@arizeai/phoenix-evals
Version:
A library for running evaluations for AI use cases
49 lines • 2.59 kB
JavaScript
;
var __rest = (this && this.__rest) || function (s, e) {
var t = {};
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)
t[p] = s[p];
if (s != null && typeof Object.getOwnPropertySymbols === "function")
for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {
if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))
t[p[i]] = s[p[i]];
}
return t;
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.createDocumentRelevancyEvaluator = createDocumentRelevancyEvaluator;
const createClassifier_1 = require("./createClassifier");
const DOCUMENT_RELEVANCY_TEMPLATE_1 = require("../default_templates/DOCUMENT_RELEVANCY_TEMPLATE");
/**
* Creates a document relevancy 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 relevancy evaluator.
* @param args.model - The model to use for classification.
* @param args.choices - The possible classification choices (defaults to DOCUMENT_RELEVANCY_CHOICES).
* @param args.promptTemplate - The prompt template to use (defaults to DOCUMENT_RELEVANCY_TEMPLATE).
* @param args.telemetry - The telemetry to use for the evaluator.
*
* @returns An evaluator function that takes a {@link DocumentRelevancyExample} and returns a classification result
* indicating whether the document is relevant to the input question.
*
* @example
* ```ts
* const evaluator = createDocumentRelevancyEvaluator({ model: openai("gpt-4o-mini") });
* const result = await evaluator({
* 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"
* ```
*/
function createDocumentRelevancyEvaluator(args) {
const { choices = DOCUMENT_RELEVANCY_TEMPLATE_1.DOCUMENT_RELEVANCY_CHOICES, promptTemplate = DOCUMENT_RELEVANCY_TEMPLATE_1.DOCUMENT_RELEVANCY_TEMPLATE } = args, rest = __rest(args, ["choices", "promptTemplate"]);
const documentRelevancyEvaluatorFn = (0, createClassifier_1.createClassifier)(Object.assign(Object.assign(Object.assign({}, args), { promptTemplate,
choices }), rest));
return documentRelevancyEvaluatorFn;
}
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