开始使用ChatGPT导言

• Introduction 导言
• • Overview 概述
  • Key concepts 关键概念
  • Prompts and completions 提示和完成
  • Tokens 标记/符号
  • Models 模型
  • Next steps 下一步步骤
• 其它资料下载

Introduction 导言

Overview 概述

The OpenAI API can be applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.
OpenAI API可以应用于几乎任何涉及理解或生成自然语言或代码的任务。我们提供了一系列适合不同任务的不同功率级别的模型，并能够微调您自己的定制模型。这些模型可以用于从内容生成到语义搜索和分类的所有方面。

Key concepts 关键概念

We recommend completing our quickstart tutorial to get acquainted with key concepts through a hands-on, interactive example.
我们建议完成快速入门教程，通过一个动手的交互式示例来熟悉关键概念。

Quickstart tutorial 官方快速入门教程
Learn by building a quick sample application
通过构建快速示例应用程序来学习

Prompts and completions 提示和完成

The completions endpoint is at the center of our API. It provides a simple interface to our models that is extremely flexible and powerful. You input some text as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. For example, if you give the API the prompt, “Write a tagline for an ice cream shop”, it will return a completion like “We serve up smiles with every scoop!”
补全端点是我们API的核心。它为我们的模型提供了一个简单的接口，但非常灵活和强大。您输入一些文本作为提示，模型将生成一个完成文本，尝试匹配您提供的任何上下文或模式。例如，如果您向API提供提示“为冰淇淋店写一个标语”，它将返回类似“我们为每一勺都提供微笑！”

Designing your prompt is essentially how you “program” the model, usually by providing some instructions or a few examples. This is different from most other NLP services which are designed for a single task, such as sentiment classification or named entity recognition. Instead, the completions endpoint can be used for virtually any task including content or code generation, summarization, expansion, conversation, creative writing, style transfer, and more.
设计提示本质上是如何“编程”模型，通常是通过提供一些说明或几个示例。这与大多数其他为单个任务设计的NLP服务不同，例如情感分类或命名实体识别。相反，补全端点可以用于几乎任何任务，包括内容或代码生成、总结、扩展、对话、创意写作、风格转换等。

Tokens 标记/符号

Our models understand and process text by breaking it down into tokens. Tokens can be words or just chunks of characters. For example, the word “hamburger” gets broken up into the tokens “ham”, “bur” and “ger”, while a short and common word like “pear” is a single token. Many tokens start with a whitespace, for example “ hello” and “ bye”.
我们的模型通过将文本分解为标记来理解和处理文本。标记可以是单词，也可以是字符块。例如，单词“hamburger”被分解为标记“ham”、“bur”和“ger”，而像“pear”这样简短而常见的单词是单个标记。许多标记以空格开头，例如“hello”和“bye”。

The number of tokens processed in a given API request depends on the length of both your inputs and outputs. As a rough rule of thumb, 1 token is approximately 4 characters or 0.75 words for English text. One limitation to keep in mind is that your text prompt and generated completion combined must be no more than the model’s maximum context length (for most models this is 2048 tokens, or about 1500 words). Check out our tokenizer tool to learn more about how text translates to tokens.
给定API请求中处理的标记数取决于输入和输出的长度。根据粗略的经验，对于英语文本，1个标记大约为4个字符或0.75个单词。需要记住的一个限制是，文本提示和生成的补全的总和不能超过模型的最大上下文长度（对于大多数模型，这是2048个标记，或大约1500个单词）。查看我们的标记器工具，了解更多关于文本如何转换为标记的信息。

Models 模型

The API is powered by a set of models with different capabilities and price points. Our base GPT-3 models are called Davinci, Curie, Babbage and Ada. Our Codex series is a descendant of GPT-3 that’s been trained on both natural language and code. To learn more, visit our models documentation.
API由一组具有不同功能和价位的模型提供支持。我们的GPT-3基本模型有Davinci, Curie, Babbage and Ada。我们的Codex系列是GPT-3的后代，经过了自然语言和代码方面的培训。要了解更多信息，请访问我们的模型文档。

Next steps 下一步步骤

Keep our usage policies in mind as you start building your application.
在开始构建应用程序时，请牢记我们的使用策略。
Explore our examples library for inspiration.
探索我们的示例库以获得灵感。
Jump into one of our guides to start building.
跳到我们的指南之一开始建设。

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