OpenAI Dive is an unofficial async Rust library that allows you to interact with the OpenAI API.
Sign up for an account on https://platform.openai.com/overview to get your API key.
[dependencies]
openai_dive = "0.3"
More information: set API key, add proxy, rate limit headers, use model names
- Models
- Chat
- Images
- Audio
- Embeddings
- Files
- Fine tuning
- Moderation
- Assistants
List and describe the various models available in the API.
Lists the currently available models, and provides basic information about each one such as the owner and availability.
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let result = client.models().list().await.unwrap();
println!("{:#?}", result);
}
More information: List models
Retrieves a model instance, providing basic information about the model such as the owner and permissioning.
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let result = client.models().get("gpt-3.5-turbo-16k-0613").await.unwrap();
println!("{:#?}", result);
}
More information: Retrieve model
Delete a fine-tuned model. You must have the Owner role in your organization to delete a model.
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let result = client.models().delete("my-custom-model").await.unwrap();
println!("{:#?}", result);
}
More information: Delete fine-tune model
Given a list of messages comprising a conversation, the model will return a response.
Creates a model response for the given chat conversation.
Note
This endpoint also has stream
support. See the examples/chat/create_chat_completion_stream example.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::chat::{ChatCompletionParameters, ChatMessage, Role};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = ChatCompletionParameters {
model: "gpt-3.5-turbo-16k-0613".to_string(),
messages: vec![
ChatMessage {
role: Role::User,
content: Some("Hello!".to_string()),
..Default::default()
},
ChatMessage {
role: Role::User,
content: Some("Where are you located?".to_string()),
..Default::default()
},
],
max_tokens: Some(12),
..Default::default()
};
let result = client.chat().create(parameters).await.unwrap();
println!("{:#?}", result);
}
More information: Create chat completion
In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call one or many functions. The Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code.
Note
This endpoint also has stream
support. See the examples/chat/function_calling_stream example.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::chat::{
ChatCompletionFunction, ChatCompletionParameters, ChatCompletionTool, ChatCompletionToolChoice,
ChatCompletionToolChoiceFunction, ChatCompletionToolChoiceFunctionName, ChatCompletionToolType,
ChatMessage, Role,
};
use openai_dive::v1::resources::shared::FinishReason;
use rand::Rng;
use serde::{Deserialize, Serialize};
use serde_json::{json, Value};
#[tokio::main]
async fn main() {
let api_key = std::env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let mut messages = vec![ChatMessage {
content: Some("Give me a random number between 25 and 50?".to_string()),
..Default::default()
}];
let parameters = ChatCompletionParameters {
model: "gpt-3.5-turbo-0613".to_string(),
messages: messages.clone(),
tool_choice: Some(ChatCompletionToolChoice::ChatCompletionToolChoiceFunction(
ChatCompletionToolChoiceFunction {
r#type: Some(ChatCompletionToolType::Function),
function: ChatCompletionToolChoiceFunctionName {
name: "get_random_number".to_string(),
},
},
)),
tools: Some(vec![ChatCompletionTool {
r#type: ChatCompletionToolType::Function,
function: ChatCompletionFunction {
name: "get_random_number".to_string(),
description: Some("Get a random number between two values".to_string()),
parameters: json!({
"type": "object",
"properties": {
"min": {"type": "integer", "description": "Minimum value of the random number."},
"max": {"type": "integer", "description": "Maximum value of the random number."},
}
}),
},
}]),
..Default::default()
};
let result = client.chat().create(parameters).await.unwrap();
for choice in result.choices.iter() {
if choice.finish_reason == FinishReason::StopSequenceReached {
if let Some(tool_calls) = &choice.message.tool_calls {
for tool_call in tool_calls.iter() {
let random_numbers =
serde_json::from_str(&tool_call.function.arguments).unwrap();
if tool_call.function.name == "get_random_number" {
let random_number_result = get_random_number(random_numbers);
messages.push(ChatMessage {
role: Role::Function,
content: Some(serde_json::to_string(&random_number_result).unwrap()),
name: Some("get_random_number".to_string()),
..Default::default()
});
let parameters = ChatCompletionParameters {
model: "gpt-3.5-turbo-0613".to_string(),
messages: messages.clone(),
..Default::default()
};
let result = client.chat().create(parameters).await.unwrap();
println!("{:#?}", result);
}
}
}
}
}
}
#[derive(Serialize, Deserialize)]
pub struct RandomNumber {
min: u32,
max: u32,
}
fn get_random_number(params: RandomNumber) -> Value {
let random_number = rand::thread_rng().gen_range(params.min..params.max);
random_number.into()
}
More information: Function calling
Given a prompt and/or an input image, the model will generate a new image.
Creates an image given a prompt.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::image::{CreateImageParameters, ImageSize, ResponseFormat};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = CreateImageParameters {
prompt: "A cute baby dog".to_string(),
model: None,
n: Some(1),
quality: None,
response_format: Some(ResponseFormat::Url),
size: Some(ImageSize::Size256X256),
style: None,
user: None,
};
let result = client.images().create(parameters).await.unwrap();
let paths = result.save("./images").await.unwrap();
println!("{:?}", paths);
println!("{:#?}", result);
}
More information: Create image
Creates an edited or extended image given an original image and a prompt.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::image::{EditImageParameters, ImageSize};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = EditImageParameters {
image: "./images/image_edit_original.png".to_string(),
prompt: "A cute baby sea otter".to_string(),
mask: Some("./images/image_edit_mask.png".to_string()),
model: None,
n: Some(1),
size: Some(ImageSize::Size256X256),
response_format: None,
user: None,
};
let result = client.images().edit(parameters).await.unwrap();
println!("{:#?}", result);
}
More information: Create image edit
Creates a variation of a given image.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::image::{CreateImageVariationParameters, ImageSize};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = CreateImageVariationParameters {
image: "./images/image_edit_original.png".to_string(),
model: None,
n: Some(1),
response_format: None,
size: Some(ImageSize::Size256X256),
user: None,
};
let result = client.images().variation(parameters).await.unwrap();
println!("{:#?}", result);
}
More information: Create image variation
Learn how to turn audio into text or text into audio.
Generates audio from the input text.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::audio::{
AudioSpeechParameters, AudioSpeechResponseFormat, AudioVoice,
};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = AudioSpeechParameters {
model: "tts-1".to_string(),
input: "Hallo, this is a test from OpenAI Dive.".to_string(),
voice: AudioVoice::Alloy,
response_format: Some(AudioSpeechResponseFormat::Mp3),
speed: Some(1.0),
};
let response = client.audio().create_speech(parameters).await.unwrap();
response.save("files/example.mp3").await.unwrap();
}
More information: Create speech
Transcribes audio into the input language.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::audio::{AudioOutputFormat, AudioTranscriptionParameters};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = AudioTranscriptionParameters {
file: "./audio/micro-machines.mp3".to_string(),
model: "whisper-1".to_string(),
language: None,
prompt: None,
response_format: Some(AudioOutputFormat::Text),
temperature: None,
};
let result = client
.audio()
.create_transcription(parameters)
.await
.unwrap();
println!("{:#?}", result);
}
More information: Create transcription
Translates audio into English.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::audio::{AudioOutputFormat, AudioTranslationParameters};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = AudioTranslationParameters {
file: "./audio/multilingual.mp3".to_string(),
model: "whisper-1".to_string(),
prompt: None,
response_format: Some(AudioOutputFormat::Srt),
temperature: None,
};
let result = client.audio().create_translation(parameters).await.unwrap();
println!("{:#?}", result);
}
More information: Create translation
Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
Creates an embedding vector representing the input text.
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::embedding::EmbeddingParameters;
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = EmbeddingParameters {
model: "text-embedding-ada-002".to_string(),
input: "The food was delicious and the waiter...".to_string(),
encoding_format: None,
user: None,
};
let result = client.embeddings().create(parameters).await.unwrap();
println!("{:#?}", result);
}
More information: Create embeddings
Files are used to upload documents that can be used with features like Assistants and Fine-tuning.
Returns a list of files that belong to the user's organization.
use openai_dive::v1::{
api::Client,
resources::file::{FilePurpose, ListFilesParameters},
};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let query = ListFilesParameters {
purpose: Some(FilePurpose::FineTune),
};
let result = client.files().list(Some(query)).await.unwrap();
println!("{:#?}", result);
}
More information: List files
Upload a file that can be used across various endpoints.
use openai_dive::v1::{
api::Client,
resources::file::{FilePurpose, UploadFileParameters},
};
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = UploadFileParameters {
file: "./files/FineTuningJobSample2.jsonl".to_string(),
purpose: FilePurpose::FineTune,
};
let result = client.files().upload(parameters).await.unwrap();
println!("{:#?}", result);
}
More information Upload file
Delete a file.
use dotenv::dotenv;
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let file_id = env::var("FILE_ID").expect("FILE_ID is not set in the .env file.");
let result = client.files().delete(&file_id).await.unwrap();
println!("{:#?}", result);
}
More information Delete file
Returns information about a specific file.
use dotenv::dotenv;
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let file_id = env::var("FILE_ID").expect("FILE_ID is not set in the .env file.");
let result = client.files().retrieve(&file_id).await.unwrap();
println!("{:#?}", result);
}
More information Retrieve file
Returns the contents of the specified file.
use dotenv::dotenv;
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let file_id = env::var("FILE_ID").expect("FILE_ID is not set in the .env file.");
let result = client.files().retrieve_content(&file_id).await.unwrap();
println!("{:#?}", result);
}
More information Retrieve file content
Manage fine-tuning jobs to tailor a model to your specific training data.
Creates a job that fine-tunes a specified model from a given dataset.
use dotenv::dotenv;
use openai_dive::v1::{api::Client, resources::fine_tuning::CreateFineTuningJobParameters};
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let file_id = env::var("FILE_ID").expect("FILE_ID is not set in the .env file.");
let parameters = CreateFineTuningJobParameters {
model: "gpt-3.5-turbo-1106".to_string(),
training_file: file_id,
hyperparameters: None,
suffix: None,
validation_file: None,
};
let result = client.fine_tuning().create(parameters).await.unwrap();
println!("{:#?}", result);
}
More information Create fine tuning job
List your organization's fine-tuning jobs.
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let result = client.fine_tuning().list(None).await.unwrap();
println!("{:#?}", result);
}
More information List fine tuning jobs
Get info about a fine-tuning job.
use dotenv::dotenv;
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let fine_tuning_job_id =
env::var("FINE_TUNING_JOB_ID").expect("FINE_TUNING_JOB_ID is not set in the .env file.");
let result = client
.fine_tuning()
.retrieve(&fine_tuning_job_id)
.await
.unwrap();
println!("{:#?}", result);
}
More information Retrieve fine tuning jobs
Immediately cancel a fine-tune job.
use dotenv::dotenv;
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let fine_tuning_job_id =
env::var("FINE_TUNING_JOB_ID").expect("FINE_TUNING_JOB_ID is not set in the .env file.");
let result = client
.fine_tuning()
.cancel(&fine_tuning_job_id)
.await
.unwrap();
println!("{:#?}", result);
}
More information Cancel fine tuning
Get status updates for a fine-tuning job.
use dotenv::dotenv;
use openai_dive::v1::api::Client;
use std::env;
#[tokio::main]
async fn main() {
dotenv().ok();
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let fine_tuning_job_id =
env::var("FINE_TUNING_JOB_ID").expect("FINE_TUNING_JOB_ID is not set in the .env file.");
let result = client
.fine_tuning()
.list_job_events(&fine_tuning_job_id, None)
.await
.unwrap();
println!("{:#?}", result);
}
More information List fine tuning events
Given a input text, outputs if the model classifies it as violating OpenAI's content policy.
Classifies if text violates OpenAI's Content Policy
use openai_dive::v1::api::Client;
use openai_dive::v1::resources::moderation::ModerationParameters;
use std::env;
#[tokio::main]
async fn main() {
let api_key = env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client::new(api_key);
let parameters = ModerationParameters {
input: "I want to kill them.".to_string(),
model: "text-moderation-latest".to_string(),
};
let result = client.moderations().create(parameters).await.unwrap();
println!("{:#?}", result);
}
More information Create moderation
Build assistants that can call models and use tools to perform tasks.
For more information see the examples in the examples/assistants directory.
- Assistants
- Files
- Threads
- Messages
- Runs
More information Assistants
Add the OpenAI API key to your environment variables.
# Windows PowerShell
$Env:OPENAI_API_KEY='sk-...'
# Windows cmd
set OPENAI_API_KEY=sk-...
# Linux/macOS
export OPENAI_API_KEY='sk-...'
This crate uses reqwest
as HTTP Client. Reqwest has proxies enabled by default. You can set the proxy via the system environment variable or by overriding the default client.
You can set the proxy in the system environment variables (https://docs.rs/reqwest/latest/reqwest/#proxies).
export HTTPS_PROXY=socks5://127.0.0.1:1086
use openai_dive::v1::api::Client;
let http_client = reqwest::Client::builder()
.proxy(reqwest::Proxy::https("socks5://127.0.0.1:1086")?)
.build()?;
let api_key = std::env::var("OPENAI_API_KEY").expect("$OPENAI_API_KEY is not set");
let client = Client {
http_client,
base_url: "https://api.openai.com/v1".to_string(),
api_key,
};
In addition to seeing your rate limit on your account page, you can also view important information about your rate limits such as the remaining requests, tokens, and other metadata in the headers of the HTTP response.
The following endpoints have rate limit headers support:
You can access them by calling the create_wrapped
method instead of the create
method. The create_wrapped
method returns a Result<WrappedResponse<T>, Error>
.
use openai_dive::v1::api::Client;
let result = client.chat().create_wrapped(parameters).await.unwrap();
// the chat completion response
println!("{:#?}", result.data);
// the rate limit headers
println!("{:#?}", result.headers);
More information: Rate limit headers
- Gpt4Engine
- Gpt41106Preview
gpt-4-1106-preview
- Gpt4VisionPreview
gpt-4-vision-preview
- Gpt4
gpt-4
- Gpt432K
gpt-4-32k
- Gpt40613
gpt-4-0613
- Gpt432K0613
gpt-4-32k-0613
- Gpt41106Preview
- Gpt35Engine
- Gpt35Turbo1106
gpt-3.5-turbo-1106
- Gpt35Turbo
gpt-3.5-turbo
- Gpt35Turbo16K
gpt-3.5-turbo-16k
- Gpt35TurboInstruct
gpt-3.5-turbo-instruct
- Gpt35Turbo1106
- DallEEngine
- DallE3
dall-e-2
- DallE2
dall-e-3
- DallE3
- TTSEngine
- Tts1
tts-1
- Tts1HD
tts-1-hd
- Tts1
- WhisperEngine
- Whisper1
whisper-1
- Whisper1
- EmbeddingsEngine
- TextEmbeddingAda002
text-embedding-ada-002
- TextEmbeddingAda002
- ModerationsEngine
- TextModerationLatest
text-moderation-latest
- TextModerationStable
text-moderation-stable
- TextModerationLatest
More information: Models