@arizeai/phoenix-evals/dist/esm/llm/createToolResponseHandlingEvaluator.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 ToolResponseHandlingEvaluatorArgs<RecordType extends Record<string, unknown> = ToolResponseHandlingEvaluationRecord> 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 record to be evaluated by the tool response handling evaluator.
 */
export type ToolResponseHandlingEvaluationRecord = {
    /**
     * The user query or conversation context.
     */
    input: string;
    /**
     * The tool invocation(s) made by the agent, including arguments.
     */
    toolCall: string;
    /**
     * The tool's response (data, errors, or partial results).
     */
    toolResult: string;
    /**
     * The agent's handling after receiving the tool result
     * (may include retries, follow-ups, or final response).
     */
    output: string;
};
/**
 * Creates a tool response handling evaluator function.
 *
 * This function returns an evaluator that determines whether an AI agent properly
 * handled a tool's response, including error handling, data extraction,
 * transformation, and safe information disclosure.
 *
 * @param args - The arguments for creating the tool response handling evaluator.
 * @param args.model - The model to use for classification.
 * @param args.choices - The possible classification choices (defaults to correct/incorrect).
 * @param args.promptTemplate - The prompt template to use.
 * @param args.telemetry - The telemetry to use for the evaluator.
 *
 * @returns An evaluator function that takes a {@link ToolResponseHandlingEvaluationRecord}
 * and returns a classification result indicating whether the tool response handling
 * is correct or incorrect.
 *
 * @example
 * ```ts
 * const evaluator = createToolResponseHandlingEvaluator({ model: openai("gpt-4o-mini") });
 *
 * // Example: Correct extraction from tool result
 * const result = await evaluator.evaluate({
 *   input: "What's the weather in Seattle?",
 *   toolCall: 'get_weather(location="Seattle")',
 *   toolResult: JSON.stringify({
 *     temperature: 58,
 *     unit: "fahrenheit",
 *     conditions: "partly cloudy"
 *   }),
 *   output: "The weather in Seattle is 58°F and partly cloudy."
 * });
 * console.log(result.label); // "correct"
 *
 * // Example: Hallucinated data (incorrect)
 * const resultHallucinated = await evaluator.evaluate({
 *   input: "What restaurants are nearby?",
 *   toolCall: 'search_restaurants(location="downtown")',
 *   toolResult: JSON.stringify({
 *     results: [{ name: "Cafe Luna", rating: 4.2 }]
 *   }),
 *   output: "I found Cafe Luna (4.2 stars) and Mario's Italian (4.8 stars) nearby."
 * });
 * console.log(resultHallucinated.label); // "incorrect" - Mario's was hallucinated
 * ```
 */
export declare function createToolResponseHandlingEvaluator<RecordType extends Record<string, unknown> = ToolResponseHandlingEvaluationRecord>(args: ToolResponseHandlingEvaluatorArgs<RecordType>): ClassificationEvaluator<RecordType>;
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