@langchain/community/dist/embeddings/gradient_ai.cjs

Third-party integrations for LangChain.js

Object.defineProperty(exports, Symbol.toStringTag, { value: "Module" });
const require_runtime = require("../_virtual/_rolldown/runtime.cjs");
let _langchain_core_utils_env = require("@langchain/core/utils/env");
let _langchain_core_utils_chunk_array = require("@langchain/core/utils/chunk_array");
let _langchain_core_embeddings = require("@langchain/core/embeddings");
let _gradientai_nodejs_sdk = require("@gradientai/nodejs-sdk");
//#region src/embeddings/gradient_ai.ts
var gradient_ai_exports = /* @__PURE__ */ require_runtime.__exportAll({ GradientEmbeddings: () => GradientEmbeddings });
/**
* Class for generating embeddings using the Gradient AI's API. Extends the
* Embeddings class and implements GradientEmbeddingsParams and
*/
var GradientEmbeddings = class extends _langchain_core_embeddings.Embeddings {
	gradientAccessKey;
	workspaceId;
	batchSize = 128;
	model;
	constructor(fields) {
		super(fields);
		this.gradientAccessKey = fields?.gradientAccessKey ?? (0, _langchain_core_utils_env.getEnvironmentVariable)("GRADIENT_ACCESS_TOKEN");
		this.workspaceId = fields?.workspaceId ?? (0, _langchain_core_utils_env.getEnvironmentVariable)("GRADIENT_WORKSPACE_ID");
		if (!this.gradientAccessKey) throw new Error("Missing Gradient AI Access Token");
		if (!this.workspaceId) throw new Error("Missing Gradient AI Workspace ID");
	}
	/**
	* Method to generate embeddings for an array of documents. Splits the
	* documents into batches and makes requests to the Gradient 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) {
		await this.setModel();
		const batches = (0, _langchain_core_utils_chunk_array.chunkArray)(texts.map((text) => ({ input: text })), this.batchSize);
		const batchRequests = batches.map((batch) => this.caller.call(async () => this.model.generateEmbeddings({ inputs: batch })));
		const batchResponses = await Promise.all(batchRequests);
		const embeddings = [];
		for (let i = 0; i < batchResponses.length; i += 1) {
			const batch = batches[i];
			const { embeddings: 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];
	}
	/**
	* Method to set the model to use for generating embeddings.
	* @sets the class' `model` value to that of the retrieved Embeddings Model.
	*/
	async setModel() {
		if (this.model) return;
		this.model = await new _gradientai_nodejs_sdk.Gradient({
			accessToken: this.gradientAccessKey,
			workspaceId: this.workspaceId
		}).getEmbeddingsModel({ slug: "bge-large" });
	}
};
//#endregion
exports.GradientEmbeddings = GradientEmbeddings;
Object.defineProperty(exports, "gradient_ai_exports", {
	enumerable: true,
	get: function() {
		return gradient_ai_exports;
	}
});

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