@forge-ml/rag/dist/src/embedders/openaiEmbedder.js

A RAG (Retrieval-Augmented Generation) package for Forge ML

"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
const openai_1 = __importDefault(require("openai"));
const tokenEstimate_1 = require("../../utils/tokenEstimate");
class OpenAIEmbedder {
    openai;
    constructor({ apiKey }) {
        this.openai = new openai_1.default({ apiKey });
    }
    /**
     * Generate an embedding for a given text.
     * @param text The text to generate an embedding for.
     * @returns The embedding for the text.
     */
    async generateEmbedding(text) {
        const embedding = await this.openai.embeddings.create({
            model: "text-embedding-3-large",
            input: text,
        });
        return embedding.data[0].embedding;
    }
    async generateEmbeddingBatch(texts) {
        const batchSize = 80;
        const batches = [];
        for (let i = 0; i < texts.length; i += batchSize) {
            batches.push(texts.slice(i, i + batchSize));
        }
        const embeddings = await Promise.all(batches.map(async (batch) => {
            const response = await this.openai.embeddings.create({
                model: "text-embedding-3-large",
                input: batch.filter((text) => !text.includes("$")),
            });
            return response.data.map((embedding) => embedding.embedding);
        }));
        return embeddings.flat();
    }
    /**
     * Embed an array of chunks.
     * @param chunks The chunks to embed.
     * @returns The embeddings for the chunks.
     */
    async embedChunks(chunks, documentId) {
        // Truncate chunks to 800,000 tokens (rate limit is 1,000,000 tokens)
        const { chunks: truncatedChunks, totalLength } = chunks.reduce((acc, chunk, index) => {
            const currentChunkLength = acc.totalLength + chunk.text.length;
            if ((0, tokenEstimate_1.estimateTokensByLength)(currentChunkLength) > 800000 ||
                index > 2900) {
                return acc;
            }
            acc.chunks.push(chunk);
            acc.totalLength = currentChunkLength;
            return acc;
        }, { chunks: [], totalLength: 0 });
        // Generate embeddings for the truncated chunks
        const embeddings = await this.generateEmbeddingBatch(truncatedChunks.map((chunk) => chunk.text));
        return truncatedChunks.map((chunk, index) => ({
            chunkId: chunk.forgeMetadata.chunkId,
            embedding: embeddings[index],
            documentId,
        }));
    }
}
exports.default = OpenAIEmbedder;