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nowadays0421
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "63651c26",
"metadata": {},
"source": [
"第三章 评估输入——分类"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "87d9de1d",
"metadata": {},
"source": [
"## Setup\n",
"加载 API_KEY 并封装一个调用 API 的函数"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "55ee24ab",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import openai\n",
"from dotenv import load_dotenv, find_dotenv\n",
"_ = load_dotenv(find_dotenv()) # read local .env file\n",
"os.environ['OPENAI_API_KEY']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0318b89e",
"metadata": {},
"outputs": [],
"source": [
"def get_completion_from_messages(messages, \n",
" model=\"gpt-3.5-turbo\", \n",
" temperature=0, \n",
" max_tokens=500):\n",
" response = openai.ChatCompletion.create(\n",
" model=model,\n",
" messages=messages,\n",
" temperature=temperature, \n",
" max_tokens=max_tokens,\n",
" )\n",
" return response.choices[0].message[\"content\"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "af0ff2b2",
"metadata": {},
"source": [
"在本节中,我们将专注于评估输入的任务,这对于确保系统的质量和安全性非常重要。\n",
"\n",
"对于需要处理不同情况下的许多独立指令集的任务,首先对查询类型进行分类,然后根据该分类确定要使用哪些指令会很有好处。\n",
"\n",
"这可以通过定义固定的类别和hard-coding与处理给定类别任务相关的指令来实现。\n",
"\n",
"例如,在构建客户服务助手时,首先对查询类型进行分类,然后根据该分类确定要使用哪些指令可能比较重要。\n",
"\n",
"因此,例如,如果用户要求关闭其帐户,您可能会给出不同的辅助指令,而如果用户询问特定产品,则可能会添加其他产品信息。\n",
"\n",
"让我们看一个例子,帮助我们理解的更清晰。"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f2b55807",
"metadata": {},
"source": [
"#### 对用户指令进行分类"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "c3216166",
"metadata": {},
"source": [
"在这里,我们有我们的系统消息,它是对整个系统的指导,并且我们正在使用这个分隔符。\n",
"\n",
"分隔符只是一种分隔指令或输出不同部分的方式,并且它有助于模型确定不同的部分。\n",
"\n",
"因此,对于这个例子,我们将使用#作为分隔符。\n",
"\n",
"这是一个很好的分隔符因为它实际上被表示为一个token。"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3b406ba8",
"metadata": {},
"outputs": [],
"source": [
"delimiter = \"####\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "049d0d82",
"metadata": {},
"source": [
"这是我们的系统消息,我们正在以下面的方式询问模型。"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "29e2d170",
"metadata": {},
"outputs": [],
"source": [
"system_message = f\"\"\"\n",
"You will be provided with customer service queries. \\\n",
"The customer service query will be delimited with \\\n",
"{delimiter} characters.\n",
"Classify each query into a primary category \\\n",
"and a secondary category. \n",
"Provide your output in json format with the \\\n",
"keys: primary and secondary.\n",
"\n",
"Primary categories: Billing, Technical Support, \\\n",
"Account Management, or General Inquiry.\n",
"\n",
"Billing secondary categories:\n",
"Unsubscribe or upgrade\n",
"Add a payment method\n",
"Explanation for charge\n",
"Dispute a charge\n",
"\n",
"Technical Support secondary categories:\n",
"General troubleshooting\n",
"Device compatibility\n",
"Software updates\n",
"\n",
"Account Management secondary categories:\n",
"Password reset\n",
"Update personal information\n",
"Close account\n",
"Account security\n",
"\n",
"General Inquiry secondary categories:\n",
"Product information\n",
"Pricing\n",
"Feedback\n",
"Speak to a human\n",
"\n",
"\"\"\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e6a932ce",
"metadata": {},
"source": [
"现在我们来看一个用户消息的例子,我们将使用以下内容。"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "2b2df0bf",
"metadata": {},
"outputs": [],
"source": [
"user_message = f\"\"\"\\ \n",
"I want you to delete my profile and all of my user data\"\"\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3a2c1cf0",
"metadata": {},
"source": [
"将这个消息格式化为一个消息列表,系统消息和用户消息使用####\"进行分隔。\n",
"\n",
"让我们想一想,作为人类,这句话什么意思:\"我想让您删除我的个人资料。\"\n",
"\n",
"这句话看上去属于\"Account Management\"类别,也许是属于\"Close account\"这一项。 "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "6e2b9049",
"metadata": {},
"outputs": [],
"source": [
"messages = [ \n",
"{'role':'system', \n",
" 'content': system_message}, \n",
"{'role':'user', \n",
" 'content': f\"{delimiter}{user_message}{delimiter}\"}, \n",
"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4b295207",
"metadata": {},
"source": [
"让我们看看模型是如何思考的\n",
"\n",
"模型的分类是\"Account Management\"作为\"primary\"\"Close account\"作为\"secondary\"。\n",
"\n",
"请求结构化输出如JSON的好处是您可以轻松地将其读入某个对象中\n",
"\n",
"例如Python中的字典或者如果您使用其他语言则可以使用其他对象作为输入到后续步骤中。"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "77328388",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"primary\": \"General Inquiry\",\n",
" \"secondary\": \"Product information\"\n",
"}\n"
]
}
],
"source": [
"response = get_completion_from_messages(messages)\n",
"print(response)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2f6b353b",
"metadata": {},
"source": [
"我将向您展示另一个示例,但您也可以随时暂停视频,自己尝试提问,并查看模型如何对其进行分类。\n",
"\n",
"这是另一个用户消息: \"告诉我更多关于你们的平板电视\"\n",
"\n",
"我们只是有相同的消息列表,模型的响应,然后我们打印它。\n",
"\n",
"结果这里是我们的第二个分类,看起来应该是正确的。"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "edf8fbe9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"primary\": \"General Inquiry\",\n",
" \"secondary\": \"Product information\"\n",
"}\n"
]
}
],
"source": [
"user_message = f\"\"\"\\\n",
"Tell me more about your flat screen tvs\"\"\"\n",
"messages = [ \n",
"{'role':'system', \n",
" 'content': system_message}, \n",
"{'role':'user', \n",
" 'content': f\"{delimiter}{user_message}{delimiter}\"}, \n",
"] \n",
"response = get_completion_from_messages(messages)\n",
"print(response)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8f87f68d",
"metadata": {},
"source": [
"所以总的来说,根据客户咨询的分类,我们现在可以提供一套更具体的指令来处理后续步骤。\n",
"\n",
"在这种情况下,我们可能会添加关于电视的额外信息,而不同情况下,我们可能希望提供关闭账户的链接或类似的内容。\n",
"\n",
"我们将在以后的视频中了解更多有关处理输入的不同方法。\n",
"\n",
"在下一个视频中,我们将探讨更多评估输入的方法,特别是确保用户以负责任的方式使用系统的方法。"
]
}
],
"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": "3.8.16"
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"nbformat": 4,
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