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:
curl https://api.openai.com/v1/models \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Organization: YOUR_ORG_ID"
Example with the openai Python package: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:
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 $OPENAI_API_KEY with your secret API key.
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:
{
"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 requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/models \ -H "Authorization: Bearer $OPENAI_API_KEY"
ResponseCopy
{
"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
The ID of the model to use for this request
Example requesttext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003curlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/models/text-davinci-003 \
-H "Authorization: Bearer $OPENAI_API_KEY"Responsetext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003Copy
{
"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
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.
promptstring or arrayOptionalDefaults 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.
suffixstringOptionalDefaults to null
The suffix that comes after a completion of inserted text.
max_tokensintegerOptionalDefaults 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).
temperaturenumberOptionalDefaults 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_pnumberOptionalDefaults 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.
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.
streambooleanOptionalDefaults 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.
logprobsintegerOptionalDefaults 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.
echobooleanOptionalDefaults to false
Echo back the prompt in addition to the completion
stopstring or arrayOptionalDefaults to null
Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
presence_penaltynumberOptionalDefaults 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_penaltynumberOptionalDefaults 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_ofintegerOptionalDefaults 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_biasmapOptionalDefaults 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.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example requesttext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003curlSelect librarycurlpythonnode.jsCopy
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
}'Parameterstext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003Copy
{ "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" }
Responsetext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003Copy
{ "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 list of messages describing a conversation, the model will return a response.
Create chat completionBeta
POST https://api.openai.com/v1/chat/completions
Creates a model response for the given chat conversation.
Request body
ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
A list of messages describing the conversation so far.
The role of the author of this message. One of system, user, or assistant.
The contents of the message.
The name of the author of this message. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
temperaturenumberOptionalDefaults 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_pnumberOptionalDefaults 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.
How many chat completion choices to generate for each input message.
streambooleanOptionalDefaults 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.
stopstring or arrayOptionalDefaults to null
Up to 4 sequences where the API will stop generating further tokens.
max_tokensintegerOptionalDefaults 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_penaltynumberOptionalDefaults 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_penaltynumberOptionalDefaults 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_biasmapOptionalDefaults 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.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example requestcurlSelect librarycurlpythonnode.jsCopy
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!"}]
}'ParametersCopy
{ "model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hello!"}] }
ResponseCopy
{
"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
ID of the model to use. You can use the text-davinci-edit-001 or code-davinci-edit-001 model with this endpoint.
inputstringOptionalDefaults to ''
The input text to use as a starting point for the edit.
The instruction that tells the model how to edit the prompt.
How many edits to generate for the input and instruction.
temperaturenumberOptionalDefaults 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_pnumberOptionalDefaults 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 requesttext-davinci-edit-001text-davinci-edit-001curlSelect librarycurlpythonnode.jsCopy
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"
}'Parameterstext-davinci-edit-001text-davinci-edit-001Copy
{
"model": "text-davinci-edit-001",
"input": "What day of the wek is it?",
"instruction": "Fix the spelling mistakes",
}ResponseCopy
{ "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 imageBeta
POST https://api.openai.com/v1/images/generations
Creates an image given a prompt.
Request body
A text description of the desired image(s). The maximum length is 1000 characters.
The number of images to generate. Must be between 1 and 10.
sizestringOptionalDefaults to 1024x1024
The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
response_formatstringOptionalDefaults to url
The format in which the generated images are returned. Must be one of url or b64_json.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example requestcurlSelect librarycurlpythonnode.jsCopy
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"
}'ParametersCopy
{
"prompt": "A cute baby sea otter",
"n": 2,
"size": "1024x1024"
}ResponseCopy
{
"created": 1589478378,
"data": [
{
"url": "https://..."
},
{
"url": "https://..."
}
]
}Create image editBeta
POST https://api.openai.com/v1/images/edits
Creates an edited or extended image given an original image and a prompt.
Request body
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.
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.
A text description of the desired image(s). The maximum length is 1000 characters.
The number of images to generate. Must be between 1 and 10.
sizestringOptionalDefaults to 1024x1024
The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
response_formatstringOptionalDefaults to url
The format in which the generated images are returned. Must be one of url or b64_json.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example requestcurlSelect librarycurlpythonnode.jsCopy
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"ResponseCopy
{ "created": 1589478378, "data": [ { "url": "https://..." }, { "url": "https://..." } ] }
Create image variationBeta
POST https://api.openai.com/v1/images/variations
Creates a variation of a given image.
Request body
The image to use as the basis for the variation(s). Must be a valid PNG file, less than 4MB, and square.
The number of images to generate. Must be between 1 and 10.
sizestringOptionalDefaults to 1024x1024
The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
response_formatstringOptionalDefaults to url
The format in which the generated images are returned. Must be one of url or b64_json.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/images/variations \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-F image="@otter.png" \
-F n=2 \
-F size="1024x1024"ResponseCopy
{ "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
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 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.
A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
Example requestcurlSelect librarycurlpythonnode.jsCopy
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"
}'ParametersCopy
{
"model": "text-embedding-ada-002",
"input": "The food was delicious and the waiter..."
}ResponseCopy
{ "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 transcriptionBeta
POST https://api.openai.com/v1/audio/transcriptions
Transcribes audio into the input language.
Request body
The audio file to transcribe, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.
ID of the model to use. Only whisper-1 is currently available.
An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
response_formatstringOptionalDefaults to json
The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
temperaturenumberOptionalDefaults 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.
The language of the input audio. Supplying the input language in ISO-639-1 format will improve accuracy and latency.
Example requestcurlSelect librarycurlpythonnodeCopy
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"ParametersCopy
{ "file": "audio.mp3", "model": "whisper-1" }
ResponseCopy
{
"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 translationBeta
POST https://api.openai.com/v1/audio/translations
Translates audio into into English.
Request body
The audio file to translate, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.
ID of the model to use. Only whisper-1 is currently available.
An optional text to guide the model's style or continue a previous audio segment. The prompt should be in English.
response_formatstringOptionalDefaults to json
The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
temperaturenumberOptionalDefaults 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 requestcurlSelect librarycurlpythonnodeCopy
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"ParametersCopy
{ "file": "german.m4a", "model": "whisper-1" }
ResponseCopy
{ "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 requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/files \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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
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.
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 requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/files \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-F purpose="fine-tune" \
-F file="@mydata.jsonl"ResponseCopy
{ "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
The ID of the file to use for this request
Example requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
-X DELETE \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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
The ID of the file to use for this request
Example requestcurlSelect librarycurlpythonnode.jsCopy
1
2
curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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
The ID of the file to use for this request
Example requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3/content \
-H "Authorization: Bearer $OPENAI_API_KEY" > file.jsonlFine-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.
Request body
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.
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.
modelstringOptionalDefaults 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_epochsintegerOptionalDefaults to 4
The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
batch_sizeintegerOptionalDefaults 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_multipliernumberOptionalDefaults 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_weightnumberOptionalDefaults 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_metricsbooleanOptionalDefaults 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_classesintegerOptionalDefaults to null
The number of classes in a classification task.
This parameter is required for multiclass classification.
classification_positive_classstringOptionalDefaults to null
The positive class in binary classification.
This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.
classification_betasarrayOptionalDefaults 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.
suffixstringOptionalDefaults 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 requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/fine-tunes \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"training_file": "file-XGinujblHPwGLSztz8cPS8XY"
}'ResponseCopy
{ "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 requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/fine-tunes \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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.
Path parameters
The ID of the fine-tune job
Example requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F \ -H "Authorization: Bearer $OPENAI_API_KEY"
ResponseCopy
{
"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
The ID of the fine-tune job to cancel
Example requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/cancel \ -H "Authorization: Bearer $OPENAI_API_KEY"
ResponseCopy
{ "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
The ID of the fine-tune job to get events for.
Query parameters
streambooleanOptionalDefaults 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 requestcurlSelect librarycurlpythonnode.jsCopy
1
2
curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/events \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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
The model to delete
Example requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/models/curie:ft-acmeco-2021-03-03-21-44-20 \
-X DELETE \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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
The input text to classify
modelstringOptionalDefaults 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 requestcurlSelect librarycurlpythonnode.jsCopy
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."
}'ParametersCopy
{
"input": "I want to kill them."
}ResponseCopy
{ "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 enginesDeprecated
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 requestcurlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/engines \
-H "Authorization: Bearer $OPENAI_API_KEY"ResponseCopy
{ "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 engineDeprecated
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
The ID of the engine to use for this request
Example requesttext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003curlSelect librarycurlpythonnode.jsCopy
curl https://api.openai.com/v1/engines/text-davinci-003 \
-H "Authorization: Bearer $OPENAI_API_KEY"Responsetext-davinci-003text-ada-001text-babbage-001text-curie-001text-davinci-003Copy
{ "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.