Struct Predict 

pub struct Predict {
    pub signature: Box<dyn MetaSignature>,
    pub tools: Vec<Arc<dyn ToolDyn>>,
}

Fields

signature: Box<dyn MetaSignature>

tools: Vec<Arc<dyn ToolDyn>>

Implementations

impl Predict

pub fn new(signature: impl MetaSignature + 'static) -> Self

Examples found in repository

examples/05-heterogenous-examples.rs (lines 26-28)

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/11-custom-client.rs (line 54)

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(())
}

examples/09-gepa-sentiment.rs (line 170)

async fn main() -> Result<()> {
    println!("GEPA Sentiment Analysis Optimization Example\n");

    // Setup LM
    let lm = LM::builder().temperature(0.7).build().await.unwrap();

    configure(lm.clone(), ChatAdapter);

    // Create training examples with diverse sentiments
    let trainset = vec![
        example! {
            "text": "input" => "This movie was absolutely fantastic! I loved every minute of it.",
            "expected_sentiment": "input" => "positive"
        },
        example! {
            "text": "input" => "Terrible service, will never come back again.",
            "expected_sentiment": "input" => "negative"
        },
        example! {
            "text": "input" => "The weather is okay, nothing special.",
            "expected_sentiment": "input" => "neutral"
        },
        example! {
            "text": "input" => "Despite some minor issues, I'm quite happy with the purchase.",
            "expected_sentiment": "input" => "positive"
        },
        example! {
            "text": "input" => "I have mixed feelings about this product.",
            "expected_sentiment": "input" => "neutral"
        },
        example! {
            "text": "input" => "This is the worst experience I've ever had!",
            "expected_sentiment": "input" => "negative"
        },
        example! {
            "text": "input" => "It's fine. Does what it's supposed to do.",
            "expected_sentiment": "input" => "neutral"
        },
        example! {
            "text": "input" => "Exceeded all my expectations! Highly recommend!",
            "expected_sentiment": "input" => "positive"
        },
        example! {
            "text": "input" => "Disappointed and frustrated with the outcome.",
            "expected_sentiment": "input" => "negative"
        },
        example! {
            "text": "input" => "Standard quality, nothing remarkable.",
            "expected_sentiment": "input" => "neutral"
        },
    ];

    // Create module
    let mut module = SentimentAnalyzer::builder()
        .predictor(Predict::new(SentimentSignature::new()))
        .build();

    // Evaluate baseline performance
    println!("Baseline Performance:");
    let baseline_score = module.evaluate(trainset.clone()).await;
    println!("  Average score: {:.3}\n", baseline_score);

    // Configure GEPA optimizer
    let gepa = GEPA::builder()
        .num_iterations(5)
        .minibatch_size(5)
        .num_trials(3)
        .temperature(0.9)
        .track_stats(true)
        .build();

    // Run optimization
    println!("Starting GEPA optimization...\n");
    let result = gepa
        .compile_with_feedback(&mut module, trainset.clone())
        .await?;

    // Display results
    println!("\nOptimization Results:");
    println!(
        "  Best average score: {:.3}",
        result.best_candidate.average_score()
    );
    println!("  Total rollouts: {}", result.total_rollouts);
    println!("  Total LM calls: {}", result.total_lm_calls);
    println!("  Generations: {}", result.evolution_history.len());

    println!("\nBest Instruction:");
    println!("  {}", result.best_candidate.instruction);

    if !result.evolution_history.is_empty() {
        println!("\nEvolution History:");
        for entry in &result.evolution_history {
            println!("  Generation {}: {:.3}", entry.0, entry.1);
        }
    }

    // Test optimized module on a new example
    println!("\nTesting Optimized Module:");
    let test_example = example! {
        "text": "input" => "This product changed my life! Absolutely amazing!",
        "expected_sentiment": "input" => "positive"
    };

    let test_prediction = module.forward(test_example.clone()).await?;
    let test_feedback = module
        .feedback_metric(&test_example, &test_prediction)
        .await;

    println!(
        "  Test prediction: {}",
        test_prediction.get("sentiment", None)
    );
    println!("  Test score: {:.3}", test_feedback.score);
    println!("  Feedback:\n{}", test_feedback.feedback);

    Ok(())
}

examples/10-gepa-llm-judge.rs (line 259)

async fn main() -> Result<()> {
    println!("GEPA with LLM-as-a-Judge Example\n");
    println!("This example shows how to use an LLM judge to automatically");
    println!("generate rich feedback for optimizing a math solver.\n");

    // Setup: Configure the LLM
    // Main LM for the task
    let task_lm = LM::builder().temperature(0.7).build().await.unwrap();

    // Judge LM (could use a different/cheaper model)
    let judge_lm = LM::builder().temperature(0.3).build().await.unwrap();

    configure(task_lm, ChatAdapter);

    // Create training examples
    let trainset = vec![
        example! {
            "problem": "input" => "Sarah has 12 apples. She gives 3 to her friend and buys 5 more. How many apples does she have now?",
            "expected_answer": "input" => "14"
        },
        example! {
            "problem": "input" => "A train travels 60 miles in 1 hour. How far will it travel in 3.5 hours at the same speed?",
            "expected_answer": "input" => "210"
        },
        example! {
            "problem": "input" => "There are 24 students in a class. If 1/3 of them are absent, how many students are present?",
            "expected_answer": "input" => "16"
        },
        example! {
            "problem": "input" => "A rectangle has length 8 cm and width 5 cm. What is its area?",
            "expected_answer": "input" => "40"
        },
        example! {
            "problem": "input" => "John has $50. He spends $12 on lunch and $8 on a book. How much money does he have left?",
            "expected_answer": "input" => "30"
        },
    ];

    // Create the module
    let mut module = MathSolver::builder()
        .solver(Predict::new(MathWordProblem::new()))
        .judge(Predict::new(MathJudge::new()))
        .judge_lm(Arc::new(judge_lm))
        .build();

    // Evaluate baseline performance
    println!("Step 1: Baseline Performance");
    println!("Testing the solver before optimization...\n");
    let baseline_score = module.evaluate(trainset.clone()).await;
    println!("  Baseline average score: {:.3}\n", baseline_score);

    // Configure GEPA optimizer
    println!("Step 2: Configure GEPA");
    println!("Setting up the optimizer with budget controls...\n");

    let gepa = GEPA::builder()
        .num_iterations(3) // Fewer iterations for demo
        .minibatch_size(3) // Smaller batches
        .temperature(0.9)
        .track_stats(true)
        .maybe_max_lm_calls(Some(100)) // Important: we're using 2x LM calls (task + judge)
        .build();

    // Run GEPA optimization
    println!("Step 3: Run GEPA Optimization");
    println!("The judge will analyze reasoning quality and provide feedback...\n");

    let result = gepa
        .compile_with_feedback(&mut module, trainset.clone())
        .await?;

    // Display results
    println!("\nStep 4: Results");
    println!("===============\n");
    println!("Optimization complete!");
    println!(
        "  Best average score: {:.3}",
        result.best_candidate.average_score()
    );
    println!(
        "  Improvement: {:.3}",
        result.best_candidate.average_score() - baseline_score
    );
    println!("  Total rollouts: {}", result.total_rollouts);
    println!(
        "  Total LM calls: {} (includes judge evaluations)",
        result.total_lm_calls
    );

    println!("\nEvolution over time:");
    for (generation, score) in &result.evolution_history {
        println!("  Generation {}: {:.3}", generation, score);
    }

    println!("\nOptimized instruction:");
    println!("  {}", result.best_candidate.instruction);

    // Test the optimized solver
    println!("\nStep 5: Test Optimized Solver");
    println!("==============================\n");

    let test_problem = example! {
        "problem": "input" => "A store sells pencils for $0.25 each. If you buy 8 pencils, how much will you pay?",
        "expected_answer": "input" => "2"
    };

    let test_prediction = module.forward(test_problem.clone()).await?;
    let test_feedback = module
        .feedback_metric(&test_problem, &test_prediction)
        .await;

    println!(
        "Test problem: A store sells pencils for $0.25 each. If you buy 8 pencils, how much will you pay?"
    );
    println!("\nAnswer: {}", test_prediction.get("answer", None));
    println!("Score: {:.3}\n", test_feedback.score);
    println!("Detailed Feedback from Judge:");
    println!("{}", test_feedback.feedback);

    Ok(())
}

pub fn new_with_tools( signature: impl MetaSignature + 'static, tools: Vec<Box<dyn ToolDyn>>, ) -> Self

pub fn with_tools(self, tools: Vec<Box<dyn ToolDyn>>) -> Self

pub fn add_tool(self, tool: Box<dyn ToolDyn>) -> Self

Trait Implementations

impl Optimizable for Predict

fn get_signature(&self) -> &dyn MetaSignature

fn parameters(&mut self) -> IndexMap<String, &mut dyn Optimizable>

fn update_signature_instruction(&mut self, instruction: String) -> Result<()>

impl Predictor for Predict

async fn forward(&self, inputs: Example) -> Result<Prediction>

async fn forward_with_config( &self, inputs: Example, lm: Arc<LM>, ) -> Result<Prediction>

async fn batch(&self, inputs: Vec<Example>) -> Result<Vec<Prediction>>

async fn batch_with_config( &self, inputs: Vec<Example>, lm: Arc<LM>, ) -> Result<Vec<Prediction>>