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from opik_optimizer import FewShotBayesianOptimizer
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optimizer = FewShotBayesianOptimizer(
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    model="openai/gpt-4",  # or "azure/gpt-4"
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    project_name="my-project",
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    temperature=0.1,
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    max_tokens=5000,
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    num_threads=8,
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    seed=42
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)

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optimizer = FewShotBayesianOptimizer(
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    model="openai/gpt-4",
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    project_name="my-project",
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    temperature=0.1,
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    max_tokens=5000,
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    num_threads=8,
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    seed=42,
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    min_examples=2,
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    max_examples=8,
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    n_initial_prompts=5,
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    n_iterations=10,
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    acquisition_function="ei",  # Expected Improvement
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    kernel="matern",           # Kernel for Gaussian Process
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    length_scale=1.0,          # Kernel length scale
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    noise_level=0.1           # Observation noise level
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)

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from opik_optimizer import FewShotBayesianOptimizer
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from opik.evaluation.metrics import LevenshteinRatio
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from opik_optimizer import (
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    MetricConfig,
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    TaskConfig,
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    from_llm_response_text,
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    from_dataset_field,
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)
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from opik_optimizer.demo import get_or_create_dataset
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# Initialize optimizer
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optimizer = FewShotBayesianOptimizer(
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    model="openai/gpt-4",
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    temperature=0.1,
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    max_tokens=5000
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)
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# Prepare dataset
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dataset = get_or_create_dataset("hotpot-300")
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# Define metric and task configuration (see docs for more options)
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metric_config = MetricConfig(
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    metric=LevenshteinRatio(),
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    inputs={
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        "output": from_llm_response_text(),  # Model's output
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        "reference": from_dataset_field(name="answer"),  # Ground truth
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    }
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)
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task_config = TaskConfig(
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    type="text_generation",
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    instruction_prompt="Provide an answer to the question.",
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    input_dataset_fields=["question"],
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    output_dataset_field="answer",
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    use_chat_prompt=True
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)
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# Run optimization
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results = optimizer.optimize_prompt(
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    dataset=dataset,
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    metric_config=metric_config,
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    task_config=task_config
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)
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# Access results
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results.display()

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optimizer = FewShotBayesianOptimizer(
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    model="openai/gpt-4",  # or any LiteLLM supported model
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    project_name="my-project",
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    temperature=0.1,
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    max_tokens=5000
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)