@langchain/community/dist/embeddings/deepinfra.js

Third-party integrations for LangChain.js

import { __exportAll } from "../_virtual/_rolldown/runtime.js";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import { Embeddings } from "@langchain/core/embeddings";
//#region src/embeddings/deepinfra.ts
var deepinfra_exports = /* @__PURE__ */ __exportAll({ DeepInfraEmbeddings: () => DeepInfraEmbeddings });
/**
* 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 });
* ```
*/
var DeepInfraEmbeddings = class extends Embeddings {
	apiToken;
	batchSize;
	modelName;
	/**
	* 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);
		const apiKey = fieldsWithDefaults?.apiToken || 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 = 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) {
		return 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()));
	}
};
//#endregion
export { DeepInfraEmbeddings, deepinfra_exports };

//# sourceMappingURL=deepinfra.js.map