What is OpenAI

Installation

dependencies:[.package(url:"https://github.com/MacPaw/OpenAI.git", branch:"main")]

Usage

Initialization

letopenAI=OpenAI(apiToken:"YOUR_TOKEN_HERE")

letconfiguration=OpenAI.Configuration(token:"YOUR_TOKEN_HERE", organizationIdentifier:"YOUR_ORGANIZATION_ID_HERE", timeoutInterval:60.0)letopenAI=OpenAI(configuration: configuration)

Chats

structChatQuery:Codable{    /// ID of the model to use.publicletmodel:Model    /// An object specifying the format that the model must output.publicletresponseFormat:ResponseFormat?    /// The messages to generate chat completions forpublicletmessages:[Message]    /// A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.publiclettools:[Tool]?    /// Controls how the model responds to tool calls. "none" means the model does not call a function, and responds to the end-user. "auto" means the model can pick between and end-user or calling a function. Specifying a particular function via `{"name": "my_function"}` forces the model to call that function. "none" is the default when no functions are present. "auto" is the default if functions are present.publiclettoolChoice:ToolChoice?    /// 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  We generally recommend altering this or top_p but not both.publiclettemperature:Double?    /// 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.publiclettopP:Double?    /// How many chat completion choices to generate for each input message.publicletn:Int?    /// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.publicletstop:[String]?    /// The maximum number of tokens to generate in the completion.publicletmaxTokens:Int?    /// 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.publicletpresencePenalty:Double?    /// 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.publicletfrequencyPenalty:Double?    /// Modify the likelihood of specified tokens appearing in the completion.publicletlogitBias:[String:Int]?    /// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.publicletuser:String?}

structChatResult:Codable,Equatable{publicstructChoice:Codable,Equatable{publicletindex:Intpublicletmessage:ChatpublicletfinishReason:String}publicstructUsage:Codable,Equatable{publicletpromptTokens:IntpublicletcompletionTokens:IntpubliclettotalTokens:Int}publicletid:Stringpublicletobject:Stringpublicletcreated:TimeIntervalpublicletmodel:Modelpublicletchoices:[Choice]publicletusage:Usage}

letquery=ChatQuery(model:.gpt3_5Turbo, messages:[.init(role:.user, content:"who are you")])letresult=tryawait openAI.chats(query: query)

(lldb) po result▿ ChatResult  - id : "chatcmpl-6pwjgxGV2iPP4QGdyOLXnTY0LE3F8"  - object : "chat.completion"  - created : 1677838528.0  - model : "gpt-3.5-turbo-0301"  ▿ choices : 1 element    ▿ 0 : Choice      - index : 0      ▿ message : Chat        - role : "assistant"        - content : "\n\nI\'m an AI language model developed by OpenAI, created to provide assistance and support for various tasks such as answering questions, generating text, and providing recommendations. Nice to meet you!"      - finish_reason : "stop"  ▿ usage : Usage    - prompt_tokens : 10    - completion_tokens : 39    - total_tokens : 49

Chats Streaming

openAI.chatsStream(query: query){ partialResultinswitch partialResult{case.success(let result):print(result.choices)case.failure(let error):        //Handle chunk error here}} completion:{ errorin    //Handle streaming error here}

openAI.chatsStream(query: query).sink{ completionin        //Handle completion result here} receiveValue:{ resultin        //Handle chunk here}.store(in:&cancellables)

fortryawaitresultin openAI.chatsStream(query: query){   //Handle result here}

letopenAI=OpenAI(apiToken:"...")// Declare functions which GPT-3 might decide to call.letfunctions=[FunctionDeclaration(      name:"get_current_weather",      description:"Get the current weather in a given location",      parameters:JSONSchema(          type:.object,          properties:["location":.init(type:.string, description:"The city and state, e.g. San Francisco, CA"),"unit":.init(type:.string, enumValues:["celsius","fahrenheit"])],          required:["location"]))]letquery=ChatQuery(  model:"gpt-3.5-turbo-0613",  // 0613 is the earliest version with function calls support.  messages:[Chat(role:.user, content:"What's the weather like in Boston?")],  tools: functions.map{Tool.function($0)})letresult=tryawait openAI.chats(query: query)

{"id":"chatcmpl-1234","object":"chat.completion","created":1686000000,"model":"gpt-3.5-turbo-0613","choices": [    {"index":0,"message": {"role":"assistant","tool_calls": [          {"id":"call-0","type":"function","function": {"name":"get_current_weather","arguments":"{\n\"location\":\"Boston, MA\"\n}"            }          }        ]      },"finish_reason":"function_call"    }  ],"usage": {"total_tokens":100,"completion_tokens":18,"prompt_tokens":82 }}

Structured Output

structMovieInfo:StructuredOutput{lettitle:Stringletdirector:Stringletrelease:Dateletgenres:[MovieGenre]letcast:[String]staticletexample:Self={.init(            title:"Earth",            director:"Alexander Dovzhenko",            release:Calendar.current.date(from:DateComponents(year:1930, month:4, day:1))!,            genres:[.drama],            cast:["Stepan Shkurat","Semyon Svashenko","Yuliya Solntseva"])}()}enumMovieGenre:String,Codable,StructuredOutputEnum{case action, drama, comedy, scifivarcaseNames:[String]{Self.allCases.map{ $0.rawValue}}}letquery=ChatQuery(    messages:[.system(.init(content:"Best Picture winner at the 2011 Oscars"))],    model:.gpt4_o,    responseFormat:.jsonSchema(name:"movie-info", type:MovieInfo.self))letresult=tryawait openAI.chats(query: query)

Images

Create Image

structImagesQuery:Codable{    /// A text description of the desired image(s). The maximum length is 1000 characters.publicletprompt:String    /// The number of images to generate. Must be between 1 and 10.publicletn:Int?    /// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.publicletsize:String?}

structImagesResult:Codable,Equatable{publicstructURLResult:Codable,Equatable{publicleturl:String}publicletcreated:TimeIntervalpublicletdata:[URLResult]}

letquery=ImagesQuery(prompt:"White cat with heterochromia sitting on the kitchen table", n:1, size:"1024x1024")openAI.images(query: query){ resultin  //Handle result here}//orletresult=tryawait openAI.images(query: query)

(lldb) po result▿ ImagesResult  - created : 1671453505.0  ▿ data : 1 element    ▿ 0 : URLResult      - url : "https://oaidalleapiprodscus.blob.core.windows.net/private/org-CWjU5cDIzgCcVjq10pp5yX5Q/user-GoBXgChvLBqLHdBiMJBUbPqF/img-WZVUK2dOD4HKbKwW1NeMJHBd.png?st=2022-12-19T11%3A38%3A25Z&se=2022-12-19T13%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2022-12-19T09%3A35%3A16Z&ske=2022-12-20T09%3A35%3A16Z&sks=b&skv=2021-08-06&sig=mh52rmtbQ8CXArv5bMaU6lhgZHFBZz/ePr4y%2BJwLKOc%3D"

Create Image Edit

publicstructImageEditsQuery:Codable{    /// 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.publicletimage:DatapublicletfileName:String    /// 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.publicletmask:Data?publicletmaskFileName:String?    /// A text description of the desired image(s). The maximum length is 1000 characters.publicletprompt:String    /// The number of images to generate. Must be between 1 and 10.publicletn:Int?    /// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.publicletsize:String?}

letdata= image.pngData()letquery=ImageEditQuery(image: data, fileName:"whitecat.png", prompt:"White cat with heterochromia sitting on the kitchen table with a bowl of food", n:1, size:"1024x1024")openAI.imageEdits(query: query){ resultin  //Handle result here}//orletresult=tryawait openAI.imageEdits(query: query)

Create Image Variation

publicstructImageVariationsQuery:Codable{    /// 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.publicletimage:DatapublicletfileName:String    /// The number of images to generate. Must be between 1 and 10.publicletn:Int?    /// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.publicletsize:String?}

letdata= image.pngData()letquery=ImageVariationQuery(image: data, fileName:"whitecat.png", n:1, size:"1024x1024")openAI.imageVariations(query: query){ resultin  //Handle result here}//orletresult=tryawait openAI.imageVariations(query: query)

Audio

Audio Create Speech

publicstructAudioSpeechQuery:Codable,Equatable{    //...publicletmodel:Model // tts-1 or tts-1-hdpublicletinput:Stringpublicletvoice:AudioSpeechVoicepublicletresponseFormat:AudioSpeechResponseFormatpublicletspeed:String? // Initializes with Double?    //...}

/// Audio data for one of the following formats :`mp3`, `opus`, `aac`, `flac`, `pcm`publicletaudioData:Data?

letquery=AudioSpeechQuery(model:.tts_1, input:"Hello, world!", voice:.alloy, responseFormat:.mp3, speed:1.0)openAI.audioCreateSpeech(query: query){ resultin    // Handle response here}//orletresult=tryawait openAI.audioCreateSpeech(query: query)

Audio Create Speech Streaming

openAI.audioCreateSpeechStream(query: query){ partialResultinswitch partialResult{case.success(let result):print(result.audio)case.failure(let error):        //Handle chunk error here}} completion:{ errorin    //Handle streaming error here}

openAI.audioCreateSpeechStream(query: query).sink{ completionin        //Handle completion result here} receiveValue:{ resultin        //Handle chunk here}.store(in:&cancellables)

fortryawaitresultin openAI.audioCreateSpeechStream(query: query){   //Handle result here}

Audio Transcriptions

publicstructAudioTranscriptionQuery:Codable,Equatable{publicletfile:DatapublicletfileName:Stringpublicletmodel:Modelpublicletprompt:String?publiclettemperature:Double?publicletlanguage:String?}

publicstructAudioTranscriptionResult:Codable,Equatable{publiclettext:String}

letdata=Data(contentsOfURL:...)letquery=AudioTranscriptionQuery(file: data, fileName:"audio.m4a", model:.whisper_1)        openAI.audioTranscriptions(query: query){ resultin    //Handle result here}//orletresult=tryawait openAI.audioTranscriptions(query: query)

Audio Translations

publicstructAudioTranslationQuery:Codable,Equatable{publicletfile:DatapublicletfileName:Stringpublicletmodel:Modelpublicletprompt:String?publiclettemperature:Double?}

publicstructAudioTranslationResult:Codable,Equatable{publiclettext:String}

letdata=Data(contentsOfURL:...)letquery=AudioTranslationQuery(file: data, fileName:"audio.m4a", model:.whisper_1)  openAI.audioTranslations(query: query){ resultin    //Handle result here}//orletresult=tryawait openAI.audioTranslations(query: query)

Embeddings

structEmbeddingsQuery:Codable{    /// ID of the model to use.publicletmodel:Model    /// Input text to get embeddings forpublicletinput:String}

structEmbeddingsResult:Codable,Equatable{publicstructEmbedding:Codable,Equatable{publicletobject:Stringpublicletembedding:[Double]publicletindex:Int}publicletdata:[Embedding]publicletusage:Usage}

letquery=EmbeddingsQuery(model:.textSearchBabbageDoc, input:"The food was delicious and the waiter...")openAI.embeddings(query: query){ resultin  //Handle response here}//orletresult=tryawait openAI.embeddings(query: query)

(lldb) po result▿ EmbeddingsResult  ▿ data : 1 element    ▿ 0 : Embedding      - object : "embedding"      ▿ embedding : 2048 elements        - 0 : 0.0010535449        - 1 : 0.024234328        - 2 : -0.0084999        - 3 : 0.008647452    .......        - 2044 : 0.017536353        - 2045 : -0.005897616        - 2046 : -0.026559394        - 2047 : -0.016633155      - index : 0(lldb)

Models

publicextensionModel{staticletgpt4_turbo_preview="gpt-4-turbo-preview"staticletgpt4_vision_preview="gpt-4-vision-preview"staticletgpt4_0125_preview="gpt-4-0125-preview"staticletgpt4_1106_preview="gpt-4-1106-preview"staticletgpt4="gpt-4"staticletgpt4_0613="gpt-4-0613"staticletgpt4_0314="gpt-4-0314"staticletgpt4_32k="gpt-4-32k"staticletgpt4_32k_0613="gpt-4-32k-0613"staticletgpt4_32k_0314="gpt-4-32k-0314"staticletgpt3_5Turbo="gpt-3.5-turbo"staticletgpt3_5Turbo_0125="gpt-3.5-turbo-0125"staticletgpt3_5Turbo_1106="gpt-3.5-turbo-1106"staticletgpt3_5Turbo_0613="gpt-3.5-turbo-0613"staticletgpt3_5Turbo_0301="gpt-3.5-turbo-0301"staticletgpt3_5Turbo_16k="gpt-3.5-turbo-16k"staticletgpt3_5Turbo_16k_0613="gpt-3.5-turbo-16k-0613"staticlettextDavinci_003="text-davinci-003"staticlettextDavinci_002="text-davinci-002"staticlettextCurie="text-curie-001"staticlettextBabbage="text-babbage-001"staticlettextAda="text-ada-001"staticlettextDavinci_001="text-davinci-001"staticletcodeDavinciEdit_001="code-davinci-edit-001"staticlettts_1="tts-1"staticlettts_1_hd="tts-1-hd"staticletwhisper_1="whisper-1"staticletdall_e_2="dall-e-2"staticletdall_e_3="dall-e-3"staticletdavinci="davinci"staticletcurie="curie"staticletbabbage="babbage"staticletada="ada"staticlettextEmbeddingAda="text-embedding-ada-002"staticlettextSearchAda="text-search-ada-doc-001"staticlettextSearchBabbageDoc="text-search-babbage-doc-001"staticlettextSearchBabbageQuery001="text-search-babbage-query-001"staticlettextEmbedding3="text-embedding-3-small"staticlettextEmbedding3Large="text-embedding-3-large"staticlettextModerationStable="text-moderation-stable"staticlettextModerationLatest="text-moderation-latest"staticletmoderation="text-moderation-007"}

letquery=ChatQuery(model:.gpt4_turbo_preview, messages:[.init(role:.system, content:"You are Librarian-GPT. You know everything about the books."),.init(role:.user, content:"Who wrote Harry Potter?")])letresult=tryawait openAI.chats(query: query)XCTAssertFalse(result.choices.isEmpty)

List Models

publicstructModelsResult:Codable,Equatable{publicletdata:[ModelResult]publicletobject:String}

openAI.models(){ resultin  //Handle result here}//orletresult=tryawait openAI.models()

Retrieve Model

publicstructModelQuery:Codable,Equatable{publicletmodel:Model}

publicstructModelResult:Codable,Equatable{publicletid:Modelpublicletobject:StringpublicletownedBy:String}

letquery=ModelQuery(model:.gpt4)openAI.model(query: query){ resultin  //Handle result here}//orletresult=tryawait openAI.model(query: query)

Moderations

publicstructModerationsQuery:Codable{publicletinput:Stringpublicletmodel:Model?}

publicstructModerationsResult:Codable,Equatable{publicletid:Stringpublicletmodel:Modelpublicletresults:[CategoryResult]}

letquery=ModerationsQuery(input:"I want to kill them.")openAI.moderations(query: query){ resultin  //Handle result here}//orletresult=tryawait openAI.moderations(query: query)

Utilities

publicstructVector{    /// Returns the similarity between two vectors    ///    /// - Parameters:    ///     - a: The first vector    ///     - b: The second vectorpublicstaticfunc cosineSimilarity(a:[Double], b:[Double])->Double{returndot(a, b)/(mag(a)* mag(b))}    /// Returns the difference between two vectors. Cosine distance is defined as `1 - cosineSimilarity(a, b)`    ///    /// - Parameters:    ///     - a: The first vector    ///     - b: The second vectorpublicfunc cosineDifference(a:[Double], b:[Double])->Double{return1- Self.cosineSimilarity(a: a, b: b)}}

letvector1=[0.213123,0.3214124,0.421412,0.3214521251,0.412412,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.4214214,0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3214521251]letvector2=[0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.511515,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3213213]letsimilarity=Vector.cosineSimilarity(a: vector1, b: vector2)print(similarity) //0.9510201910206734

Combine Extensions

func images(query:ImagesQuery)->AnyPublisher<ImagesResult,Error>func embeddings(query:EmbeddingsQuery)->AnyPublisher<EmbeddingsResult,Error>func chats(query:ChatQuery)->AnyPublisher<ChatResult,Error>func model(query:ModelQuery)->AnyPublisher<ModelResult,Error>func models()->AnyPublisher<ModelsResult,Error>func moderations(query:ModerationsQuery)->AnyPublisher<ModerationsResult,Error>func audioTranscriptions(query:AudioTranscriptionQuery)->AnyPublisher<AudioTranscriptionResult,Error>func audioTranslations(query:AudioTranslationQuery)->AnyPublisher<AudioTranslationResult,Error>

Assistants

Create Assistant

letquery=AssistantsQuery(model:Model.gpt4_o_mini, name: name, description: description, instructions: instructions, tools: tools, toolResources: toolResources)openAI.assistantCreate(query: query){ resultin   //Handle response here}

Modify Assistant

letquery=AssistantsQuery(model:Model.gpt4_o_mini, name: name, description: description, instructions: instructions, tools: tools, toolResources: toolResources)openAI.assistantModify(query: query, assistantId:"asst_1234"){ resultin    //Handle response here}

List Assistants

openAI.assistants(){ resultin   //Handle response here}

Threads

Create Thread

letthreadsQuery=ThreadsQuery(messages:[Chat(role: message.role, content: message.content)])openAI.threads(query: threadsQuery){ resultin  //Handle response here}

Create and Run Thread

letthreadsQuery=ThreadQuery(messages:[Chat(role: message.role, content: message.content)])letthreadRunQuery=ThreadRunQuery(assistantId:"asst_1234"  thread: threadsQuery)openAI.threadRun(query: threadRunQuery){ resultin  //Handle response here}

Get Threads Messages

openAI.threadsMessages(threadId: currentThreadId){ resultin  //Handle response here}

Add Message to Thread

letquery=MessageQuery(role: message.role.rawValue, content: message.content)openAI.threadsAddMessage(threadId: currentThreadId, query: query){ resultin  //Handle response here}

Runs

Create Run

letrunsQuery=RunsQuery(assistantId:  currentAssistantId)openAI.runs(threadId: threadsResult.id, query: runsQuery){ resultin  //Handle response here}

Retrieve Run

openAI.runRetrieve(threadId: currentThreadId, runId: currentRunId){ resultin  //Handle response here}

Retrieve Run Steps

openAI.runRetrieveSteps(threadId: currentThreadId, runId: currentRunId){ resultin  //Handle response here}

Submit Tool Outputs for Run

letoutput=RunToolOutputsQuery.ToolOutput(toolCallId:"call123", output:"Success")letquery=RunToolOutputsQuery(toolOutputs:[output])openAI.runSubmitToolOutputs(threadId: currentThreadId, runId: currentRunId, query: query){ resultin  //Handle response here}

Files

Upload file

letquery=FilesQuery(purpose:"assistants", file: fileData, fileName: url.lastPathComponent, contentType:"application/pdf")openAI.files(query: query){ resultin  //Handle response here}

Cancelling requests

Closure based API

letcancellableRequest= object.chats(query: query, completion:{ _in})cancellableReques

Swift Concurrency

lettask=Task{do{letchatResult=tryawait openAIClient.chats(query:.init(messages:[], model:"asd"))}catch{        // Handle cancellation or error}}            task.cancel()

Combine

letsubscription= openAIClient.images(query: query).sink(receiveCompletion:{ completionin}, receiveValue:{ imagesResultin})    subscription.cancel()

Support for other providers

Perplexity - Chat Completions Response

Example Project

Contribution Guidelines

UseConventional Commits principles in naming PRs and branches:

Write description to pull requests in following format:

Links

License

MIT LicenseCopyright (c) 2023 MacPaw Inc.Permission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.