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

# metrics

## Metrics

### create\_custom\_llm\_metric

```python theme={null}
def create_custom_llm_metric(self,
                             name: str,
                             user_prompt: str,
                             node_level: StepType=StepType.llm,
                             cot_enabled: bool=True,
                             model_name: str='gpt-4.1-mini',
                             num_judges: int=3,
                             description: str='',
                             tags: Optional[list[str]]=None,
                             output_type: OutputTypeEnum=OutputTypeEnum.BOOLEAN) -> BaseScorerVersionResponse
```

Create a custom LLM metric.

**Arguments**

* `name` (`str`): Name of the metric.
* `user_prompt` (`str`): User prompt for the metric.
* `node_level` (`StepType`): Node level for the metric.
* `cot_enabled` (`bool`): Whether chain-of-thought is enabled.
* `model_name` (`str`): Model name to use.
* `num_judges` (`int`): Number of judges for the metric.
* `description` (`str`): Description of the metric.
* `tags` (`List[str]`): Tags associated with the metric.
* `output_type` (`OutputTypeEnum`): Output type for the metric.

**Returns**

* `BaseScorerVersionResponse`: Response containing the created metric details.

## create\_custom\_llm\_metric

```python theme={null}
def create_custom_llm_metric(name: str,
                             user_prompt: str,
                             node_level: StepType=StepType.llm,
                             cot_enabled: bool=True,
                             model_name: str='gpt-4.1-mini',
                             num_judges: int=3,
                             description: str='',
                             tags: Optional[list[str]]=None,
                             output_type: OutputTypeEnum=OutputTypeEnum.BOOLEAN) -> BaseScorerVersionResponse
```

Create a custom LLM metric.

**Arguments**

* `name` (`str`): Name of the metric.
* `user_prompt` (`str`): User prompt for the metric.
* `node_level` (`StepType`): Node level for the metric.
* `cot_enabled` (`bool`): Whether chain-of-thought is enabled.
* `model_name` (`str`): Model name to use.
* `num_judges` (`int`): Number of judges for the metric.
* `description` (`str`): Description of the metric.
* `tags` (`List[str]`): Tags associated with the metric.
* `output_type` (`OutputTypeEnum`): Output type for the metric.

**Returns**

* `BaseScorerVersionResponse`: Response containing the created metric details.

## delete\_metric

```python theme={null}
def delete_metric(name: str) -> None
```

Deletes a metric by its name.

**Arguments**

* `name`: The name of the metric to delete.

## get\_metrics

```python theme={null}
def get_metrics(project_id: str,
                start_time: datetime.datetime,
                end_time: datetime.datetime,
                experiment_id: Optional[str]=None,
                log_stream_id: Optional[str]=None,
                filters: Optional[list[FilterType]]=None,
                group_by: Optional[str]=None,
                interval: int=5) -> LogRecordsMetricsResponse
```

Queries for metrics in a project.

**Arguments**

* `project_id`: The unique identifier of the project.
* `start_time`: The start of the time range for the query.
* `end_time`: The end of the time range for the query.
* `experiment_id`: Filter records by a specific experiment ID.
* `log_stream_id`: Filter records by a specific run ID.
* `filters`: A list of filters to apply to the query.
* `group_by`: The field to group the results by.
* `interval`: The time interval for the query in seconds.

**Returns**

* `LogRecordsMetricsResponse`: A LogRecordsMetricsResponse object containing the query results, or None if the query fails.