diff --git a/content/Prompt Engineering/8. 聊天机器人 Chatbot.ipynb b/content/Prompt Engineering/8. 聊天机器人 Chatbot.ipynb index f66b240..1a363b2 100644 --- a/content/Prompt Engineering/8. 聊天机器人 Chatbot.ipynb +++ b/content/Prompt Engineering/8. 聊天机器人 Chatbot.ipynb @@ -7,12 +7,36 @@ "id": "JMXGlIvAwn30" }, "source": [ - "# 对话聊天\n", + "# 第八章 聊天机器人" + ] + }, + { + "cell_type": "markdown", + "id": "4164d820", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f0bdc2c9", + "metadata": {}, + "source": [ + "## 一、引言\n", "\n", - "使用一个大型语言模型的一个令人兴奋的事情是，我们可以用它来构建一个定制的聊天机器人，只需要很少的工作量。在这一节中，我们将探索如何利用聊天格式（接口）与个性化或专门针对特定任务或行为的聊天机器人进行延伸对话。\n", - "\n", - "\n", - "## 启动" + "使用一个大型语言模型的一个令人兴奋的事情是，我们可以用它来构建一个定制的聊天机器人 (Chatbot) ，只需要很少的工作量。在这一节中，我们将探索如何利用聊天的方式，与个性化（或专门针对特定任务或行为的）聊天机器人进行扩展对话。" ] }, { @@ -31,18 +55,34 @@ "# 设置 API_KEY, 请替换成您自己的 API_KEY" ] }, + { + "cell_type": "markdown", + "id": "e6fae355", + "metadata": {}, + "source": [ + "像 ChatGPT 这样的聊天模型实际上是组装成以一系列消息作为输入，并返回一个模型生成的消息作为输出的。这种聊天格式原本的设计目标是简便多轮对话，但我们通过之前的学习可以知道，它对于不会涉及任何对话的**单轮任务**也同样有用。\n" + ] + }, + { + "cell_type": "markdown", + "id": "78344a7e", + "metadata": {}, + "source": [ + "## 二、身份与上下文构建" + ] + }, { "cell_type": "markdown", "id": "2c9b885b", "metadata": {}, "source": [ - "像 ChatGPT 这样的聊天模型实际上是组装成以一系列消息作为输入，并返回一个模型生成的消息作为输出的。虽然聊天格式的设计旨在使这种多轮对话变得容易，但我们通过之前的学习可以知道，它对于没有任何对话的单轮任务也同样有用。\n", + "接下来，我们将定义两个辅助函数。\n", "\n", - "接下来，我们将定义两个辅助函数。第一个是单轮的，我们将prompt放入看起来像是某种用户消息的东西中。另一个则传入一个消息列表。这些消息可以来自不同的角色，我们会描述一下这些角色。\n", + "第一个方法已经陪伴了您一整个教程，即 ```get_completion``` ，其适用于单轮对话。我们将 Prompt 放入某种类似**用户消息**的对话框中。另一个称为 ```get_completion_from_messages``` ，传入一个消息列表。这些消息可以来自大量不同的**角色** (roles) ，我们会描述一下这些角色。\n", "\n", - "第一条消息是一个系统消息，它提供了一个总体的指示，然后在这个消息之后，我们有用户和助手之间的交替。如果你曾经使用过 ChatGPT 网页界面，那么你的消息是用户消息，而 ChatGPT 的消息是助手消息。系统消息则有助于设置助手的行为和角色，并作为对话的高级指示。你可以想象它在助手的耳边低语，引导它的回应，而用户不会注意到系统消息。\n", + "第一条消息中，我们以系统身份发送系统消息 (system message) ，它提供了一个总体的指示。系统消息则有助于设置助手的行为和角色，并作为对话的高级指示。你可以想象它在助手的耳边低语，引导它的回应，而用户不会注意到系统消息。因此，作为用户，如果你曾经使用过 ChatGPT，您可能从来不知道 ChatGPT 的系统消息是什么，这是有意为之的。系统消息的好处是为开发者提供了一种方法，在不让请求本身成为对话的一部分的情况下，引导助手并指导其回应。\n", "\n", - "因此，作为用户，如果你曾经使用过 ChatGPT，你可能不知道 ChatGPT 的系统消息是什么，这是有意为之的。系统消息的好处是为开发者提供了一种方法，在不让请求本身成为对话的一部分的情况下，引导助手并指导其回应。" + "在 ChatGPT 网页界面中，您的消息称为用户消息，而 ChatGPT 的消息称为助手消息。但在构建聊天机器人时，在发送了系统消息之后，您的角色可以仅作为用户 (user) ；也可以在用户和助手 (assistant) 之间交替，从而提供对话上下文。" ] }, { @@ -78,9 +118,9 @@ "id": "46caaa5b", "metadata": {}, "source": [ - "现在让我们尝试在对话中使用这些消息。我们将使用上面的函数来获取从这些消息中得到的回答，同时，使用更高的 temperature（越高生成的越多样）。\n", + "现在让我们尝试在对话中使用这些消息。我们将使用上面的函数来获取从这些消息中得到的回答，同时，使用更高的温度 (temperature)（越高生成的越多样，更多内容见第七章）。\n", "\n", - "系统消息说，你是一个说话像莎士比亚的助手。这是我们向助手描述它应该如何表现的方式。然后，第一个用户消息是，给我讲个笑话。接下来的消息是，为什么鸡会过马路？然后最后一个用户消息是，我不知道。" + "系统消息说，你是一个说话像莎士比亚的助手。这是我们向助手描述**它应该如何表现的方式**。然后，第一个用户消息是*给我讲个笑话*。接下来以助手身份给出回复是，*为什么鸡会过马路？* 最后发送用户消息是*我不知道*。" ] }, { @@ -154,12 +194,61 @@ "print(response)" ] }, + { + "cell_type": "markdown", + "id": "3b3e5b83", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "7f51a7e0", + "metadata": {}, + "source": [ + "（注：上述例子中由于选定 temperature = 1，模型的回答会比较随机且迥异（不乏很有创意）。此处附上另一个回答：\n", + "\n", + "让我用一首莎士比亚式的诗歌来回答你的问题：\n", + "\n", + "当鸡之心欲往前，\n", + "马路之际是其选择。\n", + "驱车徐行而天晴，\n", + "鸣笛吹响伴交错。\n", + "\n", + "问之何去何从也？\n", + "因大道之上未有征，\n", + "而鸡乃跃步前进，\n", + "其决策毋需犹豫。\n", + "\n", + "鸡之智慧何可言，\n", + "道路孤独似乌漆。\n", + "然其勇气令人叹，\n", + "勇往直前没有退。\n", + "\n", + "故鸡过马路何解？\n", + "忍受车流喧嚣之困厄。\n", + "因其鸣鸣悍然一跃，\n", + "成就夸夸骄人壁画。\n", + "\n", + "所以笑话之妙处，\n", + "伴随鸡之勇气满溢。\n", + "笑谈人生不畏路，\n", + "有智有勇尽显妙。\n", + "\n", + "希望这个莎士比亚风格的回答给你带来一些欢乐！" + ] + }, + { + "cell_type": "markdown", + "id": "d70fd298", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "id": "5f76bedb", "metadata": {}, "source": [ - "让我们做另一个例子。助手的消息是，你是一个友好的聊天机器人，第一个用户消息是，嗨，我叫Isa。我们想要得到第一个用户消息。" + "让我们看另一个例子。助手的消息是*你是一个友好的聊天机器人*，第一个用户消息是*嗨，我叫Isa*。我们想要得到第一个用户消息。" ] }, { @@ -209,6 +298,12 @@ "print(response)" ] }, + { + "cell_type": "markdown", + "id": "63c2010b", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "id": "1e9f96ba", @@ -264,6 +359,12 @@ "print(response)" ] }, + { + "cell_type": "markdown", + "id": "da1e4df5", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "id": "05c65d16", @@ -271,7 +372,7 @@ "source": [ "如上所见，模型实际上并不知道我的名字。\n", "\n", - "因此，每次与语言模型的交互都是一个独立的交互，这意味着我们必须提供所有相关的消息，以便模型在当前对话中进行引用。如果想让模型引用或 “记住” 对话的早期部分，则必须在模型的输入中提供早期的交流。我们将其称为上下文。让我们试试。" + "因此，每次与语言模型的交互都互相独立，这意味着我们必须提供所有相关的消息，以便模型在当前对话中进行引用。如果想让模型引用或 “记住” 对话的早期部分，则必须在模型的输入中提供早期的交流。我们将其称为上下文 (context) 。尝试以下示例。" ] }, { @@ -301,6 +402,16 @@ "print(response)" ] }, + { + "cell_type": "markdown", + "id": "a7b40fb0", + "metadata": {}, + "source": [ + "附上另一次回答：\n", + "\n", + "*Your name is Isa! How could I forget?*" + ] + }, { "cell_type": "code", "execution_count": 16, @@ -343,11 +454,11 @@ "id": "bBg_MpXeYnTq" }, "source": [ - "# 订餐机器人\n", + "## 三、订餐机器人\n", "\n", "现在，我们构建一个 “订餐机器人”，我们需要它自动收集用户信息，接受比萨饼店的订单。\n", "\n", - "下面这个函数将收集我们的用户消息，以便我们可以避免手动输入，就像我们在刚刚上面做的那样。这个函数将从我们下面构建的用户界面中收集提示，然后将其附加到一个名为上下文的列表中，并在每次调用模型时使用该上下文。模型的响应也会被添加到上下文中，所以模型消息和用户消息都被添加到上下文中，因此上下文逐渐变长。这样，模型就有了需要的信息来确定下一步要做什么。" + "下面这个函数将收集我们的用户消息，以便我们可以避免像刚才一样手动输入。这个函数将从我们下面构建的用户界面中收集 Prompt ，然后将其附加到一个名为上下文( ```context``` )的列表中，并在每次调用模型时使用该上下文。模型的响应也会添加到上下文中，所以用户消息和模型消息都被添加到上下文中，上下文逐渐变长。这样，模型就有了需要的信息来确定下一步要做什么。" ] }, { @@ -378,7 +489,7 @@ "id": "8a3b003e", "metadata": {}, "source": [ - "现在，我们将设置并运行这个 UI 来显示订单机器人。初始的上下文包含了包含菜单的系统消息。请注意，上下文会随着时间的推移而不断增长。" + "现在，我们将设置并运行这个 UI 来显示订单机器人。初始的上下文包含了包含菜单的系统消息，在每次调用时都会使用。此后随着对话进行，上下文也会不断增长。" ] }, { @@ -393,43 +504,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "c9e746f5", "metadata": { "tags": [] }, - "outputs": [ - { - "data": 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PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", - "application/vnd.holoviews_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import panel as pn # GUI\n", "pn.extension()\n", @@ -476,19 +556,31 @@ " inp,\n", " pn.Row(button_conversation),\n", " pn.panel(interactive_conversation, loading_indicator=True, height=300),\n", - ")" + ")\n", + "\n", + "dashboard" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "65f0416e", - "metadata": { - "tags": [] - }, - "outputs": [], + "cell_type": "markdown", + "id": "42fff07d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "327b15b7", + "metadata": {}, "source": [ - "dashboard" + "运行结果可交互，请见下文中文版。" + ] + }, + { + "cell_type": "markdown", + "id": "668ea96d", + "metadata": {}, + "source": [ + "### 3.1 创建JSON摘要" ] }, { @@ -496,11 +588,11 @@ "id": "2a2c9822", "metadata": {}, "source": [ - "现在我们可以要求模型创建一个 JSON 摘要发送给订单系统。\n", + "此处我们另外要求模型创建一个 JSON 摘要，方便我们发送给订单系统。\n", "\n", - "所以我们现在追加另一个系统消息，它是另一条prompt，我们说创建一个刚刚订单的 JSON 摘要，列出每个项目的价格，字段应包括1）披萨，包括尺寸，2）配料列表，3）饮料列表，4）辅菜列表，包括尺寸，最后是总价格。这里也可以在这里使用用户消息，不一定是系统消息。\n", + "因此我们需要在上下文的基础上追加另一个系统消息，作为另一条指示 (instruction) 。我们说*创建一个刚刚订单的 JSON 摘要，列出每个项目的价格，字段应包括 1）披萨，包括尺寸，2）配料列表，3）饮料列表，4）辅菜列表，包括尺寸，最后是总价格*。此处也可以定义为用户消息，不一定是系统消息。\n", "\n", - "请注意，这里我们使用了一个较低的temperature，因为对于这些类型的任务，我们希望输出相对可预测。" + "请注意，这里我们使用了一个较低的温度，因为对于这些类型的任务，我们希望输出相对可预测。" ] }, { @@ -552,8 +644,6 @@ "{'role':'system', 'content':'create a json summary of the previous food order. Itemize the price for each item\\\n", " The fields should be 1) pizza, include size 2) list of toppings 3) list of drinks, include size 4) list of sides include size 5)total price '}, \n", ")\n", - " #The fields should be 1) pizza, price 2) list of toppings 3) list of drinks, include size include price 4) list of sides include size include price, 5)total price '}, \n", - "\n", "response = get_completion_from_messages(messages, temperature=0)\n", "print(response)" ] @@ -777,44 +867,39 @@ "print(response)" ] }, + { + "cell_type": "markdown", + "id": "018de2cd", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "id": "ef17c2b2", "metadata": {}, "source": [ - "现在，我们已经建立了自己的订餐聊天机器人。请随意自定义并修改系统消息，以更改聊天机器人的行为，并使其扮演不同的角色和拥有不同的知识。" + "现在，我们已经建立了自己的订餐聊天机器人。请随意自定义并修改系统消息，以更改聊天机器人的行为，并使其扮演不同的角色，拥有不同的知识。" ] }, { "cell_type": "markdown", - "id": "3153c581-1c72-497a-9293-8db3bcb804fc", + "id": "7f688397", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "d9f43e62", "metadata": {}, "source": [ - "## 尝试你的实验！\n", - "\n", - "你可以修改菜单或指令来创建自己的订单机器人！" + "附：下图展示了订餐机器人一次完整的对话流程：\n", + "![image.png](../../figures/Chatbot-pizza-cn.png)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b0df540", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2cc84122", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -828,7 +913,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.11.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/figures/Chatbot-pizza-cn.png b/figures/Chatbot-pizza-cn.png new file mode 100644 index 0000000..54807eb Binary files /dev/null and b/figures/Chatbot-pizza-cn.png differ