@arizeai/phoenix-evals/dist/esm/llm/createConcisenessEvaluator.js

A library for running evaluations for AI use cases

import { CONCISENESS_CLASSIFICATION_EVALUATOR_CONFIG } from "../__generated__/default_templates/index.js";
import { createClassificationEvaluator } from "./createClassificationEvaluator.js";
/**
 * Creates a conciseness evaluator function.
 *
 * This function returns an evaluator that determines whether a given output
 * is concise and free of unnecessary content such as pleasantries, hedging,
 * meta-commentary, or redundant information.
 *
 * @param args - The arguments for creating the conciseness evaluator.
 * @param args.model - The model to use for classification.
 * @param args.choices - The possible classification choices (defaults to CONCISENESS_CHOICES).
 * @param args.promptTemplate - The prompt template to use (defaults to CONCISENESS_TEMPLATE).
 * @param args.telemetry - The telemetry to use for the evaluator.
 *
 * @returns An evaluator function that takes a {@link ConcisenessEvaluationRecord} and returns a classification result
 * indicating whether the output is concise or verbose.
 *
 * @example
 * ```ts
 * const evaluator = createConcisenessEvaluator({ model: openai("gpt-4o-mini") });
 * const result = await evaluator.evaluate({
 *   input: "What is the capital of France?",
 *   output: "Paris.",
 * });
 * console.log(result.label); // "concise" or "verbose"
 * ```
 */
export function createConcisenessEvaluator(args) {
    const { choices = CONCISENESS_CLASSIFICATION_EVALUATOR_CONFIG.choices, promptTemplate = CONCISENESS_CLASSIFICATION_EVALUATOR_CONFIG.template, optimizationDirection = CONCISENESS_CLASSIFICATION_EVALUATOR_CONFIG.optimizationDirection, name = CONCISENESS_CLASSIFICATION_EVALUATOR_CONFIG.name, ...rest } = args;
    return createClassificationEvaluator({
        ...rest,
        promptTemplate,
        choices,
        optimizationDirection,
        name,
    });
}
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