@arizeai/phoenix-evals/dist/src/llm/createDocumentRelevanceEvaluator.d.ts

A library for running evaluations for AI use cases

import type { CreateClassificationEvaluatorArgs } from "../types/evals";
import type { ClassificationEvaluator } from "./ClassificationEvaluator";
export interface DocumentRelevanceEvaluatorArgs<RecordType extends Record<string, unknown> = DocumentRelevanceEvaluationRecord> 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 document relevance evaluator.
 */
export interface DocumentRelevanceEvaluationRecord {
    input: string;
    documentText: string;
    [key: string]: unknown;
}
/**
 * Creates a document relevance evaluator function.
 *
 * This function returns an evaluator that determines whether a given document text
 * is relevant to a provided input question. The evaluator uses a classification model
 * and a prompt template to make its determination.
 *
 * @param args - The arguments for creating the document relevance evaluator.
 * @param args.model - The model to use for classification.
 * @param args.choices - The possible classification choices (defaults to DOCUMENT_RELEVANCE_CHOICES).
 * @param args.promptTemplate - The prompt template to use (defaults to DOCUMENT_RELEVANCE_TEMPLATE).
 * @param args.telemetry - The telemetry to use for the evaluator.
 *
 * @returns An evaluator function that takes a {@link DocumentRelevanceExample} and returns a classification result
 * indicating whether the document is relevant to the input question.
 *
 * @example
 * ```ts
 * const evaluator = createDocumentRelevanceEvaluator({ model: openai("gpt-4o-mini") });
 * const result = await evaluator.evaluate({
 *   input: "What is the capital of France?",
 *   documentText: "Paris is the capital and most populous city of France.",
 * });
 * console.log(result.label); // "relevant" or "unrelated"
 * ```
 */
export declare function createDocumentRelevanceEvaluator<RecordType extends Record<string, unknown> = DocumentRelevanceEvaluationRecord>(args: DocumentRelevanceEvaluatorArgs<RecordType>): ClassificationEvaluator<RecordType>;
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