Struct LM 

pub struct LM {
    pub base_url: Option<String>,
    pub api_key: Option<String>,
    pub model: String,
    pub temperature: f32,
    pub max_tokens: u32,
    pub max_tool_iterations: u32,
    pub cache: bool,
    pub cache_handler: Option<Arc<Mutex<ResponseCache>>>,
    /* private fields */
}

Fields

base_url: Option<String>

api_key: Option<String>

model: String

temperature: f32

max_tokens: u32

max_tool_iterations: u32

cache: bool

cache_handler: Option<Arc<Mutex<ResponseCache>>>

Implementations

impl LM

pub fn builder() -> LMBuilder

Create an instance of LM using the builder syntax

Examples found in repository

examples/01-simple.rs (line 73)

async fn main() -> Result<()> {
    configure(
        LM::builder()
            .model("openai:gpt-4o-mini".to_string())
            .build()
            .await
            .unwrap(),
        ChatAdapter,
    );

    let example = example! {
        "question": "input" => "What is the capital of France?",
    };

    let qa_rater = QARater::builder().build();
    let prediction = qa_rater.forward(example).await.unwrap();
    println!("{prediction:?}");

    Ok(())
}

examples/05-heterogenous-examples.rs (line 15)

async fn main() -> anyhow::Result<()> {
    configure(
        LM::builder()
            .model("openai:gpt-4o-mini".to_string())
            .build()
            .await
            .unwrap(),
        ChatAdapter {},
    );

    let exp = example! {
        "number": "input" => 10,
    };
    let predict = Predict::new(sign! {
        (number: i32) -> number_squared: i32, number_cubed: i32
    });

    let prediction = predict.forward(exp).await?;
    println!("{prediction:?}");

    Ok(())
}

examples/07-inspect-history.rs (line 31)

async fn main() {
    let lm = LM::builder()
        .model("openai:gpt-4o-mini".to_string())
        .build()
        .await
        .unwrap();
    configure(lm, ChatAdapter);

    let example = example! {
        "question": "input" => "What is the capital of France?",
    };

    let qa_rater = QARater::builder().build();
    let prediction = qa_rater.forward(example.clone()).await.unwrap();
    println!("Prediction: {prediction:?}");

    let history = get_lm().inspect_history(1).await;
    println!("History: {history:?}");
}

examples/03-evaluate-hotpotqa.rs (line 66)

async fn main() -> anyhow::Result<()> {
    configure(
        LM::builder()
            .model("openai:gpt-4o-mini".to_string())
            .build()
            .await?,
        ChatAdapter {},
    );

    let examples = DataLoader::load_hf(
        "hotpotqa/hotpot_qa",
        vec!["question".to_string()],
        vec!["answer".to_string()],
        "fullwiki",
        "validation",
        true,
    )?[..128]
        .to_vec();

    let evaluator = QARater::builder().build();
    let metric = evaluator.evaluate(examples).await;

    println!("Metric: {metric}");
    Ok(())
}

examples/04-optimize-hotpotqa.rs (line 62)

async fn main() -> anyhow::Result<()> {
    configure(
        LM::builder()
            .model("openai:gpt-4o-mini".to_string())
            .build()
            .await
            .unwrap(),
        ChatAdapter {},
    );

    let examples = DataLoader::load_hf(
        "hotpotqa/hotpot_qa",
        vec!["question".to_string()],
        vec!["answer".to_string()],
        "fullwiki",
        "validation",
        true,
    )?[..10]
        .to_vec();

    let mut rater = QARater::builder().build();
    let optimizer = COPRO::builder().breadth(10).depth(1).build();

    println!("Rater: {:?}", rater.answerer.get_signature().instruction());

    optimizer.compile(&mut rater, examples.clone()).await?;

    println!("Rater: {:?}", rater.answerer.get_signature().instruction());

    Ok(())
}

examples/06-other-providers-batch.rs (line 80)

async fn main() {
    // Anthropic
    configure(
        LM::builder()
            .model("anthropic:claude-sonnet-4-5-20250929".to_string())
            .build()
            .await
            .unwrap(),
        ChatAdapter,
    );

    let example = vec![
        example! {
            "question": "input" => "What is the capital of France?",
        },
        example! {
            "question": "input" => "What is the capital of Germany?",
        },
        example! {
            "question": "input" => "What is the capital of Italy?",
        },
    ];

    let qa_rater = QARater::builder().build();
    let prediction = qa_rater.batch(example.clone(), 2, true).await.unwrap();
    println!("Anthropic: {prediction:?}");

    // Gemini
    configure(
        LM::builder()
            .model("gemini:gemini-2.0-flash".to_string())
            .build()
            .await
            .unwrap(),
        ChatAdapter,
    );

    let prediction = qa_rater.batch(example, 2, true).await.unwrap();
    println!("Gemini: {prediction:?}");
}

Additional examples can be found in:

• examples/11-custom-client.rs
• examples/09-gepa-sentiment.rs
• examples/10-gepa-llm-judge.rs

impl LM

pub async fn with_client(self, client: LMClient) -> Result<Self>

Examples found in repository

examples/11-custom-client.rs (line 44)

async fn main() -> Result<()> {
    // Create a custom OpenAI completion model directly
    let api_key = env::var("OPENAI_API_KEY").unwrap_or_else(|_| "dummy-key".to_string());

    let openai_client: openai::Client<reqwest::Client> =
        openai::ClientBuilder::new(&api_key).build();
    let openai_model = openai::completion::CompletionModel::new(openai_client, "gpt-4o-mini");

    // Convert to LMClient using Into trait (enum_dispatch generates From implementations)
    let custom_lm_client: LMClient = openai_model.into();

    // Create LM with the custom client
    let lm = LM::builder()
        .build()
        .await?
        .with_client(custom_lm_client)
        .await?;

    // Configure the global settings with our custom LM
    configure(lm, ChatAdapter);

    let example = example! {
        "question": "input" => "What is the capital of France?",
    };

    let qa_predictor = Predict::new(QASignature::new());
    let prediction = qa_predictor.forward(example).await?;
    println!("{prediction:?}");

    Ok(())
}

impl LM

pub async fn call( &self, messages: Chat, tools: Vec<Arc<dyn ToolDyn>>, ) -> Result<LMResponse>

pub async fn inspect_history(&self, n: usize) -> Vec<CallResult>

Returns the n most recent cached calls.

Panics if caching is disabled for this LM.

Examples found in repository

examples/07-inspect-history.rs (line 46)

async fn main() {
    let lm = LM::builder()
        .model("openai:gpt-4o-mini".to_string())
        .build()
        .await
        .unwrap();
    configure(lm, ChatAdapter);

    let example = example! {
        "question": "input" => "What is the capital of France?",
    };

    let qa_rater = QARater::builder().build();
    let prediction = qa_rater.forward(example.clone()).await.unwrap();
    println!("Prediction: {prediction:?}");

    let history = get_lm().inspect_history(1).await;
    println!("History: {history:?}");
}

Trait Implementations

impl Clone for LM

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for LM

fn default() -> Self

Returns the “default value” for a type.