Eval List Response

• EvalListResponse object \{ id, created_at, data_source_config, 4 more \}

  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-6-astra

  • 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 or object \{ schema, type, metadata \} or EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • 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.

        • "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.

        • "logs"

      • metadata: optional Metadata or null

        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.

    • 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.

        • "stored_completions"

      • metadata: optional Metadata or null

        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.

  • metadata: Metadata or null

    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: string

    The name of the evaluation.

  • object: "eval"

    The object type.

    • "eval"

  • testing_criteria: array of LabelModelGrader or StringCheckGrader or TextSimilarityGrader or 2 more

    A list of testing criteria.

    • 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 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.

          • TextInput = string

            A text input to the model.

          • ResponseInputText object \{ text, type, prompt_cache_breakpoint \}

            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.

              • "input_text"

            • prompt_cache_breakpoint: optional object \{ mode \}

              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.

              • mode: "explicit"

                The breakpoint mode. Always explicit.

                • "explicit"

          • 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.

              • "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.

              • "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.

                • "mp3"

                • "wav"

            • type: "input_audio"

              The type of the input item. Always input_audio.

              • "input_audio"

          • GraderInputs = array of string or ResponseInputText 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.

            • TextInput = string

              A text input to the model.

            • ResponseInputText object \{ text, type, prompt_cache_breakpoint \}

              A text input to the model.

            • 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.

                • "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.

                • "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.

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

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: optional "message"

          The type of the message input. Always message.

          • "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.

      • name: string

        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.

        • "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.

      • name: string

        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.

        • "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.

        • "string_check"

    • TextSimilarityGrader = TextSimilarityGrader

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: number

        The threshold for the score.

    • PythonGrader = PythonGrader

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

      • pass_threshold: optional number

        The threshold for the score.

    • ScoreModelGrader = ScoreModelGrader

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

      • pass_threshold: optional number

        The threshold for the score.