为什么我们需要Chains ？\n", "链允许我们将多个组件组合在一起，以创建一个单一的、连贯的应用程序。链（Chains）通常将一个LLM（大语言模型）与提示结合在一起，使用这个构建块，您还可以将一堆这些构建块组合在一起，对您的文本或其他数据进行一系列操作。例如，我们可以创建一个链，该链接受用户输入，使用提示模板对其进行格式化，然后将格式化的响应传递给LLM。我们可以通过将多个链组合在一起，或者通过将链与其他组件组合在一起来构建更复杂的链。" ] }, + { + "cell_type": "markdown", + "id": "67fbb012-bee5-49a8-9b62-9bad7ee87e94", + "metadata": {}, + "source": [ + "## 一、设置OpenAI API Key\n", + "\n", + "登陆[OpenAI账户获取你的API Key](https://platform.openai.com/account/api-keys) " + ] + }, { "cell_type": "code", "execution_count": 44, @@ -68,7 +67,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "663fc885", "metadata": {}, @@ -167,16 +165,14 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "b940ce7c", "metadata": {}, "source": [ - "## 1. LLMChain" + "## 二、LLMChain" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "e000bd16", "metadata": {}, @@ -197,7 +193,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "94a32c6f", "metadata": {}, @@ -216,7 +211,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "81887434", "metadata": {}, @@ -238,7 +232,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "5c22cb13", "metadata": {}, @@ -257,7 +250,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "8d7d5ff6", "metadata": {}, @@ -288,7 +280,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "1e1ede1c", "metadata": {}, @@ -324,21 +315,19 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "49158430", "metadata": {}, "source": [ - "## 2. Sequential Chains" + "## 三、Sequential Chains" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "69b03469", "metadata": {}, "source": [ - "### 2.1 SimpleSequentialChain\n", + "### 3.1 SimpleSequentialChain\n", "\n", "顺序链（Sequential Chains）是按预定义顺序执行其链接的链。具体来说，我们将使用简单顺序链（SimpleSequentialChain），这是顺序链的最简单类型，其中每个步骤都有一个输入/输出，一个步骤的输出是下一个步骤的输入" ] @@ -364,7 +353,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "0e732589", "metadata": {}, @@ -391,7 +379,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "dcfca7bd", "metadata": {}, @@ -417,7 +404,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "3a1991f4", "metadata": {}, @@ -438,7 +424,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "5122f26a", "metadata": {}, @@ -534,16 +519,14 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "7b5ce18c", "metadata": {}, "source": [ - "### 2.2 SequentialChain" + "### 3.2 SequentialChain" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "1e69f4c0", "metadata": {}, @@ -567,7 +550,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "3d4be4e8", "metadata": {}, @@ -586,7 +568,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "9811445c", "metadata": {}, @@ -693,7 +674,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "0509de01", "metadata": {}, @@ -869,16 +849,14 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "3041ea4c", "metadata": {}, "source": [ - "## 3. Router Chain（路由链）" + "## 四、 Router Chain（路由链）" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "f0c32f97", "metadata": {}, @@ -896,12 +874,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "cb1b4708", "metadata": {}, "source": [ - "### 定义提示模板" + "### 4.1 定义提示模板" ] }, { @@ -1017,7 +994,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "6922b35e", "metadata": {}, @@ -1028,6 +1004,7 @@ { "cell_type": "code", "execution_count": 27, + "id": "141a3d32", "metadata": {}, "outputs": [], "source": [ @@ -1088,12 +1065,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "80eb1de8", "metadata": {}, "source": [ - "### 导入相关的包" + "### 4.2 导入相关的包" ] }, { @@ -1109,12 +1085,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "50c16f01", "metadata": {}, "source": [ - "### 定义语言模型" + "### 4.3 定义语言模型" ] }, { @@ -1128,12 +1103,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "8795cd42", "metadata": {}, "source": [ - "### LLMRouterChain（此链使用 LLM 来确定如何路由事物）\n", + "### 4.4 LLMRouterChain（此链使用 LLM 来确定如何路由事物）\n", "\n", "在这里，我们需要一个**多提示链**。这是一种特定类型的链，用于在多个不同的提示模板之间进行路由。\n", "但是，这只是你可以路由的一种类型。你也可以在任何类型的链之间进行路由。\n", @@ -1143,12 +1117,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "46633b43", "metadata": {}, "source": [ - "#### 创建目标链 \n", + "#### 4.4.1 创建目标链 \n", "目标链是由路由链调用的链，每个目标链都是一个语言模型链" ] }, @@ -1192,12 +1165,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "eba115de", "metadata": {}, "source": [ - "#### 创建默认目标链\n", + "#### 4.4.2 创建默认目标链\n", "除了目标链之外，我们还需要一个默认目标链。这是一个当路由器无法决定使用哪个子链时调用的链。在上面的示例中，当输入问题与物理、数学、历史或计算机科学无关时，可能会调用它。" ] }, @@ -1213,17 +1185,15 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "948700c4", "metadata": {}, "source": [ - "#### 创建LLM用于在不同链之间进行路由的模板\n", + "#### 4.4.3 创建LLM用于在不同链之间进行路由的模板\n", "这包括要完成的任务的说明以及输出应该采用的特定格式。" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "24f30c2c", "metadata": {}, @@ -1348,12 +1318,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "de5c46d0", "metadata": {}, "source": [ - "#### 构建路由链\n", + "#### 4.4.4 构建路由链\n", "首先，我们通过格式化上面定义的目标创建完整的路由器模板。这个模板可以适用许多不同类型的目标。\n", "因此，在这里，您可以添加一个不同的学科，如英语或拉丁语，而不仅仅是物理、数学、历史和计算机科学。\n", "\n", @@ -1382,12 +1351,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "7e92355c", "metadata": {}, "source": [ - "#### 最后，将所有内容整合在一起，创建整体链路" + "#### 4.4.5 创建整体链路" ] }, { @@ -1406,16 +1374,14 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "086503f7", "metadata": {}, "source": [ - "#### 进行提问" + "#### 4.4.6 进行提问" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "969cd878", "metadata": {}, @@ -1522,7 +1488,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "289c5ca9", "metadata": {}, @@ -1611,7 +1576,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "4186a2b9", "metadata": {}, @@ -1704,7 +1668,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/content/LangChain for LLM Application Development/5.基于文档的问答.ipynb b/content/LangChain for LLM Application Development/5.基于文档的问答 Question and Answer.ipynb similarity index 95% rename from content/LangChain for LLM Application Development/5.基于文档的问答.ipynb rename to content/LangChain for LLM Application Development/5.基于文档的问答 Question and Answer.ipynb index 811e8df..2ddeced 100644 --- a/content/LangChain for LLM Application Development/5.基于文档的问答.ipynb +++ b/content/LangChain for LLM Application Development/5.基于文档的问答 Question and Answer.ipynb @@ -5,8 +5,7 @@ "id": "f200ba9a", "metadata": {}, "source": [ - "# 5 基于文档的问答 \n", - "
" + "# 第五章 基于文档的问答" ] }, { @@ -94,7 +93,7 @@ "height": 30 }, "source": [ - "## 5.1 导入embedding模型和向量存储组件\n", + "## 一、导入embedding模型和向量存储组件\n", "使用Dock Array内存搜索向量存储，作为一个内存向量存储，不需要连接外部数据库" ] }, @@ -289,7 +288,7 @@ "id": "2963fc63", "metadata": {}, "source": [ - "### 5.1.2 创建向量存储\n", + "### 1.1 创建向量存储\n", "将导入一个索引，即向量存储索引创建器" ] }, @@ -396,7 +395,7 @@ "id": "dd34e50e", "metadata": {}, "source": [ - "### 5.1.3 使用语言模型与文档结合使用\n", + "### 1.2 使用语言模型与文档结合使用\n", "想要使用语言模型并将其与我们的许多文档结合使用，但是语言模型一次只能检查几千个单词，如果我们有非常大的文档，如何让语言模型回答关于其中所有内容的问题呢？通过embedding和向量存储实现\n", "* embedding \n", "文本片段创建数值表示文本语义，相似内容的文本片段将具有相似的向量，这使我们可以在向量空间中比较文本片段\n", @@ -607,7 +606,7 @@ "id": "fe41b36f", "metadata": {}, "source": [ - "## 5.2 如何回答我们文档的相关问题\n", + "## 二、 如何回答我们文档的相关问题\n", "首先，我们需要从这个向量存储中创建一个检索器，检索器是一个通用接口，可以由任何接受查询并返回文档的方法支持。接下来，因为我们想要进行文本生成并返回自然语言响应\n" ] }, @@ -798,7 +797,7 @@ "id": "44f1fa38", "metadata": {}, "source": [ - "### 5.2.1 不同类型的chain链\n", + "### 1.3 不同类型的chain链\n", "想在许多不同类型的块上执行相同类型的问答，该怎么办？之前的实验中只返回了4个文档，如果有多个文档，那么我们可以使用几种不同的方法\n", "* Map Reduce \n", "将所有块与问题一起传递给语言模型，获取回复，使用另一个语言模型调用将所有单独的回复总结成最终答案，它可以在任意数量的文档上运行。可以并行处理单个问题，同时也需要更多的调用。它将所有文档视为独立的\n", @@ -809,6 +808,14 @@ "* Stuff \n", "将所有内容组合成一个文档" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41c9d68a-251a-41f1-a571-f6a13b3d7b40", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/content/LangChain for LLM Application Development/6.评估.ipynb b/content/LangChain for LLM Application Development/6.评估 Evaluation.ipynb similarity index 99% rename from content/LangChain for LLM Application Development/6.评估.ipynb rename to content/LangChain for LLM Application Development/6.评估 Evaluation.ipynb index 4601453..8f93fce 100644 --- a/content/LangChain for LLM Application Development/6.评估.ipynb +++ b/content/LangChain for LLM Application Development/6.评估 Evaluation.ipynb @@ -5,7 +5,7 @@ "id": "52824b89-532a-4e54-87e9-1410813cd39e", "metadata": {}, "source": [ - "# 6.评估\n", + "# 第六章 评估\n", "\n", "当使用llm构建复杂应用程序时，评估应用程序的表现是一个重要但有时棘手的步骤，它是否满足某些准确性标准？\n", "通常更有用的是从许多不同的数据点中获得更全面的模型表现情况\n", @@ -1087,7 +1087,6 @@ "source": [] }, { - "attachments": {}, "cell_type": "markdown", "id": "0b6680d2", "metadata": {}, @@ -1367,7 +1366,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "952fc528", "metadata": {}, @@ -1419,7 +1417,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "b5b994b0", "metadata": {}, @@ -1428,7 +1425,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "0214fe01", "metadata": {}, @@ -1456,7 +1452,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "27f3bc68", "metadata": {}, @@ -1475,7 +1470,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "d2117da8", "metadata": {}, @@ -1484,7 +1478,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "9e76a2ae", "metadata": {}, @@ -1539,7 +1532,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "13e72db4", "metadata": {}, @@ -1600,6 +1592,7 @@ { "cell_type": "code", "execution_count": 39, + "id": "82ec0488", "metadata": {}, "outputs": [], "source": [ @@ -1743,7 +1736,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "0f076f4c", "metadata": {}, @@ -1794,7 +1786,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "9737c0cc", "metadata": {}, @@ -1934,7 +1925,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "9f3ebc93", "metadata": {}, @@ -1955,7 +1945,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "dd57860a", "metadata": {}, @@ -2340,7 +2329,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "ece7c7b4", "metadata": {}, diff --git a/content/LangChain for LLM Application Development/7.代理.ipynb b/content/LangChain for LLM Application Development/7.代理 Agent.ipynb similarity index 97% rename from content/LangChain for LLM Application Development/7.代理.ipynb rename to content/LangChain for LLM Application Development/7.代理 Agent.ipynb index d746699..fb955b1 100644 --- a/content/LangChain for LLM Application Development/7.代理.ipynb +++ b/content/LangChain for LLM Application Development/7.代理 Agent.ipynb @@ -1,28 +1,11 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "id": "2caa79ba-45e3-437c-9cb6-e433f443f0bf", "metadata": {}, "source": [ - "# 7. 代理\n", - "\n", - "
\n", - " \n", - "
\n", - "\n", + "# 第七章 代理\n", "\n", "大语言模型学习并记住许多的网络公开信息，大语言模型最常见的应用场景是，将它当作知识库，让它对给定的问题做出回答。\n", "\n", @@ -48,7 +31,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "631c764b-68fa-483a-80a5-9a322cd1117c", "metadata": {}, @@ -83,7 +65,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "d5a3655c-4d5e-4a86-8bf4-a11bd1525059", "metadata": {}, @@ -92,7 +73,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "e484d285-2110-4a06-a360-55313d6d9ffc", "metadata": {}, @@ -116,7 +96,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "d7f75023-1825-4d74-bc8e-2c362f551fd1", "metadata": { @@ -142,7 +121,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "e5b4fcc8-8817-4a94-b154-3f328480e441", "metadata": {}, @@ -171,7 +149,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "b980137a-a1d2-4c19-80c8-ec380f9c1efe", "metadata": { @@ -193,13 +170,7 @@ "text": [ "\n", "\n", - "\u001b[1m> Entering new chain...\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\u001b[1m> Entering new chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3mQuestion: 计算300的25%\n", "Thought: 我可以使用计算器来计算这个百分比\n", "Action:\n", @@ -233,7 +204,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "05bb0811-d71e-4016-868b-efbb651d8e59", "metadata": {}, @@ -258,7 +228,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "bdd13d06-0f0e-4918-ac28-339f524f8c76", "metadata": {}, @@ -568,7 +537,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "39e2f6fb-f229-4235-ad58-98a5f505800c", "metadata": {}, @@ -595,7 +563,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "5901cab6-a7c9-4590-b35d-d41c29e39a39", "metadata": {}, @@ -604,7 +571,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "12b8d837-524c-46b8-9189-504808cf1f93", "metadata": {}, @@ -630,7 +596,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "d32ed40c-cbcd-4efd-b044-57e611f5fab5", "metadata": { @@ -672,13 +637,7 @@ "text": [ "\n", "\n", - "\u001b[1m> Entering new chain...\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\u001b[1m> Entering new chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m我需要使用拼音库来将客户名字转换为拼音。我可以使用Python的pinyin库来实现这个功能。\n", "Action: Python_REPL\n", "Action Input: import pinyin\u001b[0m\n", @@ -732,7 +691,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "16f530a5-8c50-4648-951f-4e65d15ca93f", "metadata": { @@ -744,7 +702,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "951048dc-0414-410d-a0e9-6ef32bbdda89", "metadata": { @@ -977,7 +934,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "6b2ba50d-53c4-4eab-9467-98f562a072cc", "metadata": { @@ -999,7 +955,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "fb92f6e4-21ab-494d-9c22-d0678050fd37", "metadata": {}, @@ -1020,7 +975,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "8990b3c6-fe05-45b6-9567-06baec267a99", "metadata": {}, @@ -1060,7 +1014,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "440cb348-0ec6-4999-a6cd-81cb48b8c546", "metadata": {}, @@ -1085,7 +1038,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "f1a10657-23f2-4f78-a247-73f272119eb4", "metadata": {}, @@ -1143,7 +1095,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "923a199f-08e8-4e70-afb5-70cd3e123acf", "metadata": {}, @@ -1187,7 +1138,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.12" }, "toc": { "base_numbering": 1, diff --git a/content/LangChain for LLM Application Development/8.总结 Conclusion.md b/content/LangChain for LLM Application Development/8.总结 Conclusion.md new file mode 100644 index 0000000..7b52340 --- /dev/null +++ b/content/LangChain for LLM Application Development/8.总结 Conclusion.md @@ -0,0 +1,19 @@ +# 第八章 总结 + + +本次简短课程涵盖了一系列LangChain的应用实践，包括处理顾客评论和基于文档回答问题，以及通过LLM判断何时求助外部工具 (如网站) 来回答复杂问题。 + +**👍🏻 LangChain如此强大** + +构建这类应用曾经需要耗费数周时间，而现在只需要非常少的代码，就可以通过LangChain高效构建所需的应用程序。LangChain已成为开发大模型应用的有力范式，希望大家拥抱这个强大工具，积极探索更多更广泛的应用场景。 + +**🌈 不同组合, 更多可能性** + +LangChain还可以协助我们做什么呢：基于CSV文件回答问题、查询sql数据库、与api交互，有很多例子通过Chain以及不同的提示（Prompts）和输出解析器（output parsers）组合得以实现。 + +**💪🏻 出发 去探索新世界吧** + +因此非常感谢社区中做出贡献的每一个人，无论是协助文档的改进，还是让其他人更容易上手，还是构建新的Chain打开一个全新的世界。 + +如果你还没有这样做，快去打开电脑，运行 pip install LangChain，然后去使用LangChain、搭建惊艳的应用吧~ + diff --git a/content/LangChain for LLM Application Development/8.总结.ipynb b/content/LangChain for LLM Application Development/8.总结.ipynb deleted file mode 100644 index 1a3e885..0000000 --- a/content/LangChain for LLM Application Development/8.总结.ipynb +++ /dev/null @@ -1,64 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "87f7cfaa", - "metadata": {}, - "source": [ - "# 8. 总结\n", - "\n", - "\n", - "本次简短课程涵盖了一系列LangChain的应用实践，包括处理顾客评论和基于文档回答问题，以及通过LLM判断何时求助外部工具 (如网站) 来回答复杂问题。\n", - "\n", - "**👍🏻 LangChain如此强大**\n", - "\n", - "构建这类应用曾经需要耗费数周时间，而现在只需要非常少的代码，就可以通过LangChain高效构建所需的应用程序。LangChain已成为开发大模型应用的有力范式，希望大家拥抱这个强大工具，积极探索更多更广泛的应用场景。\n", - "\n", - "**🌈 不同组合, 更多可能性**\n", - "\n", - "LangChain还可以协助我们做什么呢：基于CSV文件回答问题、查询sql数据库、与api交互，有很多例子通过Chain以及不同的提示（Prompts）和输出解析器（output parsers）组合得以实现。\n", - "\n", - "**💪🏻 出发 去探索新世界吧**\n", - "\n", - "因此非常感谢社区中做出贡献的每一个人，无论是协助文档的改进，还是让其他人更容易上手，还是构建新的Chain打开一个全新的世界。\n", - "\n", - "如果你还没有这样做，快去打开电脑，运行 pip install LangChain，然后去使用LangChain、搭建惊艳的应用吧~\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": false, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}