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