> ## 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 Metric

> Create a metric for evaluating your products.

## Returns

Returns a [Metric](/concepts/metric) object for the given parameters, or `None` if an error occurs.

## Examples

<Tabs>
  <Tab title="AI Evaluation">
    ```python theme={"system"}
    metric = galtea.metrics.create(
        name="accuracy_v1_" + run_identifier,
        evaluator_model_name="GPT-4.1",
        source="partial_prompt",
        judge_prompt="Determine whether the actual output is equivalent to the expected output",
        evaluation_params=["input", "actual_output", "expected_output"],
        tags=["custom", "accuracy"],
        description="A custom accuracy metric.",
    )
    ```
  </Tab>

  <Tab title="Human Evaluation">
    ```python theme={"system"}
    metric = galtea.metrics.create(
        name="human_eval_v1_" + run_identifier,
        source="human_evaluation",
        judge_prompt="Review the actual output and score it based on helpfulness, accuracy, and tone. Score 1 if the response is helpful and accurate. Score 0 if it is unhelpful or incorrect.",
        evaluation_params=["input", "actual_output", "expected_output"],
        user_group_ids=[user_group_id],
        tags=["human", "quality"],
        description="A human evaluation metric scored by QA annotators.",
    )
    ```
  </Tab>

  <Tab title="Self-Hosted">
    ```python theme={"system"}
    metric = galtea.metrics.create(
        name="accuracy_v1_self_" + run_identifier,
        source="self_hosted",
        tags=["custom", "accuracy"],
        description="A custom accuracy metric calculated by our custom score function.",
    )
    ```
  </Tab>
</Tabs>

## Parameters

<ResponseField name="name" type="string" required>
  The name of the metric.
</ResponseField>

<ResponseField name="test_type" type="string" optional>
  <Warning>
    **Deprecated.** This parameter is ignored and will be removed in a future release.
  </Warning>
</ResponseField>

<ResponseField name="evaluator_model_name" type="string" optional>
  The name of the model used to evaluate the metric. Required for metrics using `judge_prompt`.

  **Available models:**

  * `"Claude-Sonnet-4.5"`
  * `"Claude-Sonnet-3.7"`
  * `"GPT-4.1-mini"`
  * `"Gemini-2.5-Flash-Lite"`
  * `"Gemini-2.5-Flash"`
  * `"Gemini-2.0-flash"`
  * `"GPT-4o"`
  * `"GPT-4.1"`

  <Note>
    It should not be provided if the metric is "self hosted" (has no `judge_prompt`) since it does not require a model for evaluation.
  </Note>
</ResponseField>

<ResponseField name="judge_prompt" type="string" optional>
  A custom prompt that defines the evaluation logic for the metric. For AI Evaluation metrics, write the evaluation criteria and scoring rubric — Galtea will prepend the selected `evaluation_params` automatically. For Human Evaluation metrics, this serves as the annotation rubric. If omitted, the metric is considered a deterministic "Custom Score" metric.
</ResponseField>

<ResponseField name="source" type="string" optional>
  The evaluation method for the metric. Possible values:

  * `"partial_prompt"` — [AI Evaluation](/concepts/metric#creating-custom-metrics-via-ai-evaluation): You provide the core evaluation criteria and rubric. Galtea dynamically constructs the final prompt by prepending selected evaluation parameters to your criteria.
  * `"human_evaluation"` — **Human Evaluation**: Human annotators manually review and score evaluations using the annotation criteria you define. Evaluations enter a `PENDING_HUMAN` status and are completed when an annotator submits a score.
  * `"self_hosted"` — [Self-Hosted](/concepts/metric#self-hosted): For deterministic metrics scored locally using the SDK's `CustomScoreEvaluationMetric`. Your custom logic runs on your infrastructure, and the resulting score is uploaded to the platform.
</ResponseField>

<ResponseField name="evaluation_params" type="list[string]" optional>
  Evaluation parameters to include in the judge prompt. These parameters are prepended to the judge prompt to construct the final evaluation prompt.
  To check the available evaluation parameters, see the [Evaluation Parameters](/concepts/metric#param-evaluation-parameters) section.

  <Note>
    Only applicable for AI Evaluation and Human Evaluation metrics.
  </Note>
</ResponseField>

<ResponseField name="user_group_ids" type="list[string]" optional>
  A list of [user group](/concepts/user-group) IDs to associate with this metric. Only applicable when `source` is `human_evaluation`.

  * If user group IDs are specified, only users in those groups can annotate evaluations for this metric.
  * If no user group IDs are specified, any user in the organization can annotate.
</ResponseField>

<ResponseField name="tags" type="list[string]" optional>
  Tags to categorize the metric.
</ResponseField>

<ResponseField name="description" type="string" optional>
  A brief description of what the metric evaluates.
</ResponseField>

<ResponseField name="documentation_url" type="string" optional>
  A URL pointing to more detailed documentation about the metric.
</ResponseField>