@ideadesignmedia/open-ai.js v1.0.15
open-ai.js
Easily interact with the OpenAI API through our SDK.
Installation
yarn add @ideadesignmedia/open-ai.js
You must include new environment variables in node before requiring this package:
process.env.OPEN_AI_ORGANIZATION = <your-organization>
process.env.OPEN_AI_SECRET = <your-secret-key>You can also pass these as arguments when starting your node process:
node <filename> <...other_arguments> OPEN_AI_ORGANIZATION=<your-organization> OPEN_AI_SECRET=<your-secret-key>
Usage
/* process.env.OPEN_AI_ORGANIZATION and process.env.OPEN_AI_SECRET should already be initialized */
const {
Message,
getModel,
getModels,
edit,
completion,
completionStream,
chatCompletion,
chatCompletionStream,
getFineTunedModels,
getFineTunedModel,
createFineTunedModel,
cancelFineTune,
deleteFineTune,
getFineTuneEvents,
generateImage,
editImage,
getImageVariations,
getEmbedding,
getTranscription,
getTranslation,
getFiles,
getFile,
getFileContent,
uploadFile,
deleteFile,
moderation
} = require('@ideadesignmedia/open-ai.js');Message
Message(content, role) => {content, role}
This function is used to format a chat completion message. Possible roles for completion are system, user, assistant.
Parameters:
content: string: The content of the message.
role: string: The role of the message.
Sample usage:
const message = Message('Test message, send me back 10 resposes.', 'user'); // { content: 'Test message, send me back 10 resposes.', role: 'user' }getModels
getModels() => Promise<response>
Sample Response:
{
"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"
}getModel
getModel(modelId) => Promise<response>
Parameters:
modelId: string: The id of the model to retrieve.
Sample Response:
{
"id": "text-davinci-003",
"object": "model",
"owned_by": "openai",
"permission": [...]
}completion
completion(prompt, resultCount = 1, stop, options = { model: 'text-ada-001' }) => Promise<response>
Parameters:
prompt: string: A prompt to use for the completion.
resultCount: number: The number of responses to return.
stop: string[]: An array of strings to use as stop tokens.
options: object: The options to use for the completion. Possible options are:
{
model: 'text-ada-001', // possible models are: text-davinci-003, text-davinci-002, text-curie-001, text-babbage-001, text-ada-001, davinci, curie, babbage, ada
temperature: 0.5,
max_tokens: 32,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
stop: ['\n']
}Sample Response:
{
"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
}
}completionStream
completionStream(prompt, resultCount = 1, stop, options = { model: 'text-ada-001' }) => Promise<ResponseStream>
Note: Responses may be too fast to listen for the stream events through a response stream. Use the completion method instead.
Parameters:
prompt: string: Prompt to use for the completion.
resultCount: number: The number of responses to return.
stop: string[]: An array of strings to use as stop tokens.
options: object: The options to use for the completion. Possible options are:
{
model: 'text-ada-001', // possible models are: text-davinci-003, text-davinci-002, text-curie-001, text-babbage-001, text-ada-001, davinci, curie, babbage, ada
temperature: 0.5,
max_tokens: 32,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
stop: ['\n']
}ResponseStream
A ResponseStream EventEmitter that manages the response events from the OpenAI API. Manage the stream with the following methods:
onComplete: () => void
onData = (data: string[]) => voidSample Usage:
completionStream('Test message, send me back 10 resposes.').then(stream => new Promise((res) => {
let d = ''
stream.onComplete = () => {
console.log('Stream Complete: ', data);
res(d)
}
stream.onData = (data) => {
console.log('Stream Data: ', data);
d += data;
}
})).catch(console.error)chatCompletion
chatCompletion(messages = [], resultCount = 1, stop, options = { model: 'gpt-3.5-turbo' }) => Promise<response>
Parameters:
messages: Message[]: An array of Message objects to use for the chat completion.
resultCount: number: The number of responses to return.
stop: string[]: An array of strings to use as stop tokens.
options: object: The options to use for the chat completion. Possible options are:
{
model: 'gpt-3.5-turbo',
temperature: 0.5,
max_tokens: 32,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
stop: ['\n']
}Sample Response:
{
"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
}
}chatCompletionStream
chatCompletionStream(messages = [], resultCount = 1, stop, options = { model: 'gpt-3.5-turbo' }) => Promise<ResponseStream>
Parameters:
messages: Message[]: An array of Message objects to use for the chat completion.
resultCount: number: The number of responses to return.
stop: string[]: An array of strings to use as stop tokens.
options: object: The options to use for the chat completion. Possible options are:
{
model: 'gpt-3.5-turbo',
temperature: 0.5,
max_tokens: 32,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
stop: ['\n']
}Sample Response:
see completionStream
edit
edit(instruction, input, numberOfEdits, options = {model: 'text-davinci-edit-001'}) => Promise<response>
Parameters:
instruction: string: The instruction to use for the edit.
input: string: The input to use for the edit.
numberOfEdits: number: The number of edits to return.
options: object: The options to use for the edit. Possible options are:
{
model: 'text-davinci-edit-001',
temperature: 0.5,
max_tokens: 32,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
stop: ['\n']
}Sample Response:
{
"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
}
}generateImage
generateImage(prompt, resultCount, size = 0, responseFormat = 'url', user) => Promise<response>
Parameters:
prompt: string: The prompt to use for the image.
resultCount: number: The number of images to return. Maximum result count is 10.
size: number: The size of the image to return. Possible Image Sizes:
{
0: '256x256',
1: '512x512',
2: '1024x1024
}responseFormat: string: Possible response formats are url or b64_json or use file to return the image as a buffer as well as the url.
user: string: The user to use for the request. If not provided, the default user will be used.
Sample Response:
{
"created": 1589478378,
"data": [
{
"url": "https://..."
},
{
"url": "https://..."
}
]
}editImage
editImage(imagePath, prompt, maskPath, resultCount, size, responseFormat, user) => Promise<response>
Parameters:
imagePath: string: Use a url or relative/absolute path to an image.
prompt: string: The prompt to use for the image.
maskPath: string: Use a url or relative/absolute path to an image.
resultCount: number: The number of variations to return. Maximum result count is 10.
size: number: The size of the image to return. See generateImage for possible image sizes.
responseFormat: string: May use response types url or b64_json or use file to return the image as a buffer as well as the url.
user: string: Optional user id to associate with the request.
Sample Response:
{
"created": 1589478378,
"data": [
{
"url": "https://..."
},
{
"url": "https://..."
}
]
}getImageVariations
getImageVariations(imagePath, resultCount, size, responseFormat, user) => Promise<response>
Parameters:
imagePath: string: Use a url or relative/absolute path to an image.
resultCount: number: The number of variations to return. Maximum result count is 10.
size: number: The size of the image to return. See generateImage for possible image sizes.
responseFormat: string: May use response types url or b64_json or use file to return the image as a buffer as well as the url.
user: string: Optional user id to associate with the request.
Sample Response:
{
"created": 1589478378,
"data": [
{
"url": "https://..."
},
{
"url": "https://..."
}
]
}getEmbedding
getEmbedding(input, model = 'text-embedding-ada-002', user) => Promise<response>
Parameters:
input: string: input to get embedding for
model: string: model to use for embedding
user: string: Optional user id to associate with the request.
Sample Response:
{
"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
}
}getTranscription
getTranscription(file, prompt, language = 'en', responseFormat = 'json', temperature = 0) => Promise<response>
Parameters:
file: string: Use a url or relative/absolute path to an audio file.
prompt: string: The prompt to use for the transcription.
language: string: The language to use for the transcription. default is en.
responseFormat: string: May use response types json or .
temperature: number: The temperature to use for the transcription.
Sample Response:
{
"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."
}getTranslation
getTranslation(file, prompt, responseFormat, temperature) => Promise<response>
Parameters:
file: string: Use a url or relative/absolute path to an audio file.
prompt: string: The prompt to use for the translation.
responseFormat: string: May use response types json or .
temperature: number: The temperature to use for the translation.
Sample Response:
{
"text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
}getFiles
getFiles() => Promise<response>
Sample Response:
{
"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"
}getFile
getFile(fileId) => Promise<response>
Parameters:
fileId: string: The id of the file to retrieve.
Sample Response:
{
"id": "file-XjGxS3KTG0uNmNOK362iJua3",
"object": "file",
"bytes": 140,
"created_at": 1613779657,
"filename": "mydata.jsonl",
"purpose": "fine-tune"
}getFileContent
getFileContent(fileId) => Promise<string>
Parameters:
fileId: string: The id of the file to retrieve.
Sample Response:
File content as utf-8 string.
uploadFile
uploadFile(file, fileName, fileType, filePurpose) => Promise<response>
Parameters:
file: string: Use a url or relative/absolute path to a file.
fileName: string: The name of the file to upload.
fileType: string: The type of the file to upload. Possible types are search or fine-tune.
filePurpose: string: The purpose of the file to upload. Possible purposes are search or fine-tune.
Sample Response:
{
"id": "file-XjGxS3KTG0uNmNOK362iJua3",
"object": "file",
"bytes": 140,
"created_at": 1613779121,
"filename": "mydata.jsonl",
"purpose": "fine-tune"
}deleteFile
deleteFile(fileId) => Promise<response>
Parameters:
fileId: string: The id of the file to delete.
Sample Response:
{
"id": "file-XjGxS3KTG0uNmNOK362iJua3",
"object": "file",
"deleted": true
}getFineTunedModels
getFineTunedModels() => Promise<response>
Sample Response:
{
"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,
},
{ ... },
{ ... }
]
}getFineTunedModel
getFineTunedModel(modelId) => Promise<response>
Parameters:
modelId: string: The id of the fine-tuned model to retrieve.
Sample Response:
{
"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,
}createFineTunedModel
createFineTunedModel(trainingFile, validationFile, model, epochs, batchSize, learningRate, promptLoss, computeClassificationMetrics, classificationClasses, classificationBetas, suffix) => Promise<response>
Parameters:
trainingFile: string: File to use for training.
validationFile: string: File to use for validation
model: string: Name of model to train
epochs: Number of epochs to train for
batchSize: Batch size to use for training
learningRate: Learning rate to use for training
promptLoss: Prompt loss weight to use for training
computeClassificationMetrics: Whether to compute classification metrics
classificationClasses: List of classes to use for classification
classificationBetas: List of betas to use for classification
suffix: Suffix to append to fine-tuned model name
Sample Response:
{
"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,
}deleteFineTune
deleteFineTunedModel(modelId) => Promise<response>
Parameters:
modelId: string: ID of fine-tuned model to delete
Sample Response:
{
"id": "curie:ft-acmeco-2021-03-03-21-44-20",
"object": "model",
"deleted": true
}getFineTuneEvents
getFineTuneEvents(modelId) => Promise<response>
Parameters:
modelId: string: ID of fine-tuned model to get events for
Sample Response:
{
"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."
}
]
}cancelFineTune
cancelFineTune(modelId) => Promise<response>
Parameters:
modelId: string: ID of fine-tuned model to cancel
Sample Response:
{
"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,
}moderation
moderation(input, model = 'moderation', user) => Promise<response>
Parameters:
input: string: Text to moderate
model: string: Name of model to use for moderation. Defaults to moderation
user: string: User ID to use for moderation
Sample Response:
{
"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
}
]
}