Retrieve Results

API Reference

Agents

Evaluation Runs

Retrieve Results of an Evaluation Run Prompt

agents.evaluation_runs.retrieve_results(intprompt_id, EvaluationRunRetrieveResultsParams**kwargs) -> EvaluationRunRetrieveResultsResponse

get/v2/gen-ai/evaluation_runs/{evaluation_run_uuid}/results/{prompt_id}

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

ParametersExpand Collapse

evaluation_run_uuid: str

prompt_id: int

ReturnsExpand Collapse

class EvaluationRunRetrieveResultsResponse: …

prompt: Optional[APIEvaluationPrompt]

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.

formatuint64

output: Optional[str]

output_tokens: Optional[str]

The number of output tokens used in the prompt.

formatuint64

prompt_chunks: Optional[List[PromptChunk]]

The list of prompt chunks.

chunk_usage_pct: Optional[float]

The usage percentage of the chunk.

formatdouble

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

formatint64

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"]]

Accepts one of the following:

"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.

formatdouble

reasoning: Optional[str]

Reasoning of the metric result.

string_value: Optional[str]

The value of the metric as a string.

Retrieve Results of an Evaluation Run Prompt

python

• http
• typescript
• python

from gradient import Gradient

client = Gradient(
    access_token="My Access Token",
)
response = client.agents.evaluation_runs.retrieve_results(
    prompt_id=1,
    evaluation_run_uuid="\"123e4567-e89b-12d3-a456-426614174000\"",
)
print(response.prompt)

200 example

{
  "prompt": {
    "ground_truth": "example string",
    "input": "example string",
    "input_tokens": "12345",
    "output": "example string",
    "output_tokens": "12345",
    "prompt_chunks": [
      {
        "chunk_usage_pct": 123,
        "chunk_used": true,
        "index_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "source_name": "example name",
        "text": "example string"
      }
    ],
    "prompt_id": 123,
    "prompt_level_metric_results": [
      {
        "error_description": "example string",
        "metric_name": "example name",
        "metric_value_type": "METRIC_VALUE_TYPE_UNSPECIFIED",
        "number_value": 123,
        "reasoning": "example string",
        "string_value": "example string"
      }
    ]
  }
}

Returns Examples

200 example

{
  "prompt": {
    "ground_truth": "example string",
    "input": "example string",
    "input_tokens": "12345",
    "output": "example string",
    "output_tokens": "12345",
    "prompt_chunks": [
      {
        "chunk_usage_pct": 123,
        "chunk_used": true,
        "index_uuid": "123e4567-e89b-12d3-a456-426614174000",
        "source_name": "example name",
        "text": "example string"
      }
    ],
    "prompt_id": 123,
    "prompt_level_metric_results": [
      {
        "error_description": "example string",
        "metric_name": "example name",
        "metric_value_type": "METRIC_VALUE_TYPE_UNSPECIFIED",
        "number_value": 123,
        "reasoning": "example string",
        "string_value": "example string"
      }
    ]
  }
}