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 YOUR_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 YOUR_API_KEY' \
  -H 'OpenAI-Organization: YOUR_ORG_ID'

Example with the openai Python package:

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import os
import openai
openai.organization = "YOUR_ORG_ID"
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: "YOUR_ORG_ID",
    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 YOUR_API_KEY with your secret API key.

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curl https://api.openai.com/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{"model": "text-davinci-003", "prompt": "Say this is a test", "temperature": 0, "max_tokens": 7}'

This request queries the Davinci model to complete the text starting with a prompt of "Say this is a test". The max_tokens parameter sets an upper bound on how many tokens the API will return. You should get a response back that resembles the following:

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{
    "id": "cmpl-GERzeJQ4lvqPk8SkZu4XMIuR",
    "object": "text_completion",
    "created": 1586839808,
    "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
    }
}

Now you've generated your first completion. If you concatenate the prompt and the completion text (which the API will do for you if you set the echo parameter to true), the resulting text is "Say this is a test. This is indeed a test." You can also set the stream parameter to true for the API to stream back text (as data-only server-sent events).

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 YOUR_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 YOUR_API_KEY'

Response

text-davinci-003

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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 YOUR_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
  }
}

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 YOUR_API_KEY' \
  -d '{
  "model": "text-davinci-edit-001",
  "input": "What day of the wek is it?",
  "instruction": "Fix the spelling mistakes"
}'

Parameters

text-davinci-edit-001

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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 YOUR_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 YOUR_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 YOUR_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 \
  -X POST \
  -H "Authorization: Bearer YOUR_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
  }
}

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 YOUR_API_KEY'

Response

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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

curl

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curl https://api.openai.com/v1/files \
  -H "Authorization: Bearer YOUR_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 YOUR_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 YOUR_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 YOUR_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

curl

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curl https://api.openai.com/v1/fine-tunes \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_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 YOUR_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 YOUR_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 \
  -X POST \
  -H "Authorization: Bearer YOUR_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 YOUR_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 YOUR_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

curl

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curl https://api.openai.com/v1/moderations \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer YOUR_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 YOUR_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 YOUR_API_KEY'

Response

text-davinci-003

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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.

Introduction

Authentication

Requesting organization

Making requests

Models

List models

Retrieve model

Path parameters

Completions

Create completion

Request body

Edits

Create edit

Request body

Images

Create imageBeta

Request body

Create image editBeta

Request body

Create image variation Beta

Request body

Embeddings

Create embeddings

Request body

Files

List files

Upload file

Request body

Delete file

Path parameters

Retrieve file

Path parameters

Retrieve file content

Path parameters

Fine-tunes

Create fine-tune

Request body

List fine-tunes

Retrieve fine-tune

Path parameters

Cancel fine-tune

Path parameters

List fine-tune events

Path parameters

Query parameters

Delete fine-tune model

Path parameters

Moderations

Create moderation

Request body

Engines

List enginesDeprecated

Retrieve engineDeprecated

Path parameters

Parameter details

Create image
Beta

Create image edit
Beta

Create image variation
Beta

List engines
Deprecated

Retrieve engine
Deprecated