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[Add] Synaptic AI Pro

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https://assetstore.unity.com/packages/tools/generative-ai/synaptic-ai-pro-natural-language-control-for-unity-336030
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Konstantin Dyachenko
2026-06-06 20:12:40 +07:00
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Assets/Synaptic AI Pro/MCPServer/node_modules/openai/resources/evals/evals.d.ts
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import { APIResource } from "../../resource.js";
import * as Core from "../../core.js";
import * as Shared from "../shared.js";
import * as GraderModelsAPI from "../graders/grader-models.js";
import * as ResponsesAPI from "../responses/responses.js";
import * as RunsAPI from "./runs/runs.js";
import { CreateEvalCompletionsRunDataSource, CreateEvalJSONLRunDataSource, EvalAPIError, RunCancelResponse, RunCreateParams, RunCreateResponse, RunDeleteResponse, RunListParams, RunListResponse, RunListResponsesPage, RunRetrieveResponse, Runs } from "./runs/runs.js";
import { CursorPage, type CursorPageParams } from "../../pagination.js";
export declare class Evals extends APIResource {
runs: RunsAPI.Runs;
/**
* 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](https://platform.openai.com/docs/guides/evals).
*/
create(body: EvalCreateParams, options?: Core.RequestOptions): Core.APIPromise<EvalCreateResponse>;
/**
* Get an evaluation by ID.
*/
retrieve(evalId: string, options?: Core.RequestOptions): Core.APIPromise<EvalRetrieveResponse>;
/**
* Update certain properties of an evaluation.
*/
update(evalId: string, body: EvalUpdateParams, options?: Core.RequestOptions): Core.APIPromise<EvalUpdateResponse>;
/**
* List evaluations for a project.
*/
list(query?: EvalListParams, options?: Core.RequestOptions): Core.PagePromise<EvalListResponsesPage, EvalListResponse>;
list(options?: Core.RequestOptions): Core.PagePromise<EvalListResponsesPage, EvalListResponse>;
/**
* Delete an evaluation.
*/
del(evalId: string, options?: Core.RequestOptions): Core.APIPromise<EvalDeleteResponse>;
}
export declare class EvalListResponsesPage extends CursorPage<EvalListResponse> {
}
/**
* 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
*/
export interface EvalCustomDataSourceConfig {
/**
* The json schema for the run data source items. Learn how to build JSON schemas
* [here](https://json-schema.org/).
*/
schema: Record<string, unknown>;
/**
* The type of data source. Always `custom`.
*/
type: 'custom';
}
/**
* @deprecated Deprecated in favor of LogsDataSourceConfig.
*/
export interface EvalStoredCompletionsDataSourceConfig {
/**
* The json schema for the run data source items. Learn how to build JSON schemas
* [here](https://json-schema.org/).
*/
schema: Record<string, unknown>;
/**
* The type of data source. Always `stored_completions`.
*/
type: 'stored_completions';
/**
* 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?: Shared.Metadata | null;
}
/**
* 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
*/
export interface EvalCreateResponse {
/**
* Unique identifier for the evaluation.
*/
id: string;
/**
* The Unix timestamp (in seconds) for when the eval was created.
*/
created_at: number;
/**
* Configuration of data sources used in runs of the evaluation.
*/
data_source_config: EvalCustomDataSourceConfig | EvalCreateResponse.Logs | EvalStoredCompletionsDataSourceConfig;
/**
* 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: Shared.Metadata | null;
/**
* The name of the evaluation.
*/
name: string;
/**
* The object type.
*/
object: 'eval';
/**
* A list of testing criteria.
*/
testing_criteria: Array<GraderModelsAPI.LabelModelGrader | GraderModelsAPI.StringCheckGrader | EvalCreateResponse.EvalGraderTextSimilarity | EvalCreateResponse.EvalGraderPython | EvalCreateResponse.EvalGraderScoreModel>;
}
export declare namespace EvalCreateResponse {
/**
* 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.
*/
interface Logs {
/**
* The json schema for the run data source items. Learn how to build JSON schemas
* [here](https://json-schema.org/).
*/
schema: Record<string, unknown>;
/**
* The type of data source. Always `logs`.
*/
type: 'logs';
/**
* 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?: Shared.Metadata | null;
}
/**
* A TextSimilarityGrader object which grades text based on similarity metrics.
*/
interface EvalGraderTextSimilarity extends GraderModelsAPI.TextSimilarityGrader {
/**
* The threshold for the score.
*/
pass_threshold: number;
}
/**
* A PythonGrader object that runs a python script on the input.
*/
interface EvalGraderPython extends GraderModelsAPI.PythonGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
/**
* A ScoreModelGrader object that uses a model to assign a score to the input.
*/
interface EvalGraderScoreModel extends GraderModelsAPI.ScoreModelGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
}
/**
* 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
*/
export interface EvalRetrieveResponse {
/**
* Unique identifier for the evaluation.
*/
id: string;
/**
* The Unix timestamp (in seconds) for when the eval was created.
*/
created_at: number;
/**
* Configuration of data sources used in runs of the evaluation.
*/
data_source_config: EvalCustomDataSourceConfig | EvalRetrieveResponse.Logs | EvalStoredCompletionsDataSourceConfig;
/**
* 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: Shared.Metadata | null;
/**
* The name of the evaluation.
*/
name: string;
/**
* The object type.
*/
object: 'eval';
/**
* A list of testing criteria.
*/
testing_criteria: Array<GraderModelsAPI.LabelModelGrader | GraderModelsAPI.StringCheckGrader | EvalRetrieveResponse.EvalGraderTextSimilarity | EvalRetrieveResponse.EvalGraderPython | EvalRetrieveResponse.EvalGraderScoreModel>;
}
export declare namespace EvalRetrieveResponse {
/**
* 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.
*/
interface Logs {
/**
* The json schema for the run data source items. Learn how to build JSON schemas
* [here](https://json-schema.org/).
*/
schema: Record<string, unknown>;
/**
* The type of data source. Always `logs`.
*/
type: 'logs';
/**
* 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?: Shared.Metadata | null;
}
/**
* A TextSimilarityGrader object which grades text based on similarity metrics.
*/
interface EvalGraderTextSimilarity extends GraderModelsAPI.TextSimilarityGrader {
/**
* The threshold for the score.
*/
pass_threshold: number;
}
/**
* A PythonGrader object that runs a python script on the input.
*/
interface EvalGraderPython extends GraderModelsAPI.PythonGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
/**
* A ScoreModelGrader object that uses a model to assign a score to the input.
*/
interface EvalGraderScoreModel extends GraderModelsAPI.ScoreModelGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
}
/**
* 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
*/
export interface EvalUpdateResponse {
/**
* Unique identifier for the evaluation.
*/
id: string;
/**
* The Unix timestamp (in seconds) for when the eval was created.
*/
created_at: number;
/**
* Configuration of data sources used in runs of the evaluation.
*/
data_source_config: EvalCustomDataSourceConfig | EvalUpdateResponse.Logs | EvalStoredCompletionsDataSourceConfig;
/**
* 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: Shared.Metadata | null;
/**
* The name of the evaluation.
*/
name: string;
/**
* The object type.
*/
object: 'eval';
/**
* A list of testing criteria.
*/
testing_criteria: Array<GraderModelsAPI.LabelModelGrader | GraderModelsAPI.StringCheckGrader | EvalUpdateResponse.EvalGraderTextSimilarity | EvalUpdateResponse.EvalGraderPython | EvalUpdateResponse.EvalGraderScoreModel>;
}
export declare namespace EvalUpdateResponse {
/**
* 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.
*/
interface Logs {
/**
* The json schema for the run data source items. Learn how to build JSON schemas
* [here](https://json-schema.org/).
*/
schema: Record<string, unknown>;
/**
* The type of data source. Always `logs`.
*/
type: 'logs';
/**
* 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?: Shared.Metadata | null;
}
/**
* A TextSimilarityGrader object which grades text based on similarity metrics.
*/
interface EvalGraderTextSimilarity extends GraderModelsAPI.TextSimilarityGrader {
/**
* The threshold for the score.
*/
pass_threshold: number;
}
/**
* A PythonGrader object that runs a python script on the input.
*/
interface EvalGraderPython extends GraderModelsAPI.PythonGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
/**
* A ScoreModelGrader object that uses a model to assign a score to the input.
*/
interface EvalGraderScoreModel extends GraderModelsAPI.ScoreModelGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
}
/**
* 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
*/
export interface EvalListResponse {
/**
* Unique identifier for the evaluation.
*/
id: string;
/**
* The Unix timestamp (in seconds) for when the eval was created.
*/
created_at: number;
/**
* Configuration of data sources used in runs of the evaluation.
*/
data_source_config: EvalCustomDataSourceConfig | EvalListResponse.Logs | EvalStoredCompletionsDataSourceConfig;
/**
* 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: Shared.Metadata | null;
/**
* The name of the evaluation.
*/
name: string;
/**
* The object type.
*/
object: 'eval';
/**
* A list of testing criteria.
*/
testing_criteria: Array<GraderModelsAPI.LabelModelGrader | GraderModelsAPI.StringCheckGrader | EvalListResponse.EvalGraderTextSimilarity | EvalListResponse.EvalGraderPython | EvalListResponse.EvalGraderScoreModel>;
}
export declare namespace EvalListResponse {
/**
* 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.
*/
interface Logs {
/**
* The json schema for the run data source items. Learn how to build JSON schemas
* [here](https://json-schema.org/).
*/
schema: Record<string, unknown>;
/**
* The type of data source. Always `logs`.
*/
type: 'logs';
/**
* 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?: Shared.Metadata | null;
}
/**
* A TextSimilarityGrader object which grades text based on similarity metrics.
*/
interface EvalGraderTextSimilarity extends GraderModelsAPI.TextSimilarityGrader {
/**
* The threshold for the score.
*/
pass_threshold: number;
}
/**
* A PythonGrader object that runs a python script on the input.
*/
interface EvalGraderPython extends GraderModelsAPI.PythonGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
/**
* A ScoreModelGrader object that uses a model to assign a score to the input.
*/
interface EvalGraderScoreModel extends GraderModelsAPI.ScoreModelGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
}
export interface EvalDeleteResponse {
deleted: boolean;
eval_id: string;
object: string;
}
export interface EvalCreateParams {
/**
* The configuration for the data source used for the evaluation runs. Dictates the
* schema of the data used in the evaluation.
*/
data_source_config: EvalCreateParams.Custom | EvalCreateParams.Logs | EvalCreateParams.StoredCompletions;
/**
* 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}}`).
*/
testing_criteria: Array<EvalCreateParams.LabelModel | GraderModelsAPI.StringCheckGrader | EvalCreateParams.TextSimilarity | EvalCreateParams.Python | EvalCreateParams.ScoreModel>;
/**
* 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?: Shared.Metadata | null;
/**
* The name of the evaluation.
*/
name?: string;
}
export declare namespace EvalCreateParams {
/**
* 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
*/
interface Custom {
/**
* The json schema for each row in the data source.
*/
item_schema: Record<string, unknown>;
/**
* The type of data source. Always `custom`.
*/
type: 'custom';
/**
* Whether the eval should expect you to populate the sample namespace (ie, by
* generating responses off of your data source)
*/
include_sample_schema?: boolean;
}
/**
* 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.
*/
interface Logs {
/**
* The type of data source. Always `logs`.
*/
type: 'logs';
/**
* Metadata filters for the logs data source.
*/
metadata?: Record<string, unknown>;
}
/**
* @deprecated Deprecated in favor of LogsDataSourceConfig.
*/
interface StoredCompletions {
/**
* The type of data source. Always `stored_completions`.
*/
type: 'stored_completions';
/**
* Metadata filters for the stored completions data source.
*/
metadata?: Record<string, unknown>;
}
/**
* A LabelModelGrader object which uses a model to assign labels to each item in
* the evaluation.
*/
interface LabelModel {
/**
* A list of chat messages forming the prompt or context. May include variable
* references to the `item` namespace, ie {{item.name}}.
*/
input: Array<LabelModel.SimpleInputMessage | LabelModel.EvalItem>;
/**
* The labels to classify to each item in the evaluation.
*/
labels: Array<string>;
/**
* The model to use for the evaluation. Must support structured outputs.
*/
model: string;
/**
* The name of the grader.
*/
name: string;
/**
* The labels that indicate a passing result. Must be a subset of labels.
*/
passing_labels: Array<string>;
/**
* The object type, which is always `label_model`.
*/
type: 'label_model';
}
namespace LabelModel {
interface SimpleInputMessage {
/**
* The content of the message.
*/
content: string;
/**
* The role of the message (e.g. "system", "assistant", "user").
*/
role: string;
}
/**
* 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.
*/
interface EvalItem {
/**
* Text inputs to the model - can contain template strings.
*/
content: string | ResponsesAPI.ResponseInputText | EvalItem.OutputText;
/**
* The role of the message input. One of `user`, `assistant`, `system`, or
* `developer`.
*/
role: 'user' | 'assistant' | 'system' | 'developer';
/**
* The type of the message input. Always `message`.
*/
type?: 'message';
}
namespace EvalItem {
/**
* A text output from the model.
*/
interface OutputText {
/**
* The text output from the model.
*/
text: string;
/**
* The type of the output text. Always `output_text`.
*/
type: 'output_text';
}
}
}
/**
* A TextSimilarityGrader object which grades text based on similarity metrics.
*/
interface TextSimilarity extends GraderModelsAPI.TextSimilarityGrader {
/**
* The threshold for the score.
*/
pass_threshold: number;
}
/**
* A PythonGrader object that runs a python script on the input.
*/
interface Python extends GraderModelsAPI.PythonGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
/**
* A ScoreModelGrader object that uses a model to assign a score to the input.
*/
interface ScoreModel extends GraderModelsAPI.ScoreModelGrader {
/**
* The threshold for the score.
*/
pass_threshold?: number;
}
}
export interface EvalUpdateParams {
/**
* 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?: Shared.Metadata | null;
/**
* Rename the evaluation.
*/
name?: string;
}
export interface EvalListParams extends CursorPageParams {
/**
* Sort order for evals by timestamp. Use `asc` for ascending order or `desc` for
* descending order.
*/
order?: 'asc' | 'desc';
/**
* Evals can be ordered by creation time or last updated time. Use `created_at` for
* creation time or `updated_at` for last updated time.
*/
order_by?: 'created_at' | 'updated_at';
}
export declare namespace Evals {
export { type EvalCustomDataSourceConfig as EvalCustomDataSourceConfig, type EvalStoredCompletionsDataSourceConfig as EvalStoredCompletionsDataSourceConfig, type EvalCreateResponse as EvalCreateResponse, type EvalRetrieveResponse as EvalRetrieveResponse, type EvalUpdateResponse as EvalUpdateResponse, type EvalListResponse as EvalListResponse, type EvalDeleteResponse as EvalDeleteResponse, EvalListResponsesPage as EvalListResponsesPage, type EvalCreateParams as EvalCreateParams, type EvalUpdateParams as EvalUpdateParams, type EvalListParams as EvalListParams, };
export { Runs as Runs, type CreateEvalCompletionsRunDataSource as CreateEvalCompletionsRunDataSource, type CreateEvalJSONLRunDataSource as CreateEvalJSONLRunDataSource, type EvalAPIError as EvalAPIError, type RunCreateResponse as RunCreateResponse, type RunRetrieveResponse as RunRetrieveResponse, type RunListResponse as RunListResponse, type RunDeleteResponse as RunDeleteResponse, type RunCancelResponse as RunCancelResponse, RunListResponsesPage as RunListResponsesPage, type RunCreateParams as RunCreateParams, type RunListParams as RunListParams, };
}
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"use strict";
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
var desc = Object.getOwnPropertyDescriptor(m, k);
if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
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Object.defineProperty(exports, "__esModule", { value: true });
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const resource_1 = require("../../resource.js");
const core_1 = require("../../core.js");
const RunsAPI = __importStar(require("./runs/runs.js"));
const runs_1 = require("./runs/runs.js");
const pagination_1 = require("../../pagination.js");
class Evals extends resource_1.APIResource {
constructor() {
super(...arguments);
this.runs = new RunsAPI.Runs(this._client);
}
/**
* 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](https://platform.openai.com/docs/guides/evals).
*/
create(body, options) {
return this._client.post('/evals', { body, ...options });
}
/**
* Get an evaluation by ID.
*/
retrieve(evalId, options) {
return this._client.get(`/evals/${evalId}`, options);
}
/**
* Update certain properties of an evaluation.
*/
update(evalId, body, options) {
return this._client.post(`/evals/${evalId}`, { body, ...options });
}
list(query = {}, options) {
if ((0, core_1.isRequestOptions)(query)) {
return this.list({}, query);
}
return this._client.getAPIList('/evals', EvalListResponsesPage, { query, ...options });
}
/**
* Delete an evaluation.
*/
del(evalId, options) {
return this._client.delete(`/evals/${evalId}`, options);
}
}
exports.Evals = Evals;
class EvalListResponsesPage extends pagination_1.CursorPage {
}
exports.EvalListResponsesPage = EvalListResponsesPage;
Evals.EvalListResponsesPage = EvalListResponsesPage;
Evals.Runs = runs_1.Runs;
Evals.RunListResponsesPage = runs_1.RunListResponsesPage;
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// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
import { APIResource } from "../../resource.mjs";
import { isRequestOptions } from "../../core.mjs";
import * as RunsAPI from "./runs/runs.mjs";
import { RunListResponsesPage, Runs, } from "./runs/runs.mjs";
import { CursorPage } from "../../pagination.mjs";
export class Evals extends APIResource {
constructor() {
super(...arguments);
this.runs = new RunsAPI.Runs(this._client);
}
/**
* 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](https://platform.openai.com/docs/guides/evals).
*/
create(body, options) {
return this._client.post('/evals', { body, ...options });
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/**
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*/
retrieve(evalId, options) {
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/**
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update(evalId, body, options) {
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}
list(query = {}, options) {
if (isRequestOptions(query)) {
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}
return this._client.getAPIList('/evals', EvalListResponsesPage, { query, ...options });
}
/**
* Delete an evaluation.
*/
del(evalId, options) {
return this._client.delete(`/evals/${evalId}`, options);
}
}
export class EvalListResponsesPage extends CursorPage {
}
Evals.EvalListResponsesPage = EvalListResponsesPage;
Evals.Runs = Runs;
Evals.RunListResponsesPage = RunListResponsesPage;
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export { EvalListResponsesPage, Evals, type EvalCustomDataSourceConfig, type EvalStoredCompletionsDataSourceConfig, type EvalCreateResponse, type EvalRetrieveResponse, type EvalUpdateResponse, type EvalListResponse, type EvalDeleteResponse, type EvalCreateParams, type EvalUpdateParams, type EvalListParams, } from "./evals.js";
export { RunListResponsesPage, Runs, type CreateEvalCompletionsRunDataSource, type CreateEvalJSONLRunDataSource, type EvalAPIError, type RunCreateResponse, type RunRetrieveResponse, type RunListResponse, type RunDeleteResponse, type RunCancelResponse, type RunCreateParams, type RunListParams, } from "./runs/index.js";
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"use strict";
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
Object.defineProperty(exports, "__esModule", { value: true });
exports.Runs = exports.RunListResponsesPage = exports.Evals = exports.EvalListResponsesPage = void 0;
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export { OutputItemListResponsesPage, OutputItems, type OutputItemRetrieveResponse, type OutputItemListResponse, type OutputItemListParams, } from "./output-items.js";
export { RunListResponsesPage, Runs, type CreateEvalCompletionsRunDataSource, type CreateEvalJSONLRunDataSource, type EvalAPIError, type RunCreateResponse, type RunRetrieveResponse, type RunListResponse, type RunDeleteResponse, type RunCancelResponse, type RunCreateParams, type RunListParams, } from "./runs.js";
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"use strict";
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
Object.defineProperty(exports, "__esModule", { value: true });
exports.Runs = exports.RunListResponsesPage = exports.OutputItems = exports.OutputItemListResponsesPage = void 0;
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// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
export { OutputItemListResponsesPage, OutputItems, } from "./output-items.mjs";
export { RunListResponsesPage, Runs, } from "./runs.mjs";
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import { APIResource } from "../../../resource.js";
import * as Core from "../../../core.js";
import * as RunsAPI from "./runs.js";
import { CursorPage, type CursorPageParams } from "../../../pagination.js";
export declare class OutputItems extends APIResource {
/**
* Get an evaluation run output item by ID.
*/
retrieve(evalId: string, runId: string, outputItemId: string, options?: Core.RequestOptions): Core.APIPromise<OutputItemRetrieveResponse>;
/**
* Get a list of output items for an evaluation run.
*/
list(evalId: string, runId: string, query?: OutputItemListParams, options?: Core.RequestOptions): Core.PagePromise<OutputItemListResponsesPage, OutputItemListResponse>;
list(evalId: string, runId: string, options?: Core.RequestOptions): Core.PagePromise<OutputItemListResponsesPage, OutputItemListResponse>;
}
export declare class OutputItemListResponsesPage extends CursorPage<OutputItemListResponse> {
}
/**
* A schema representing an evaluation run output item.
*/
export interface OutputItemRetrieveResponse {
/**
* Unique identifier for the evaluation run output item.
*/
id: string;
/**
* Unix timestamp (in seconds) when the evaluation run was created.
*/
created_at: number;
/**
* Details of the input data source item.
*/
datasource_item: Record<string, unknown>;
/**
* The identifier for the data source item.
*/
datasource_item_id: number;
/**
* The identifier of the evaluation group.
*/
eval_id: string;
/**
* The type of the object. Always "eval.run.output_item".
*/
object: 'eval.run.output_item';
/**
* A list of results from the evaluation run.
*/
results: Array<Record<string, unknown>>;
/**
* The identifier of the evaluation run associated with this output item.
*/
run_id: string;
/**
* A sample containing the input and output of the evaluation run.
*/
sample: OutputItemRetrieveResponse.Sample;
/**
* The status of the evaluation run.
*/
status: string;
}
export declare namespace OutputItemRetrieveResponse {
/**
* A sample containing the input and output of the evaluation run.
*/
interface Sample {
/**
* An object representing an error response from the Eval API.
*/
error: RunsAPI.EvalAPIError;
/**
* The reason why the sample generation was finished.
*/
finish_reason: string;
/**
* An array of input messages.
*/
input: Array<Sample.Input>;
/**
* The maximum number of tokens allowed for completion.
*/
max_completion_tokens: number;
/**
* The model used for generating the sample.
*/
model: string;
/**
* An array of output messages.
*/
output: Array<Sample.Output>;
/**
* The seed used for generating the sample.
*/
seed: number;
/**
* The sampling temperature used.
*/
temperature: number;
/**
* The top_p value used for sampling.
*/
top_p: number;
/**
* Token usage details for the sample.
*/
usage: Sample.Usage;
}
namespace Sample {
/**
* An input message.
*/
interface Input {
/**
* The content of the message.
*/
content: string;
/**
* The role of the message sender (e.g., system, user, developer).
*/
role: string;
}
interface Output {
/**
* The content of the message.
*/
content?: string;
/**
* The role of the message (e.g. "system", "assistant", "user").
*/
role?: string;
}
/**
* Token usage details for the sample.
*/
interface Usage {
/**
* The number of tokens retrieved from cache.
*/
cached_tokens: number;
/**
* The number of completion tokens generated.
*/
completion_tokens: number;
/**
* The number of prompt tokens used.
*/
prompt_tokens: number;
/**
* The total number of tokens used.
*/
total_tokens: number;
}
}
}
/**
* A schema representing an evaluation run output item.
*/
export interface OutputItemListResponse {
/**
* Unique identifier for the evaluation run output item.
*/
id: string;
/**
* Unix timestamp (in seconds) when the evaluation run was created.
*/
created_at: number;
/**
* Details of the input data source item.
*/
datasource_item: Record<string, unknown>;
/**
* The identifier for the data source item.
*/
datasource_item_id: number;
/**
* The identifier of the evaluation group.
*/
eval_id: string;
/**
* The type of the object. Always "eval.run.output_item".
*/
object: 'eval.run.output_item';
/**
* A list of results from the evaluation run.
*/
results: Array<Record<string, unknown>>;
/**
* The identifier of the evaluation run associated with this output item.
*/
run_id: string;
/**
* A sample containing the input and output of the evaluation run.
*/
sample: OutputItemListResponse.Sample;
/**
* The status of the evaluation run.
*/
status: string;
}
export declare namespace OutputItemListResponse {
/**
* A sample containing the input and output of the evaluation run.
*/
interface Sample {
/**
* An object representing an error response from the Eval API.
*/
error: RunsAPI.EvalAPIError;
/**
* The reason why the sample generation was finished.
*/
finish_reason: string;
/**
* An array of input messages.
*/
input: Array<Sample.Input>;
/**
* The maximum number of tokens allowed for completion.
*/
max_completion_tokens: number;
/**
* The model used for generating the sample.
*/
model: string;
/**
* An array of output messages.
*/
output: Array<Sample.Output>;
/**
* The seed used for generating the sample.
*/
seed: number;
/**
* The sampling temperature used.
*/
temperature: number;
/**
* The top_p value used for sampling.
*/
top_p: number;
/**
* Token usage details for the sample.
*/
usage: Sample.Usage;
}
namespace Sample {
/**
* An input message.
*/
interface Input {
/**
* The content of the message.
*/
content: string;
/**
* The role of the message sender (e.g., system, user, developer).
*/
role: string;
}
interface Output {
/**
* The content of the message.
*/
content?: string;
/**
* The role of the message (e.g. "system", "assistant", "user").
*/
role?: string;
}
/**
* Token usage details for the sample.
*/
interface Usage {
/**
* The number of tokens retrieved from cache.
*/
cached_tokens: number;
/**
* The number of completion tokens generated.
*/
completion_tokens: number;
/**
* The number of prompt tokens used.
*/
prompt_tokens: number;
/**
* The total number of tokens used.
*/
total_tokens: number;
}
}
}
export interface OutputItemListParams extends CursorPageParams {
/**
* Sort order for output items by timestamp. Use `asc` for ascending order or
* `desc` for descending order. Defaults to `asc`.
*/
order?: 'asc' | 'desc';
/**
* Filter output items by status. Use `failed` to filter by failed output items or
* `pass` to filter by passed output items.
*/
status?: 'fail' | 'pass';
}
export declare namespace OutputItems {
export { type OutputItemRetrieveResponse as OutputItemRetrieveResponse, type OutputItemListResponse as OutputItemListResponse, OutputItemListResponsesPage as OutputItemListResponsesPage, type OutputItemListParams as OutputItemListParams, };
}
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// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
import { APIResource } from "../../../resource.mjs";
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"use strict";
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
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class Runs extends resource_1.APIResource {
constructor() {
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/**
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create(evalId, body, options) {
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/**
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retrieve(evalId, runId, options) {
return this._client.get(`/evals/${evalId}/runs/${runId}`, options);
}
list(evalId, query = {}, options) {
if ((0, core_1.isRequestOptions)(query)) {
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return this._client.getAPIList(`/evals/${evalId}/runs`, RunListResponsesPage, { query, ...options });
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/**
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/**
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cancel(evalId, runId, options) {
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}
exports.Runs = Runs;
class RunListResponsesPage extends pagination_1.CursorPage {
}
exports.RunListResponsesPage = RunListResponsesPage;
Runs.RunListResponsesPage = RunListResponsesPage;
Runs.OutputItems = output_items_1.OutputItems;
Runs.OutputItemListResponsesPage = output_items_1.OutputItemListResponsesPage;
//# sourceMappingURL=runs.js.map
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// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
import { APIResource } from "../../../resource.mjs";
import { isRequestOptions } from "../../../core.mjs";
import * as OutputItemsAPI from "./output-items.mjs";
import { OutputItemListResponsesPage, OutputItems, } from "./output-items.mjs";
import { CursorPage } from "../../../pagination.mjs";
export class Runs extends APIResource {
constructor() {
super(...arguments);
this.outputItems = new OutputItemsAPI.OutputItems(this._client);
}
/**
* Kicks off a new run for a given evaluation, specifying the data source, and what
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create(evalId, body, options) {
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/**
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retrieve(evalId, runId, options) {
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list(evalId, query = {}, options) {
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/**
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del(evalId, runId, options) {
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}
/**
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*/
cancel(evalId, runId, options) {
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}
}
export class RunListResponsesPage extends CursorPage {
}
Runs.RunListResponsesPage = RunListResponsesPage;
Runs.OutputItems = OutputItems;
Runs.OutputItemListResponsesPage = OutputItemListResponsesPage;
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