The FewShotBayesianOptimizer is a sophisticated prompt optimization tool adds relevant examples from
your sample questions to the system prompt using Bayesian optimization techniques.

:::callout{intent="note"}
The `FewShotBayesianOptimizer` is a strong choice when your primary goal is to find the optimal number and
combination of few-shot examples (demonstrations) to accompany your main instruction prompt,
particularly for **chat models**. If your task performance heavily relies on the quality and relevance of in-context examples, this optimizer is ideal.
:::

## How It Works

The `FewShotBayesianOptimizer` uses Bayesian optimization to find the optimal set and number of
few-shot examples to include with your base instruction prompt for chat models. It Uses
[Optuna](https://optuna.org/), a hyperparameter optimization framework, to guide the search for the
optimal set and number of few-shot examples.

:::frame
<img src="../img/apps/opik-documentation/documentation/fern/img/agent_optimization/fewshot_bayesian_optimizer.png" alt="FewShot Bayesian Optimizer">
:::

## Quickstart

You can use the `FewShotBayesianOptimizer` to optimize a prompt by following these steps:

```python maxLines=1000
from opik_optimizer import FewShotBayesianOptimizer
from opik.evaluation.metrics import LevenshteinRatio
from opik_optimizer import datasets, ChatPrompt

# Initialize optimizer
optimizer = FewShotBayesianOptimizer(
    model="openai/gpt-4",
    model_parameters={
        "temperature": 0.1,
        "max_tokens": 5000
    },
)

# Prepare dataset
dataset = datasets.hotpot(count=300)

# Define metric and prompt (see docs for more options)
def levenshtein_ratio(dataset_item, llm_output):
    return LevenshteinRatio().score(reference=dataset_item["answer"], output=llm_output)

prompt = ChatPrompt(
    messages=[
        {"role": "system", "content": "Provide an answer to the question."},
        {"role": "user", "content": "{question}"}
    ]
)

# Run optimization
results = optimizer.optimize_prompt(
    prompt=prompt,
    dataset=dataset,
    metric=levenshtein_ratio,
    n_samples=100
)

# Access results
results.display()
```

## Configuration Options

### Optimizer parameters

The optimizer has the following parameters:

- `model` (str, default: openai/gpt-5-nano) — LiteLLM model name for optimizer's internal reasoning (generating few-shot templates)

- `model_parameters` (dict\[str, typing.Any] | None) — Optional dict of LiteLLM parameters for optimizer's internal LLM calls. Common params: temperature, max\_tokens, max\_completion\_tokens, top\_p.

- `min_examples` (int, default: 2) — Minimum number of examples to include in the prompt

- `max_examples` (int, default: 8) — Maximum number of examples to include in the prompt

- `n_threads` (int, default: 8) — Number of threads for parallel evaluation

- `verbose` (int, default: 1) — Controls internal logging/progress bars (0=off, 1=on)

- `seed` (int, default: 42) — Random seed for reproducibility

### `optimize_prompt` parameters

The `optimize_prompt` method has the following parameters:

- `prompt` (ChatPrompt) — The prompt to optimize

- `dataset` (Dataset) — Opik Dataset to optimize on

- `metric` (Callable) — Metric function to evaluate on

- `experiment_config` (dict | None) — Optional configuration for the experiment, useful to log additional metadata

- `n_samples` (int | float | str | None) — Number of dataset items to use per evaluation. Use counts (e.g., `50`), fractions (e.g., `0.1`), percentages (e.g., "10%"), or "all"/"full"/None for the full dataset.

- `n_samples_minibatch` (int | None) — Optional number of samples for inner-loop minibatches (defaults to n\_samples).

- `n_samples_strategy` (str | None) — Sampling strategy for subsampling (default: "random\_sorted").

- `auto_continue` (bool, default: False) — Whether to auto-continue optimization

- `agent_class` (type\[opik\_optimizer.optimizable\_agent.OptimizableAgent] | None) — Optional agent class to use

- `project_name` (str, default: Optimization) — Opik project name for logging traces (default: "Optimization")

- `max_trials` (int, default: 10) — Number of trials for Bayesian Optimization (default: 10)

- `args` (Any) —

- `kwargs` (Any) —

### Model Support

There are two models to consider when using the `FewShotBayesianOptimizer`:

- `FewShotBayesianOptimizer.model`: The model used to generate the few-shot template and placeholder.
- `ChatPrompt.model`: The model used to evaluate the prompt.

The `model` parameter accepts any LiteLLM-supported model string (e.g., `"gpt-4o"`, `"azure/gpt-4"`,
`"anthropic/claude-3-opus"`, `"gemini/gemini-1.5-pro"`). You can also pass in extra model parameters
using the `model_parameters` parameter:

```python
optimizer = FewShotBayesianOptimizer(
    model="anthropic/claude-3-opus-20240229",
    model_parameters={
        "temperature": 0.7,
        "max_tokens": 4096
    }
)
```

## Next Steps

1. Explore specific [Optimizers](https://www.comet.com/development/optimization-runs/algorithms/overview) for algorithm details.
2. Refer to the [FAQ](https://www.comet.com/development/optimization-runs/faq) for common questions and troubleshooting.
3. Refer to the [API Reference](https://www.comet.com/development/optimization-runs/advanced/api_reference) for detailed configuration options.

## Related pages

- [Optimization algorithms overview](./development-optimization-runs-algorithms-overview.md)
- [Optimizer benchmarks](./development-optimization-runs-algorithms-benchmarks.md)
- [MetaPrompt Optimizer](./development-optimization-runs-algorithms-metaprompt-optimizer.md)
- [HRPO (Hierarchical Reflective Prompt Optimizer)](./development-optimization-runs-algorithms-hierarchical-adaptive-optimizer.md)
- [Evolutionary Optimizer: Genetic Algorithms](./development-optimization-runs-algorithms-evolutionary-optimizer.md)
- [GEPA Optimizer](./development-optimization-runs-algorithms-gepa-optimizer.md)
- [Parameter Optimizer: Bayesian Parameter Tuning](./development-optimization-runs-algorithms-parameter-optimizer.md)
- [Tool Optimization (MCP & Function Calling)](./development-optimization-runs-algorithms-tool-optimization.md)

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