"use strict"; Object.defineProperty(exports, "__esModule", { value: true }); exports.DeepInfraEmbeddings = void 0; const env_1 = require("@langchain/core/utils/env"); const embeddings_1 = require("@langchain/core/embeddings"); const chunk_array_1 = require("@langchain/core/utils/chunk_array"); /** * The default model name to use for generating embeddings. */ const DEFAULT_MODEL_NAME = "sentence-transformers/clip-ViT-B-32"; /** * The default batch size to use for generating embeddings. * This is limited by the DeepInfra API to a maximum of 1024. */ const DEFAULT_BATCH_SIZE = 1024; /** * Environment variable name for the DeepInfra API token. */ const API_TOKEN_ENV_VAR = "DEEPINFRA_API_TOKEN"; /** * A class for generating embeddings using the DeepInfra API. * @example * ```typescript * // Embed a query using the DeepInfraEmbeddings class * const model = new DeepInfraEmbeddings(); * const res = await model.embedQuery( * "What would be a good company name for a company that makes colorful socks?", * ); * console.log({ res }); * ``` */ class DeepInfraEmbeddings extends embeddings_1.Embeddings { /** * Constructor for the DeepInfraEmbeddings class. * @param fields - An optional object with properties to configure the instance. */ constructor(fields) { const fieldsWithDefaults = { modelName: DEFAULT_MODEL_NAME, batchSize: DEFAULT_BATCH_SIZE, ...fields, }; super(fieldsWithDefaults); Object.defineProperty(this, "apiToken", { enumerable: true, configurable: true, writable: true, value: void 0 }); Object.defineProperty(this, "batchSize", { enumerable: true, configurable: true, writable: true, value: void 0 }); Object.defineProperty(this, "modelName", { enumerable: true, configurable: true, writable: true, value: void 0 }); const apiKey = fieldsWithDefaults?.apiToken || (0, env_1.getEnvironmentVariable)(API_TOKEN_ENV_VAR); if (!apiKey) { throw new Error("DeepInfra API token not found"); } this.modelName = fieldsWithDefaults?.modelName ?? this.modelName; this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize; this.apiToken = apiKey; } /** * Generates embeddings for an array of texts. * @param inputs - An array of strings to generate embeddings for. * @returns A Promise that resolves to an array of embeddings. */ async embedDocuments(inputs) { const batches = (0, chunk_array_1.chunkArray)(inputs, this.batchSize); const batchRequests = batches.map((batch) => this.embeddingWithRetry({ inputs: batch, })); const batchResponses = await Promise.all(batchRequests); const out = []; for (let i = 0; i < batchResponses.length; i += 1) { const batch = batches[i]; const { embeddings } = batchResponses[i]; for (let j = 0; j < batch.length; j += 1) { out.push(embeddings[j]); } } return out; } /** * Generates an embedding for a single text. * @param text - A string to generate an embedding for. * @returns A Promise that resolves to an array of numbers representing the embedding. */ async embedQuery(text) { const { embeddings } = await this.embeddingWithRetry({ inputs: [text], }); return embeddings[0]; } /** * Generates embeddings with retry capabilities. * @param request - An object containing the request parameters for generating embeddings. * @returns A Promise that resolves to the API response. */ async embeddingWithRetry(request) { const response = await this.caller.call(() => fetch(`https://api.deepinfra.com/v1/inference/${this.modelName}`, { method: "POST", headers: { Authorization: `Bearer ${this.apiToken}`, "Content-Type": "application/json", }, body: JSON.stringify(request), }).then((res) => res.json())); return response; } } exports.DeepInfraEmbeddings = DeepInfraEmbeddings;