## List Results

`agents.evaluation_runs.list_results(strevaluation_run_uuid, EvaluationRunListResultsParams**kwargs)  -> EvaluationRunListResultsResponse`

**get** `/v2/gen-ai/evaluation_runs/{evaluation_run_uuid}/results`

To retrieve results of an evaluation run, send a GET request to `/v2/gen-ai/evaluation_runs/{evaluation_run_uuid}/results`.

### Parameters

- **evaluation\_run\_uuid:** `str`

- **page:** `int`

  Page number.

- **per\_page:** `int`

  Items per page.

### Returns

- `class EvaluationRunListResultsResponse`

  Gets the full results of an evaluation run with all prompts.

  - **evaluation\_run:** `Optional[APIEvaluationRun]`

  - **links:** `Optional[APILinks]`

    Links to other pages

  - **meta:** `Optional[APIMeta]`

    Meta information about the data set

  - **prompts:** `Optional[List[APIEvaluationPrompt]]`

    The prompt level results.

    - **ground\_truth:** `Optional[str]`

      The ground truth for the prompt.

    - **input:** `Optional[str]`

    - **input\_tokens:** `Optional[str]`

      The number of input tokens used in the prompt.

    - **output:** `Optional[str]`

    - **output\_tokens:** `Optional[str]`

      The number of output tokens used in the prompt.

    - **prompt\_chunks:** `Optional[List[PromptChunk]]`

      The list of prompt chunks.

      - **chunk\_usage\_pct:** `Optional[float]`

        The usage percentage of the chunk.

      - **chunk\_used:** `Optional[bool]`

        Indicates if the chunk was used in the prompt.

      - **index\_uuid:** `Optional[str]`

        The index uuid (Knowledge Base) of the chunk.

      - **source\_name:** `Optional[str]`

        The source name for the chunk, e.g., the file name or document title.

      - **text:** `Optional[str]`

        Text content of the chunk.

    - **prompt\_id:** `Optional[int]`

      Prompt ID

    - **prompt\_level\_metric\_results:** `Optional[List[APIEvaluationMetricResult]]`

      The metric results for the prompt.

      - **error\_description:** `Optional[str]`

        Error description if the metric could not be calculated.

      - **metric\_name:** `Optional[str]`

        Metric name

      - **metric\_value\_type:** `Optional[Literal["METRIC_VALUE_TYPE_UNSPECIFIED", "METRIC_VALUE_TYPE_NUMBER", "METRIC_VALUE_TYPE_STRING", "METRIC_VALUE_TYPE_PERCENTAGE"]]`

        - `"METRIC_VALUE_TYPE_UNSPECIFIED"`

        - `"METRIC_VALUE_TYPE_NUMBER"`

        - `"METRIC_VALUE_TYPE_STRING"`

        - `"METRIC_VALUE_TYPE_PERCENTAGE"`

      - **number\_value:** `Optional[float]`

        The value of the metric as a number.

      - **reasoning:** `Optional[str]`

        Reasoning of the metric result.

      - **string\_value:** `Optional[str]`

        The value of the metric as a string.

### Example

```python
from do_gradientai import GradientAI

client = GradientAI()
response = client.agents.evaluation_runs.list_results(
    evaluation_run_uuid="\"123e4567-e89b-12d3-a456-426614174000\"",
)
print(response.evaluation_run)
```