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Introduction

You can interact with the API through HTTP requests from any language, via our official Python bindings, our official Node.js library, or a community-maintained library.

To install the official Python bindings, run the following command:

pip install openai

To install the official Node.js library, run the following command in your Node.js project directory:

npm install openai

Authentication

The OpenAI API uses API keys for authentication. Visit your API Keys page to retrieve the API key you'll use in your requests.

Remember that your API key is a secret! Do not share it with others or expose it in any client-side code (browsers, apps). Production requests must be routed through your own backend server where your API key can be securely loaded from an environment variable or key management service.

All API requests should include your API key in an Authorization HTTP header as follows:

Authorization: Bearer OPENAI_API_KEY

Requesting organization

For users who belong to multiple organizations, you can pass a header to specify which organization is used for an API request. Usage from these API requests will count against the specified organization's subscription quota.

Example curl command:

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curl https://api.openai.com/v1/models \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "OpenAI-Organization: org-Ki0lPeKX1Jq97R9m9nJyapLf"

Example with the openai Python package:

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import os
import openai
openai.organization = "org-Ki0lPeKX1Jq97R9m9nJyapLf"
openai.api_key = os.getenv("OPENAI_API_KEY")
openai.Model.list()

Example with the openai Node.js package:

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import { Configuration, OpenAIApi } from "openai";
const configuration = new Configuration({
    organization: "org-Ki0lPeKX1Jq97R9m9nJyapLf",
    apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);
const response = await openai.listEngines();

Organization IDs can be found on your Organization settings page.

Making requests

You can paste the command below into your terminal to run your first API request. Make sure to replace $OPENAI_API_KEY with your secret API key.

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curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
     "model": "gpt-3.5-turbo",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

This request queries the gpt-3.5-turbo model to complete the text starting with a prompt of "Say this is a test". You should get a response back that resembles the following:

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{
   "id":"chatcmpl-abc123",
   "object":"chat.completion",
   "created":1677858242,
   "model":"gpt-3.5-turbo-0301",
   "usage":{
      "prompt_tokens":13,
      "completion_tokens":7,
      "total_tokens":20
   },
   "choices":[
      {
         "message":{
            "role":"assistant",
            "content":"\n\nThis is a test!"
         },
         "finish_reason":"stop",
         "index":0
      }
   ]
}

Now you've generated your first chat completion. We can see the finish_reason is stop which means the API returned the full completion generated by the model. In the above request, we only generated a single message but you can set the n parameter to generate multiple messages choices. In this example, gpt-3.5-turbo is being used for more of a traditional text completion task. The model is also optimized for chat applications as well.

Models

List and describe the various models available in the API. You can refer to the Models documentation to understand what models are available and the differences between them.

List models

get https://api.openai.com/v1/models

Lists the currently available models, and provides basic information about each one such as the owner and availability.

Example request
curl
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curl https://api.openai.com/v1/models \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "data": [
    {
      "id": "model-id-0",
      "object": "model",
      "owned_by": "organization-owner",
      "permission": [...]
    },
    {
      "id": "model-id-1",
      "object": "model",
      "owned_by": "organization-owner",
      "permission": [...]
    },
    {
      "id": "model-id-2",
      "object": "model",
      "owned_by": "openai",
      "permission": [...]
    },
  ],
  "object": "list"
}

Retrieve model

get https://api.openai.com/v1/models/{model}

Retrieves a model instance, providing basic information about the model such as the owner and permissioning.

Path parameters

model
string
Required

The ID of the model to use for this request

Example request
text-davinci-003
curl
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curl https://api.openai.com/v1/models/text-davinci-003 \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "text-davinci-003",
  "object": "model",
  "owned_by": "openai",
  "permission": [...]
}

Completions

Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.

Create completion

post https://api.openai.com/v1/completions

Creates a completion for the provided prompt and parameters

Request body

model
string
Required

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

prompt
string or array
Optional
Defaults to <|endoftext|>

The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.

Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.

suffix
string
Optional
Defaults to null

The suffix that comes after a completion of inserted text.

max_tokens
integer
Optional
Defaults to 16

The maximum number of tokens to generate in the completion.

The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).

temperature
number
Optional
Defaults to 1

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

top_p
number
Optional
Defaults to 1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

n
integer
Optional
Defaults to 1

How many completions to generate for each prompt.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

stream
boolean
Optional
Defaults to false

Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

logprobs
integer
Optional
Defaults to null

Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.

The maximum value for logprobs is 5. If you need more than this, please contact us through our Help center and describe your use case.

echo
boolean
Optional
Defaults to false

Echo back the prompt in addition to the completion

stop
string or array
Optional
Defaults to null

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

presence_penalty
number
Optional
Defaults to 0

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

See more information about frequency and presence penalties.

frequency_penalty
number
Optional
Defaults to 0

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

See more information about frequency and presence penalties.

best_of
integer
Optional
Defaults to 1

Generates best_of completions server-side and returns the "best" (the one with the highest log probability per token). Results cannot be streamed.

When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

logit_bias
map
Optional
Defaults to null

Modify the likelihood of specified tokens appearing in the completion.

Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.

user
string
Optional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example request
text-davinci-003
curl
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curl https://api.openai.com/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "text-davinci-003",
    "prompt": "Say this is a test",
    "max_tokens": 7,
    "temperature": 0
  }'
Parameters
text-davinci-003
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{
  "model": "text-davinci-003",
  "prompt": "Say this is a test",
  "max_tokens": 7,
  "temperature": 0,
  "top_p": 1,
  "n": 1,
  "stream": false,
  "logprobs": null,
  "stop": "\n"
}
Response
text-davinci-003
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{
  "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
  "object": "text_completion",
  "created": 1589478378,
  "model": "text-davinci-003",
  "choices": [
    {
      "text": "\n\nThis is indeed a test",
      "index": 0,
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 5,
    "completion_tokens": 7,
    "total_tokens": 12
  }
}

Chat

Given a chat conversation, the model will return a chat completion response.

Create chat completion
Beta

post https://api.openai.com/v1/chat/completions

Creates a completion for the chat message

Request body

model
string
Required

ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.

messages
array
Required

The messages to generate chat completions for, in the chat format.

temperature
number
Optional
Defaults to 1

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

top_p
number
Optional
Defaults to 1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

n
integer
Optional
Defaults to 1

How many chat completion choices to generate for each input message.

stream
boolean
Optional
Defaults to false

If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. See the OpenAI Cookbook for example code.

stop
string or array
Optional
Defaults to null

Up to 4 sequences where the API will stop generating further tokens.

max_tokens
integer
Optional
Defaults to inf

The maximum number of tokens to generate in the chat completion.

The total length of input tokens and generated tokens is limited by the model's context length.

presence_penalty
number
Optional
Defaults to 0

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

See more information about frequency and presence penalties.

frequency_penalty
number
Optional
Defaults to 0

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

See more information about frequency and presence penalties.

logit_bias
map
Optional
Defaults to null

Modify the likelihood of specified tokens appearing in the completion.

Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

user
string
Optional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example request
curl
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curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
Parameters
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{
  "model": "gpt-3.5-turbo",
  "messages": [{"role": "user", "content": "Hello!"}]
}
Response
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{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "\n\nHello there, how may I assist you today?",
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 12,
    "total_tokens": 21
  }
}

Edits

Given a prompt and an instruction, the model will return an edited version of the prompt.

Create edit

post https://api.openai.com/v1/edits

Creates a new edit for the provided input, instruction, and parameters.

Request body

model
string
Required

ID of the model to use. You can use the text-davinci-edit-001 or code-davinci-edit-001 model with this endpoint.

input
string
Optional
Defaults to ''

The input text to use as a starting point for the edit.

instruction
string
Required

The instruction that tells the model how to edit the prompt.

n
integer
Optional
Defaults to 1

How many edits to generate for the input and instruction.

temperature
number
Optional
Defaults to 1

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

top_p
number
Optional
Defaults to 1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

Example request
text-davinci-edit-001
curl
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curl https://api.openai.com/v1/edits \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "text-davinci-edit-001",
    "input": "What day of the wek is it?",
    "instruction": "Fix the spelling mistakes"
  }'
Parameters
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{
  "model": "text-davinci-edit-001",
  "input": "What day of the wek is it?",
  "instruction": "Fix the spelling mistakes",
}
Response
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{
  "object": "edit",
  "created": 1589478378,
  "choices": [
    {
      "text": "What day of the week is it?",
      "index": 0,
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 32,
    "total_tokens": 57
  }
}

Images

Given a prompt and/or an input image, the model will generate a new image.

Related guide: Image generation

Create image
Beta

post https://api.openai.com/v1/images/generations

Creates an image given a prompt.

Request body

prompt
string
Required

A text description of the desired image(s). The maximum length is 1000 characters.

n
integer
Optional
Defaults to 1

The number of images to generate. Must be between 1 and 10.

size
string
Optional
Defaults to 1024x1024

The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.

response_format
string
Optional
Defaults to url

The format in which the generated images are returned. Must be one of url or b64_json.

user
string
Optional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example request
curl
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curl https://api.openai.com/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "prompt": "A cute baby sea otter",
    "n": 2,
    "size": "1024x1024"
  }'
Parameters
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{
  "prompt": "A cute baby sea otter",
  "n": 2,
  "size": "1024x1024"
}
Response
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{
  "created": 1589478378,
  "data": [
    {
      "url": "https://..."
    },
    {
      "url": "https://..."
    }
  ]
}

Create image edit
Beta

post https://api.openai.com/v1/images/edits

Creates an edited or extended image given an original image and a prompt.

Request body

image
string
Required

The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.

mask
string
Optional

An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where image should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as image.

prompt
string
Required

A text description of the desired image(s). The maximum length is 1000 characters.

n
integer
Optional
Defaults to 1

The number of images to generate. Must be between 1 and 10.

size
string
Optional
Defaults to 1024x1024

The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.

response_format
string
Optional
Defaults to url

The format in which the generated images are returned. Must be one of url or b64_json.

user
string
Optional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example request
curl
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curl https://api.openai.com/v1/images/edits \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F image="@otter.png" \
  -F mask="@mask.png" \
  -F prompt="A cute baby sea otter wearing a beret" \
  -F n=2 \
  -F size="1024x1024"
Response
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{
  "created": 1589478378,
  "data": [
    {
      "url": "https://..."
    },
    {
      "url": "https://..."
    }
  ]
}

Create image variation
Beta

post https://api.openai.com/v1/images/variations

Creates a variation of a given image.

Request body

image
string
Required

The image to use as the basis for the variation(s). Must be a valid PNG file, less than 4MB, and square.

n
integer
Optional
Defaults to 1

The number of images to generate. Must be between 1 and 10.

size
string
Optional
Defaults to 1024x1024

The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.

response_format
string
Optional
Defaults to url

The format in which the generated images are returned. Must be one of url or b64_json.

user
string
Optional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example request
curl
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curl https://api.openai.com/v1/images/variations \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F image="@otter.png" \
  -F n=2 \
  -F size="1024x1024"
Response
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{
  "created": 1589478378,
  "data": [
    {
      "url": "https://..."
    },
    {
      "url": "https://..."
    }
  ]
}

Embeddings

Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.

Related guide: Embeddings

Create embeddings

post https://api.openai.com/v1/embeddings

Creates an embedding vector representing the input text.

Request body

model
string
Required

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

input
string or array
Required

Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed 8192 tokens in length.

user
string
Optional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example request
curl
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curl https://api.openai.com/v1/embeddings \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-ada-002"
  }'
Parameters
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{
  "model": "text-embedding-ada-002",
  "input": "The food was delicious and the waiter..."
}
Response
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{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222,
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Audio

Learn how to turn audio into text.

Related guide: Speech to text

Create transcription
Beta

post https://api.openai.com/v1/audio/transcriptions

Transcribes audio into the input language.

Request body

file
string
Required

The audio file to transcribe, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.

model
string
Required

ID of the model to use. Only whisper-1 is currently available.

prompt
string
Optional

An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.

response_format
string
Optional
Defaults to json

The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.

temperature
number
Optional
Defaults to 0

The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

language
string
Optional

The language of the input audio. Supplying the input language in ISO-639-1 format will improve accuracy and latency.

Example request
curl
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curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F model="whisper-1"
Parameters
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{
  "file": "audio.mp3",
  "model": "whisper-1"
}
Response
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{
  "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
}

Create translation
Beta

post https://api.openai.com/v1/audio/translations

Translates audio into into English.

Request body

file
string
Required

The audio file to translate, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.

model
string
Required

ID of the model to use. Only whisper-1 is currently available.

prompt
string
Optional

An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English.

response_format
string
Optional
Defaults to json

The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.

temperature
number
Optional
Defaults to 0

The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

Example request
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curl https://api.openai.com/v1/audio/translations \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/german.m4a" \
  -F model="whisper-1"
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{
  "file": "german.m4a",
  "model": "whisper-1"
}
Response
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{
  "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}

Files

Files are used to upload documents that can be used with features like Fine-tuning.

List files

get https://api.openai.com/v1/files

Returns a list of files that belong to the user's organization.

Example request
curl
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curl https://api.openai.com/v1/files \
  -H "Authorization: Bearer $OPENAI_API_KEY"
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{
  "data": [
    {
      "id": "file-ccdDZrC3iZVNiQVeEA6Z66wf",
      "object": "file",
      "bytes": 175,
      "created_at": 1613677385,
      "filename": "train.jsonl",
      "purpose": "search"
    },
    {
      "id": "file-XjGxS3KTG0uNmNOK362iJua3",
      "object": "file",
      "bytes": 140,
      "created_at": 1613779121,
      "filename": "puppy.jsonl",
      "purpose": "search"
    }
  ],
  "object": "list"
}

Upload file

post https://api.openai.com/v1/files

Upload a file that contains document(s) to be used across various endpoints/features. Currently, the size of all the files uploaded by one organization can be up to 1 GB. Please contact us if you need to increase the storage limit.

Request body

file
string
Required

Name of the JSON Lines file to be uploaded.

If the purpose is set to "fine-tune", each line is a JSON record with "prompt" and "completion" fields representing your training examples.

purpose
string
Required

The intended purpose of the uploaded documents.

Use "fine-tune" for Fine-tuning. This allows us to validate the format of the uploaded file.

Example request
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curl https://api.openai.com/v1/files \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F purpose="fine-tune" \
  -F file="@mydata.jsonl"
Response
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{
  "id": "file-XjGxS3KTG0uNmNOK362iJua3",
  "object": "file",
  "bytes": 140,
  "created_at": 1613779121,
  "filename": "mydata.jsonl",
  "purpose": "fine-tune"
}

Delete file

delete https://api.openai.com/v1/files/{file_id}

Delete a file.

Path parameters

file_id
string
Required

The ID of the file to use for this request

Example request
curl
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curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
  -X DELETE \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "file-XjGxS3KTG0uNmNOK362iJua3",
  "object": "file",
  "deleted": true
}

Retrieve file

get https://api.openai.com/v1/files/{file_id}

Returns information about a specific file.

Path parameters

file_id
string
Required

The ID of the file to use for this request

Example request
curl
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curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "file-XjGxS3KTG0uNmNOK362iJua3",
  "object": "file",
  "bytes": 140,
  "created_at": 1613779657,
  "filename": "mydata.jsonl",
  "purpose": "fine-tune"
}

Retrieve file content

get https://api.openai.com/v1/files/{file_id}/content

Returns the contents of the specified file

Path parameters

file_id
string
Required

The ID of the file to use for this request

Example request
curl
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curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3/content \
  -H "Authorization: Bearer $OPENAI_API_KEY" > file.jsonl

Fine-tunes

Manage fine-tuning jobs to tailor a model to your specific training data.

Related guide: Fine-tune models

Create fine-tune

post https://api.openai.com/v1/fine-tunes

Creates a job that fine-tunes a specified model from a given dataset.

Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

Learn more about Fine-tuning

Request body

training_file
string
Required

The ID of an uploaded file that contains training data.

See upload file for how to upload a file.

Your dataset must be formatted as a JSONL file, where each training example is a JSON object with the keys "prompt" and "completion". Additionally, you must upload your file with the purpose fine-tune.

See the fine-tuning guide for more details.

validation_file
string
Optional

The ID of an uploaded file that contains validation data.

If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. Your train and validation data should be mutually exclusive.

Your dataset must be formatted as a JSONL file, where each validation example is a JSON object with the keys "prompt" and "completion". Additionally, you must upload your file with the purpose fine-tune.

See the fine-tuning guide for more details.

model
string
Optional
Defaults to curie

The name of the base model to fine-tune. You can select one of "ada", "babbage", "curie", "davinci", or a fine-tuned model created after 2022-04-21. To learn more about these models, see the Models documentation.

n_epochs
integer
Optional
Defaults to 4

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

batch_size
integer
Optional
Defaults to null

The batch size to use for training. The batch size is the number of training examples used to train a single forward and backward pass.

By default, the batch size will be dynamically configured to be ~0.2% of the number of examples in the training set, capped at 256 - in general, we've found that larger batch sizes tend to work better for larger datasets.

learning_rate_multiplier
number
Optional
Defaults to null

The learning rate multiplier to use for training. The fine-tuning learning rate is the original learning rate used for pretraining multiplied by this value.

By default, the learning rate multiplier is the 0.05, 0.1, or 0.2 depending on final batch_size (larger learning rates tend to perform better with larger batch sizes). We recommend experimenting with values in the range 0.02 to 0.2 to see what produces the best results.

prompt_loss_weight
number
Optional
Defaults to 0.01

The weight to use for loss on the prompt tokens. This controls how much the model tries to learn to generate the prompt (as compared to the completion which always has a weight of 1.0), and can add a stabilizing effect to training when completions are short.

If prompts are extremely long (relative to completions), it may make sense to reduce this weight so as to avoid over-prioritizing learning the prompt.

compute_classification_metrics
boolean
Optional
Defaults to false

If set, we calculate classification-specific metrics such as accuracy and F-1 score using the validation set at the end of every epoch. These metrics can be viewed in the results file.

In order to compute classification metrics, you must provide a validation_file. Additionally, you must specify classification_n_classes for multiclass classification or classification_positive_class for binary classification.

classification_n_classes
integer
Optional
Defaults to null

The number of classes in a classification task.

This parameter is required for multiclass classification.

classification_positive_class
string
Optional
Defaults to null

The positive class in binary classification.

This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.

classification_betas
array
Optional
Defaults to null

If this is provided, we calculate F-beta scores at the specified beta values. The F-beta score is a generalization of F-1 score. This is only used for binary classification.

With a beta of 1 (i.e. the F-1 score), precision and recall are given the same weight. A larger beta score puts more weight on recall and less on precision. A smaller beta score puts more weight on precision and less on recall.

suffix
string
Optional
Defaults to null

A string of up to 40 characters that will be added to your fine-tuned model name.

For example, a suffix of "custom-model-name" would produce a model name like ada:ft-your-org:custom-model-name-2022-02-15-04-21-04.

Example request
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curl https://api.openai.com/v1/fine-tunes \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "training_file": "file-XGinujblHPwGLSztz8cPS8XY"
  }'
Response
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{
  "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
  "object": "fine-tune",
  "model": "curie",
  "created_at": 1614807352,
  "events": [
    {
      "object": "fine-tune-event",
      "created_at": 1614807352,
      "level": "info",
      "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
    }
  ],
  "fine_tuned_model": null,
  "hyperparams": {
    "batch_size": 4,
    "learning_rate_multiplier": 0.1,
    "n_epochs": 4,
    "prompt_loss_weight": 0.1,
  },
  "organization_id": "org-...",
  "result_files": [],
  "status": "pending",
  "validation_files": [],
  "training_files": [
    {
      "id": "file-XGinujblHPwGLSztz8cPS8XY",
      "object": "file",
      "bytes": 1547276,
      "created_at": 1610062281,
      "filename": "my-data-train.jsonl",
      "purpose": "fine-tune-train"
    }
  ],
  "updated_at": 1614807352,
}

List fine-tunes

get https://api.openai.com/v1/fine-tunes

List your organization's fine-tuning jobs

Example request
curl
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curl https://api.openai.com/v1/fine-tunes \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "object": "list",
  "data": [
    {
      "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
      "object": "fine-tune",
      "model": "curie",
      "created_at": 1614807352,
      "fine_tuned_model": null,
      "hyperparams": { ... },
      "organization_id": "org-...",
      "result_files": [],
      "status": "pending",
      "validation_files": [],
      "training_files": [ { ... } ],
      "updated_at": 1614807352,
    },
    { ... },
    { ... }
  ]
}

Retrieve fine-tune

get https://api.openai.com/v1/fine-tunes/{fine_tune_id}

Gets info about the fine-tune job.

Learn more about Fine-tuning

Path parameters

fine_tune_id
string
Required

The ID of the fine-tune job

Example request
curl
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curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
  "object": "fine-tune",
  "model": "curie",
  "created_at": 1614807352,
  "events": [
    {
      "object": "fine-tune-event",
      "created_at": 1614807352,
      "level": "info",
      "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807356,
      "level": "info",
      "message": "Job started."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807861,
      "level": "info",
      "message": "Uploaded snapshot: curie:ft-acmeco-2021-03-03-21-44-20."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807864,
      "level": "info",
      "message": "Uploaded result files: file-QQm6ZpqdNwAaVC3aSz5sWwLT."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807864,
      "level": "info",
      "message": "Job succeeded."
    }
  ],
  "fine_tuned_model": "curie:ft-acmeco-2021-03-03-21-44-20",
  "hyperparams": {
    "batch_size": 4,
    "learning_rate_multiplier": 0.1,
    "n_epochs": 4,
    "prompt_loss_weight": 0.1,
  },
  "organization_id": "org-...",
  "result_files": [
    {
      "id": "file-QQm6ZpqdNwAaVC3aSz5sWwLT",
      "object": "file",
      "bytes": 81509,
      "created_at": 1614807863,
      "filename": "compiled_results.csv",
      "purpose": "fine-tune-results"
    }
  ],
  "status": "succeeded",
  "validation_files": [],
  "training_files": [
    {
      "id": "file-XGinujblHPwGLSztz8cPS8XY",
      "object": "file",
      "bytes": 1547276,
      "created_at": 1610062281,
      "filename": "my-data-train.jsonl",
      "purpose": "fine-tune-train"
    }
  ],
  "updated_at": 1614807865,
}

Cancel fine-tune

post https://api.openai.com/v1/fine-tunes/{fine_tune_id}/cancel

Immediately cancel a fine-tune job.

Path parameters

fine_tune_id
string
Required

The ID of the fine-tune job to cancel

Example request
curl
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curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/cancel \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "ft-xhrpBbvVUzYGo8oUO1FY4nI7",
  "object": "fine-tune",
  "model": "curie",
  "created_at": 1614807770,
  "events": [ { ... } ],
  "fine_tuned_model": null,
  "hyperparams": { ... },
  "organization_id": "org-...",
  "result_files": [],
  "status": "cancelled",
  "validation_files": [],
  "training_files": [
    {
      "id": "file-XGinujblHPwGLSztz8cPS8XY",
      "object": "file",
      "bytes": 1547276,
      "created_at": 1610062281,
      "filename": "my-data-train.jsonl",
      "purpose": "fine-tune-train"
    }
  ],
  "updated_at": 1614807789,
}

List fine-tune events

get https://api.openai.com/v1/fine-tunes/{fine_tune_id}/events

Get fine-grained status updates for a fine-tune job.

Path parameters

fine_tune_id
string
Required

The ID of the fine-tune job to get events for.

Query parameters

stream
boolean
Optional
Defaults to false

Whether to stream events for the fine-tune job. If set to true, events will be sent as data-only server-sent events as they become available. The stream will terminate with a data: [DONE] message when the job is finished (succeeded, cancelled, or failed).

If set to false, only events generated so far will be returned.

Example request
curl
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curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/events \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "object": "list",
  "data": [
    {
      "object": "fine-tune-event",
      "created_at": 1614807352,
      "level": "info",
      "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807356,
      "level": "info",
      "message": "Job started."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807861,
      "level": "info",
      "message": "Uploaded snapshot: curie:ft-acmeco-2021-03-03-21-44-20."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807864,
      "level": "info",
      "message": "Uploaded result files: file-QQm6ZpqdNwAaVC3aSz5sWwLT."
    },
    {
      "object": "fine-tune-event",
      "created_at": 1614807864,
      "level": "info",
      "message": "Job succeeded."
    }
  ]
}

Delete fine-tune model

delete https://api.openai.com/v1/models/{model}

Delete a fine-tuned model. You must have the Owner role in your organization.

Path parameters

model
string
Required

The model to delete

Example request
curl
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curl https://api.openai.com/v1/models/curie:ft-acmeco-2021-03-03-21-44-20 \
  -X DELETE \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "curie:ft-acmeco-2021-03-03-21-44-20",
  "object": "model",
  "deleted": true
}

Moderations

Given a input text, outputs if the model classifies it as violating OpenAI's content policy.

Related guide: Moderations

Create moderation

post https://api.openai.com/v1/moderations

Classifies if text violates OpenAI's Content Policy

Request body

input
string or array
Required

The input text to classify

model
string
Optional
Defaults to text-moderation-latest

Two content moderations models are available: text-moderation-stable and text-moderation-latest.

The default is text-moderation-latest which will be automatically upgraded over time. This ensures you are always using our most accurate model. If you use text-moderation-stable, we will provide advanced notice before updating the model. Accuracy of text-moderation-stable may be slightly lower than for text-moderation-latest.

Example request
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curl https://api.openai.com/v1/moderations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "input": "I want to kill them."
  }'
Parameters
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{
  "input": "I want to kill them."
}
Response
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{
  "id": "modr-5MWoLO",
  "model": "text-moderation-001",
  "results": [
    {
      "categories": {
        "hate": false,
        "hate/threatening": true,
        "self-harm": false,
        "sexual": false,
        "sexual/minors": false,
        "violence": true,
        "violence/graphic": false
      },
      "category_scores": {
        "hate": 0.22714105248451233,
        "hate/threatening": 0.4132447838783264,
        "self-harm": 0.005232391878962517,
        "sexual": 0.01407341007143259,
        "sexual/minors": 0.0038522258400917053,
        "violence": 0.9223177433013916,
        "violence/graphic": 0.036865197122097015
      },
      "flagged": true
    }
  ]
}

Engines

The Engines endpoints are deprecated.

Please use their replacement, Models, instead. Learn more.

These endpoints describe and provide access to the various engines available in the API.

List engines
Deprecated

get https://api.openai.com/v1/engines

Lists the currently available (non-finetuned) models, and provides basic information about each one such as the owner and availability.

Example request
curl
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curl https://api.openai.com/v1/engines \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "data": [
    {
      "id": "engine-id-0",
      "object": "engine",
      "owner": "organization-owner",
      "ready": true
    },
    {
      "id": "engine-id-2",
      "object": "engine",
      "owner": "organization-owner",
      "ready": true
    },
    {
      "id": "engine-id-3",
      "object": "engine",
      "owner": "openai",
      "ready": false
    },
  ],
  "object": "list"
}

Retrieve engine
Deprecated

get https://api.openai.com/v1/engines/{engine_id}

Retrieves a model instance, providing basic information about it such as the owner and availability.

Path parameters

engine_id
string
Required

The ID of the engine to use for this request

Example request
text-davinci-003
curl
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curl https://api.openai.com/v1/engines/text-davinci-003 \
  -H "Authorization: Bearer $OPENAI_API_KEY"
Response
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{
  "id": "text-davinci-003",
  "object": "engine",
  "owner": "openai",
  "ready": true
}

Parameter details

Frequency and presence penalties

The frequency and presence penalties found in the Completions API can be used to reduce the likelihood of sampling repetitive sequences of tokens. They work by directly modifying the logits (un-normalized log-probabilities) with an additive contribution.

mu[j] -> mu[j] - c[j] * alpha_frequency - float(c[j] > 0) * alpha_presence

Where:

  • mu[j] is the logits of the j-th token
  • c[j] is how often that token was sampled prior to the current position
  • float(c[j] > 0) is 1 if c[j] > 0 and 0 otherwise
  • alpha_frequency is the frequency penalty coefficient
  • alpha_presence is the presence penalty coefficient

As we can see, the presence penalty is a one-off additive contribution that applies to all tokens that have been sampled at least once and the frequency penalty is a contribution that is proportional to how often a particular token has already been sampled.

Reasonable values for the penalty coefficients are around 0.1 to 1 if the aim is to just reduce repetitive samples somewhat. If the aim is to strongly suppress repetition, then one can increase the coefficients up to 2, but this can noticeably degrade the quality of samples. Negative values can be used to increase the likelihood of repetition.