import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
export class AlibabaTongyiEmbeddings extends Embeddings {
    constructor(fields) {
        const fieldsWithDefaults = { maxConcurrency: 2, ...fields };
        super(fieldsWithDefaults);
        Object.defineProperty(this, "modelName", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: "text-embedding-v2"
        });
        Object.defineProperty(this, "batchSize", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: 24
        });
        Object.defineProperty(this, "stripNewLines", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: true
        });
        Object.defineProperty(this, "apiKey", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        Object.defineProperty(this, "parameters", {
            enumerable: true,
            configurable: true,
            writable: true,
            value: void 0
        });
        const apiKey = fieldsWithDefaults?.apiKey ?? getEnvironmentVariable("ALIBABA_API_KEY");
        if (!apiKey)
            throw new Error("AlibabaAI API key not found");
        this.apiKey = apiKey;
        this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
        this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
        this.stripNewLines =
            fieldsWithDefaults?.stripNewLines ?? this.stripNewLines;
        this.parameters = {
            text_type: fieldsWithDefaults?.parameters?.text_type ?? "document",
        };
    }
    /**
     * Method to generate embeddings for an array of documents. Splits the
     * documents into batches and makes requests to the AlibabaTongyi 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(this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts, this.batchSize);
        const batchRequests = batches.map((batch) => {
            const params = this.getParams(batch);
            return this.embeddingWithRetry(params);
        });
        const batchResponses = await Promise.all(batchRequests);
        const embeddings = [];
        for (let i = 0; i < batchResponses.length; i += 1) {
            const batch = batches[i];
            const batchResponse = batchResponses[i] || [];
            for (let j = 0; j < batch.length; j += 1) {
                embeddings.push(batchResponse[j]);
            }
        }
        return embeddings;
    }
    /**
     * Method to generate an embedding for a single document. Calls the
     * embeddingWithRetry 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) {
        const params = this.getParams([
            this.stripNewLines ? text.replace(/\n/g, " ") : text,
        ]);
        const embeddings = (await this.embeddingWithRetry(params)) || [[]];
        return embeddings[0];
    }
    /**
     * Method to generate an embedding params.
     * @param texts Array of documents to generate embeddings for.
     * @returns an embedding params.
     */
    getParams(texts) {
        return {
            model: this.modelName,
            input: {
                texts,
            },
            parameters: this.parameters,
        };
    }
    /**
     * Private method to make a request to the OpenAI API to generate
     * embeddings. Handles the retry logic and returns the response from the
     * API.
     * @param request Request to send to the OpenAI API.
     * @returns Promise that resolves to the response from the API.
     */
    async embeddingWithRetry(body) {
        return fetch("https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding", {
            method: "POST",
            headers: {
                "Content-Type": "application/json",
                Authorization: `Bearer ${this.apiKey}`,
            },
            body: JSON.stringify(body),
        }).then(async (response) => {
            const embeddingData = await response.json();
            if ("code" in embeddingData && embeddingData.code) {
                throw new Error(`${embeddingData.code}: ${embeddingData.message}`);
            }
            return embeddingData.output.embeddings.map(({ embedding }) => embedding);
        });
    }
}