const gpt3encoder = require('gpt-3-encoder');
const { typeCheck } = require('type-check');
const FormData = require('form-data');
const axios = require('axios').create({
baseURL: 'https://api.openai.com/v1'
});
const stream = require('stream');
const assert = require('assert');
const parameters = require('./utils/parameters.json');
const RequestError = require('./utils/RequestError');
const { ReadStream } = require('fs');
/**
* OpenAI client library.
*/
class OpenAI {
#api_key;
#organization_id;
/**
* @param {String} key API key
* @param {String} [organization=null] Organization ID
* @param {String} [engine=davinci] The engine you will use in your requests
* @see https://beta.openai.com/docs/api-reference/authentication
*/
constructor(key, organization = null, engine = 'davinci') {
assert.strictEqual(typeof key, 'string', 'key must be a string');
assert.ok(organization ? typeof organization === 'string' : true, 'organization must be a string');
assert.ok(engine ? typeof engine === 'string' : true, 'engine must be a string');
this.#api_key = key;
this.#organization_id = organization;
this.engine = engine;
}
_request(endpoint = '', request_content = {}, request_type = 'POST', custom_header = {}) {
assert.strictEqual(typeof endpoint, 'string', 'endpoint must be a string');
assert.strictEqual(typeof request_content, 'object', 'request_content must be an object');
assert.strictEqual(typeof request_type, 'string', 'request_type must be a string');
assert.strictEqual(typeof custom_header, 'object', 'custom_header must be a string');
request_type = request_type.toUpperCase();
assert.ok(request_type === 'GET' || request_type === 'POST' || request_type === 'DELETE', 'invalid request_type');
let headers = {
'Authorization': 'Bearer ' + this.#api_key,
}
if (this.#organization_id)
headers['OpenAI-Organization'] = this.#organization_id;
if (request_type === 'POST')
headers['content-type'] = 'application/json';
headers = {...headers, ...custom_header};
if (request_type === 'POST')
return axios.post(endpoint, request_content, {headers});
else if (request_type === 'GET')
return axios.get(endpoint, {headers, params: request_content});
else if (request_type === 'DELETE')
return axios.delete(endpoint, {headers, request_content});
}
/**
* @typedef {Object} Engine
*
* @property {String} id
* @property {String} object
* @property {Number|null} created
* @property {Number|null} max_replicas
* @property {String} owner
* @property {any} permissions
* @property {boolean} ready
* @property {boolean|null} ready_replicas
* @property {any} ready_replicas
* @see https://beta.openai.com/docs/api-reference/engines
*/
/**
* Lists the currently available engines, and provides basic information about each one such as the owner and availability.
* @returns {Promise<Array<Engine>>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* client.getEngines()
* .then(engines => {
* console.log(`Engines: ${engines.map(engine => engine.id).join(', ')}`);
* })
* .catch(console.error);
*
* @see https://beta.openai.com/docs/api-reference/engines/list
*/
async getEngines() {
return this._request('/engines', {}, 'GET')
.then(res => res.data.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* Retrieves an engine instance, providing basic information about the engine such as the owner and availability.
* @param {String} engine The ID of the engine to use for this request
* @returns {Promise<Engine>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* client.getEngine('davinci')
* .then(engine => {
* console.log(`Engine owner: ${engine.owner}`);
* })
* .catch(console.error);
*
* @see https://beta.openai.com/docs/api-reference/engines/retrieve
*/
async getEngine(engine) {
assert.strictEqual(typeof engine, 'string', 'engine must be a string');
return this._request('/engines/' + engine, {}, 'GET')
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* @typedef {Object} Completion
*
* @property {String} id
* @property {String} object
* @property {Number|null} created
* @property {String} model
* @property {array<CompletionChoice>} choices
* @see https://beta.openai.com/docs/api-reference/completions
*/
/**
* @typedef {Object} CompletionBody
*
* @property {Number} [max_tokens] The maximum number of tokens to generate.
* @property {Number} [temperature] What sampling temperature to use.
* @property {Number} [top_p] An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
* @property {Number} [n] How many completions to generate for each prompt.
* @property {Boolean} [stream] Whether to stream back partial progress.
* @property {Number} [logprobs] Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens.
* @property {Boolean} [echo] Echo back the prompt in addition to the completion
* @property {String|Array<String>} [stop] Up to 4 sequences where the API will stop generating further tokens.
* @property {Number} [presence_penalty] Number between 0 and 1 that penalizes new tokens based on whether they appear in the text so far.
* @property {Number} [frequency_penalty] Number between 0 and 1 that penalizes new tokens based on their existing frequency in the text so far.
* @property {Number} [best_of] Generates best_of completions server-side and returns the "best" (the one with the lowest log probability per token).
* @property {Object} [logit_bias] Modify the likelihood of specified tokens appearing in the completion.
* @see https://beta.openai.com/docs/api-reference/completions
*/
/**
* @typedef {Object} CompletionChoice
*
* @property {String} text
* @property {Number} index
* @property {Number|null} logprobs
* @property {String} finish_reason
* @see https://beta.openai.com/docs/api-reference/completions
*/
/**
* Creates a new completion for the provided prompt and parameters.
* @param {string|array} [prompt=<|endoftext|>] The prompt(s) to generate completions for, encoded as a string, a list of strings, or a list of token lists.
* @param {CompletionBody} [body={}]
* @returns {Promise<Completion>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* var prompt = 'My name is Bond';
* client.complete(prompt, {stop: ['\n', '"'], temperature: 0})
* .then(completion => {
* console.log(`Result: ${prompt}${completion.choices[0].text}`);
* })
* .catch(console.error);
*
* @see https://beta.openai.com/docs/api-reference/completions/create
*/
async complete(prompt, body = {}) {
assert.ok(typeof prompt === 'string' || Array.isArray(prompt), 'prompt must be a string or array');
assert.ok(typeCheck(parameters.completion, body), 'invalid body content');
if (body.engine) {
var engine = body.engine;
delete body.engine;
}
return this._request(`/engines/${engine || this.engine}/completions`, {prompt, ...body})
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* @typedef {Object} Search
*
* @property {Number} document
* @property {String} object
* @property {Number} score
* @see https://beta.openai.com/docs/api-reference/searches
*/
/**
* @typedef {Object} SearchBody
*
* @property {Array<String>} [documents] Up to 200 documents to search over, provided as a list of strings.
* @property {String} [file] The ID of an uploaded file that contains documents to search over.
* @property {Number} [max_rerank] The maximum number of documents to be re-ranked and returned by search.
* @property {Boolean} [return_metadata] A special boolean flag for showing metadata.
* @see https://beta.openai.com/docs/api-reference/searches
*/
/**
* Given a query and a set of documents or labels, the model ranks each document based on its semantic similarity to the provided query.
* @param {string|array} query
* @param {SearchBody} [body={}]
* @returns {Promise<Search>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* const documents = ['Dancing', 'Programming', 'Skating', 'Drawing'];
*
* client.search('I do not like CSS', {documents})
* .then(result => {
* var max_score = Math.max(...result.map(e => e.score), 0);
*
* console.log(`Likely subject: ${documents[result.find(e => e.score === max_score).document]} (${max_score})`);
* })
* .catch(console.error);
*
* @see https://beta.openai.com/docs/guides/search/create
*/
async search(query, body = {}) {
assert.strictEqual(typeof query, 'string', 'query must be a string');
assert.ok(typeCheck(parameters.search, body), 'invalid body content');
if (body.engine) {
var engine = body.engine;
delete body.engine;
}
return this._request(`/engines/${engine || this.engine}/search`, {query, ...body})
.then(res => res.data.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* @typedef {Object} Classification
*
* @property {String|Completion} completion
* @property {String} label
* @property {String} model
* @property {String} object
* @property {String} search_model
* @property {String} [prompt]
* @property {Array<ClassificationExample>} selected_examples
* @see https://beta.openai.com/docs/api-reference/classifications
*/
/**
* @typedef {Object} ClassificationBody
*
* @property {Array<Array<String>>} [examples] A list of examples with labels.
* @property {String} [file] The ID of the uploaded file that contains training examples.
* @property {Array<String>} [labels] The set of categories being classified.
* @property {String} [search_model] ID of the engine to use for Search.
* @property {Number} [temperature] What sampling temperature to use.
* @property {Number} [logprobs] Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens.
* @property {Number} [max_examples] The maximum number of examples to be ranked by Search when using file.
* @property {Object} [logit_bias] Modify the likelihood of specified tokens appearing in the completion.
* @property {Boolean} [return_prompt] If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion.
* @property {Boolean} [return_metadata] A special boolean flag for showing metadata.
* @property {Array<String>} [expand] If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID.
* @see https://beta.openai.com/docs/api-reference/classifications
*/
/**
* @typedef {Object} ClassificationExample
*
* @property {Number} document
* @property {String} label
* @property {String} text
* @see https://beta.openai.com/docs/api-reference/classifications
*/
/**
* [BETA] Classifies the specified query using provided examples.
* @param {string|array} query
* @param {ClassificationBody} [body={}]
* @returns {Promise<Classification>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* const examples = [
* ['A happy moment', 'Positive'],
* ['I am sad.', 'Negative'],
* ['I am feeling awesome', 'Positive']
* ];
*
* const labels = ['Positive', 'Negative', 'Neutral'];
*
* client.classificate('It is a raining day :(', {examples, labels, 'search_model': 'ada', 'model': 'curie'})
* .then(classification => {
* console.log(`Classification: ${classification.label}`);
* })
* .catch(console.error);
*
* @see https://beta.openai.com/docs/api-reference/classifications/create
*/
async classificate(query, body = {}) {
assert.strictEqual(typeof query, 'string', 'query must be a string');
assert.ok(typeCheck(parameters.classification, body), 'invalid body content');
if (body.model) {
var model = body.model;
delete body.model;
}
return this._request(`/classifications`, {model: model || this.engine, query, ...body})
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* @typedef {Object} Answer
*
* @property {Array<String>} answers
* @property {String|Completion} completion
* @property {String} model
* @property {String} object
* @property {String} search_model
* @property {String} [prompt]
* @property {Array<AnswerDocument>} selected_documents
* @see https://beta.openai.com/docs/api-reference/answers
*/
/**
* @typedef {Object} AnswerBody
*
* @property {Array<Array<String>>} examples List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like.
* @property {String} examples_context A text snippet containing the contextual information used to generate the answers for the examples you provide.
* @property {Array<String>} [documents] List of documents from which the answer for the input question should be derived.
* @property {String} [file] The ID of an uploaded file that contains documents to search over.
* @property {String} [search_model] ID of the engine to use for Search.
* @property {Boolean} [return_prompt] If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion.
* @property {Array<String>} [expand] If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID.
* @property {Number} [max_rerank] The maximum number of documents to be ranked by Search when using file.
* @property {Boolean} [return_metadata] A special boolean flag for showing metadata.
* @property {Number} [max_tokens] The maximum number of tokens allowed for the generated answer.
* @property {Number} [temperature] What sampling temperature to use.
* @property {Number} [n] How many answers to generate for each question.
* @property {Number} [logprobs] Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens.
* @property {String|Array<String>} [stop] Up to 4 sequences where the API will stop generating further tokens.
* @property {Object} [logit_bias] Modify the likelihood of specified tokens appearing in the completion.
* @see https://beta.openai.com/docs/api-reference/answers
*/
/**
* @typedef {Object} AnswerDocument
*
* @property {Number} document
* @property {String} text
* @see https://beta.openai.com/docs/api-reference/answers
*/
/**
* [BETA] Answers the specified question using the provided documents and examples.
* @param {string|array} question
* @param {AnswerBody} [body={}]
* @returns {Promise<Answer>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* const documents = ['Puppy A is happy.', 'Puppy B is sad.'];
* const examples_context = 'In 2017, U.S. life expectancy was 78.6 years.';
* const examples = [
* ['What is human life expectancy in the United States?', '78 years.']
* ];
*
* const stop = ['\n', '<|endoftext|>'];
*
* client.answer('Which puppy is happy?', {documents, examples_context, examples, stop})
* .then(answer => {
* console.log(`Answer: ${answer.answers[0]}`);
* })
* .catch(console.error);
*
* @see https://beta.openai.com/docs/api-reference/answers/create
*/
async answer(question, body = {}) {
assert.strictEqual(typeof question, 'string', 'question must be a string');
assert.ok(typeCheck(parameters.answer, body), 'invalid body content');
if (body.model) {
var model = body.model;
delete body.model;
}
return this._request(`/answers`, {model: model || this.engine, question: question, ...body})
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* @typedef {Object} File
*
* @property {String} id
* @property {String} object
* @property {Number} bytes
* @property {Number|null} created_at
* @property {String} filename
* @property {String} purpose
* @property {String} status
* @property {String|null} status_details
* @see https://beta.openai.com/docs/api-reference/files
*/
/**
* Returns a list of files that belong to the user's organization.
* @returns {Promise<Array<File>>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* client.getFiles()
* .then(console.log)
* .catch(console.error);
* @see https://beta.openai.com/docs/api-reference/files/list
*/
async getFiles() {
return this._request('/files', {}, 'GET')
.then(res => res.data.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* Returns information about a specific file.
* @param {String} filename
* @returns {Promise<File>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* client.getFile('FILE_ID')
* .then(console.log)
* .catch(console.error);
* @see https://beta.openai.com/docs/api-reference/files/retrieve
*/
async getFile(fileId) {
assert.strictEqual(typeof fileId, 'string', 'fileId must be a string');
return this._request('/files/' + fileId, {}, 'GET')
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* @typedef {Object} DeletedFile
*
* @property {String} id
* @property {String} object
* @property {Boolean} deleted
* @see https://beta.openai.com/docs/api-reference/files
*/
/**
* Delete a File.
* @param {String} fileId
* @returns {DeletedFile}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* client.deleteFile('FILE_ID')
* .then(console.log)
* .catch(console.error);
* @see https://beta.openai.com/docs/api-reference/files/delete
*/
async deleteFile(fileId) {
assert.strictEqual(typeof fileId, 'string', 'fileId must be a string');
return this._request('/files/' + fileId, {}, 'DELETE')
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* Upload a file that contains document(s) to be used across various endpoints/features.
* @param {string|ReadStream} file The content of the JSON to be uploaded.
* @param {String} purpose The intended purpose of the uploaded documents.
* @returns {File}
* @example
* const fs = require('fs');
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* client.uploadFile(fs.createReadStream('file.jsonl'), 'answers')
* .then(console.log)
* .catch(console.error);
*
* client.uploadFile('{"text": "A text here"}', 'answers')
* .then(console.log)
* .catch(console.error);
* @see https://beta.openai.com/docs/api-reference/files/upload
*/
async uploadFile(file, purpose) {
assert.ok(typeof file === 'string' || file instanceof stream.Readable, 'file must be a string or readableStream');
assert.strictEqual(typeof purpose, 'string', 'purpose must be a string');
assert.ok(purpose === 'search' || purpose === 'answers' || purpose === 'classifications', 'invalid purpose');
if (typeof file === 'string') {
let content = file;
file = new stream.Readable();
file.push(content);
file.push(null);
file.path = 'file.jsonl';
}
let data = new FormData();
data.append('file', file);
data.append('purpose', purpose);
return this._request('/files', data, 'POST', data.getHeaders())
.then(res => res.data)
.catch(err => {
throw new RequestError(err.response.data.error);
});
}
/**
* Split text by keys.
* @param {String} text The string to be encoded
* @returns {Array<String>}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* var encoded = client.encode('Hello, world!');
* console.log(encoded);
* @see https://beta.openai.com/docs/introduction/key-concepts
*/
encode(text) {
assert.strictEqual(typeof text, 'string', 'text must be a string');
return gpt3encoder.encode(text);
}
/**
* Decode keys to text.
* @param {Array<String>} text The encoded text
* @returns {string}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* var encoded = client.encode('Hello, world!');
* var decoded = client.decode(encoded);
* console.log(`Decoded: ${decoded}`);
* @see https://beta.openai.com/docs/introduction/key-concepts
*/
decode(encoded_text) {
assert.ok(Array.isArray(encoded_text), 'encoded_text must be an array');
return gpt3encoder.decode(encoded_text);
}
/**
* Shows the token amount of the inserted text.
* @param {String} text The string to get the token amount
* @returns {Number}
* @example
* const OpenAI = require('openai-nodejs');
* const client = new OpenAI('YOUR_API_KEY');
*
* var tokens = client.tokens('Hello, world!');
* console.log(`Tokens count: ${tokens}`);
* @see https://beta.openai.com/docs/introduction/key-concepts
*/
tokens(text) {
assert.strictEqual(typeof text, 'string', 'text must be a string');
return this.encode(text).length;
}
}
module.exports = OpenAI;source