ai-utils.js/model-function/embed-text/embedText.js

Build AI applications, chatbots, and agents with JavaScript and TypeScript.

import { executeCall } from "../executeCall.js";
/**
 * Generate embeddings for multiple texts.
 *
 * @example
 * const { embeddings } = await embedTexts(
 *   new OpenAITextEmbeddingModel(...),
 *   [
 *     "At first, Nox didn't know what to do with the pup.",
 *     "He keenly observed and absorbed everything around him, from the birds in the sky to the trees in the forest.",
 *   ]
 * );
 */
export async function embedTexts(model, texts, options) {
    const result = await executeCall({
        model,
        options,
        generateResponse: (options) => {
            // split the texts into groups that are small enough to be sent in one call:
            const maxTextsPerCall = model.maxTextsPerCall;
            const textGroups = [];
            for (let i = 0; i < texts.length; i += maxTextsPerCall) {
                textGroups.push(texts.slice(i, i + maxTextsPerCall));
            }
            return Promise.all(textGroups.map((textGroup) => model.generateEmbeddingResponse(textGroup, options)));
        },
        extractOutputValue: (result) => {
            const embeddings = [];
            for (const response of result) {
                embeddings.push(...model.extractEmbeddings(response));
            }
            return embeddings;
        },
        getStartEvent: (metadata, settings) => ({
            type: "text-embedding-started",
            metadata,
            settings,
            texts,
        }),
        getAbortEvent: (metadata, settings) => ({
            type: "text-embedding-finished",
            status: "abort",
            metadata,
            settings,
            texts,
        }),
        getFailureEvent: (metadata, settings, error) => ({
            type: "text-embedding-finished",
            status: "failure",
            metadata,
            settings,
            error,
            texts,
        }),
        getSuccessEvent: (metadata, settings, response, output) => ({
            type: "text-embedding-finished",
            status: "success",
            metadata,
            settings,
            texts,
            response,
            generatedEmbeddings: output,
        }),
    });
    return {
        embeddings: result.output,
        metadata: result.metadata,
    };
}
/**
 * Generate an embedding for a single text.
 *
 * @example
 * const { embedding } = await embedText(
 *   new OpenAITextEmbeddingModel(...),
 *   "At first, Nox didn't know what to do with the pup."
 * );
 */
export async function embedText(model, text, options) {
    const result = await embedTexts(model, [text], options);
    return {
        embedding: result.embeddings[0],
        metadata: result.metadata,
    };
}