Struct openai_rust::Client

pub struct Client { /* private fields */ }

This is the main interface to interact with the api.

Implementations

impl Client

pub fn new(api_key: &str) -> Client

Create a new client. This will automatically build a reqwest::Client used internally.

Examples found in repository

examples/chat_example.rs (line 6)

async fn main() {
    let client = openai_rust::Client::new(&std::env::var("OPENAI_API_KEY").unwrap());
    let args = openai_rust::chat::ChatArguments::new(
        "gpt-3.5-turbo",
        vec![openai_rust::chat::Message {
            role: "user".to_owned(),
            content: "Hello GPT!".to_owned(),
        }],
    );
    let res = client.create_chat(args).await.unwrap();
    println!("{}", res);
}

examples/chat_stream_example.rs (line 8)

async fn main() {
    let client = openai_rust::Client::new(&std::env::var("OPENAI_API_KEY").unwrap());
    let args = openai_rust::chat::ChatArguments::new(
        "gpt-3.5-turbo",
        vec![openai_rust::chat::Message {
            role: "user".to_owned(),
            content: "Hello GPT!".to_owned(),
        }],
    );
    let mut res = client.create_chat_stream(args).await.unwrap();
    while let Some(events) = res.next().await {
        for event in events.unwrap() {
            print!("{}", event);
            std::io::stdout().flush().unwrap();
        }
    }
}

pub fn new_with_client(api_key: &str, req_client: Client) -> Client

Build a client using your own reqwest::Client.

pub async fn list_models(&self) -> Result<Vec<Model>, Error>

List and describe the various models available in the API. You can refer to the Models documentation to understand what models are available and the differences between them.

let client = openai_rust::Client::new(api_key);
let models = client.list_models().await.unwrap();

See https://platform.openai.com/docs/api-reference/models/list.

pub async fn create_chat( &self, args: ChatArguments ) -> Result<ChatResponse, Error>

Given a list of messages comprising a conversation, the model will return a response.

See https://platform.openai.com/docs/api-reference/chat.

let client = openai_rust::Client::new(api_key);
let args = openai_rust::chat::ChatArguments::new("gpt-3.5-turbo", vec![
   openai_rust::chat::Message {
       role: "user".to_owned(),
       content: "Hello GPT!".to_owned(),
   }
]);
let res = client.create_chat(args).await.unwrap();
println!("{}", res.choices[0].message.content);

Examples found in repository

examples/chat_example.rs (line 14)

async fn main() {
    let client = openai_rust::Client::new(&std::env::var("OPENAI_API_KEY").unwrap());
    let args = openai_rust::chat::ChatArguments::new(
        "gpt-3.5-turbo",
        vec![openai_rust::chat::Message {
            role: "user".to_owned(),
            content: "Hello GPT!".to_owned(),
        }],
    );
    let res = client.create_chat(args).await.unwrap();
    println!("{}", res);
}

pub async fn create_chat_stream( &self, args: ChatArguments ) -> Result<impl Stream<Item = Result<Vec<ChatResponseEvent>>>>

Like Client::create_chat but with streaming.

See https://platform.openai.com/docs/api-reference/chat.

This method will return a stream. Calling next on it will return a vector of chat::stream::ChatResponseEvents.

use openai_rust::futures_util::StreamExt;
let mut res = client.create_chat_stream(args).await.unwrap();
while let Some(events) = res.next().await {
    for event in events.unwrap() {
        print!("{}", event.choices[0].delta.content.as_ref().unwrap_or(&"".to_owned()));
        std::io::stdout().flush().unwrap();
    }
}

Examples found in repository

examples/chat_stream_example.rs (line 16)

async fn main() {
    let client = openai_rust::Client::new(&std::env::var("OPENAI_API_KEY").unwrap());
    let args = openai_rust::chat::ChatArguments::new(
        "gpt-3.5-turbo",
        vec![openai_rust::chat::Message {
            role: "user".to_owned(),
            content: "Hello GPT!".to_owned(),
        }],
    );
    let mut res = client.create_chat_stream(args).await.unwrap();
    while let Some(events) = res.next().await {
        for event in events.unwrap() {
            print!("{}", event);
            std::io::stdout().flush().unwrap();
        }
    }
}

pub async fn create_completion( &self, args: CompletionArguments ) -> Result<CompletionResponse>

Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.

See https://platform.openai.com/docs/api-reference/completions

let c = openai_rust::Client::new(api_key);
let args = openai_rust::completions::CompletionArguments::new("text-davinci-003", "The quick brown fox".to_owned());
println!("{}", c.create_completion(args).await.unwrap().choices[0].text);

pub async fn create_edit(&self, args: EditArguments) -> Result<EditResponse>

Given a prompt and an instruction, the model will return an edited version of the prompt.

See https://platform.openai.com/docs/api-reference/edits

let c = openai_rust::Client::new(api_key);
let args = openai_rust::edits::EditArguments::new("text-davinci-edit-001", "The quick brown fox".to_owned(), "Complete this sentence.".to_owned());
println!("{}", c.create_edit(args).await.unwrap().to_string());

pub async fn create_embeddings( &self, args: EmbeddingsArguments ) -> Result<EmbeddingsResponse>

Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.

See https://platform.openai.com/docs/api-reference/embeddings

let c = openai_rust::Client::new(api_key);
let args = openai_rust::embeddings::EmbeddingsArguments::new("text-embedding-ada-002", "The food was delicious and the waiter...".to_owned());
println!("{:?}", c.create_embeddings(args).await.unwrap().data);