java/resources/evals/methods/update/index.md

Update an eval

EvalUpdateResponse evals().update(EvalUpdateParamsparams = EvalUpdateParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

post /evals/{eval_id}

Update certain properties of an evaluation.

Parameters

EvalUpdateParams params
- Optional<String> evalId
- Optional<Metadata> 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.
- Optional<String> name
  
  Rename the evaluation.

Returns

class EvalUpdateResponse:

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
- String id
  
  Unique identifier for the evaluation.
- long createdAt
  
  The Unix timestamp (in seconds) for when the eval was created.
- DataSourceConfig dataSourceConfig
  
  Configuration of data sources used in runs of the evaluation.
  - class EvalCustomDataSourceConfig:
    
    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 schema
      
      The json schema for the run data source items. Learn how to build JSON schemas here.
    - JsonValue; type "custom"constant
      
      The type of data source. Always custom.
      - CUSTOM("custom")
  - class Logs:
    
    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 schema
      
      The json schema for the run data source items. Learn how to build JSON schemas here.
    - JsonValue; type "logs"constant
      
      The type of data source. Always logs.
      - LOGS("logs")
    - Optional<Metadata> 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.
  - class EvalStoredCompletionsDataSourceConfig:
    
    Deprecated in favor of LogsDataSourceConfig.
    - Schema schema
      
      The json schema for the run data source items. Learn how to build JSON schemas here.
    - JsonValue; type "stored_completions"constant
      
      The type of data source. Always stored_completions.
      - STORED_COMPLETIONS("stored_completions")
    - Optional<Metadata> 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.
- Optional<Metadata> 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.
- String name
  
  The name of the evaluation.
- JsonValue; object_ "eval"constant
  
  The object type.
  - EVAL("eval")
- List<TestingCriterion> testingCriteria
  
  A list of testing criteria.
  - class LabelModelGrader:
    
    A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
    - List<Input> input
      - Content content
        
        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.
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
      - Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
      - Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
    - List<String> labels
      
      The labels to assign to each item in the evaluation.
    - String model
      
      The model to use for the evaluation. Must support structured outputs.
    - String name
      
      The name of the grader.
    - List<String> passingLabels
      
      The labels that indicate a passing result. Must be a subset of labels.
    - JsonValue; type "label_model"constant
      
      The object type, which is always label_model.
      - LABEL_MODEL("label_model")
  - class StringCheckGrader:
    
    A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
    - String input
      
      The input text. This may include template strings.
    - String name
      
      The name of the grader.
    - Operation operation
      
      The string check operation to perform. One of eq, ne, like, or ilike.
      - EQ("eq")
      - NE("ne")
      - LIKE("like")
      - ILIKE("ilike")
    - String reference
      
      The reference text. This may include template strings.
    - JsonValue; type "string_check"constant
      
      The object type, which is always string_check.
      - STRING_CHECK("string_check")
  - class EvalGraderTextSimilarity:
    
    A TextSimilarityGrader object which grades text based on similarity metrics.
    - double passThreshold
      
      The threshold for the score.
  - class EvalGraderPython:
    
    A PythonGrader object that runs a python script on the input.
    - Optional<Double> passThreshold
      
      The threshold for the score.
  - class EvalGraderScoreModel:
    
    A ScoreModelGrader object that uses a model to assign a score to the input.
    - Optional<Double> passThreshold
      
      The threshold for the score.

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.evals.EvalUpdateParams;
import com.openai.models.evals.EvalUpdateResponse;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        EvalUpdateResponse eval = client.evals().update("eval_id");
    }
}

Response

{
  "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"
    }
  ]
}

java/resources/evals/methods/update/index.md 2026-07-10 23:02 UTC to 2026-07-12 06:58 UTC