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Get Started - Introduction

2023. 1. 3. 11:35 | Posted by 솔웅


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https://beta.openai.com/docs/introduction/overview

 

OpenAI API

An API for accessing new AI models developed by OpenAI

beta.openai.com

Introduction

 

Overview

 

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 API는 자연어 또는 코드를 이해하거나 생성하는 것과 관련된 거의 모든 작업에 적용할 수 있습니다. 우리는 다양한 작업에 적합한 다양한 수준의 성능을 가진 다양한 모델과 사용자 지정 모델을 미세 조정할 수 있는 기능을 제공합니다. 이러한 모델은 콘텐츠 생성에서 시맨틱 검색 및 분류에 이르기까지 모든 것에 사용할 수 있습니다.


Key concepts

We recommend completing our quickstart tutorial to get acquainted with key concepts through a hands-on, interactive example.

 

실습 대화형 예제를 통해 주요 개념에 익숙해지려면 빠른 시작 자습서를 완료하는 것이 좋습니다.

 

 

Prompts and completions

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!”

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.

 

완료 끝점은 API의 중심에 있습니다. 매우 유연하고 강력한 모델에 간단한 인터페이스를 제공합니다. 일부 텍스트를 프롬프트로 입력하면 모델은 사용자가 제공한 컨텍스트나 패턴과 일치하도록 시도하는 텍스트 완성을 생성합니다. 예를 들어 API에 "아이스크림 가게에 대한 태그라인 작성"이라는 프롬프트를 제공하면 "We serve up smiles with every scoop!"과 같은 완성을 반환합니다.
프롬프트를 디자인하는 것은 기본적으로 모델을 "프로그래밍"하는 방법이며 일반적으로 몇 가지 지침이나 몇 가지 예를 제공합니다. 이는 감정 분류 또는 명명된 엔터티 인식과 같은 단일 작업용으로 설계된 대부분의 다른 NLP 서비스와 다릅니다. 대신 완성 엔드포인트는 콘텐츠 또는 코드 생성, 요약, 확장, 대화, 창작, 스타일 전송 등을 포함한 거의 모든 작업에 사용할 수 있습니다.

 

Tokens

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”.

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.

 

우리의 모델은 텍스트를 토큰으로 분해하여 텍스트를 이해하고 처리합니다. 토큰은 단어일 수도 있고 문자 덩어리일 수도 있습니다. 예를 들어 "hamburger"라는 단어는 "ham", "bur" 및 "ger" 토큰으로 분해되는 반면 "pear"와 같은 짧고 일반적인 단어는 단일 토큰입니다. 많은 토큰이 공백으로 시작합니다(예: "hello" 및 "bye").
지정된 API 요청에서 처리되는 토큰 수는 입력 및 출력의 길이에 따라 다릅니다. 대략적인 경험 법칙으로 1 토큰은 약 4자 또는 영어 텍스트의 경우 0.75단어입니다. 명심해야 할 한 가지 제한 사항은 텍스트 프롬프트와 생성된 완성을 합친 것이 모델의 최대 컨텍스트 길이(대부분의 모델의 경우 2048 토큰 또는 약 1500단어)를 넘지 않아야 한다는 것입니다. 텍스트가 토큰으로 변환되는 방법에 대해 자세히 알아보려면 토크나이저 도구를 확인하십시오.

Models

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.

 

API는 기능과 가격대가 다른 일련의 모델로 구동됩니다. 기본 GPT-3 모델은 Davinci, Curie, Babbage 및 Ada라고 합니다. Codex 시리즈는 자연어와 코드 모두에 대해 교육을 받은 GPT-3의 후예입니다. 자세한 내용은 모델 설명서를 참조하십시오.


Next steps

  • Keep our usage policies in mind as you start building your application.
  • Explore our examples library for inspiration.
  • Jump into one of our guides to start building.

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Guides

 

 

 

 

 

 

 

 

 

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