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

Third-party integrations for LangChain.js

Object.defineProperty(exports, Symbol.toStringTag, { value: "Module" });
const require_runtime = require("../_virtual/_rolldown/runtime.cjs");
const require_zhipuai = require("../utils/zhipuai.cjs");
let _langchain_core_utils_env = require("@langchain/core/utils/env");
let _langchain_core_embeddings = require("@langchain/core/embeddings");
//#region src/embeddings/zhipuai.ts
var zhipuai_exports = /* @__PURE__ */ require_runtime.__exportAll({ ZhipuAIEmbeddings: () => ZhipuAIEmbeddings });
var ZhipuAIEmbeddings = class extends _langchain_core_embeddings.Embeddings {
	modelName = "embedding-2";
	apiKey;
	stripNewLines = true;
	embeddingsAPIURL = "https://open.bigmodel.cn/api/paas/v4/embeddings";
	constructor(fields) {
		super(fields ?? {});
		this.modelName = fields?.modelName ?? this.modelName;
		this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
		this.apiKey = fields?.apiKey ?? (0, _langchain_core_utils_env.getEnvironmentVariable)("ZHIPUAI_API_KEY");
		if (!this.apiKey) throw new Error("ZhipuAI API key not found");
	}
	/**
	* Private method to make a request to the TogetherAI API to generate
	* embeddings. Handles the retry logic and returns the response from the API.
	* @param {string} input The input text to embed.
	* @returns Promise that resolves to the response from the API.
	* @TODO Figure out return type and statically type it.
	*/
	async embeddingWithRetry(input) {
		const text = this.stripNewLines ? input.replace(/\n/g, " ") : input;
		const body = JSON.stringify({
			input: text,
			model: this.modelName
		});
		const headers = {
			Accept: "application/json",
			"Content-Type": "application/json",
			Authorization: require_zhipuai.encodeApiKey(this.apiKey)
		};
		return this.caller.call(async () => {
			const fetchResponse = await fetch(this.embeddingsAPIURL, {
				method: "POST",
				headers,
				body
			});
			if (fetchResponse.status === 200) return fetchResponse.json();
			throw new Error(`Error getting embeddings from ZhipuAI. ${JSON.stringify(await fetchResponse.json(), null, 2)}`);
		});
	}
	/**
	* Method to generate an embedding for a single document. Calls the
	* embeddingWithRetry method with the document as the input.
	* @param {string} text Document to generate an embedding for.
	* @returns {Promise<number[]>} Promise that resolves to an embedding for the document.
	*/
	async embedQuery(text) {
		const { data } = await this.embeddingWithRetry(text);
		return data[0].embedding;
	}
	/**
	* Method that takes an array of documents as input and returns a promise
	* that resolves to a 2D array of embeddings for each document. It calls
	* the embedQuery method for each document in the array.
	* @param documents Array of documents for which to generate embeddings.
	* @returns Promise that resolves to a 2D array of embeddings for each input document.
	*/
	embedDocuments(documents) {
		return Promise.all(documents.map((doc) => this.embedQuery(doc)));
	}
};
//#endregion
exports.ZhipuAIEmbeddings = ZhipuAIEmbeddings;
Object.defineProperty(exports, "zhipuai_exports", {
	enumerable: true,
	get: function() {
		return zhipuai_exports;
	}
});

//# sourceMappingURL=zhipuai.cjs.map