Create eval

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.

Body ParametersJSONExpand Collapse

data_source_config: object { item_schema, type, include_sample_schema } or object { type, metadata } or object { type, metadata }

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

Accepts one of the following:

CustomDataSourceConfig = object { item_schema, type, include_sample_schema }

A CustomDataSourceConfig object that defines the schema for the data source used for the evaluation runs. This schema is used to define the shape of the data that will be:

Used to define your testing criteria and
What data is required when creating a run

item_schema: map[unknown]

The json schema for each row in the data source.

type: "custom"

The type of data source. Always custom.

include_sample_schema: optional boolean

Whether the eval should expect you to populate the sample namespace (ie, by generating responses off of your data source)

LogsDataSourceConfig = object { type, metadata }

A data source config which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc.

type: "logs"

The type of data source. Always logs.

metadata: optional map[unknown]

Metadata filters for the logs data source.

StoredCompletionsDataSourceConfig = object { type, metadata }

Deprecated in favor of LogsDataSourceConfig.

type: "stored_completions"

The type of data source. Always stored_completions.

metadata: optional map[unknown]

Metadata filters for the stored completions data source.

testing_criteria: array of object { input, labels, model, 3 more } or StringCheckGrader { input, name, operation, 2 more } or TextSimilarityGrader { evaluation_metric, input, name, 2 more } or 2 more

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}}).

Accepts one of the following:

LabelModelGrader = object { input, labels, model, 3 more }

A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

input: array of object { content, role } or object { content, role, type }

A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

Accepts one of the following:

SimpleInputMessage = object { content, role }

content: string

The content of the message.

role: string

The role of the message (e.g. "system", "assistant", "user").

EvalMessageObject = object { content, role, type }

A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:

"user"

"assistant"

"system"

"developer"

type: optional "message"

The type of the message input. Always message.

labels: array of string

The labels to classify to each item in the evaluation.

model: string

The model to use for the evaluation. Must support structured outputs.

The name of the grader.

passing_labels: array of string

The labels that indicate a passing result. Must be a subset of labels.

type: "label_model"

The object type, which is always label_model.

StringCheckGrader = object { input, name, operation, 2 more }

A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

input: string

The input text. This may include template strings.

The name of the grader.

operation: "eq" or "ne" or "like" or "ilike"

The string check operation to perform. One of eq, ne, like, or ilike.

Accepts one of the following:

"eq"

"ne"

"like"

"ilike"

reference: string

The reference text. This may include template strings.

type: "string_check"

The object type, which is always string_check.

TextSimilarityGrader = TextSimilarityGrader { evaluation_metric, input, name, 2 more }

A TextSimilarityGrader object which grades text based on similarity metrics.

pass_threshold: number

The threshold for the score.

PythonGrader = PythonGrader { name, source, type, image_tag }

A PythonGrader object that runs a python script on the input.

pass_threshold: optional number

The threshold for the score.

ScoreModelGrader = ScoreModelGrader { input, model, name, 3 more }

A ScoreModelGrader object that uses a model to assign a score to the input.

pass_threshold: optional number

The threshold for the score.

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.

The name of the evaluation.

ReturnsExpand Collapse

id: string

Unique identifier for the evaluation.

created_at: number

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

data_source_config: EvalCustomDataSourceConfig { schema, type } or object { schema, type, metadata } or EvalStoredCompletionsDataSourceConfig { schema, type, metadata }

Configuration of data sources used in runs of the evaluation.

Accepts one of the following:

EvalCustomDataSourceConfig = object { schema, type }

A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

Used to define your testing criteria and
What data is required when creating a run

schema: map[unknown]

The json schema for the run data source items. Learn how to build JSON schemas here.

type: "custom"

The type of data source. Always custom.

LogsDataSourceConfig = object { schema, type, metadata }

A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

schema: map[unknown]

The json schema for the run data source items. Learn how to build JSON schemas here.

type: "logs"

The type of data source. Always logs.

metadata: optional Metadata

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

EvalStoredCompletionsDataSourceConfig = object { schema, type, metadata }

Deprecated in favor of LogsDataSourceConfig.

schema: map[unknown]

The json schema for the run data source items. Learn how to build JSON schemas here.

type: "stored_completions"

The type of data source. Always stored_completions.

metadata: optional Metadata

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

metadata: Metadata

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

The name of the evaluation.

object: "eval"

The object type.

testing_criteria: array of LabelModelGrader { input, labels, model, 3 more } or StringCheckGrader { input, name, operation, 2 more } or TextSimilarityGrader { evaluation_metric, input, name, 2 more } or 2 more

A list of testing criteria.

Accepts one of the following:

LabelModelGrader = object { input, labels, model, 3 more }

A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

input: array of object { content, role, type }

content: string or ResponseInputText { text, type } or object { text, type } or 3 more

Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = array of string or ResponseInputText { text, type } or object { text, type } or 2 more

A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

Accepts one of the following:

TextInput = string

A text input to the model.

ResponseInputText = object { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText = object { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage = object { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail: optional string

The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

ResponseInputAudio = object { input_audio, type }

An audio input to the model.

input_audio: object { data, format }

data: string

Base64-encoded audio data.

format: "mp3" or "wav"

The format of the audio data. Currently supported formats are mp3 and wav.

Accepts one of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

role: "user" or "assistant" or "system" or "developer"

The role of the message input. One of user, assistant, system, or developer.

Accepts one of the following:

"user"

"assistant"

"system"

"developer"

type: optional "message"

The type of the message input. Always message.

labels: array of string

The labels to assign to each item in the evaluation.

model: string

The model to use for the evaluation. Must support structured outputs.

The name of the grader.

passing_labels: array of string

The labels that indicate a passing result. Must be a subset of labels.

type: "label_model"

The object type, which is always label_model.

StringCheckGrader = object { input, name, operation, 2 more }

A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

input: string

The input text. This may include template strings.

The name of the grader.

operation: "eq" or "ne" or "like" or "ilike"

The string check operation to perform. One of eq, ne, like, or ilike.

Accepts one of the following:

"eq"

"ne"

"like"

"ilike"

reference: string

The reference text. This may include template strings.

type: "string_check"

The object type, which is always string_check.

TextSimilarityGrader = TextSimilarityGrader { evaluation_metric, input, name, 2 more }

A TextSimilarityGrader object which grades text based on similarity metrics.

pass_threshold: number

The threshold for the score.

PythonGrader = PythonGrader { name, source, type, image_tag }

A PythonGrader object that runs a python script on the input.

pass_threshold: optional number

The threshold for the score.

ScoreModelGrader = ScoreModelGrader { input, model, name, 3 more }

A ScoreModelGrader object that uses a model to assign a score to the input.

pass_threshold: optional number

The threshold for the score.

Create eval

curl https://api.openai.com/v1/evals \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -d '{
          "data_source_config": {
            "item_schema": {
              "foo": "bar"
            },
            "type": "custom"
          },
          "testing_criteria": [
            {
              "input": [
                {
                  "content": "content",
                  "role": "role"
                }
              ],
              "labels": [
                "string"
              ],
              "model": "model",
              "name": "name",
              "passing_labels": [
                "string"
              ],
              "type": "label_model"
            }
          ]
        }'

{
  "id": "id",
  "created_at": 0,
  "data_source_config": {
    "schema": {
      "foo": "bar"
    },
    "type": "custom"
  },
  "metadata": {
    "foo": "string"
  },
  "name": "Chatbot effectiveness Evaluation",
  "object": "eval",
  "testing_criteria": [
    {
      "input": [
        {
          "content": "string",
          "role": "user",
          "type": "message"
        }
      ],
      "labels": [
        "string"
      ],
      "model": "model",
      "name": "name",
      "passing_labels": [
        "string"
      ],
      "type": "label_model"
    }
  ]
}

Returns Examples

{
  "id": "id",
  "created_at": 0,
  "data_source_config": {
    "schema": {
      "foo": "bar"
    },
    "type": "custom"
  },
  "metadata": {
    "foo": "string"
  },
  "name": "Chatbot effectiveness Evaluation",
  "object": "eval",
  "testing_criteria": [
    {
      "input": [
        {
          "content": "string",
          "role": "user",
          "type": "message"
        }
      ],
      "labels": [
        "string"
      ],
      "model": "model",
      "name": "name",
      "passing_labels": [
        "string"
      ],
      "type": "label_model"
    }
  ]
}

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Create eval

Body ParametersJSONExpand Collapse

ReturnsExpand Collapse

Create eval

Returns Examples