> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galtea.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Create Single-Turn Evaluation

> Create an evaluation for a single-turn interaction, either from a test case or production data.

<Warning>
  **Deprecated**: This method is deprecated and will be removed in a future version.

  Use one of the following alternatives instead:

  * `galtea.inference_results.create_and_evaluate()` - Creates an inference result and evaluates it in a single call
  * `galtea.evaluations.create(inference_result_id=...)` - Evaluates an existing inference result
</Warning>

## Returns

Returns a list of [Evaluation](/concepts/product/version/session/evaluation) objects, one for each metric provided.

## Usage

This method is versatile and can be used for two main scenarios:

1. **Test-Based Evaluation**: When you provide a `test_case_id`, Galtea evaluates your product's performance against a predefined challenge.
2. **Production Monitoring**: When you set `is_production=True` and provide an `input`, Galtea logs and evaluates your product's performance in a live environment.

### Development Testing

**For non self-hosted metrics**

```python theme={"system"}
evaluations = galtea.evaluations.create_single_turn(
    version_id=version_id,
    test_case_id=test_case_id,
    actual_output="Paris",
    metrics=[{"name": "Factual Accuracy"}],
)
```

**If you pre-computed the score**

```python theme={"system"}
evaluations = galtea.evaluations.create_single_turn(
    version_id=version_id,
    test_case_id=test_case_id,
    actual_output="Paris",
    metrics=[{"name": self_hosted_metric.name, "score": 0.75}],
)
```

### Production Monitoring

In order to monitor your product in a production environment, you can create evaluations not linked to a specific test case, but you need to set the `is_production` flag to `True`.

```python theme={"system"}
evaluations = galtea.evaluations.create_single_turn(
    version_id=version_id,
    is_production=True,
    input="What is the capital of France?",
    actual_output="Paris",
    metrics=[{"name": "Answer Relevancy"}],
)
```

### Advanced Usage

You can also create evaluations using self-hosted metrics with dynamically calculated scores by utilizing the `CustomScoreEvaluationMetric` class, which allows for more complex evaluation scenarios.

```python theme={"system"}
# Define scoring logic as a class
class PolitenessCheck(CustomScoreEvaluationMetric):
    def __init__(self):
        super().__init__(name="politeness-check")

    def measure(self, *args, actual_output: str | None = None, **kwargs) -> float:
        if actual_output is None:
            return 0.0
        polite_words = ["please", "thank you", "you're welcome"]
        return 1.0 if any(word in actual_output.lower() for word in polite_words) else 0.0


# Create the metric in the platform if it doesn't exist yet
# Note: This can be done via the Dashboard too
try:
    metric = galtea.metrics.get_by_name(name="politeness-check")
except Exception:
    metric = None
if metric is None:
    galtea.metrics.create(
        name="politeness-check",
        source="self_hosted",
        description="Checks if polite words appear in the output",
    )

# Now, evaluate the single turn using both Galtea-hosted and self-hosted metrics
evaluations = galtea.evaluations.create_single_turn(
    is_production=True,
    version_id=version_id,
    input="Hello!",
    actual_output="Hi there! How can I assist you today?",
    metrics=[
        {"name": "Role Adherence"},  # You can use galtea-hosted metrics simultaneously
        {"score": PolitenessCheck()},  # Self-hosted with dynamic scoring
        # Note: No 'name' or 'id' in dict - it comes from PolitenessCheck(name="...")
    ],
)
```

## Parameters

<ResponseField name="version_id" type="string" required>
  The ID of the version you want to evaluate.
</ResponseField>

<ResponseField name="metrics" type="List[Union[str, CustomScoreEvaluationMetric, Dict]]" required>
  A list of metrics to use for the evaluation.

  The `MetricInput` dictionary supports the following keys:

  * `id` (string, optional): The ID of an existing metric
  * `name` (string, optional): The name of the metric
  * `score` (float | CustomScoreEvaluationMetric, optional): For self-hosted metrics only
    * If `float`: Pre-computed score (0.0 to 1.0). Requires `id` or `name` in the dict.
    * If `CustomScoreEvaluationMetric`: Score will be calculated dynamically. The CustomScoreEvaluationMetric instance must be initialized with `name` or `id`. Do NOT provide `id` or `name` in the dict when using this option.

  <Note>
    For self-hosted metrics, both score options are equally valid: pre-compute as a float, or use CustomScoreEvaluationMetric for dynamic calculation. Galtea-hosted metrics automatically compute scores and should not include a `score` field.
  </Note>
</ResponseField>

<ResponseField name="actual_output" type="string" required>
  The actual output produced by the product.
</ResponseField>

<ResponseField name="test_case_id" type="string" optional>
  The ID of the test case to be evaluated. Required for non-production evaluations.
</ResponseField>

<ResponseField name="input" type="string" optional>
  The input text/prompt. Required for production evaluations where no `test_case_id` is provided.
</ResponseField>

<ResponseField name="is_production" type="boolean" optional>
  Set to `True` to indicate the evaluation is from a production environment. Defaults to `False`.
</ResponseField>

<ResponseField name="retrieval_context" type="string" optional>
  The context retrieved by your RAG system that was used to generate the `actual_output`.
</ResponseField>

<ResponseField name="latency" type="float" optional>
  Time in milliseconds from the request to the LLM until the response was received.
</ResponseField>

<ResponseField name="usage_info" type="dict[str, int]" optional>
  Information about token usage during the model call.
  Possible keys include:

  * `input_tokens`: Number of input tokens sent to the model.
  * `output_tokens`: Number of output tokens generated by the model.
  * `cache_read_input_tokens`: Number of input tokens read from the cache.
</ResponseField>

<ResponseField name="cost_info" type="dict[str, float]" optional>
  Information about the cost per token during the model call.
  Possible keys include:

  * `cost_per_input_token`: Cost per input token sent to the model.
  * `cost_per_output_token`: Cost per output token generated by the model.
  * `cost_per_cache_read_input_token`: Cost per input token read from the cache.
</ResponseField>

<ResponseField name="conversation_simulator_version" type="string" optional>
  The version of Galtea's conversation simulator used to generate the user message (input). This should only be provided if the input was generated using the `conversation_simulator_service`.
</ResponseField>