Crate async_openai

Rust library for OpenAI

Creating client

use async_openai::{Client, config::OpenAIConfig};

// Create a OpenAI client with api key from env var OPENAI_API_KEY and default base url.
let client = Client::new();

// Above is shortcut for
let config = OpenAIConfig::default();
let client = Client::with_config(config);

// OR use API key from different source and a non default organization
let api_key = "sk-..."; // This secret could be from a file, or environment variable.
let config = OpenAIConfig::new()
    .with_api_key(api_key)
    .with_org_id("the-continental");

let client = Client::with_config(config);

// Use custom reqwest client
let http_client = reqwest::ClientBuilder::new().user_agent("async-openai").build().unwrap();
let client = Client::new().with_http_client(http_client);

Microsoft Azure Endpoints

use async_openai::{Client, config::AzureConfig};

let config = AzureConfig::new()
    .with_api_base("https://my-resource-name.openai.azure.com")
    .with_api_version("2023-03-15-preview")
    .with_deployment_id("deployment-id")
    .with_api_key("...");

let client = Client::with_config(config);

// Note that `async-openai` only implements OpenAI spec
// and doesn't maintain parity with the spec of Azure OpenAI service.

Making requests

 use async_openai::{Client, types::{CreateCompletionRequestArgs}};

 // Create client
 let client = Client::new();

 // Create request using builder pattern
 // Every request struct has companion builder struct with same name + Args suffix
 let request = CreateCompletionRequestArgs::default()
     .model("gpt-3.5-turbo-instruct")
     .prompt("Tell me the recipe of alfredo pasta")
     .max_tokens(40_u32)
     .build()
     .unwrap();

 // Call API
 let response = client
     .completions()      // Get the API "group" (completions, images, etc.) from the client
     .create(request)    // Make the API call in that "group"
     .await
     .unwrap();

 println!("{}", response.choices.first().unwrap().text);

Examples

For full working examples for all supported features see examples directory in the repository.

Modules

• config
  Client configurations: OpenAIConfig for OpenAI, AzureConfig for Azure OpenAI Service.
• error
  Errors originating from API calls, parsing responses, and reading-or-writing to the file system.
• types
  Types used in OpenAI API requests and responses. These types are created from component schemas in the OpenAPI spec

Structs

• AssistantFiles
  Files attached to an assistant.
• Assistants
  Build assistants that can call models and use tools to perform tasks.
• Audio
  Turn audio into text or text into audio. Related guide: Speech to text
• Batches
  Create large batches of API requests for asynchronous processing. The Batch API returns completions within 24 hours for a 50% discount.
• Chat
  Given a list of messages comprising a conversation, the model will return a response.
• Client
  Client is a container for config, backoff and http_client used to make API calls.
• Completions
  Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position. We recommend most users use our Chat completions API. Learn more
• Embeddings
  Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
• Files
  Files are used to upload documents that can be used with features like Assistants and Fine-tuning.
• FineTuning
  Manage fine-tuning jobs to tailor a model to your specific training data.
• Images
  Given a prompt and/or an input image, the model will generate a new image.
• MessageFiles
  Files attached to a message.
• Messages
  Represents a message within a thread.
• Models
  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.
• Moderations
  Given some input text, outputs if the model classifies it as potentially harmful across several categories.
• Runs
  Represents an execution run on a thread.
• Steps
  Represents a step in execution of a run.
• Threads
  Create threads that assistants can interact with.
• VectorStoreFileBatches
  Vector store file batches represent operations to add multiple files to a vector store.
• VectorStoreFiles
  Vector store files represent files inside a vector store.
• VectorStores