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Evals

Create eval

evals.create(EvalCreateParams**kwargs) -> EvalCreateResponse
POST/evals

Create the structure of an evaluation that can be used to test a model’s performance. An evaluation is a set of testing criteria and the config for a data source, which dictates the schema of the data used in the evaluation. After creating an evaluation, you can run it on different models and model parameters. We support several types of graders and datasources. For more information, see the Evals guide.

ParametersExpand Collapse
data_source_config: DataSourceConfig

The configuration for the data source used for the evaluation runs. Dictates the schema of the data used in the evaluation.

testing_criteria: Iterable[TestingCriterion]

A list of graders for all eval runs in this group. Graders can reference variables in the data source using double curly braces notation, like {{item.variable_name}}. To reference the model’s output, use the sample namespace (ie, {{sample.output_text}}).

metadata: Optional[Metadata]

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

name: Optional[str]

The name of the evaluation.

ReturnsExpand Collapse
class EvalCreateResponse: …

An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot
  • See how well my chatbot handles customer support
  • Check if o4-mini is better at my usecase than gpt-4o
id: str

Unique identifier for the evaluation.

created_at: int

The Unix timestamp (in seconds) for when the eval was created.

formatunixtime
data_source_config: DataSourceConfig

Configuration of data sources used in runs of the evaluation.

metadata: Optional[Metadata]

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

name: str

The name of the evaluation.

object: Literal["eval"]

The object type.

testing_criteria: List[TestingCriterion]

A list of testing criteria.

Create eval

from openai import OpenAI
client = OpenAI()

eval_obj = client.evals.create(
  name="Sentiment",
  data_source_config={
    "type": "stored_completions",
    "metadata": {"usecase": "chatbot"}
  },
  testing_criteria=[
    {
      "type": "label_model",
      "model": "o3-mini",
      "input": [
        {"role": "developer", "content": "Classify the sentiment of the following statement as one of 'positive', 'neutral', or 'negative'"},
        {"role": "user", "content": "Statement: {{item.input}}"}
      ],
      "passing_labels": ["positive"],
      "labels": ["positive", "neutral", "negative"],
      "name": "Example label grader"
    }
  ]
)
print(eval_obj)

{
  "object": "eval",
  "id": "eval_67b7fa9a81a88190ab4aa417e397ea21",
  "data_source_config": {
    "type": "stored_completions",
    "metadata": {
      "usecase": "chatbot"
    },
    "schema": {
      "type": "object",
      "properties": {
        "item": {
          "type": "object"
        },
        "sample": {
          "type": "object"
        }
      },
      "required": [
        "item",
        "sample"
      ]
  },
  "testing_criteria": [
    {
      "name": "Example label grader",
      "type": "label_model",
      "model": "o3-mini",
      "input": [
        {
          "type": "message",
          "role": "developer",
          "content": {
            "type": "input_text",
            "text": "Classify the sentiment of the following statement as one of positive, neutral, or negative"
          }
        },
        {
          "type": "message",
          "role": "user",
          "content": {
            "type": "input_text",
            "text": "Statement: {{item.input}}"
          }
        }
      ],
      "passing_labels": [
        "positive"
      ],
      "labels": [
        "positive",
        "neutral",
        "negative"
      ]
    }
  ],
  "name": "Sentiment",
  "created_at": 1740110490,
  "metadata": {
    "description": "An eval for sentiment analysis"
  }
}
Returns Examples
{
  "object": "eval",
  "id": "eval_67b7fa9a81a88190ab4aa417e397ea21",
  "data_source_config": {
    "type": "stored_completions",
    "metadata": {
      "usecase": "chatbot"
    },
    "schema": {
      "type": "object",
      "properties": {
        "item": {
          "type": "object"
        },
        "sample": {
          "type": "object"
        }
      },
      "required": [
        "item",
        "sample"
      ]
  },
  "testing_criteria": [
    {
      "name": "Example label grader",
      "type": "label_model",
      "model": "o3-mini",
      "input": [
        {
          "type": "message",
          "role": "developer",
          "content": {
            "type": "input_text",
            "text": "Classify the sentiment of the following statement as one of positive, neutral, or negative"
          }
        },
        {
          "type": "message",
          "role": "user",
          "content": {
            "type": "input_text",
            "text": "Statement: {{item.input}}"
          }
        }
      ],
      "passing_labels": [
        "positive"
      ],
      "labels": [
        "positive",
        "neutral",
        "negative"
      ]
    }
  ],
  "name": "Sentiment",
  "created_at": 1740110490,
  "metadata": {
    "description": "An eval for sentiment analysis"
  }
}