@arizeai/phoenix-evals/dist/esm/llm/createUserFrictionEvaluator.d.ts

A library for running evaluations for AI use cases

import type { CreateClassificationEvaluatorArgs } from "../types/evals.js";
import type { ClassificationEvaluator } from "./ClassificationEvaluator.js";
export interface UserFrictionEvaluatorArgs<RecordType extends Record<string, unknown> = UserFrictionEvaluationRecord> extends Omit<CreateClassificationEvaluatorArgs<RecordType>, "promptTemplate" | "choices" | "optimizationDirection" | "name"> {
    optimizationDirection?: CreateClassificationEvaluatorArgs<RecordType>["optimizationDirection"];
    name?: CreateClassificationEvaluatorArgs<RecordType>["name"];
    choices?: CreateClassificationEvaluatorArgs<RecordType>["choices"];
    promptTemplate?: CreateClassificationEvaluatorArgs<RecordType>["promptTemplate"];
}
/** A conversation and the latest user message to evaluate for expressed friction. */
export interface UserFrictionEvaluationRecord {
    /**
     * Human-readable conversation history before the target user message.
     */
    conversation: string;
    /**
     * The latest user message to classify for expressed friction.
     */
    userMessage: string;
    [key: string]: unknown;
}
/**
 * Creates a user friction evaluator function.
 *
 * This function returns an evaluator that detects expressed user friction
 * with an assistant's preceding behavior, such as corrections, retries,
 * frustration, and challenges to unrequested or unexplained actions.
 *
 * @param args - The arguments for creating the user friction evaluator.
 * @param args.model - The model to use for classification.
 * @param args.choices - The possible classification choices (defaults to friction/no_friction).
 * @param args.promptTemplate - The prompt template to use (defaults to USER_FRICTION_CLASSIFICATION_EVALUATOR_CONFIG.template).
 * @param args.telemetry - The telemetry to use for the evaluator.
 *
 * @returns An evaluator function that takes a {@link UserFrictionEvaluationRecord} and returns a classification result
 * indicating whether the latest user message expresses friction or no friction.
 *
 * @example
 * ```ts
 * const evaluator = createUserFrictionEvaluator({ model: openai("gpt-4o-mini") });
 * const result = await evaluator.evaluate({
 *   conversation: "User: Show recent orders.\nAssistant: Here are last month's orders.",
 *   userMessage: "No, I asked for this week.",
 * });
 * console.log(result.label); // "friction"
 * ```
 */
export declare function createUserFrictionEvaluator<RecordType extends Record<string, unknown> = UserFrictionEvaluationRecord>(args: UserFrictionEvaluatorArgs<RecordType>): ClassificationEvaluator<RecordType>;
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