First Chat

This tutorial walks through installing Synwire and making your first chat model invocation.

Add dependencies

Add the following to your Cargo.toml:

[dependencies]
synwire-core = "0.1"
tokio = { version = "1", features = ["full"] }

For testing without API keys, synwire-core includes FakeChatModel. To use a real provider, add synwire-llm-openai or synwire-llm-ollama.

Using FakeChatModel (no API key)

use synwire_core::language_models::{FakeChatModel, BaseChatModel};
use synwire_core::messages::Message;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // FakeChatModel returns pre-configured responses in order
    let model = FakeChatModel::new(vec![
        "I'm doing well, thanks for asking!".into(),
    ]);

    let messages = vec![Message::human("How are you?")];
    let result = model.invoke(&messages, None).await?;

    println!("{}", result.message.content().as_text());
    // Output: I'm doing well, thanks for asking!

    Ok(())
}

Using ChatOpenAI

Add synwire-llm-openai to your dependencies:

[dependencies]
synwire-llm-openai = "0.1"

use synwire_core::language_models::BaseChatModel;
use synwire_core::messages::Message;
use synwire_llm_openai::ChatOpenAI;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let model = ChatOpenAI::builder()
        .model("gpt-4o-mini")
        .api_key_env("OPENAI_API_KEY")
        .build()?;

    let messages = vec![Message::human("What is Rust?")];
    let result = model.invoke(&messages, None).await?;

    println!("{}", result.message.content().as_text());

    Ok(())
}

Multi-turn conversations

All chat models accept a slice of Message values. Build a conversation by including system, human, and AI messages:

use synwire_core::language_models::{FakeChatModel, BaseChatModel};
use synwire_core::messages::Message;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let model = FakeChatModel::new(vec![
        "Paris is the capital of France.".into(),
    ]);

    let messages = vec![
        Message::system("You are a helpful geography assistant."),
        Message::human("What is the capital of France?"),
    ];

    let result = model.invoke(&messages, None).await?;
    println!("{}", result.message.content().as_text());

    Ok(())
}

Batch invocation

Call batch to invoke on multiple inputs:

let inputs = vec![
    vec![Message::human("Question 1")],
    vec![Message::human("Question 2")],
];
let results = model.batch(&inputs, None).await?;
for r in &results {
    println!("{}", r.message.content().as_text());
}

Key types

Next steps

• Prompt Chains -- compose templates with models
• Streaming -- stream responses token by token

See also

• Local Inference with Ollama — run the same example with a local model, no API key required
• LLM Providers Explanation — choosing and swapping providers

Background: Zero-shot Prompting — how LLMs respond to instructions without examples.