ChatGPT-rs

This library has now been rewritten to use official OpenAI's ChatGPT API, instead of other unofficial workarounds.

Usage

Here is a simple usage of the API, getting completion for a single message. You can see more practical examples in the examples directory.

use chatgpt::prelude::*;

#[tokio::main]
async fn main() -> Result<()> {
    // Getting the API key here
    let key = args().nth(1).unwrap();

    /// Creating a new ChatGPT client.
    /// Note that it requires an API key, and uses
    /// tokens from your OpenAI API account balance.
    let client = ChatGPT::new(key)?;

    /// Sending a message and getting the completion
    let response: CompletionResponse = client
        .send_message("Describe in five words the Rust programming language.")
        .await?;

    println!("Response: {}", response.message().content);

    Ok(())
}

Streaming Responses

If you wish to gradually build the response message, you may use the streams feature (not enabled by default) of the crate, and special methods to request streamed responses.

Here is an example:

// Acquiring a streamed response
// Note, that the `futures_util` crate is required for most
// stream related utility methods
let stream = client
    .send_message_streaming("Could you name me a few popular Rust backend server frameworks?")
    .await?;

// Iterating over stream contents
stream
    .for_each(|each| async move {
        match each {
            ResponseChunk::Content {
                delta,
                response_index: _,
            } => {
                // Printing part of response without the newline
                print!("{delta}");
                // Manually flushing the standard output, as `print` macro does not do that
                stdout().lock().flush().unwrap();
            }
            _ => {}
        }
    })
    .await;
}

Note that the returned streams normally don't have any utility methods, so you will have to use a StreamExt method from your async library of choice (e.g. futures-util or tokio).

Conversations

Conversations are the threads in which ChatGPT can analyze previous messages and chain it's thoughts. They also automatically store all the message history.

Here is an example:

// Creating a new conversation
let mut conversation: Conversation = client.new_conversation();

// Sending messages to the conversation
let response_a: CompletionResponse = conversation
    .send_message("Could you describe the Rust programming language in 5 words?")
    .await?;
let response_b: CompletionResponse = conversation
    .send_message("Now could you do the same, but for Kotlin?")
    .await?;

// You can also access the message history itself
for message in &conversation.history {
    println!("{message:#?}")
}

This way of creating a conversation creates it with the default introductory message, which roughly is: You are ChatGPT, an AI model developed by OpenAI. Answer as concisely as possible. Today is: {today's date}.

However, you can specify the introductory message yourself this way:

let mut conversation: Conversation = client.new_conversation_directed("You are RustGPT, when answering any questions, you always shift the topic of the conversation to the Rust programming language.");
// Continue with the new conversation

Conversation Streaming

Conversations also support returning streamed responses (with the streams feature).

NOTE: Streamed responses do not automatically save returned message to history, so you will have to do it manually by yourself.

Here is an example:

// Acquiring a streamed response
// Note, that the `futures_util` crate is required for most
// stream related utility methods
let mut stream = conversation
    .send_message_streaming("Could you name me a few popular Rust backend server frameworks?")
    .await?;

    // Iterating over a stream and collecting the results into a vector
let mut output: Vec<ResponseChunk> = Vec::new();
while let Some(chunk) = stream.next().await {
    match chunk {
        ResponseChunk::Content {
            delta,
            response_index,
        } => {
            // Printing part of response without the newline
            print!("{delta}");
            // Manually flushing the standard output, as `print` macro does not do that
            stdout().lock().flush().unwrap();
            output.push(ResponseChunk::Content {
                delta,
                response_index,
            });
        }
        // We don't really care about other types, other than parsing them into a ChatMessage later
        other => output.push(other),
    }
}

// Parsing ChatMessage from the response chunks and saving it to the conversation history
let messages = ChatMessage::from_response_chunks(output);
conversation.history.push(messages[0].to_owned());

Conversation Persistence

You can currently store the conversation's message in two formats: JSON or postcard. They can be toggled on or off using the json and postcard features respectively.

Since the ChatMessage struct derives serde's Serialize and Deserialize traits, you can also use any serde-compatible serialization library, as the history field and the Conversation::new_with_history() method are public in the Conversation struct.

Persistence with JSON

Requires the json feature (enabled by default)

// Create a new conversation here
let mut conversation: Conversation = ...;

// ... send messages to the conversation ...

// Saving the conversation
conversation.save_history_json("my-conversation.json").await?;

// You can later read this conversation history again
let mut restored = client
    .restore_conversation_json("my-conversation.json")
    .await?;

Persistence with Postcard

Requires the postcard feature (disabled by default)

// Create a new conversation here
let mut conversation: Conversation = ...;

// ... send messages to the conversation ...

// Saving the conversation
conversation.save_history_postcard("my-conversation.bin").await?;

// You can later read this conversation history again
let mut restored = client
    .restore_conversation_postcard("my-conversation.bin")
    .await?;

Advanced configuration

You can configure your model further with ModelConfigurationBuilder, which also allows to use proxies:

// Getting the API key here
let key = args().nth(1).unwrap();

// Creating a new ChatGPT client with extra settings.
// Note that it might not require an API key depending on proxy
let client = ChatGPT::new_with_config(
    key,
    ModelConfigurationBuilder::default()
        .api_url("https://api.pawan.krd/v1/chat/completions")
        .temperature(1.0)
        .engine(ChatGPTEngine::Gpt4_32k)
        .build()
        .unwrap(),
)?;