本参考指南当前包含的内容：
- GPT-4 API 请求/响应模式
- Python示例：OpenAI Python 库和LangChain
- 为什么使用GPT-4 API
Postman 中的 GPT-4 API
GPT-4 API 请求
Endpoint
POST https://api.openai.com/v1/chat/completions
Headers
Content-Type： application/json
**Authorization：**YOUR_OPENAI_API_KEY
Body
{
"model": "gpt-4",
"messages": [
{"role": "system", "content": "Set the behavior"},
{"role": "assistant", "content": "Provide examples"},
{"role": "user", "content": "Set the instructions"}
],
"temperature": 0.05,
"max_tokens": 256,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0
}
model**：**正在使用哪个版本（例如“gpt-4”）
messages：
- system：设置助手应该如何运作。
- assistant：提供助手应该如何运作的实例。
- user：设置助手应该按照什么指示来运作。
temperature：控制机器人响应的创造性或随机性。较低的数字（如 0.05）意味着助手会更加专注和一致，而较高的数字会使助手更具创造力和不可预测性。
**max_tokens：**允许机器人在其响应中使用的最大单词数或单词部分（标记）。
**top_p：**帮助控制响应的随机性。值为 1 意味着机器人会考虑范围广泛的响应，而较低的值会使机器人更专注于一些特定的响应。
**frequency_penalty：**控制机器人在其响应中使用罕见或不常见单词的可能性。值为 0 表示使用生僻词不会受到惩罚。
**presence_penalty：**控制机器人重复自己或使用相似短语的可能性。值为 0 表示没有对重复单词或短语的惩罚。
GPT-4 API 响应
{
"id": "chatcmpl-6viHI5cWjA8QWbeeRtZFBnYMl1EKV",
"object": "chat.completion",
"created": 1679212920,
"model": "gpt-4-0314",
"usage": {
"prompt_tokens": 21,
"completion_tokens": 5,
"total_tokens": 26
},
"choices": [
{
"message": {
"role": "assistant",
"content": "GPT-4 response returned here"
},
"finish_reason": "stop",
"index": 0
}
]
}
**id：**唯一标识符
object： chat.completion
**created：**表示响应创建时间的数字—自 1970 年 1 月 1 日以来的秒数。(UTC)。
**model：**正在使用哪个版本（例如“gpt-4”）
usage：
- prompt_tokens：每 750 个单词定价为0.03 美元的请求令牌数量（1k 令牌）
- completion_tokens：每 750 个单词定价为0.06 美元的响应令牌数量（1k 令牌）
- total_tokens： prompt_tokens + completion_tokens
choices：
- message：角色（即“assistant”）和内容（实际响应文本）。
- **finish_reason：**告诉我们为什么助手停止生成响应。在这种情况下，它停止是因为它到达了一个自然停止点（“停止”）。有效值为 stop、length、content_filter 和 null。
- **index：**这只是一个用于跟踪响应的数字（0 表示它是第一个响应）。
Python 示例
虽然可以使用上述信息向 GPT-4 API 发出 HTTPS 请求，但建议使用官方 OpenAI 库或更进一步使用 LLM 抽象层，如 LangChain。
用于 OpenAI Python 库和 LangChain 的 Jupyter Notebook
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNin3vntcd4FM6CYdBsLcIr",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/IvanCampos/c3f70e58efdf012a6422f2444dc3c261/langchain-gpt-4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"**OPENAI PYTHON LIBRARY**"
],
"metadata": {
"id": "wIs-na6UdiRX"
}
},
{
"cell_type": "code",
"source": [
"!pip install openai"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TjnIGuQfNvzt",
"outputId": "7d1ebf99-f7b5-4664-c100-228fa94a5576"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 frozenlist-1.3.3 multidict-6.0.4 openai-0.27.2 yarl-1.8.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import openai"
],
"metadata": {
"id": "FW_xUOJ5eZd-"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"openai.api_key = \"YOUR_OPENAI_API_KEY\""
],
"metadata": {
"id": "-dbOFoxWdh6L"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"completion = openai.ChatCompletion.create(\n",
" model=\"gpt-4\", \n",
" messages=[{\"role\": \"user\", \"content\": \"in 7 words, explain why artificial intelligence is the future\"}]\n",
" )\n",
"print(completion.choices[0].message.content)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ygGcf8IwdxVr",
"outputId": "1f9956a0-fc3c-40b3-d1a9-a6cd717da057"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Automation, efficiency, accuracy, data analysis, safety, cost-effective, adaptability.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**LANGCHAIN**"
],
"metadata": {
"id": "upvQ5UgjelMR"
}
},
{
"cell_type": "code",
"source": [
"!pip install langchain"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jc25fuF-NrQt",
"outputId": "255a94ae-6573-4eab-d9bd-14e5589bf251"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting langchain\n",
" Downloading langchain-0.0.117-py3-none-any.whl (414 kB)\n",
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" Downloading marshmallow_enum-1.5.1-py2.py3-none-any.whl (4.2 kB)\n",
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"Successfully installed dataclasses-json-0.5.7 langchain-0.0.117 marshmallow-3.19.0 marshmallow-enum-1.5.1 mypy-extensions-1.0.0 typing-inspect-0.8.0\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "9Mh-o0TWNV6l"
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain import PromptTemplate, LLMChain\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import (\n",
" AIMessage,\n",
" HumanMessage,\n",
" SystemMessage\n",
")"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ['OPENAI_API_KEY'] = 'YOUR_OPENAI_API_KEY'"
],
"metadata": {
"id": "Ak433tRsNyyb"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**CHAT**"
],
"metadata": {
"id": "isebouwnT5V0"
}
},
{
"cell_type": "code",
"source": [
"chat = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
],
"metadata": {
"id": "DIz2zrJ7N7iD"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"chat([HumanMessage(content=\"Translate this sentence from English to Spanish. Artificial Intelligence is the future.\")])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x_0OHs4wQmTx",
"outputId": "40c8d939-072d-4e54-ce8c-4c36da47c159"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:/usr/local/lib/python3.9/dist-packages/langchain/chat_models/openai.py:Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: The server had an error while processing your request. Sorry about that!.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AIMessage(content='La inteligencia artificial es el futuro.', additional_kwargs={})"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"chat([HumanMessage(content=\"what are the three largest cities in MA\"),AIMessage(content=\"\"),SystemMessage(content=\"you are a snarky assistant\")])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AsFSC_D4S1C_",
"outputId": "ca57aaec-0e92-4c83-a926-9126f4953cd9"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AIMessage(content='I apologize if my previous response seemed snarky. The three largest cities in Massachusetts are:\\n\\n1. Boston\\n2. Worcester\\n3. Springfield', additional_kwargs={})"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"source": [
"**PROMPT TEMPLATE**"
],
"metadata": {
"id": "CEGPefQzTu7Z"
}
},
{
"cell_type": "code",
"source": [
"template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
"system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
"human_template=\"{text}\"\n",
"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
],
"metadata": {
"id": "CVyDs6wcSNqn"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
"\n",
"# get a chat completion from the formatted messages\n",
"chat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"Spanish\", text=\"Artificial Intelligence is the future.\").to_messages())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ON8-_WnlSS0j",
"outputId": "907d6683-8572-47c9-850c-164d8dc3d935"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AIMessage(content='La inteligencia artificial es el futuro.', additional_kwargs={})"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"source": [
"**LLMChain**"
],
"metadata": {
"id": "rZ91aUK4UAR0"
}
},
{
"cell_type": "code",
"source": [
"chain = LLMChain(llm=chat, prompt=chat_prompt)"
],
"metadata": {
"id": "1chnqKppR-D4"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"chain.run(input_language=\"English\", output_language=\"Spanish\", text=\"Artificial Intelligence is the future.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "6tqIT0B2SnZv",
"outputId": "1bf9c358-668a-416b-b375-f7b41ea2f9c7"
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'La inteligencia artificial es el futuro.'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 22
}
]
}
]
}
OpenAI Python 库演练
OpenAI Python 库使可以轻松地与 OpenAI 的服务进行连接和通信。该库带有一组内置类，可帮助这些程序非常容易与 OpenAI 的服务进行交互。这些功能自动适应从 OpenAI 的服务接收到的信息，这使得该库与 OpenAI 提供的许多不同版本的服务兼容。
!pip install openai：这一行安装了一个名为“openai”的包，它提供了与 OpenAI API 通信的工具。import openai: 这一行导入了“openai”包，所以它的工具可以在脚本中使用。openai.api_key = "YOUR_OPENAI_API_KEY"：设置访问 OpenAI API 所需的密钥。将用实际密钥替换“YOUR_OPENAI_API_KEY”。completion = openai.ChatCompletion.create(...)：向聊天完成端点发送一条消息，要求它用 7 个词解释为什么人工智能是未来。它指定要使用的模型版本（“gpt-4”）和消息内容。print(completion.choices[0].message.content)：此行打印 GPT-4 对控制台的响应。
LangChain演练
LangChain 是一种抽象化和简化大型语言模型 (LLM) 工作的工具。它可以用于各种目的，例如创建聊天机器人、回答问题或总结文本。LangChain 背后的主要思想是可以将不同的部分连接在一起，以使用这些 LLM 制作更复杂和高级的应用程序。
LangChain notebook cells如果独立运行，需要pip安装导入openai
!pip install langchain：安装了一个名为“langchain”的包，它提供了与 LLM 通信的工具。from ... import ...: 这些行从“langchain”包中导入特定的函数和类（或工具），因此它们可以在脚本中使用。os.environ['OPENAI_API_KEY'] = 'YOUR_OPENAI_API_KEY'：此行设置访问 OpenAI API 所需的密钥。您将用您的实际密钥替换“YOUR_OPENAI_API_KEY”。chat = ChatOpenAI(...)：此行创建具有特定设置（如温度）的 GPT-4 API 实例。chat([...])：这些行显示了如何向 API 发送消息和接收响应的两个示例。第一个示例要求翻译一个句子，第二个示例要求翻译马萨诸塞州的三个最大城市。template = "You are a helpful assistant...: 这部分设置一个提示模板来定义请求的行为。在这种情况下，它告诉 GPT-4 它是一个有用的翻译器。system_message_prompt = ...：此行创建一个 SystemMessagePromptTemplate，它将用于使用之前定义的模板设置助手的行为。human_message_prompt = ...：此行创建一个 HumanMessagePromptTemplate，它将用于格式化用户发送的消息。chat_prompt = ...：此行将系统和人工消息提示组合到一个 ChatPromptTemplate 中。chat(chat_prompt.format_prompt(...))：此行使用 ChatPromptTemplate 向 API 发送一条消息，要求它将一个句子从英语翻译成西班牙语。chain = LLMChain(llm=chat, prompt=chat_prompt)：这一行创建了一个 LLMChain 对象，它简化了与 LLM 的交互。chain.run(...)：此行使用 LLMChain 对象向 GPT-4 API 发送一条消息，再次要求它将一个句子从英语翻译成西班牙语。
为什么选择 GPT-4？
一个客观的好处是 GPT-4 API 接受上下文长度为8,192 个令牌（12.5 页文本）的请求——这是GPT-3.5 上下文长度的2 倍。
此外，与之前的模型相比，GPT-4 在推理和完成响应的简洁性方面表现出色。
多少钱呐
在决定是否使用 GPT-4 API 时最困难的选择是定价—因为 GPT-4 定价的如下：
- 提示：每 750 个单词 0.03 美元（1k 代币）
- 完成：每 750 个单词 0.06 美元（1k 代币）
GPT-4 API比 ChatGPT 的默认模型 gpt-3.5-turbo 贵 14-29 倍
未来的改进
- 在发布的 GPT-4 API 中，它将是多模式的。在这种情况下，“多模型”是指 API 不仅可以接受文本，还可以接受图像的能力。
- 还有一个 32,768 上下文长度模型（50 页文本）目前处于预览状态—与 gpt-4-0314 相比，上下文长度增加了4 倍。然而，这将是8,192 上下文长度 GPT-4 模型成本的2 倍。
- 虽然在 GPT-3 API 是微调模型（davinci、curie、babbage 和 ada）中可用，但预计将在未来版本中我们也可以在 GPT-4 API 上微调。
- 目前的训练数据只到**2021 年 9 月。**这也有望在未来的版本中增加最新数据，以及联网测试，当然公布的插件还无法使用。
- 当通过 ChatGPT Plus 使用 GPT-4 时，截至今天：“GPT-4 目前每 3 小时有 25 条消息的上限**。**随着我们根据需求进行调整，预计上限会大大降低。” 随着此上限的降低，预计直接 GPT-4 API 的使用会增加。