This repository contains Swift community-maintained implementation over OpenAI public API.
OpenAI is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. It was created with the purpose of advancing digital intelligence in ways that benefit humanity as a whole and promote societal progress. The organization strives to develop AI (Artificial Intelligence) programs and systems that can think, act and adapt quickly on their own – autonomously. OpenAI's mission is to ensure safe and responsible use of AI for civic good, economic growth and other public benefits; this includes cutting-edge research into important topics such as general AI safety, natural language processing, applied reinforcement learning methods, machine vision algorithms etc.
The OpenAI API can be applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification.
OpenAI is available with Swift Package Manager. The Swift Package Manager is a tool for automating the distribution of Swift code and is integrated into the swift compiler. Once you have your Swift package set up, adding OpenAI as a dependency is as easy as adding it to the dependencies value of your Package.swift.
dependencies: [
.package(url: "https://github.com/MacPaw/OpenAI.git", branch: "main")
]To initialize API instance you need to obtain API token from your Open AI organization.
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.
Once you have a token, you can initialize OpenAI class, which is an entry point to the API.
⚠️ OpenAI strongly recommends developers of client-side applications proxy requests through a separate backend service to keep their API key safe. API keys can access and manipulate customer billing, usage, and organizational data, so it's a significant risk to expose them.
let openAI = OpenAI(apiToken: "YOUR_TOKEN_HERE")Optionally you can initialize OpenAI with token, organization identifier and timeoutInterval.
let configuration = OpenAI.Configuration(token: "YOUR_TOKEN_HERE", organizationIdentifier: "YOUR_ORGANIZATION_ID_HERE", timeoutInterval: 60.0)
let openAI = OpenAI(configuration: configuration)Once token you posses the token, and the instance is initialized you are ready to make requests.
Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.
Request
struct CompletionsQuery: Codable {
/// ID of the model to use.
public let model: Model
/// The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.
public let prompt: String
/// What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
public let temperature: Double?
/// The maximum number of tokens to generate in the completion.
public let maxTokens: Int?
/// 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.
public let topP: 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.
public let frequencyPenalty: Double?
/// 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.
public let presencePenalty: Double?
/// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
public let stop: [String]?
/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
public let user: String?
}Response
struct CompletionsResult: Codable, Equatable {
public struct Choice: Codable, Equatable {
public let text: String
public let index: Int
}
public let id: String
public let object: String
public let created: TimeInterval
public let model: Model
public let choices: [Choice]
public let usage: Usage
}Example
let query = CompletionsQuery(model: .textDavinci_003, prompt: "What is 42?", temperature: 0, maxTokens: 100, topP: 1, frequencyPenalty: 0, presencePenalty: 0, stop: ["\\n"])
openAI.completions(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.completions(query: query)(lldb) po result
▿ CompletionsResult
- id : "cmpl-6P9be2p2fQlwB7zTOl0NxCOetGmX3"
- object : "text_completion"
- created : 1671453146.0
- model : OpenAI.Model.textDavinci_003
▿ choices : 1 element
▿ 0 : Choice
- text : "\n\n42 is the answer to the ultimate question of life, the universe, and everything, according to the book The Hitchhiker\'s Guide to the Galaxy."
- index : 0
Completions streaming is available by using completionsStream function. Tokens will be sent one-by-one.
Closures
openAI.completionsStream(query: query) { partialResult in
switch partialResult {
case .success(let result):
print(result.choices)
case .failure(let error):
//Handle chunk error here
}
} completion: { error in
//Handle streaming error here
}Combine
openAI
.completionsStream(query: query)
.sink { completion in
//Handle completion result here
} receiveValue: { result in
//Handle chunk here
}.store(in: &cancellables)Structured concurrency
for try await result in openAI.completionsStream(query: query) {
//Handle result here
}Review Completions Documentation for more info.
Using the OpenAI Chat API, you can build your own applications with gpt-3.5-turbo to do things like:
- Draft an email or other piece of writing
- Write Python code
- Answer questions about a set of documents
- Create conversational agents
- Give your software a natural language interface
- Tutor in a range of subjects
- Translate languages
- Simulate characters for video games and much more
Request
struct ChatQuery: Codable {
/// ID of the model to use. Currently, only gpt-3.5-turbo and gpt-3.5-turbo-0301 are supported.
public let model: Model
/// The messages to generate chat completions for
public let messages: [Chat]
/// A list of functions the model may generate JSON inputs for.
public let functions: [ChatFunctionDeclaration]?
/// 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.
public let temperature: 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.
public let topP: Double?
/// How many chat completion choices to generate for each input message.
public let n: Int?
/// Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
public let stop: [String]?
/// The maximum number of tokens to generate in the completion.
public let maxTokens: 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.
public let presencePenalty: 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.
public let frequencyPenalty: Double?
///Modify the likelihood of specified tokens appearing in the completion.
public let logitBias: [String:Int]?
/// A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
public let user: String?
}Response
struct ChatResult: Codable, Equatable {
public struct Choice: Codable, Equatable {
public let index: Int
public let message: Chat
public let finishReason: String
}
public struct Usage: Codable, Equatable {
public let promptTokens: Int
public let completionTokens: Int
public let totalTokens: Int
}
public let id: String
public let object: String
public let created: TimeInterval
public let model: Model
public let choices: [Choice]
public let usage: Usage
}Example
let query = ChatQuery(model: .gpt3_5Turbo, messages: [.init(role: .user, content: "who are you")])
let result = try await 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 is available by using chatStream function. Tokens will be sent one-by-one.
Closures
openAI.chatsStream(query: query) { partialResult in
switch partialResult {
case .success(let result):
print(result.choices)
case .failure(let error):
//Handle chunk error here
}
} completion: { error in
//Handle streaming error here
}Combine
openAI
.chatsStream(query: query)
.sink { completion in
//Handle completion result here
} receiveValue: { result in
//Handle chunk here
}.store(in: &cancellables)Structured concurrency
for try await result in openAI.chatsStream(query: query) {
//Handle result here
}Function calls
let openAI = OpenAI(apiToken: "...")
// Declare functions which GPT-3 might decide to call.
let functions = [
ChatFunctionDeclaration(
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"]
)
)
]
let query = 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?")
],
functions: functions
)
let result = try await openAI.chats(query: query)Result will be (serialized as JSON here for readability):
{
"id": "chatcmpl-1234",
"object": "chat.completion",
"created": 1686000000,
"model": "gpt-3.5-turbo-0613",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"function_call": {
"name": "get_current_weather",
"arguments": "{\n \"location\": \"Boston, MA\"\n}"
}
},
"finish_reason": "function_call"
}
],
"usage": { "total_tokens": 100, "completion_tokens": 18, "prompt_tokens": 82 }
}
Review Chat Documentation for more info.
Given a prompt and/or an input image, the model will generate a new image.
As Artificial Intelligence continues to develop, so too does the intriguing concept of Dall-E. Developed by OpenAI, a research lab for artificial intelligence purposes, Dall-E has been classified as an AI system that can generate images based on descriptions provided by humans. With its potential applications spanning from animation and illustration to design and engineering - not to mention the endless possibilities in between - it's easy to see why there is such excitement over this new technology.
Request
struct ImagesQuery: Codable {
/// A text description of the desired image(s). The maximum length is 1000 characters.
public let prompt: String
/// The number of images to generate. Must be between 1 and 10.
public let n: Int?
/// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
public let size: String?
}Response
struct ImagesResult: Codable, Equatable {
public struct URLResult: Codable, Equatable {
public let url: String
}
public let created: TimeInterval
public let data: [URLResult]
}Example
let query = ImagesQuery(prompt: "White cat with heterochromia sitting on the kitchen table", n: 1, size: "1024x1024")
openAI.images(query: query) { result in
//Handle result here
}
//or
let result = try await 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"
Generated image
Creates an edited or extended image given an original image and a prompt.
Request
public struct ImageEditsQuery: 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.
public let image: Data
public let fileName: 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.
public let mask: Data?
public let maskFileName: String?
/// A text description of the desired image(s). The maximum length is 1000 characters.
public let prompt: String
/// The number of images to generate. Must be between 1 and 10.
public let n: Int?
/// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
public let size: String?
}Response
Uses the ImagesResult response similarly to ImagesQuery.
Example
let data = image.pngData()
let query = 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) { result in
//Handle result here
}
//or
let result = try await openAI.imageEdits(query: query)Creates a variation of a given image.
Request
public struct ImageVariationsQuery: 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.
public let image: Data
public let fileName: String
/// The number of images to generate. Must be between 1 and 10.
public let n: Int?
/// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
public let size: String?
}Response
Uses the ImagesResult response similarly to ImagesQuery.
Example
let data = image.pngData()
let query = ImageVariationQuery(image: data, fileName: "whitecat.png", n: 1, size: "1024x1024")
openAI.imageVariations(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.imageVariations(query: query)Review Images Documentation for more info.
The speech to text API provides two endpoints, transcriptions and translations, based on our state-of-the-art open source large-v2 Whisper model. They can be used to:
Transcribe audio into whatever language the audio is in. Translate and transcribe the audio into english. File uploads are currently limited to 25 MB and the following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm.
This function sends an AudioSpeechQuery to the OpenAI API to create audio speech from text using a specific voice and format.
Learn more about voices.
Learn more about models.
Request:
public struct AudioSpeechQuery: Codable, Equatable {
//...
public let model: Model // tts-1 or tts-1-hd
public let input: String
public let voice: AudioSpeechVoice
public let responseFormat: AudioSpeechResponseFormat
public let speed: String? // Initializes with Double?
//...
}Response:
/// Audio data for one of the following formats :`mp3`, `opus`, `aac`, `flac`
public let audioData: Data?Example:
let query = AudioSpeechQuery(model: .tts_1, input: "Hello, world!", voice: .alloy, responseFormat: .mp3, speed: 1.0)
openAI.audioCreateSpeech(query: query) { result in
// Handle response here
}
//or
let result = try await openAI.audioCreateSpeech(query: query)OpenAI Create Speech – Documentation
Transcribes audio into the input language.
Request
public struct AudioTranscriptionQuery: Codable, Equatable {
public let file: Data
public let fileName: String
public let model: Model
public let prompt: String?
public let temperature: Double?
public let language: String?
}Response
public struct AudioTranscriptionResult: Codable, Equatable {
public let text: String
}Example
let data = Data(contentsOfURL:...)
let query = AudioTranscriptionQuery(file: data, fileName: "audio.m4a", model: .whisper_1)
openAI.audioTranscriptions(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.audioTranscriptions(query: query)Translates audio into into English.
Request
public struct AudioTranslationQuery: Codable, Equatable {
public let file: Data
public let fileName: String
public let model: Model
public let prompt: String?
public let temperature: Double?
} Response
public struct AudioTranslationResult: Codable, Equatable {
public let text: String
}Example
let data = Data(contentsOfURL:...)
let query = AudioTranslationQuery(file: data, fileName: "audio.m4a", model: .whisper_1)
openAI.audioTranslations(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.audioTranslations(query: query)Review Audio Documentation for more info.
Creates a new edit for the provided input, instruction, and parameters.
Request
struct EditsQuery: Codable {
/// ID of the model to use.
public let model: Model
/// Input text to get embeddings for.
public let input: String?
/// The instruction that tells the model how to edit the prompt.
public let instruction: String
/// The number of images to generate. Must be between 1 and 10.
public let n: Int?
/// What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
public let temperature: 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.
public let topP: Double?
}Response
struct EditsResult: Codable, Equatable {
public struct Choice: Codable, Equatable {
public let text: String
public let index: Int
}
public struct Usage: Codable, Equatable {
public let promptTokens: Int
public let completionTokens: Int
public let totalTokens: Int
enum CodingKeys: String, CodingKey {
case promptTokens = "prompt_tokens"
case completionTokens = "completion_tokens"
case totalTokens = "total_tokens"
}
}
public let object: String
public let created: TimeInterval
public let choices: [Choice]
public let usage: Usage
}Example
let query = EditsQuery(model: .gpt4, input: "What day of the wek is it?", instruction: "Fix the spelling mistakes")
openAI.edits(query: query) { result in
//Handle response here
}
//or
let result = try await openAI.edits(query: query)Review Edits Documentation for more info.
Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
Request
struct EmbeddingsQuery: Codable {
/// ID of the model to use.
public let model: Model
/// Input text to get embeddings for
public let input: String
}Response
struct EmbeddingsResult: Codable, Equatable {
public struct Embedding: Codable, Equatable {
public let object: String
public let embedding: [Double]
public let index: Int
}
public let data: [Embedding]
public let usage: Usage
}Example
let query = EmbeddingsQuery(model: .textSearchBabbageDoc, input: "The food was delicious and the waiter...")
openAI.embeddings(query: query) { result in
//Handle response here
}
//or
let result = try await 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)
Review Embeddings Documentation for more info.
Models are represented as a typealias typealias Model = String.
public extension Model {
static let gpt4_1106_preview = "gpt-4-1106-preview"
static let gpt4_vision_preview = "gpt-4-vision-preview"
static let gpt4 = "gpt-4"
static let gpt4_0314 = "gpt-4-0314"
static let gpt4_32k = "gpt-4-32k"
static let gpt4_32k_0314 = "gpt-4-32k-0314"
static let gpt3_5Turbo = "gpt-3.5-turbo"
static let gpt3_5Turbo_1106 = "gpt-3.5-turbo-1106"
static let gpt3_5Turbo0301 = "gpt-3.5-turbo-0301"
static let textDavinci_003 = "text-davinci-003"
static let textDavinci_002 = "text-davinci-002"
static let textCurie = "text-curie-001"
static let textBabbage = "text-babbage-001"
static let textAda = "text-ada-001"
static let textDavinci_001 = "text-davinci-001"
static let codeDavinciEdit_001 = "code-davinci-edit-001"
static let whisper_1 = "whisper-1"
static let davinci = "davinci"
static let curie = "curie"
static let babbage = "babbage"
static let ada = "ada"
static let textEmbeddingAda = "text-embedding-ada-002"
static let textSearchAda = "text-search-ada-doc-001"
static let textSearchBabbageDoc = "text-search-babbage-doc-001"
static let textSearchBabbageQuery001 = "text-search-babbage-query-001"
static let textModerationStable = "text-moderation-stable"
static let textModerationLatest = "text-moderation-latest"
static let moderation = "text-moderation-001"
static let dall_e_2 = "dall-e-2"
static let dall_e_3 = "dall-e-3"
}GPT-4 models are supported.
For example to use basic GPT-4 8K model pass .gpt4 as a parameter.
let query = ChatQuery(model: .gpt4, messages: [
.init(role: .system, content: "You are Librarian-GPT. You know everything about the books."),
.init(role: .user, content: "Who wrote Harry Potter?")
])
let result = try await openAI.chats(query: query)
XCTAssertFalse(result.choices.isEmpty)You can also pass a custom string if you need to use some model, that is not represented above.
Lists the currently available models.
Response
public struct ModelsResult: Codable, Equatable {
public let data: [ModelResult]
public let object: String
}Example
openAI.models() { result in
//Handle result here
}
//or
let result = try await openAI.models()Retrieves a model instance, providing ownership information.
Request
public struct ModelQuery: Codable, Equatable {
public let model: Model
} Response
public struct ModelResult: Codable, Equatable {
public let id: Model
public let object: String
public let ownedBy: String
}Example
let query = ModelQuery(model: .gpt4)
openAI.model(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.model(query: query)Review Models Documentation for more info.
Given a input text, outputs if the model classifies it as violating OpenAI's content policy.
Request
public struct ModerationsQuery: Codable {
public let input: String
public let model: Model?
} Response
public struct ModerationsResult: Codable, Equatable {
public let id: String
public let model: Model
public let results: [CategoryResult]
}Example
let query = ModerationsQuery(input: "I want to kill them.")
openAI.moderations(query: query) { result in
//Handle result here
}
//or
let result = try await openAI.moderations(query: query)Review Moderations Documentation for more info.
The component comes with several handy utility functions to work with the vectors.
public struct Vector {
/// Returns the similarity between two vectors
///
/// - Parameters:
/// - a: The first vector
/// - b: The second vector
public static func cosineSimilarity(a: [Double], b: [Double]) -> Double {
return dot(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 vector
public func cosineDifference(a: [Double], b: [Double]) -> Double {
return 1 - Self.cosineSimilarity(a: a, b: b)
}
}Example
let vector1 = [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]
let vector2 = [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]
let similarity = Vector.cosineSimilarity(a: vector1, b: vector2)
print(similarity) //0.9510201910206734In data analysis, cosine similarity is a measure of similarity between two sequences of numbers.
Read more about Cosine Similarity here.
The library contains built-in Combine extensions.
func completions(query: CompletionsQuery) -> AnyPublisher<CompletionsResult, Error>
func images(query: ImagesQuery) -> AnyPublisher<ImagesResult, Error>
func embeddings(query: EmbeddingsQuery) -> AnyPublisher<EmbeddingsResult, Error>
func chats(query: ChatQuery) -> AnyPublisher<ChatResult, Error>
func edits(query: EditsQuery) -> AnyPublisher<EditsResult, 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>You can find example iOS application in Demo folder.
Make your Pull Requests clear and obvious to anyone viewing them.
Set main as your target branch.
Use Conventional Commits principles in naming PRs and branches:
Feat: ...for new features and new functionality implementations.Bug: ...for bug fixes.Fix: ...for minor issues fixing, like typos or inaccuracies in code.Chore: ...for boring stuff like code polishing, refactoring, deprecation fixing etc.
PR naming example: Feat: Add Threads API handling or Bug: Fix message result duplication
Branch naming example: feat/add-threads-API-handling or bug/fix-message-result-duplication
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What
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Why
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Affected Areas
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More Info
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We'll appreciate you including tests to your code if it is needed and possible. ❤️
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