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目 录CONTENT

文章目录

GPT-4 API Python接入指南

MobotStone
2023-03-30 / 0 评论 / 0 点赞 / 1404 阅读 / 29719 字

image-1680159157677
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

image-1680159168990

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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            "\u001b[?25hInstalling collected packages: multidict, frozenlist, async-timeout, yarl, aiosignal, aiohttp, openai\n",
            "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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            "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。

image-1680159209963

image-1680159218475

image-1680159226625

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

image-1680159244530

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。
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