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