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

Third-party integrations for LangChain.js

import { __exportAll } from "../_virtual/_rolldown/runtime.js";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import Prem from "@premai/prem-sdk";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import { Embeddings } from "@langchain/core/embeddings";
//#region src/embeddings/premai.ts
var premai_exports = /* @__PURE__ */ __exportAll({ PremEmbeddings: () => PremEmbeddings });
/**
* Class for generating embeddings using the Prem AI's API. Extends the
* Embeddings class and implements PremEmbeddingsParams and
*/
var PremEmbeddings = class extends Embeddings {
	client;
	batchSize = 128;
	apiKey;
	project_id;
	model;
	encoding_format;
	constructor(fields) {
		super(fields);
		const apiKey = fields?.apiKey || getEnvironmentVariable("PREM_API_KEY");
		if (!apiKey) throw new Error(`Prem API key not found. Please set the PREM_API_KEY environment variable or provide the key into "apiKey"`);
		const projectId = fields?.project_id ?? parseInt(getEnvironmentVariable("PREM_PROJECT_ID") ?? "-1", 10);
		if (!projectId || projectId === -1 || typeof projectId !== "number") throw new Error(`Prem project ID not found. Please set the PREM_PROJECT_ID environment variable or provide the key into "project_id"`);
		this.client = new Prem({ apiKey });
		this.project_id = projectId;
		this.model = fields.model ?? this.model;
		this.encoding_format = fields.encoding_format ?? this.encoding_format;
	}
	/**
	* Method to generate embeddings for an array of documents. Splits the
	* documents into batches and makes requests to the Prem API to generate
	* embeddings.
	* @param texts Array of documents to generate embeddings for.
	* @returns Promise that resolves to a 2D array of embeddings for each document.
	*/
	async embedDocuments(texts) {
		const batches = chunkArray(texts.map((text) => text), this.batchSize);
		const batchRequests = batches.map((batch) => this.caller.call(async () => this.client.embeddings.create({
			input: batch,
			model: this.model,
			encoding_format: this.encoding_format,
			project_id: this.project_id
		})));
		const batchResponses = await Promise.all(batchRequests);
		const embeddings = [];
		for (let i = 0; i < batchResponses.length; i += 1) {
			const batch = batches[i];
			const { data: batchResponse } = batchResponses[i];
			for (let j = 0; j < batch.length; j += 1) embeddings.push(batchResponse[j].embedding);
		}
		return embeddings;
	}
	/**
	* Method to generate an embedding for a single document. Calls the
	* embedDocuments method with the document as the input.
	* @param text Document to generate an embedding for.
	* @returns Promise that resolves to an embedding for the document.
	*/
	async embedQuery(text) {
		return (await this.embedDocuments([text]))[0];
	}
};
//#endregion
export { PremEmbeddings, premai_exports };

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