import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings } from "@langchain/core/embeddings";
/**
 * Class for generating embeddings using the Tencent Hunyuan API.
 */
export class TencentHunyuanEmbeddings extends Embeddings {
    constructor(fields) {
        super(fields ?? {});
        Object.defineProperty(this, "tencentSecretId", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "tencentSecretKey", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "host", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "hunyuan.tencentcloudapi.com"
        });
        Object.defineProperty(this, "sign", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        this.tencentSecretId =
            fields?.tencentSecretId ?? getEnvironmentVariable("TENCENT_SECRET_ID");
        if (!this.tencentSecretId) {
            throw new Error("Tencent SecretID not found");
        }
        this.tencentSecretKey =
            fields?.tencentSecretKey ?? getEnvironmentVariable("TENCENT_SECRET_KEY");
        if (!this.tencentSecretKey) {
            throw new Error("Tencent SecretKey not found");
        }
        this.host = fields?.host ?? this.host;
        if (!fields?.sign) {
            throw new Error("Sign method undefined");
        }
        this.sign = fields?.sign;
    }
    /**
     * 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 request = { Input: input };
        const timestamp = Math.trunc(Date.now() / 1000);
        const headers = {
            "Content-Type": "application/json",
            "X-TC-Action": "GetEmbedding",
            "X-TC-Version": "2023-09-01",
            "X-TC-Timestamp": timestamp.toString(),
            Authorization: "",
        };
        headers.Authorization = this.sign(this.host, request, timestamp, this.tencentSecretId ?? "", this.tencentSecretKey ?? "", headers);
        return this.caller.call(async () => {
            const response = await fetch(`https://${this.host}`, {
                headers,
                method: "POST",
                body: JSON.stringify(request),
            });
            if (response.ok) {
                return response.json();
            }
            throw new Error(`Error getting embeddings from Tencent Hunyuan. ${JSON.stringify(await response.json(), null, 2)}`);
        });
    }
    /**
     * Method to generate an embedding for a single document. Calls the
     * embeddingWithRetry method with the document as the input.
     * @param {string} text Document to generate an embedding for.
     * @returns {Promise<number[]>} Promise that resolves to an embedding for the document.
     */
    async embedQuery(text) {
        const { Response } = await this.embeddingWithRetry(text);
        if (Response?.Error?.Message) {
            throw new Error(`[${Response.RequestId}] ${Response.Error.Message}`);
        }
        return Response.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)));
    }
}