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split runtime file
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‎packages/runtime/src/classify.ts‎

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/* eslint-disable @typescript-eslint/no-unused-expressions */
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// Copyright (c) Microsoft Corporation.
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// Licensed under the MIT License.
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/**
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* GenAIScript supporting runtime
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* This module provides core functionality for text classification, data transformation,
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* PDF processing, and file system operations in the GenAIScript environment.
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*/
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import type {
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ChatGenerationContext,
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Logprob,
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PromptGenerator,
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PromptGeneratorOptions,
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RunPromptUsage,
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StringLike,
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} from "@genaiscript/core";
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import { uniq } from "es-toolkit";
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/**
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* Options for classifying data using AI models.
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*
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* @property {boolean} [other] - Inject a 'other' label.
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* @property {boolean} [explanations] - Explain answers before returning token.
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* @property {ChatGenerationContext} [ctx] - Options runPrompt context.
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*/
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export type ClassifyOptions = {
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/**
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* When true, adds an 'other' category to handle cases that don't match defined labels
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*/
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other?: boolean;
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/**
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* When true, provides explanatory text before the classification result
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*/
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explanations?: boolean;
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/**
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* Context for running the classification prompt
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*/
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ctx?: ChatGenerationContext;
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} & Omit<PromptGeneratorOptions, "choices">;
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/**
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* Classifies input text into predefined categories using AI.
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* Inspired by https://github.com/prefecthq/marvin.
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*
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* @param text - Text content to classify or a prompt generator function.
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* @param labels - Object mapping label names to their descriptions.
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* @param options - Configuration options for classification, including whether to add an "other" category, provide explanations, and specify context.
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* @returns Classification result containing the chosen label, confidence metrics, log probabilities, the full answer text, and usage statistics.
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* @throws Error if fewer than two labels are provided (including "other").
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*/
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export async function classify<L extends Record<string, string>>(
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text: StringLike | PromptGenerator,
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labels: L,
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options?: ClassifyOptions,
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): Promise<{
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label: keyof typeof labels | "other";
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entropy?: number;
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logprob?: number;
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probPercent?: number;
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answer: string;
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logprobs?: Record<keyof typeof labels | "other", Logprob>;
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usage?: RunPromptUsage;
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}> {
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const { other, explanations, ...rest } = options || {};
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const entries = Object.entries({
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...labels,
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...(other
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? {
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other: "This label is used when the text does not fit any of the available labels.",
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}
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: {}),
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}).map(([k, v]) => [k.trim().toLowerCase(), v]);
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if (entries.length < 2) throw Error("classify must have at least two label (including other)");
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const choices = entries.map(([k]) => k);
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const allChoices = uniq<keyof typeof labels | "other">(choices);
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const ctx = options?.ctx || globalPromptContext.env.generator;
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const res = await ctx.runPrompt(
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async (_) => {
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_.$`## Expert Classifier
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You are a specialized text classification system.
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Your task is to carefully read and classify any input text or image into one
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of the predefined labels below.
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For each label, you will find a short description. Use these descriptions to guide your decision.
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`.role("system");
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_.$`## Labels
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You must classify the data as one of the following labels.
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${entries.map(([id, descr]) => `- Label '${id}': ${descr}`).join("\n")}
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## Output
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${explanations ? "Provide a single short sentence justification for your choice." : ""}
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Output the label as a single word on the last line (do not emit "Label").
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`;
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_.fence(
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`- Label 'yes': funny
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- Label 'no': not funny
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DATA:
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Why did the chicken cross the road? Because moo.
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Output:
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${explanations ? "It's a classic joke but the ending does not relate to the start of the joke." : ""}
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no
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`,
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{ language: "example" },
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);
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if (typeof text === "function") await text(_);
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else _.def("DATA", text);
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},
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{
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model: "classify",
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choices: choices,
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label: `classify ${choices.join(", ")}`,
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logprobs: true,
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topLogprobs: Math.min(3, choices.length),
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maxTokens: explanations ? 100 : 1,
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system: [
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"system.output_plaintext",
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"system.safety_jailbreak",
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"system.safety_harmful_content",
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"system.safety_protected_material",
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],
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...rest,
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},
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);
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// find the last label
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const answer = res.text.toLowerCase();
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const indexes = choices.map((l) => answer.lastIndexOf(l));
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const labeli = indexes.reduce((previ, _label, i) => {
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if (indexes[i] > indexes[previ]) return i;
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else return previ;
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}, 0);
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const label = entries[labeli][0];
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const logprobs = res.choices
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? (Object.fromEntries(
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res.choices.filter((c) => !isNaN(c?.logprob)).map((c, i) => [allChoices[i], c]),
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) as Record<keyof typeof labels | "other", Logprob>)
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: undefined;
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const logprob = logprobs?.[label];
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const usage = res.usage;
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return {
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label,
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entropy: logprob?.entropy,
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logprob: logprob?.logprob,
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probPercent: logprob?.probPercent,
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answer,
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logprobs,
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usage,
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};
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}

‎packages/runtime/src/index.ts‎

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@@ -7,6 +7,7 @@ export * from "./log.js";
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export * from "./nodehost.js";
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export * from "./playwright.js";
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export * from "./runtime.js";
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export * from "./classify.js";
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export * from "./version.js";
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import { installGlobals } from "@genaiscript/core";

‎packages/runtime/src/runtime.ts‎

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FileStats,
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JSONSchema,
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JSONSchemaArray,
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Logprob,
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OptionsOrString,
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ParsePDFOptions,
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PromptContext,
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PromptGenerator,
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PromptGeneratorOptions,
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RunPromptUsage,
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StringLike,
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WorkspaceFile,
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WorkspaceGrepOptions,
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} from "@genaiscript/core";
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import { delay, uniq, uniqBy, chunk } from "es-toolkit";
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import { z } from "zod";
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const globalPromptContext: PromptContext = globalThis as unknown as PromptContext;
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/**
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* Utility functions exported for general use
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*/
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export { delay, uniq, uniqBy, z, chunk };
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/**
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* Options for classifying data using AI models.
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*
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* @property {boolean} [other] - Inject a 'other' label.
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* @property {boolean} [explanations] - Explain answers before returning token.
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* @property {ChatGenerationContext} [ctx] - Options runPrompt context.
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*/
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export type ClassifyOptions = {
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/**
46-
* When true, adds an 'other' category to handle cases that don't match defined labels
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*/
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other?: boolean;
49-
/**
50-
* When true, provides explanatory text before the classification result
51-
*/
52-
explanations?: boolean;
53-
/**
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* Context for running the classification prompt
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*/
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ctx?: ChatGenerationContext;
57-
} & Omit<PromptGeneratorOptions, "choices">;
58-
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/**
60-
* Classifies input text into predefined categories using AI.
61-
* Inspired by https://github.com/prefecthq/marvin.
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*
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* @param text - Text content to classify or a prompt generator function.
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* @param labels - Object mapping label names to their descriptions.
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* @param options - Configuration options for classification, including whether to add an "other" category, provide explanations, and specify context.
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* @returns Classification result containing the chosen label, confidence metrics, log probabilities, the full answer text, and usage statistics.
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* @throws Error if fewer than two labels are provided (including "other").
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*/
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export async function classify<L extends Record<string, string>>(
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text: StringLike | PromptGenerator,
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labels: L,
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options?: ClassifyOptions,
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): Promise<{
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label: keyof typeof labels | "other";
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entropy?: number;
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logprob?: number;
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probPercent?: number;
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answer: string;
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logprobs?: Record<keyof typeof labels | "other", Logprob>;
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usage?: RunPromptUsage;
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}> {
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const { other, explanations, ...rest } = options || {};
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const entries = Object.entries({
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...labels,
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...(other
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? {
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other: "This label is used when the text does not fit any of the available labels.",
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}
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: {}),
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}).map(([k, v]) => [k.trim().toLowerCase(), v]);
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if (entries.length < 2) throw Error("classify must have at least two label (including other)");
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const choices = entries.map(([k]) => k);
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const allChoices = uniq<keyof typeof labels | "other">(choices);
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const ctx = options?.ctx || globalPromptContext.env.generator;
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const res = await ctx.runPrompt(
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async (_) => {
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_.$`## Expert Classifier
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You are a specialized text classification system.
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Your task is to carefully read and classify any input text or image into one
104-
of the predefined labels below.
105-
For each label, you will find a short description. Use these descriptions to guide your decision.
106-
`.role("system");
107-
_.$`## Labels
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You must classify the data as one of the following labels.
109-
${entries.map(([id, descr]) => `- Label '${id}': ${descr}`).join("\n")}
110-
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## Output
112-
${explanations ? "Provide a single short sentence justification for your choice." : ""}
113-
Output the label as a single word on the last line (do not emit "Label").
114-
115-
`;
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_.fence(
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`- Label 'yes': funny
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- Label 'no': not funny
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120-
DATA:
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Why did the chicken cross the road? Because moo.
122-
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Output:
124-
${explanations ? "It's a classic joke but the ending does not relate to the start of the joke." : ""}
125-
no
126-
127-
`,
128-
{ language: "example" },
129-
);
130-
if (typeof text === "function") await text(_);
131-
else _.def("DATA", text);
132-
},
133-
{
134-
model: "classify",
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choices: choices,
136-
label: `classify ${choices.join(", ")}`,
137-
logprobs: true,
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topLogprobs: Math.min(3, choices.length),
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maxTokens: explanations ? 100 : 1,
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system: [
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"system.output_plaintext",
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"system.safety_jailbreak",
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"system.safety_harmful_content",
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"system.safety_protected_material",
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],
146-
...rest,
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},
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);
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// find the last label
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const answer = res.text.toLowerCase();
152-
const indexes = choices.map((l) => answer.lastIndexOf(l));
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const labeli = indexes.reduce((previ, _label, i) => {
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if (indexes[i] > indexes[previ]) return i;
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else return previ;
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}, 0);
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const label = entries[labeli][0];
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const logprobs = res.choices
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? (Object.fromEntries(
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res.choices.filter((c) => !isNaN(c?.logprob)).map((c, i) => [allChoices[i], c]),
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) as Record<keyof typeof labels | "other", Logprob>)
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: undefined;
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const logprob = logprobs?.[label];
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const usage = res.usage;
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return {
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label,
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entropy: logprob?.entropy,
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logprob: logprob?.logprob,
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probPercent: logprob?.probPercent,
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answer,
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logprobs,
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usage,
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};
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}
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/**
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* Enhances content generation by applying iterative improvements.
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*

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